February, 2011

Air quality monitoring, emission

inventory and source apportionment
study for Indian cities
National Summary Report

__________________________________________________________________
www.cpcb.nic.in Central Pollution Control Board
Technical Committee Members present in the Meeting, held on July
03, 2009

Committee Members:

1. Prof. S. P. Gautam, Chairman, CPCB and Technical Committee

2. Shri R. N. Jindal, Addl. Director, MoEF
3. Prof. H. B. Mathur, Retired Professor, IIT, Delhi
4. Shri K. K. Gandhi, Executive Director, SIAM, Delhi
5. Dr. S. A. Dutta, DGM, Tata Motors Ltd., Pune
6. Shri M. Kannan, Head Environment, Reliance Industries Ltd., Jamnagar
7. Shri G. K. Acharya, DGM, IOC (R&D), Faridabad
8. Dr. Ajay Deshpandey, Sr. Scientist, SPCB, Maharashtra, Mumbai
9. Shri B. L. Chawala, Env. Engineer, DPCC, Delhi
10. Prof. Virendra Sethi, IIT, Bombay
11. Dr. C. V. Chalapati Rao, Deputy Director, NEERI, Nagpur
12. Dr. Rakesh Kumar, Deputy Director, NEERI, Mumbai
13. Prof. S. Pushpavanam, IIT, Madras
14. Dr. T. S. Panwar, Director, TERI, Delhi
15. Shri M. K. Chaudhari, Sr. Deputy Director, ARAI, Pune
16. Dr. Prashant Gargava, Sr. Env. Engineer, CPCB & Member Secretary
Technical Committee

Distinguished Participants:

1. Dr. A. L. Aggarwal, ASEM-GTZ, Delhi

2. Shri Dilip Chenoy, Director General, SIAM, Delhi
3. Shri Mukul Maheshwari, IOC (R&D), Faridabad
4. Dr. (Ms) Indrani Gupta, NEERI, Mumbai
5. Ms. Abha Elizabeth, NEERI, Mumbai
6. Shri Rakesh K. Hooda, TERI, Delhi
7. Shri Sumit Sharma, TERI, Delhi
8. Shri Shailesh Behra, IIT, Kanpur
9. Shri M. A. Bawase, ARAI, Pune
10. Shri Abhijit Pathak, CPCB, Delhi
11. Ms. Sakshi Batra, CPCB, Delhi
Technical Committee Members present in the Meeting, held on
March 22, 2010

Committee Members:

1. Prof. S. P. Gautam, Chairman, CPCB and Technical Committee

2. Shri J. S. Kamyotra, Member Secretary, CPCB
3. Shri R. N. Jindal, Addl. Director, MoEF
4. Prof. H. B. Mathur, Retired Professor, IIT, Delhi
5. Shri K. K. Gandhi, Executive Director, SIAM, Delhi
6. Dr. S. A. Dutta, DGM, Tata Motors Ltd., Pune
7. Shri M. Kannan, Head Environment, Reliance Industries Ltd., Jamnagar
8. Shri G. K. Acharya, DGM, IOC (R&D), Faridabad
9. Shri M. P. George, Sr. Scientist, DPCC, Delhi
10. Prof. Virendra Sethi, IIT, Bombay
11. Prof. Mukesh Sharma, IIT, Kanpur
12. Dr. C. V. Chalapati Rao, Deputy Director, NEERI, Nagpur
13. Dr. Rakesh Kumar, Deputy Director, NEERI, Mumbai
14. Prof. S. Pushpavanam, IIT, Madras
15. Dr. T. S. Panwar, Director, TERI, Delhi
16. Shri M. K. Chaudhari, Sr. Deputy Director, ARAI, Pune
17. Dr. Prashant Gargava, Sr. Env. Engineer, CPCB & Member Secretary
Technical Committee

Distinguished Participants:

1. Dr. A. L. Aggarwal, ASEM-GTZ, Delhi

2. Dr. B. Basu, IOC (R&D), Faridabad
3. Prof. (Mrs.) R. S. Patil, IIT, Bombay
4. Dr. (Mrs.) Indrani Gupta, NEERI, Mumbai
5. Mr. J. K. Bhasin, NEERI, Delhi
6. Mrs. A. A. Baikerikar, ARAI, Pune
7. Shri M. A. Bawase, ARAI, Pune
8. Shri Rakesh K. Hooda, TERI, Delhi
9. Shri Sumit Sharma, TERI, Delhi
10. Shri Atanu Ganguli, SIAM, Delhi
11. Shri Abhijit Pathak, CPCB, Delhi
12. Ms. Sakshi Batra, CPCB, Delhi
Steering Committee Members present in the Meeting, held on July
08, 2010

Committee Members:

1. Sh. Vijai Sharma, Secretary MoEF & Chairman Steering Committee

2. Sh. R.H. Khwaja, Special Secretary, MoEF
3. Prof. S.P. Gautam, Chairman, CPCB
4. Dr. Rajneesh Dube, Joint Secretary, MoEF
5. Sh. J.S. Kamyotra, Member Secretary, CPCB
6. Sh. L.N. Gupta. Joint Secretary, MoP&NG
7. Dr. R.K. Malhotra, Executive Director, IOCL (R&D)
8. Shri K. K. Gandhi, Executive Director, SIAM
9. Dr. T.S. Panwar, Director, TERI
10. Dr. C.V. Chalapati Rao, Deputy Director, NEERI, Nagpur
11. Dr.(Ms.) Indrani Gupta , Scientist, NEERI, Mumbai
12. Prof.(Ms.) R.S. Patil, IIT, Bombay
13. Prof. Mukesh Sharma, IIT, Kanpur
14. Dr. M.K. Chaudhari, Sr. Deputy Director, ARAI, Pune

Distinguished Participants:

1. Sh. V.S. Yadav, Under Secretary, DHI

2. Sh. P.K. Singh, Director, MoP&NG
3. Dr. B. Basu, IOCL, R&D, Faridabad
4. Dr. G.K. Acharya, IOCL(R&D)
5. Dr. A.L. Aggarwal, Consultant
6. Sh. R.N. Jindal, Scientist ‘E’, MoEF
7. Dr. (Ms.) Susan George K., Scientist ‘C’, MoEF
8. Dr. Prashant Gargava, Sr. Environmental Engineer, CPCB
9. Ms. Sakshi Batra, CPCB

This summary report is based on the detailed study reports, prepared by

National Environmental Engineering Research Institute (NEERI), The
Energy and Resources Institute (TERI), Automotive Research Association of
India (ARAI), Indian Institute of Technology, Kanpur (IITK), Indian
Institute of Technology, Madras (IITM) in respect of cities of Delhi &
Mumbai, Bangalore, Pune, Kanpur and Chennai respectively.
 AIR QUALITY MONITORING, EMISSION INVENTORY AND SOURCE APPORTIONMENT STUDY

Institutes, approved Project Teams Overall Supervision Report Funded by Technical Peer Project
by Project Steering Guidance preparation Consultant Reviewed by Coordination
Committee Group*
The Automotive Shri M. K. Chaudhari, Prof. S. P. Shri J. S. Dr. CPCB Dr. A. L. Dr. Xavier Dr. Prashant
Research Association Ms. A. A. Baikerikar, Gautam Kamyotra, Prashant Aggarwal, Querol, Gargava
of India (ARAI), Pune Ms. Ujjawala Kalre, Chairman, Member Gargava ASEM-GTZ ASEM-GTZ Research
Shri Moqtik Bawase, CPCB Secretary, Professor,
Ms. S. A. Varade, Shri. CPCB Dr. Rakesh Oil Institute of
P. N. Pawar & others Kumar Companies Environmental
Indian Institute of Prof. Virendra Sethi, Shri J. M. Dr. B. Assessment
Technology Bombay Prof. (Ms) Rashmi Patil, Mauskar, Sengupta Prof. and Water
(IITB), Mumbai Dr. Neetu Saraf, Dr. Former Former Mukesh Research
Sumit Kumar Gautam Chairman Member Sharma Barcelona,
& others CPCB Secretary, Spain
National Dr. Rakesh Kumar, Dr. CPCB Dr. A. L.
Environmental (Ms) Indrani Gupta, Dr. V. Aggarwal
Engineering Research Shri Shivaji, Ms. Rajagopalan, Shri Martin
Institute (NEERI), Zonal Elizabeth Joseph, Shri Former Lutz,
Lab, Mumbai Mihir Herlekar & others Chairman Head of
Indian Institute of Prof. Mukesh Sharma, CPCB sector air
Technology Kanpur Shri Sailesh Behera, quality
(IITK), Kanpur Shri S. P. Shukla, Shri assessment
Pranveer Satvat & and pollution
others control
Indian Institute of Prof. S. Pushpavanam, planning
Technology Madras Dr. S. Ramanathan, Germany
(IITM), Chennai Dr. R. Ravikrishna, Dr.
Shiv Nagendra Prof. Prasad
National Dr. C. V. Rao, Dr. Modak,
Environmental Rakesh Kumar, Shri J. Executive
Engineering Research K. Bassin, Shri A. G. President
Institute (NEERI), Gavane, Dr. S. K. Environmental
Nagpur Goel, Shri A. D. Management
Bhanarkar & others Centre, and
The Energy and Dr. T. S. Panwar, Shri Adjunct
Resources Sumit Sharma, Shri Professor,
Institute (TERI), New Rakesh K Hooda, Dr. Indian
Delhi Sumit Kumar Gautam Institute of
& others Technology,
Powai, India

* Shri M. K. Chaudhari, Ms. Sakshi Batra, Shri Mihir Herlekar, Shri Shivaji and Shri Abhijit Pathak

contributed significantly
Report Structure

This synthesis report provides outcome of the Air Quality Monitoring, Emission Inventory and
Source Apportionment Study carried out in the cities of Bangalore, Chennai, Delhi, Kanpur,
Mumbai and Pune. The primary focus of the study was on respirable particulate matter (PM10),
although it also deals with other pollutants like NOx, SO2, Ozone (O3), PM2.5, etc. The report is
intended to provide scientific basis to the policy makers and other stakeholders, for formulation
of strategies and prioritizing actions for improving air quality in urban areas. It draws and
integrates the information, data, findings and conclusions contained in detailed city reports
prepared by the respective Institutes. The report deals with various elements covered in the
study and which are key to urban air quality management. These include air quality monitoring,
emission inventory, chemical speciation and source apportionment of PM10 and evaluation of
control options using dispersion modeling for evolving city-specific action plans.

The report is divided into ten chapters. Chapter One provides background to the study. Chapter
two describes project overview including need; objectives, scope of work, an outline of the
approach followed, and project administration. Chapter Three deals with air quality monitoring
including air quality levels with respect to PM10, NOx, SO2, and a few air toxics in different
project cities; chemical speciation data for PM10 as well as PM2.5; Data are given in the form of
tables, graphs, charts, etc. for better understanding of air quality status in the project cities. In
Chapter Four, methodology adopted for building up emission inventory, results of study on
development of emission factors for vehicular exhaust, emission estimates for the baseline year
2007 as well as Business as Usual (BaU) scenarios for 2012 and 2017 in respect of PM10 and NOx
are discussed. It provides information on contribution of various source groups viz. vehicles,
industries, domestic combustion, road side dust, and other fugitive area sources in emission
loads. Besides, within vehicular sources, contributions from different categories of vehicles (e.g.
two wheeler, three wheeler, passenger cars, heavy duty commercial vehicles, etc) are also
discussed. Chapter Five presents results of Factor Analysis and Chemical Mass Balance (CMB)
receptor models and source apportionment for PM10 and PM2.5. Dispersion modeling was
carried out for generating air quality profiles under different scenarios, details of which are
given in Chapter Six. Chapter Seven is on evaluation of control options and city specific action
plans. Based on source apportionment in different cities, a wide range of potential control
options were evaluated for their efficacies; and subsequent delineating appropriate city-specific
plans. As such, this chapter provides suggestions for improving the air quality. Chapter Eight
summarizes the conclusions focusing on key issues in urban air quality management. Chapter
Nine briefly mentions about some of the accomplishments made through the project. Chapter
Ten on ‘Way Forward’ enlists the next steps, in terms of actions, further research/studies that
are required to expand the knowledge base and understanding on urban air quality.
 Contents

S. No. Title Page

No.
CHAPTER – I Background 1-3
CHAPTER – II Project Overview

2.1 Objectives of the Study 4

2.2 Focus on PM10 5
2.3 Scope of the Project 6
2.4 Study Framework 6
2.5 Selection and Background of Project Cities 7-8
2.6 Quality Assurance and Quality Control 12-13
2.7 Project Administration 13
CHAPTER – III Air Quality Monitoring

3.1 Air Quality Status and Trends (2000 - 2006) 14

3.2 Ambient Air Quality Monitoring Network Design 17-20
3.3 Air Quality Monitoring Results of Project 21-33
3.4 Chemical Characterization of Particulate Matter 33-39
3.5 Molecular Markers 42-43

CHAPTER – IV Emission Inventory

4.1 Approach for developing Emission Inventory 46-47

4.2 Development of Emission Factors 56-60
4.3 Emission Inventory 60-67
CHAPTER – V Receptor Modeling and Source Apportionment

5.1 Factor Analysis: Methodology 71-72

5.2 CMB Model 8.2: Methodology 72-74
5.3 Source Apportionment of PM10 & PM2.5 74-76
5.4 Chemical characterization of PM10 and PM2.5 76-77
5.5 Source Profiling of Vehicular and Non-vehicular Emission Sources 77-78
5.6 Vehicular Source Emission Profiles 78-81
5.7 Non-Vehicular Sources Emission Profiles 85-91
S. No. Title Page
No.
5.8 Contributing Sources based on Receptor Modeling 92-99
CHAPTER – VI Dispersion Modeling

6.1 Approach and Methodology 101-102

6.2 Modeling Results 103-118
6.3 Model Performance and Calibration 125-126
CHAPTER – VII Evaluation of Control/Management Options and City Specific
Action Plans
7.1 Approach and Methodology 127-128
7.2 Evaluation of Efficacy of Control Options and Development of 140-190
City-Specific Action Plans
CHAPTER – VIII Conclusion 195-200

CHAPTER – IX Major Accomplishments 201

CHAPTER – X Way Forward 202-205

References 206-225
Annexure
I Composition of various Committees and Expert Groups I
II National Ambient Air Quality Standards, prevailing in 2007 II
III Revised National Ambient Air Quality Standards III-IV
IV Description of Monitoring Sites V - VIII
V Air Quality Monitoring: Sampling Period IX
VI Seasonal Variation in Concentration of Different Pollutants in Six X - XIV
Cities
VII Emission Factors for Vehicular Exhaust XV - XIX

VIII Non-Vehicular Emission Factors XX - XLVI

IX Projections of grid-wise emission load for 2012 and 2017 XLVII - LII
 List of Tables
Table Title Page
No. No.
2.1 Institutes responsible for carrying out various studies 4
2.2 Characteristics of the Project Cities 9-11
3.1 Monitoring protocol 21
3
3.2 Average Air Quality Levels (in µg/m ) and Percent Exceedances with 24-25
respect to 24-hourly average Standard
3.3 Concentration of Organic Pollutants 33
3.4 Sources and Associated Signature Elements 39
3.5 Molecular Markers and their Sources 43
4.1 Steps for Developing Emission Inventory 48-54
4.2 Test Driving Cycles used for development of EF 57
5.1 Target Physical and Chemical components (groups) for 76-77
Characterization of Particulate Matter
5.2 Percentage distribution of Carbon, Ions and Elements in PM10 different 83
categories of vehicles
5.3 Percentage distribution of PAHs in PM10 for different categories of 84
vehicles
5.4 List of Source Profiles Developed 86-88
5.5 Percent contribution of various sources at residential, kerbside, 100
industrial locations in all the six cities in respect of PM10 and PM2.5
6.1 Salient Features of Modeling Exercise at Each Grid and City level for all 102
Project Cities
7.1 List of Prioritized Sources 129
7.2 List of Potential Control Options 130-137
7.3 Factors considered in formulating control strategies 137-139
7.4 Emission Reductions with Implementation of Action Plan 140-141
7.5 Action Plan for Bangalore 144-148
7.6 Action Plan for Chennai 152-155
7.7 Action Plan for Delhi 159-167
7.8 Action Plan for Kanpur 171-174
7.9 Action Plan for Mumbai 178-186
7.10 Action Plan for Pune 190-192
 List of Figures
Figure Title Page
No. No.
2.1 Study Framework 7
2.2 Location of Project Cities 8
3.1 Air Quality Trends of RSPM in Residential Areas 15
3.2 Air Quality Trends of NOx in Residential Areas 15-16
3.3 Air Quality Trends of SO2 in Residential Areas 16-17
3.4 Monitoring Locations 18-19
3
3.5 Box plots of SPM, PM10, PM2.5 and NO2 Concentrations (in µg/m ) at 26
Background Locations
3
3.6 Box plots of SPM, PM10, PM2.5 and NO2 Concentrations (in µg/m ) at 26-27
Residential Locations
3
3.7 Box plots of SPM, PM10, PM2.5 and NO2 Concentrations (in µg/m ) at 27
Industrial Locations
3
3.8 Box plots of SPM, PM10, PM2.5 and NO2 Concentrations (in µg/m ) at 28
Kerbsite Locations Commercial
3
3.9 Box plots of SPM, PM10, PM2.5 and NO2 Concentrations (in µg/m ) at 29
Commercial Locations
3.10 CO concentration at Kerbsite location in Bangalore 30
3.11 CO concentration at Kerbsite location in Chennai 30
3.12 CO concentration at Kerbsite location in Delhi 30
3.13 CO concentration at Kerbsite location in Kanpur 31
3.14 CO concentration at Kerbsite location in Pune 31
3.15 Temporal variation of O3 concentration in Bangalore 31
3.16 Temporal variation of O3 concentration in Delhi 32
3.17 Temporal variation of O3 concentration in Mumbai 32
3.18 Temporal variation of O3 concentration location in Pune 32
–– –
3.19 EC/OC and SO4 /NO3 , in PM10/PM2.5 in Bangalore 35
–– –
3.20 EC/OC and SO4 /NO3 , in PM10/PM2.5 in Chennai 35-36
–– –
3.21 EC/OC and SO4 /NO3 , in PM10/PM2.5 in Delhi 36
–– –
3.22 EC/OC and SO4 /NO3 , in PM10/PM2.5 in Kanpur 36-37
–– –
3.23 EC/OC and SO4 /NO3 , in PM10/PM2.5 in Mumbai 37
–– –
3.24 EC/OC and SO4 /NO3 , in PM10/PM2.5 in Pune 37-38
3.25 Mass Closure of PM10 and PM 2.5 of Bangalore 39
3.26 Mass Closure of PM10 and PM 2.5 of Chennai 40
3.27 Mass Closure of PM10 and PM 2.5 of Delhi 40
3.28 Mass Closure of PM10 and PM 2.5 of Kanpur 41
Figure Title Page
No. No.
3.29 Mass Closure of PM10 and PM 2.5 of Mumbai 41
3.30 Mass Closure of PM10 and PM 2.5 of Pune 42
3.31 Molecular Markers at various sites of Bangalore 44
3.32 Molecular Markers at various sites of Chennai 44
3.33 Molecular Markers at various sites of Delhi 44
3.34 Molecular Markers at various sites of Kanpur 45
3.35 Molecular Markers at various sites of Mumbai 45
3.36 Molecular Markers at various sites of Pune 45
4.1 Framework on Emission Inventory 55
4.2 Schematic Test Cell Layout 59
4.3 Prominence of Sources of PM10 61
4.4 Distribution of Source Contributions of PM10 Emissions in Six Cities 62-63
4.5 Prominence of Sources of NOx 63
4.6 Distribution of Source Contributions of NOx Emissions in Six Cities 64-65
4.7 Prominence of Sources of SO2 65
4.8 Distribution of Source Contributions of SO2 Emissions in Six Cities 66
4.9 Contribution of Different Vehicle Types in PM10 Emissions in Six Cities 67-68
4.10 Contribution of Different Vehicle Types in NOx Emissions in Six Cities 68-69
5.1 Scheme of Source Apportionment 75
5.2 Overall Framework for the Work Elements in the Development of 89
Stationary Source Profiles carried out at IIT-Bombay
5.3 Sampling Strategies for different sources 90
5.4 A Typical Source Profile(Paved Road Dust, Mumbai) 92
5.5 Contribution of Sources in PM10 in Residential Locations 93-94
5.6 Contribution of Sources in PM10 in Kerbsite Locations 94-95
5.7 Contribution of Sources in PM10 in Industrial Locations 95-96
5.8 Contribution of Sources in PM2.5 in Residential Locations 97
5.9 Contribution of Sources in PM2.5 in Kerbsite Locations 98
5.10 Contribution of Sources in PM2.5 in Industrial Locations 99
6.1 Modeling results for Bangalore (Base year 2007; PM10, NOx; Winter) 105
6.2 Modeling results for Bangalore (Base year 2007; PM10, NOx; Summer) 106
6.3 Modeling results for Bangalore (Base year 2007; PM10, NOx; Pre monsoon) 107
6.4 Modeling results for Chennai (Base year 2007; PM10, NOx; Winter) 108
6.5 Modeling results for Chennai (Base year 2007; PM10, NOx; Post monsoon) 109
6.6 Modeling results for Chennai (Base year 2007; PM10, NOx; Summer) 110
Figure Title Page
No. No.
6.7 Modeling results for Delhi (Base year 2007; PM10, NOx; Summer) 112
6.8 Modeling results for Delhi (Base year 2007; PM10, NOx; Post monsoon) 113
6.9 Modeling results for Delhi (Base year 2007; PM10, NOx; Winter) 114
6.10 Modeling results for Kanpur (Base year 2007; PM10, NOx; Winter) 115
6.11 Modeling results for Kanpur (Base year 2007; PM10, NOx; Summer) 116
6.12 Modeling results for Kanpur (Base year 2007; PM10, NOx; post monsoon) 117
6.13 Modeling results for Mumbai (Base year 2007; PM10, NOx; Summer) 119
6.14 Modeling results for Mumbai (Base year 2007; PM10, NOx; Post monsoon) 120
6.15 Modeling results for Mumbai (Base year 2007; PM10, NOx; Winter) 121
6.16 Modeling results for Pune (Base year 2007; PM10, NOx; Summer) 122
6.17 Modeling results for Pune (Base year 2007; PM10, NOx; Post monsoon) 123
6.18 Modeling results for Pune (Base year 2007; PM10, NOx; Winter) 124
7.1 Air Quality Profiles for BAU 2012 and with Implementation of Action Plan 149
in Bangalore
7.2 Air Quality Profiles for BAU 2017 and with Implementation of Action Plan 150
in Bangalore
7.3 Air Quality Profiles for BAU 2012and with Implementation of Action Plan 156
in Chennai
7.4 Air Quality Profiles for BAU 2017 and with Implementation of Action Plan 157
in Chennai
7.5 Air Quality Profiles for BAU 2012 and with Implementation of Action Plan 168
in Delhi
7.6 Air Quality Profiles for BAU 2017, and with Implementation of Action Plan 169
in Delhi
7.7 Air Quality Profiles for BAU 2012, and with Implementation of Action Plan 175
in Kanpur
7.8 Air Quality Profiles for BAU 2017 and with Implementation of Action Plan 176
in Kanpur
7.9 Air Quality Profiles for BAU 2012, and with Implementation of Action Plan 187
in Mumbai
7.10 Air Quality Profiles for BAU 2017 and with Implementation of Action Plan 188
in Mumbai
7.11 Air Quality Profiles for BAU 2012 and with Implementation of Action Plan 193
in Pune
7.12 Air Quality Profiles for BAU 2017 and with Implementation of Action Plan 194
in Pune
 1

Background

Ambient air quality monitoring carried out at various cities/towns in the

country, under National Air Monitoring Programme (NAMP) provide air
quality information that form the basis for identifying areas with high air
pollution levels and subsequently, for planning the strategies for control and
abatement of air pollution. Data generated over the years reveal that
Suspended Particulate Matter (SPM) and Respirable Suspended Particulate
Matter (RSPM/PM 10 ) exceed permissible levels at many locations, particularly
in urban areas. Air pollution problem becomes complex due to multiplicity
and complexity of air polluting source mix (e.g. industries, automobiles,
generator sets, domestic fuel burning, road side dusts, construction activities,
etc.). A cost-effective approach for improving air quality in polluted areas
involves (i) identification of emission sources; (ii) assessment of extent of
contribution of these sources to ambient air; (iii) prioritization of sources that
need to be addressed; (iv) evaluation of various options for controlling the
sources with regard to feasibility and economic viability; and (v) formulation
and implementation of appropriate action plans. Source apportionment
study, which is primarily based on measurements and tracking down the
sources through receptor modeling, can help in identifying the sources and
extent of their contribution to ambient air pollution. The Auto Fuel Policy
(AFP) of Government of India (http://petroleum.nic.in/autoeng.pdf) also
recommended for carrying out source apportionment studies for better
planning related to air pollution reduction.

1.1 Recommendations of Auto Fuel Policy

The AFP document of Government of India made recommendations on

implementation of EURO III equivalent norms for new vehicles except two
and three wheelers for entire country and EURO IV equivalent norms for all
private vehicles, city public service vehicles and city commercial vehicles in
respect of 11 major cities with effect from April 01, 2010. While dealing with
air pollution scenarios, the policy document, observed that there were no
reliable emission inventories and there was a need to develop realistic
emission inventories based on representative emission and vehicle utilization
factors and undertake source apportionment studies. It also suggested that
for assessing source contribution to ambient air, an integrated approach,
which uses dispersion and receptor (Chemical Mass Balance – CMB-8)
models, could be followed.

1
1.2 Initiatives taken by Oil Companies

Oil companies, led by Indian Oil Corporation Limited (IOCL) initiated source
apportionment studies in August 2003 with National Environmental
Engineering Research Institute (NEERI), Nagpur for source apportionment
study in Delhi and with Automotive Research Association of India (ARAI),
Pune for development of emission factors for vehicles. Subsequently,
Memoranda of Collaborations (MoCs) were entered into with The Energy
and Resources Institute (TERI) in December 2004 and The Automotive
Research association of India (ARAI) in January 2005 for studies in Bangalore
and Pune respectively. The focus of the above studies was on PM 10 (particles
of size less than or equal to10µm).

Steering and Technical Committees, coordinated by IOC, were constituted

to oversee the implementation of the above studies.

1.3 Taking over of Project Management by Ministry of Environment &

Forests (MoEF) and Central Pollution Control Board (CPCB)

In October 2004, Oil and Automobile industries met the Secretary (E & F) and
proposed that MoEF should take over the project management and get
source apportionment studies done under its guidance. Although the studies
had made little progress at that stage, to ensure greater credibility, co-
ordination by MoEF was agreed upon. Steering and Technical Committees,
headed by Secretary (E&F) and Chairman, CPCB respectively, were
reconstituted.

1.4 Need for evolving an Appropriate Common Methodology

In order to ensure that the six studies produce consistent and comparable
results, it was necessary that the various institutes, conducting the studies
adopted a common methodology. CPCB reviewed the scope of
work/methodology for identifying the gaps, and for evolving an appropriate
common approach and methodology that could be followed by all the
participating institutes. Some of the important modifications made in the
earlier scope of work are presented below:

ƒ It was earlier proposed to conduct ambient air quality monitoring with

respect to PM 10 & a few other pollutants and subsequent chemical
speciation of PM 10 for a limited number of parameters. Monitoring
frequency was as per National Ambient Air Quality Standards protocol
(i.e. total 104 observations in a year @ twice a week). It was realized that
instead of analyzing a few parameters, a detailed chemical analysis

2
 (ions, elements, carbon, and molecular markers) would provide more in-
depth understanding of the constituents and origin of fine particulates.
Besides, this being a specific objective oriented study and not
compliance monitoring, number of observation was changed from 104
in a year to continuous monitoring for 20 – 30 days in each of the three
seasons viz. summer, post-monsoon and winter. These alterations would
be more appropriate for analyzing the impact of different sources and
working out management options using air quality models. Accordingly,
a detailed monitoring protocol including parameters, their measurement
principles, sampling and analytical procedures, frequency of
measurements, etc. was prepared.

ƒ Besides PM 10 , limited monitoring and source apportionment of PM 2.5

(particles of size less than or equal to 2.5µm) was also included. As a
result, assessment of contributions of different source categories to
concentrations of fine particles (PM 2.5 ) that have more severe health
impacts was also possible.
ƒ In urban areas, except for large industries in a few cases, most of the
sources are low-height sources and air quality monitoring should
specifically capture impact of these polluting sources. Therefore, for
deriving meaningful interpretation through the study, an extensive
primary survey of spatial distribution of sources and preparation of
detailed emission inventory based on primary surveys for zone of
influence (i.e. 2x2 km2 area) around each ambient air quality monitoring
location were included in the scope.
ƒ Earlier scope envisaged use of source emission profiles, available from
developed countries, as input to CMB-8 model for estimating
contributions from different source categories. It was felt that use of these
emission profiles for the sources that are relevant in Indian context may
not be appropriate. Therefore, two new studies on developing profiles for
vehicular and other sources (construction activities, roadside dust, DG
sets, combustion, etc.) were included in the scope.

The Technical Committee approved the common methodology in its

meeting held on October 20, 2005 and the same was presented before the
Steering Committee on October 25, 2005 for concurrence. The revised
scope of work and common methodology had implications in terms of
requirement of additional financial resources, which are met through Plan
allocations of CPCB. New MoCs with revised scope of work and costs were
finalized.

3
 2

Project Overview

Prevailing air quality scenario in major Indian cities demands formulation of

comprehensive action plans for improvement in the non-attainment cities
and towns. These Action Plans need to be realistic, technically feasible &
economically viable to deliver the intended benefits.

In order to gainfully utilize the existing expertise and infrastructure available

within India, the project was initiated in collaboration with premier research
institutes like The Automotive Research Association of India (ARAI), Indian
Institute of Technology (IIT), National Environmental Engineering Research
Institute (NEERI) and The Energy and Resources Institute (TERI). Various studies
were commissioned to these institutes, as per details given in Table 2.1.

Table 2.1: Institutes responsible for carrying out various studies

Study Institute
Source Apportionment Studies
Delhi NEERI
Bangalore TERI
Pune ARAI
Mumbai NEERI
Chennai IITM
Kanpur IITK
Development of Emission Factors for Vehicles ARAI
Development of Source Emission Profiles
Vehicles ARAI
Non-vehicular sources IITB

2.1 Objectives of the Study

Since, air quality in urban areas are affected by a variety of complex source
mix, the objectives of the study were defined so as to have better
understanding of major sources and their contributions to air pollution; and
to formulate strategies for improving air quality that are based on detailed
scientific investigations.

4
The study objectives are:

ƒ To profile Ground Level Concentration (GLC) of air pollutants in

different parts of the city including background, residential,
commercial/mixed areas and source specific “hot spots” viz.
kerbside/roadside, industrial zones, etc.

ƒ To develop emission factors (EF) for different categories of vehicles with

due consideration to variations in fuel quality, technology, size and
vintage, control systems, etc.

ƒ To arrive at appropriate EF for non-vehicular sources viz. industries,

industrial and domestic fuel combustions, roadside dust, construction
activities, DG sets, etc.

ƒ To prepare inventory for different air pollutants, their emission rates and
pollution loads from various sources along with spatial and temporal
distribution.

ƒ To profile the source emission characteristics of various sources typically

present in urban areas.

ƒ To apportion the sources of PM 10 and PM 2.5 (limited) and prioritize the

source categories for evolving city-specific air
pollution management strategies/plan.

ƒ To assess the impact of sources on ambient air quality under different

management/ interventions/control options and draw a roadmap of
short and long term measures as considered appropriate and cost
effective to ensure “cleaner air in urban areas”.

2.2 Focus on PM 10

Among all the criteria air pollutants, particulate matter (SPM and RSPM) has
emerged as the most critical pollutant in almost all urban areas of the
country. Coarser fraction (> PM 10 ) of SPM concentrations are primarily
irritants and may not have much relevance to direct health consequences
as compared to effects of its respirable fractions (PM 10 and PM 2.5 ), which
can penetrate the human respiratory systems deeper. Since the year 2000,
focus has shifted from SPM to PM 10 monitoring. In view of this, the main focus
of this study is on characterization and source apportionment of PM 10.
Limited exercise on characterization and source apportionment of PM 2.5 – a
relatively more hazardous particulate fraction, has also been included in
order to have a better understanding and correlation between these two
fractions.

5
2.3 Scope of the Project

A comprehensive scope of work was drawn for the project. The scope is
based on an integrated approach that includes (i) building up emission
inventories, (ii) monitoring of ambient air quality for various pollutants
relevant to urban areas viz. SPM, PM 10 , PM 2.5 , SO 2 , NO x , CO, O 3 , Benzene,
etc. and meteorological parameters at identified locations representing
various land use and activity profiles, (iii) chemical speciation of ambient
PM 10 & PM 2.5 and that of source emissions for applying dispersion and CMB-8
models to assess the contribution from various sources, and (v) future
projections and evaluation of various control options to develop cost-
effective action plans.

2.4 Study Framework

It is a comprehensive study that is based on an integrated approach

involving all major factors influencing urban air quality management. The
approach was evolved based on the study objectives, existing scientific
understanding & knowledge, technical capabilities, expertise &
infrastructure available with leading Institutes in the country, resources, etc.

In order to ensure uniformity in approach and methodology to be followed

for conducting the study, a document on ‘Conceptual guidelines and
common methodology for air quality monitoring, emission inventory and
source apportionment studies for Indian cities’
(http://cpcb.nic.in/sourceapportionmentstudies.pdf) was prepared for the
guidance/use of participating institutes.

The entire study framework was designed taking into account inter-linkages
among key components of the study viz. (i) ambient air quality monitoring;
(ii) detailed chemical speciation of PM 10 and PM 2.5 ; (iii) developing emission
inventory; (iv) receptor modeling; (v) dispersion modeling; and (vi) broad
techno-economic analysis of the prioritized control options & interventions.
The other support activities included: (i) development of emission factors for
vehicular exhaust emissions; (ii) adoption of common approach or emission
factors for non-vehicular sources; and (iii) development of indigenous source
emission profiles for vehicular and non-vehicular sources. Integrated analysis
of these components facilitates policy decisions with adequate scientific
basis; and poses unresolved questions that need to be investigated through
further research initiatives.

A schematic presentation of the framework is given in Figure 2.1 and

important elements are discussed below:

6
2.5 Selection and Background of Project Cities

It is learnt from past experiences that implementation of same interventions

in two cities having different distinctiveness in terms of source
configurations, geography, meteorology, etc may not yield similar results.
Though the choice of interventions to control urban air pollution has to be
city-specific, project cities were selected covering wide range of
characteristic distinctions so that control strategies could be applied to
other similar cities. In addition, other important factors that played key role
in identification of the cities included past data on air quality, access to
various city level information, and availability of appropriate Institutes that
could take up the study in the identified cities.

• EF developed by ARAI Emission inventory

• EF from literature and source location
• EF from CPCB database (+ future changes)
on GIS maps
City-specific meteorological
conditions (vector wind, mixing
height, stability, temperature: Air dispersion
onsite measurements and/or modeling (ISC3)
database of IMD/CPCB)

Monitored ambient Model Impact on

concentration Calibration ambient
concentration

Chemical
characterization Receptor
Source modeling
profiling of PM10 and
PM2.5 sampling (CMB)

Cost effective air

Source Interpretation of model quality
Apportionment outputs scenario(s) management
PM10 and PM2.5 (dispersion/receptor strategies
using CMB models)

Figure 2.1: Study Framework

7
Source apportionment studies were planned for following six cities:
Bangalore, Chennai, Delhi, Kanpur, Mumbai and Pune (Figure 2.2).

Figure 2.2 : Location of Project Cities (map not to scale)

City Profiles: Cities chosen under the project represent a wide spectrum of
activities, socio-economic development, geophysical character, climate,
sources of pollution, etc. Delhi and Kanpur are in Northern India with typical
dry climate and mix of industrial and commercial activities. Mumbai and
Chennai are coastal cities, which experience land/sea breeze influencing
dispersion of pollutants. Bangalore and Pune are upcoming cities that
have more of commercial/institutional activities than industrial. Delhi and
Mumbai are megacities with population of more than 13 million. The
profiles of these cities are given in Table 2.2.

8
 Table 2.2: Characteristics of the Project Cities

City Area Population (million) Vehicle Climate Remarks (including Socio-

(Km2) 2001 Projected population economic Activities)
Census for 2011 2007
(million)*
Bangalore 565 5.7 7.6 2.53 • Pleasant climate throughout Bangalore is commonly known
the year. as the Silicon Valley of India
• Max. Temp: 25-34°C, Min. because of its pre-eminent
Temp: 15-21°C. position as the nation's leading IT
• Receives rainfall from both employer. It is home to
the northeast and the innumerous software
southwest monsoons. The companies, well-recognized
wettest months are colleges and research
September and October. institutions, aerospace,
• Avg. rainfall: 970 mm/yr telecommunications, and
defence organizations

Chennai 176 4.34 4.95 2.27 • Weather is typically hot and Chennai has commercial
humid. There is only a small activities with a large number of
variation between the educational and medical
seasons due to the location institutions. Industrial activities
and proximity to the Indian are limited and located on the
Ocean. outskirts.
• Max. Temp.: 42o C and Min.
Temp: 20o C
• Receives most of the rainfall
from the north east monsoon
(mid September to mid
December) while some
rainfall is also there during the

9
City Area Population (million) Vehicle Climate Remarks (including Socio-
(Km2) 2001 Projected population economic Activities)
Census for 2011 2007
(million)*
south west monsoon (July-
August).
Delhi 1500 13.8 19.0 5.2 • Both summer and winter are Delhi is capital city with mix of
severe with June being the activities, such as commercial,
hottest month and January, small scale industries, power
the coldest. plants. The city is surrounded by
• Dust storm-cum-heat waves other major growth centres of
occur during summer. adjoining states such as
• The annual rainfall is around Haryana and Uttar Pradesh.
700 mm. Maximum rain
occurs during July to August.
Kanpur 230 2.57 3.19 0.59 • In winters minimum of -10C Kanpur, the largest industrial city
with maximum at almost 12 in Uttar Pradesh mainly having
to 140C. leather industries.
• In summer (April-June) Max.
Temp. Spiral up to 47.50C
and re accomplished by dust
storm-cum-heat-waves.
• During the rainy season the
relative humidity is generally
high over 70%
• The average annual rainfall is
792 mm.
Mumbai 468 16 22.4 1.5 • Rainfall : June to It is known as the financial &
September, 2,200 mm commercial capital. City is
• Mild Winter : Nov to surrounded by coastline on
February. western, eastern and southern
• Annual Temp. High of side.

10
City Area Population (million) Vehicle Climate Remarks (including Socio-
(Km2) 2001 Projected population economic Activities)
Census for 2011 2007
(million)*
38°C to a low of 11°C. Space constraints have given
• Average annual humidity rise to towering skyscrapers
is 90% standing majestically next to
sprawling slums (Dharavi -Asia's
biggest slum is here).
The biggest & busiest port in
India.
Pune 243.84 2.54 3.5 1.45 • Pune experiences three Pune is also known as a twin city
distinct seasons: summer, with two municipal corporations
monsoon & winter. of Pune (PMC) and Pimpri-
• Typical summer months are Chinchwad (PCMC).
March to May, with max
temperatures ranging from 35 The growth of Pune is not limited
to 39 deg C, with high diurnal to only PMC or PCMC, but all
variations in temperatures. the circumferential area with
• Wind Direction: industries, universities, institutes
Feb to Sep – Westerly; coming up in that area.
Oct to Jan – Easterly
• The city receives annual Pune is an auto hub and a
rainfall of 722 mm, mainly growing IT hub.
during June and September.

* Registered Vehicles

11
2.6 Quality Assurance and Quality Control

The institutes, participating in the project, were chosen considering their

expertise and experiences in conducting similar studies, and focusing on
capacity building in different regions of the country. The participating
institutes, being reputed scientific institutions, were responsible for ensuring
Quality Assurance and Quality Control (QA/QC). However, to facilitate
good quality data, guidelines on Standard Operating Procedures (SOPs)
for sampling and analysis were prepared (http://cpcb.nic.in/SA-
Studies%20-SOPs-for-sampling-and-analysis.pdf). The SOPs provided
complete description of the measurement process, and included the
following:

ƒ Summary of measurement methods, principles, expected accuracy

and precision, and the assumption for validity
ƒ Materials, equipment, reagents, and suppliers
ƒ Technical details
ƒ Individuals responsible for performing each part of the procedure
ƒ Traceability path, primary standards or reference, etc.

Approach and methodology for the project was presented and discussed
in the Asian Aerosol Conference, held in Mumbai in 2005. International
expert from Germany was also invited for reviewing the study design.

QA/QC was applied to various components of monitoring network design

starting from conceptual designing; selection of sampling equipment,
monitoring & analytical methods, monitoring sites; field planning; schedule
and frequency of monitoring; and documenting QA/QC procedures. The
salient features of QA/QC also included the following:

ƒ Adopting state-of art monitoring and analysis methods as well as

equipment and infrastructure support.
ƒ Uniformity in monitoring and analysis methodology.
ƒ Surveys for siting of appropriate monitoring location.
ƒ Training of field as well as analytical staff by International experts.
International training was organized to train the trainers representing all
the participating institutes, concerned State Pollution Control Boards,
with support from ASEM-GTZ.
ƒ Samples on molecular markers from Delhi and Mumbai were analyzed
at Desert Research Institute (DRI), USA.

12
ƒ A city level external group was constituted incorporating local experts
for regular interaction and external quality checks on methods and
data generated.
ƒ An Expert Group was also constituted, which interacted with project
teams of all the six cities to provide guidance on various quality related
issues.

2.7 Project Administration

ƒ Steering and Technical Committees were set up. While the Steering
Committee provides overall guidance and facilitates smooth
implementation of the project, Technical Committee is responsible for
resolving technical issues during the course of study. Technical
Committee met regularly (Ten meetings were held during last two and
half years) to monitor the progress and decide on technical issues.
ƒ An Expert Group was constituted for overseeing study on development
of emission factors for vehicles. The Group met six times for finalizing the
factors for different fuel and categories & vintage of vehicles.
ƒ A Finance Subcommittee has also been set up, which ensures timely
release of payments.
ƒ Composition of various Committees and Expert Groups are given in
Annexure – I.

13
 3

Air Quality Monitoring

3.1 Air Quality Status and Trends (2000 - 2006)

Ambient air quality is being monitored under National Ambient Air

Monitoring Program (NAMP), coordinated by Central Pollution Control
Board (CPCB), in over 115 cities/towns including the six project cities.
Figures 3.1 to 3.6 present the air quality status and trend of previous years
for Chennai, Pune, Kanpur, Bangalore, Mumbai, and Delhi in respect of
RSPM, NO 2 and SO 2 . These historical trends provide air quality status, prior
to the study. The following conclusions come to the fore by analyzing the
air quality status and trend up to the year 2006 in the six cities:

ƒ Except for Chennai, annual RSPM standard (60 µg/m3) exceeds in all
cities in all years (2000- 2006). Kanpur shows the highest concentrations
of RSPM where standard is exceeded by more than three times. It is
closely followed by Delhi.
ƒ Simple exploratory data analyses do not show any trend in RSPM levels
except for Pune and Mumbai. However, in past three years, Mumbai
shows slight rising trends for RSPM.
ƒ Generally, NO 2 levels are within the air quality standard (60 µg/m3). In
past three years, Bangalore and Pune have shown decreasing trends in
NO 2 . However, a close examination of other cities shows a definite
increasing trend of NO 2 .
ƒ SO 2 levels are within the annual standard (60 µg/m3) in all cities. The
other important point in SO 2 levels is the fact that SO 2 levels are
decreasing at all cities, which is largely attributed to sulphur reduction in
diesel.
ƒ It is clear that RSPM is the most important pollution parameter especially
in the urban environment.
ƒ The variation in annual average concentrations during different years
may be due to multiple factors including meteorology, neighbourhood
activity pattern or levels during monitoring period, etc.

14
Figure 3.1: Air Quality Trends of RSPM in Residential Areas

15
Figure 3.2: Air Quality Trends of NO 2 in Residential Areas

16
Figure 3.3: Air Quality Trends of SO 2 in Residential Areas

National Ambient Air Quality Standards, prevailing during period of

monitoring (i.e. year 2007), and revised standards notified in November 2009
are annexed for reference (Annexure – II and III).

3.2 Ambient Air Quality Monitoring Network Design

Ambient air quality monitoring network was designed to get spatial and
temporal variation of ambient air concentrations addressing a wide range
of pollutants that are considered relevant for evolving a strategic
management plan.

The primary objective being identification of “Hot Spots” representing

maximum impact zone of different land use categories and not mere
compliance monitoring, a land use based network design was considered
appropriate. Monitoring locations representing different land use namely
kerbside, residential, industrial, etc. were selected so as to capture air
quality levels under different activity profiles. In addition, one background
location (away from all the sources and in upwind direction) was also
included. However, in these cities with expanding peripheral activities, it
becomes difficult to identify absolute background locations. These are
identified, primarily, on the basis wind pattern and least polluting activities.
As such, impact of local activities (e.g. Delhi), other distant sources, re-
suspension of dust, etc. occurring during monitoring can not be ruled out.

A uniform monitoring network design was followed for all the project cities
except for Delhi. The network in each city comprised seven air monitoring
stations (with an exception for Delhi having 10 stations). The geographical
locations of monitoring sites in different cities are depicted in Figure 3.4.

17
 7
6

4
1. CSB (Kerbside)
2. IGICH (Residential)
3. Domlur (Residential)
1 4.
5.
Victoria (Kerbside)
Kammanahalli (Residential)
6. Peenya (Industrial)
7. Kanamangala (Background)

Bangalore

Chennai

Gridwise Map of Delhi City

C D E F G H J K L M N O P Q R S

1 10 1
Background:
9 10.Prahladpur
2 2
Mixed Used:
3 8 3 1.Naraina

4 7 4
Residential:
5 6 5
9.Pitampuua

6 6 Industrial:
5 8.SSI -GTK
7 7
1
8 8 Kerbside:
2.Dhuala Kuan
9 3 2 9 3.Mayapuri
4.Ashram
10 10 5.Anand Vihar
4 6.ISBT
11 WMS 11 7.Loni Road

12 12

13 13

14 14

15 15

C D E F G H J K L M N O P Q R S

Delhi

18
 1 IITK - back
2 Vikas Nagar(VN) - Residential
1
3 Colonel Ganj(CG) - Kerb
4 AHM – Commercial
5 Dada Nagar(DN) - Industrial
2 6 Govind Nagar(GN) - Residential
7 Ramadevi(RD)- kerb
3
4

Land Use and Sampling Sites on 2x2 km Grids

Kanpur

3
6
2

City : Mumbai
Monitoring Location

Residential
Site
Industrial 7
Site

1 Colaba (Background)
2 Dadar (Mixed /Kerb)
3 Dharavi (Slum
Residential)
1 4 Khar (Residential)

5 Andheri (Kerb)
6 Mahul (Industrial)
7 Mulund (Kerb)

Mumbai

Background

Residential

Kerbside Sites
Industrial Site
Institutional
7

4
Monitoring Locations
6
CWPRS Guest House, Khadakwasla

Shantiban Society, Kothrud 2

5

Sahakarnagar Colony, Sahakarnagar

3
College of Engg. Pune, Shivajinagar

Pune-Solapur Highway, Hadapsar

1
SAJ Test Plant, Mundhwa

Univ. of Pune, Shivajinagar

Pune
Figure 3.4: Monitoring Locations

19
In order to address all the expected anthropogenic emission sources
(including secondary pollutants) prevailing in the project cities, monitoring
of criteria as well as non-criteria pollutants was included in the study. This
provided insight to air quality issues including contribution from various
sources and extent of presence of secondary pollutants. The major air
pollutants covered in this project include: Particulate Matter (TSP, PM 10
and PM 2.5 ), Sulphur Dioxide (SO 2 ), Oxides of Nitrogen (NO 2 ), Carbon
Monoxide (CO), Ozone (O 3 ), Benzene (C 6 H 6 ), Formaldehyde (HCHO), Poly
Aromatic Hydrocarbon (PAH), etc. The PM 10 as well as PM 2.5 samples were
collected on different filter media to make detailed analysis of constituent
fractions including tracer elements and molecular markers. Monitoring
protocol giving details regarding schedule, frequency, averaging period,
etc. is provided in Table 3.1. Annexure – IV and V provide description of
monitoring sites and actual field sampling period in six cities respectively.
The salient features of the network design are as under:

ƒ 20/30 days monitoring for each of the three seasons i.e. summer,
post/pre-monsoon and winter.
ƒ Sampling frequency of 08 and 24-hours averaging period covering
weekdays and weekends to get air quality corresponding to varied
activity profiles was decided.
ƒ 07-10 monitoring stations with one at background and two each in
residential, traffic, industrial/commercial locations were selected.
However, in case of Delhi, in the initial stages of the study design, wide
spread traffic activities and dense road network were under focus. The
final monitoring network of Delhi represented kerbside locations under
different land-use types.
ƒ Monitoring height was kept within typical exposure range of 3 – 5 m for
obtaining ambient air pollution levels.
ƒ PM 10 and PM 2.5 being the focus of the study there collected masses were
subjected to further analysis for ions, OC, EC, major and trace elements,
SO 4 2-, NO 3 -, and source specific markers. Such analysis was designed
considering requirements of receptor modeling.
ƒ In general, PM 2.5 monitoring and related analysis had limitations of
number of days of sampling besides the days of sampling were not
concurrent. Keeping in view the changing scenario of PM control and air
quality standards, a limited prospective study was undertaken in this
project.

20
 Table 3.1: Monitoring protocol

Particulars Pollutants

SPM PM 10 /RPM SO 2 NO 2 PM 2.5 CO VOC

Equipment High Multi- Impingers Impingers FRM Automatic VOC

Volume speciation attached attached sampler Analyzer Sampler
Sampler sampler/ to to
Respirable HVS/RDS HVS/RDS
dust
sampler
(RDS)

Sampling 8/24 hrly 8/24 hrly 8/24 hrly 8/24 hrly 8/24 hrly 4/24 4/8/24
period hrly/continuous hrly

Sampling 20/30 days 20/30 days 20/30 days 20/30 days one week one week once in
frequency continuous continuous continuous continuous continuous continuous in each of
in each of in each of in each of in each of in each of each of the the
the three the three the three the three the three three seasons three
seasons seasons seasons seasons seasons seasons

3.3 Air Quality Monitoring Results of Project

The air quality sampling stations selected in this study have been
categorized based on the predominant land-use pattern at that location;
these include background (with limited human activities),
commercial/institutional, residential, industrial, kerbside, and traffic.
Ambient air was characterized for SPM, PM 10, PM 2.5 , SO 2 , NO 2 , CO, O 3 ,
Formaldehyde, VOCs (Benzene, 1-3 Butadine) OC, EC, Ions, Elements,
Benzene, PAHs and molecular markers. The air quality sampling was
conducted for three seasons: summer, post/pre monsoon and winter.

Particulate Matter (SPM, PM 10 , PM 2.5 ), Oxides of Nitrogen (NO 2 ) and Sulphur

Dioxide (SO 2 ) Levels:

Table 3.2 show summary of SPM, PM 10 , PM 2.5 , NO 2 , and SO 2 levels along

with exceedance of 24-hr standard prevailing in 2007 (for PM 2.5 , proposed
standard of 60 µg/m3 has been used). The maximum of the average values
reported for different stations in a particular land-use category. Percent
exceedance refers to the number of days out of 20/30 days, which have
violated the 24-hourly standards in terms of pollutant concentrations. At
Kanpur and Delhi, almost at all locations and in all seasons, standards of
SPM, PM 10 and PM 2.5 have exceeded (except for industrial area). Even the
background locations are highly polluted because these locations also fall
within the city area and are impacted from the city emissions. Although
SPM levels at the industrial sites are also very high, the exceedence has

21
been examined against the industrial area standard of 500 µg/m3. As
expected, Chennai has shown very good air quality for all PM parameters
followed by air quality in Bangalore. It can also be seen that for residential
area, standards are exceeded by over 90 percent time for PM 10 except for
Chennai and Bangalore. Further as expected at the kerbside in all cities,
standards have exceeded on 75 percent of time. For PM 2.5 as well,
standards are exceeded 100 percent of time at kerb stations, industrial and
residential areas (except for Bangalore and Chennai). In case of Pune
PM 2.5 standards are exceeded 100 percent of time at kerb stations only.
NO 2 levels generally exceed the ambient air quality standards at kerbside
locations, particularly during winter and post monsoon seasons (Bangalore
60 – 65%), Delhi 85 – 95%, Mumbai 20 – 43% and Pune 0 – 50%). In addition,
the standards are also exceeded at residential locations in Delhi (35 – 65%)
& Mumbai (07 – 25%), and Industrial location in Delhi (80 – 85%). During
summer, the values are comparatively low. This analysis shows that PM
pollution problem is severe and NO 2 is the emerging pollutant that requires
immediate planning to control its emissions.

Out of three prominent seasons (winter, post-monsoon, and summer)

adopted in the study design, the worst season data (post-monsoon in case
of Chennai and Delhi, winter for other cities) have been considered for
further analysis. Figure 3.5 presents the box plots of SPM, PM 10 , PM 2.5 and
NO 2 concentrations at background locations. The upper and lower limits of
box indicate 3/4th and 1/4th percentile values; and top, middle and lower
lines indicate maximum, median and minimum concentration values,
respectively. It can again be seen that in terms of PM pollution, Delhi and
Kanpur show highest air pollution levels. Further the box plots show the
variability in the observed values. Observations at Delhi and Mumbai show
much higher variability than any other city. What is more alarming is the
fact that SPM and PM 10 standards are exceeded even at the background
site supposed to have limited human activity with the exception of
Chennai and Bangalore. As regards NO 2 , levels are well within the
standards except for Mumbai and in addition, Mumbai data show
maximum variability for NO 2 levels.

Figure 3.6 presents the box plots of SPM, PM 10 , PM 2.5 and NO 2

concentrations at residential locations. It can be seen that in terms of SPM,
standards are exceeded in all cities and most cities it is exceeded for 100
percent of time. Standards also exceed in the residential areas of all cities
for PM 10 except for Bangalore and Chennai. Similar to background
locations, Delhi and Kanpur show the highest pollution levels. For PM 2.5 , the
pollution levels are the highest in Kanpur – Kanpur incidentally had more
observations for PM 2.5 than other cities. What is interesting to note is the

22
fact that Delhi show very high variability in PM levels (both for PM 10 and
PM 2.5 ). NO 2 levels exceed at the residential area sites in Delhi (35%), Pune
(6%) and Mumbai (25%). Similar to background location, NO 2 levels at
Mumbai show highest variability.

Figure 3.7 presents the box plots of SPM, PM 10 , PM 2.5 and NO 2

concentrations at industrial area sites in six cities. It can be seen that
pollution levels are the highest at industrial sites (e.g. SPM, maximum ~ 1400
µg/m3 and PM 10 maximum 1000 µg/m3 in Delhi) in all cities compared to
their corresponding residential and background locations. The standard
exceedence has not been analyzed here as the acceptable standard for
the industrial area in terms of SPM, PM 10 is much higher (500 µg/m3 for SPM
and 200 µg/m3 for PM 10 ) than the standards applicable in residential areas.
Delhi for SPM and PM 10 show highest variability but for NO 2 the highest
variability is at industrial site at Bangalore.

Figure 3.8 presents the box plots of SPM, PM 10 , PM 2.5 and NO 2

concentrations at the kerbside area in six cities. It can be seen that
pollution levels are similar to the industrial sites but for NO 2 . The NO 2 levels
are much higher at kerbside locations indicating clear influence of vehicles
on air quality. It may be noted that Delhi once again shows the highest
pollution at the kerbside locations compared to all other cities. Cities like
Kanpur, Pune and Mumbai show similar PM 10 (250-300 µg/m3). In case of
NO 2 , Pune and Mumbai show similar (70-80 µg/m3) levels however, in
Kanpur NO 2 levels are lower (46 µg/m3).It is interesting to note that
Bangalore has shown very high NO 2 level (94 µg/m3). It signifies that while
PM background levels being low the overall PM 10 levels at kerbside
locations in Bangalore may not be high but vehicular NO 2 contribution is
very high at Bangalore. As for all land-use sites, variability in the
concentration is the highest in Delhi.

Mumbai and Kanpur had sampling sites in a commercial area. The results
of sampling at these locations (Fig. 3.9) show high PM levels at Kanpur,
largely due to much higher background pollution of PM at Kanpur.
However, this is to be noted that NO 2 level at the commercial site at
Mumbai is much more (40-150 µg/m3) than at Kanpur, suggesting much
higher pollution of NO 2 in Mumbai.

23
Table 3.2: Average Air Quality Levels (in µg/m3) and Percent Exceedances with respect to 24-hourly average Standard*:
SPM PM 10 PM 2.5
W* P** S*** W P S W P S
Mean %E Mean %E Mean %E Mean %E Mean %E Mean %E Mean %E Mean %E Mean %E
Background Bangalore 110 0 82 0 83 0 47 0 105 32 66 10 27 0 23 0 27 0
Chennai 117 17 76 0 178 22 55 0 88 50 71 31 35 14 39 0 34 14
Delhi 549 100 546 100 517 100 355 100 300 100 232 100 -- -- -- -- 131 100
Kanpur 361 100 329 93 342 97 204 97 169 97 187 90 172 100 132 100 136 100
Mumbai 246 63 204 57 159 17 184 97 139 86 91 39 92 67 60 33 29 0
Pune 257 95 204 65 139 5 123 60 63 5 76 10 45 0 32 0 22 0

Residential Bangalore 294 100 301 100 177 25 133 88 93 35 69 14 36 0 41 33 29 0

Chennai 164 19 173 14 175 24 82 25 200 46 86 23 78 86 34 0 34 0
Delhi 828 100 967 100 284 90 505 100 671 100 81 40 301 100 -- -- 30 0
Kanpur 429 100 373 97 422 100 226 100 195 100 217 100 208 100 161 100 190 100
Mumbai 523 100 445 100 277 54 267 100 236 100 119 48 97 100 87 100 54 33
Pune 499 100 362 95 206 50 165 95 128 72 103 58 58 0 35 0 28 0

Industrial Bangalore 262 0 245 0 171 0 171 81 171 50 69 5 30 0 21 0 22 0

Chennai 311 8 348 11 319 5 138 31 147 44 141 38 67 57 41 0 79 30
Delhi 965 100 1239 100 611 70 546 100 781 100 229 8 197 100 314 100 52 100
Kanpur 603 62 577 58 591 61 396 76 371 74 388 74 305 100 273 100 232 100
Mumbai 395 3 388 0 238 3 271 100 218 96 99 7 127 100 87 100 17 0
Pune 400 25 164 0 270 0 216 85 71 10 121 22 63 33 26 0 37 0

Kerbside Bangalore 306 100 287 93 411 100 199 100 184 85 109 43 64 50 43 33 38 0
Chennai 350 78 243 59 211 36 111 48 128 77 271 67 73 57 56 29 51 14
Delhi 1082 100 2592 100 ##### 100 451 100 941 100 337 100 306 100 361 100 107 100
Kanpur 564 100 532 100 561 100 292 100 260 100 273 100 216 100 226 100 218 100
Mumbai 383 100 383 100 314 8 256 100 234 100 124 65 119 100 126 100 41 18
Pune 655 100 583 100 507 100 254 100 193 95 138 95 124 100 62 67 46 0

* Standard refer to 24 hourly average standards as prevailing in 2007; PM2.5 standards refers to the proposed standards in 2007

24
 Table 3.2: Contd…

NO 2 SO 2
W P S W P S
Mean %E Mean %E Mean %E Mean %E Mean %E Mean %E
Background Bangalore 18 0 45 18 91 56 6 0 14 0 9 0
Chennai 27 0 8 0 14 0 3 0 1 0 5 0
Delhi 31 0 33 0 25 0 8 0 15 0 8 0
Kanpur 23 0 20 0 20 0 8 0 8 0 4 0
Mumbai 53 10 38 0 18 3 15 0 13 0 5 0
Pune 36 0 34 0 10 0 23 0 10 0 5 0

Residential Bangalore 46 0 29 0 90 46 9 0 15 0 15 0
Chennai 32 0 17 0 28 0 4 0 3 0 3 0
Delhi 73 35 88 65 29 0 14 0 18 0 78 0
Kanpur 49 0 32 3 19 0 14 0 8 0 4 0
Mumbai 72 25 60 7 25 0 12 0 13 0 6 0
Pune 41 6 43 0 14 0 18 0 11 0 6 0

Industrial Bangalore 53 6 30 0 89 44 9 0 10 0 10 0
Chennai 45 0 20 0 42 0 6 0 4 0 6 0
Delhi 159 85 142 80 60 0 85 20 77 20 11 0
Kanpur 35 0 24 0 23 0 26 0 19 0 15 0
Mumbai 72 0 53 0 20 0 18 0 15 0 7 0
Pune 55 0 17 0 22 0 40 0 16 0 22 0

Kerbside Bangalore 94 62 105 65 66 26 10 0 19 0 13 0

Chennai 45 0 33 0 43 0 6 0 1 0 4 0
Delhi 109 85 121 95 47 0 20 0 20 0 12 0
Kanpur 46 0 42 7 37 0 15 0 9 0 8 0
Mumbai 82 43 64 20 33 2 14 0 15 0 6 0
Pune 71 50 43 0 59 20 36 7 12 0 7 0

% 25- 50- 75-

Exceedance 0-25 50 75 100

* W: Winter
** P: Post Monsoon, Summer in case of Bangalore
*** S: Summer, Pre Monsoon in case of Bangalore

25
Background:

SPM PM 10

PM 2.5 NO 2

Figure 3.5: Box plots of SPM, PM 10 , PM 2.5 and NO 2 Concentrations (in µg/m3)
at Background Locations

Residential:

SPM PM 10

26
 PM 2.5 NO 2

Figure 3.6: Box plots of SPM, PM 10 , PM 2.5 and NO 2 Concentrations (in µg/m3)
at Residential Locations

Industrial:

SPM PM 10

PM 2.5 NO 2

Figure 3.7: Box plots of SPM, PM 10 , PM 2.5 and NO 2 Concentrations (in µg/m3)
at Industrial Locations

27
Kerbside:

SPM PM 10

PM 2.5 NO 2

Figure 3.8: Box plots of SPM, PM 10 , PM 2.5 and NO 2 Concentrations (in µg/m3)
at Kerbside Locations Commercial

Commercial:

SPM PM 10

28
 PM 2.5 NO 2

Figure 3.9: Box plots of SPM, PM 10 , PM 2.5 and NO 2 Concentrations (in µg/m3)
at Commercial Locations

Carbon Monoxide (CO) and Ozone (O 3 ) Levels:

In order to study the diurnal variations of one primary pollutant (ambient air
CO) and one secondary pollutant (O 3 ) that have direct correlation with
temporal profile of vehicular activities, these parameters were monitored
for one week in each season using real time monitoring system. All six cities
measured CO levels at the kerbside to directly examine the impact of
vehicles, whereas O 3 has been measured in four cities (Bangalore, Delhi,
Mumbai, and Pune).

A typical day monitoring data of CO is shown in Figures 3.10 – 3.14 for

Bangalore, Chennai, Delhi, Pune and Kanpur. Hourly concentrations may
exceed marginally the standard of 4000 µg/m3 for CO at Delhi, Chennai
and Kanpur. It is expected that the CO levels will quickly drop off as one
move away from the road and as such the levels will not pose any health
effect. It can also be seen that in all cities, there are morning and evening
peaks in CO levels corresponding to vehicular movement. However, Delhi
experiences higher concentration of CO during night hours. The possible
reason for this could be building up of concentrations due to unfavourable
meteorological conditions, and substantial vehicular movements till late in
the night.

A typical day monitoring data of O 3 are shown in Figures 3.15 – 3.18 for
Bangalore, Delhi, Pune and Mumbai. Hourly concentrations is not
exceeding standard of 180 µg/m3 (= 90 ppb) at any of the locations. It is to
be noted that there is no definite trend in O 3 concentration over the day.
Although one would expect higher ozone concentration around 1-3 pm
but good dilution and high speed winds as a possibility, might, have
brought down the concentration. It can be concluded that at the sites,

29
where limited O 3 monitoring was done, the observed concentrations were
not significant.

Figure 3.10: CO concentration at Kerbside location in Bangalore

Figure 3.11: CO concentration at Kerbside location in Chennai

Figure 3.12: CO concentration at Kerbside location in Delhi

30
 Figure 3.13: CO concentration at Kerbside location in Kanpur

Figure 3.14: CO concentration at Kerbside location in Pune

Figure 3.15: Temporal variation of O 3 concentration in Bangalore

31
 Figure 3.16: Temporal variation of O 3 concentration in Delhi

Figure 3.17: Temporal variation of O 3 concentration in Mumbai

Figure 3.18: Temporal variation of O 3 concentration in Pune

32
Levels of Other Pollutants:

In addition to criteria pollutants, other pollutants such as Benzene, 1-3

Butadiene, Formaldehyde, Non-Methane and Total Hydrocarbon (NMHC &
THC), expected in urban environment were also monitored. However,
monitoring of these parameters was limited to once in each of the three
seasons at each monitoring location. Summary of data of these pollutants
is given in Table 3.3. The ambient concentration levels are primary
governed by the contributing sources in the neighborhood, and vide
variations were observed in different cities.

Benzene levels are higher in Bangalore, Pune and Kanpur. The values of
formaldehyde are also matter of concern in Mumbai, Pune and Bangalore.
However, more work is required to be done in future for proper
understanding of these pollutants.

Table 3.3: Concentration of Organic Pollutants

Benzene ( µg/m3 ) 1,3- Butadiene (ppb) Formaldehyde ( µg/m3 ) NMHC (ppm) HC (ppm)
city

Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg
Bangalore

7.4 237.8 119.1 0.41 3.7 2.18 8 35 20 8 12 11 9 15 13

Chennai

4 17 10.2 0.452 1.8 1.1 1.7 26 12.1 0.02 0.18 0.06 0.02 0.18 0.06
Delhi

1.92 11.19 4.96 0.2 1.6 0.78 1.94 19.0 11.27 0.2 1.7 0.9 2.6 5.3 3.7
Kanpur

4.88 68.11 26.86 - - - 4.39 18.43 10.08 0.06 0.255 0.14 0.07 0.26 0.15
Mumbai

Not monitored Not monitored 8.8 93.0 32.6 0.1 24.6 2.3 1.5 25.5 4.6
Pune

28.14 96.53 57.30 0.4 2.5 1.2 3.77 41.12 17.12 1.32 3.82 2.55 1.74 4.22 2.96

3.4 Chemical Characterization of Particulate Matter

PM 10 and PM 2.5 samples were also subjected to extensive chemical

characterizations for 36 major and trace elements and 11 ions (including
NO 3 -, SO 4 2 -, NH 4 +), OC, EC, along with wide range of molecular markers (18
in numbers) for representing typical urban emission sources in India. Before
undertaking receptor modeling, the chemical characteristics of PM 10 as

33
well as PM 2.5 were analyzed to assess the contribution of sources. Results of
chemical speciation are presented below:

Elemental Carbon (EC), Organic Carbon (OC), Sulphate (SO 4 2–) and Nitrate
(NO 3 – ) in PM 10 and PM 2.5 :

Figures 3.19 – 3.24 show the chemical composition of both PM 10 and PM 2.5
for EC, OC, SO 4 2–, and NO 3 – for the cities of Bangalore, Chennai, Delhi
Kanpur, Mumbai and Pune. These parameters constitute important fraction
both from public health point of view and as indicators for source group
contribution at a particular location of sampling. It is important to mention
that the results correspond to PM 10 samples of 20/30 days and PM 2.5
samples of one week. The following important information and
interpretations can be obtained from the EC- OC plots.

ƒ In all cities, OC (organic carbon) levels are always much higher (2 - 4.5
times) than EC levels. The ratio of EC/OC is variable from one location
to another, indicating that sources, those contribute to particulate
pollution, are variable.
ƒ In general EC and OC levels relate well with PM 10 levels. It can be seen
that Delhi and Kanpur showed the high pollution levels for PM 10 and
these two cities also show high EC and OC levels. EC levels: 15-20 µg/m3
at Delhi and 15- 40 µg/m3 at Kanpur; OC Levels: 50 – 100 µg/m3 in Delhi
and 35 – 105 µg/m3 at Kanpur. The levels in other cities range 5 – 20
µg/m3 for EC and 10 – 45 µg/m3 for OC.
ƒ EC and OC almost account for 20-45 percent of PM 10 , inclusive of all
cities which are quite high and reflect as to how badly the cities are
affected because of combustion and/or fuel related emissions.
ƒ High EC to OC ratio represents freshly contributed diesel/petrol/coal
combustion particles. Many cities have shown this ratio to be high at
kerbside and industrial locations.
ƒ There are significant quantities of SO 4 2 – and NO 3 –, (10-15% in most cities
and 20-30% in Kanpur) in PM 10 indicating contribution of secondary
particles. These contributions are even high at the background upwind
direction in all cities. It signifies long-range transport of particles in the
city as well as formation of secondary particles in the city. Any control
strategy for reduction of secondary particulate will have to consider
control of SO 2 , NO x and NH 3 .
ƒ NO 3 2- concentrations at background sites are generally lower as
compared to other sites. The instances of its higher concentrations are

34
 due to reported local activities and contribution from nearby
settlements.
ƒ EC and OC contribution to PM 2.5 is even more than what it is to PM 10 ,
and it varies from one city to another. Chennai has shown a very high
EC and OC content (60-75% in PM 2.5 ), followed by Bangalore (35-50%),
Delhi (30-45%), Mumbai (30-40%), Pune (25-40%) and Kanpur (25-35%). It
signifies an important point that PM 2.5 has much higher component of
toxic EC and OC and that mostly come from combustion sources like
vehicles, coal, biomass, garbage combustion, and others.

8
City : Banglore
7

6 PM10_NO3
Avg.Con(ug/m 3)

5
PM2.5_NO3
4

3
PM10_SO4
2
1 PM2.5_S04

0
Background Residential Kerb Industrial

Figure 3.19: EC/OC and SO 4 2–/NO 3 –, in PM 10 /PM 2.5 in Bangalore

35
 Figure 3.20: EC/OC and SO 4 2–/NO 3 –, in PM 10 /PM 2.5 in Chennai

Figure 3.21: EC/OC and SO 4 2–/NO 3 –, in PM 10 /PM 2.5 in Delhi

36
 Figure 3.22: EC/OC and SO 4 2–/NO 3 –, in PM 10 /PM 2.5 in Kanpur

Figure 3.23: EC/OC and SO 4 2 –/NO 3 –, in PM 10 /PM 2.5 in Mumbai

37
 Figure 3.24: EC/OC and SO 4 2 –/NO 3 –, in PM 10 /PM 2.5 in Pune

Mass Closure:

List of signature elements, identified for different sources, is given in Table

3.4. The data on concentrations of signature elements were analyzed for
preliminary assessment of source contributions. Figures 3.25 – 3.30 present
the overall fractions of the each component of PM 10 and PM 2.5
constituents and show mass closure of overall mass of PM 10 and PM 2.5 . It is
important to note that total mass of PM 10 and PM 2.5 has been reasonably
accounted for in most cases. About 5-30 percent of mass could not be
accounted for in PM 10 and it is reported as unidentified mass (grayish blue
stack in Figures 3.25 – 3.30) except for sampling locations at Chennai,
where 30-50 percent mass is reported as unidentified. OC is presented as
organic carbon and not organic matter. As such, other elements like
hydrogen, oxygen and nitrogen present in the organic matter are not
accounted for, and reflected in the unaccounted mass. It is also pertinent
to mention that sampling was done using Quartz, Teflon filters on different
days for analysis of OC/EC, ions and elements. Samples using Teflon filters
provided mass of PM 10 /PM 2.5 , which were used for mass closure. However,
total mass of PM 10 /PM 2.5 was not available on the days, when sampling
was done using quartz filters for OC/EC analysis. Besides, in case of PM 2.5 ,
limited sampling was carried out for seven days with data on mass
available for three days. This, probably, has resulted in negative mass
closure in some cases.

Elemental and ion analysis show abundance of soil constituents (e.g. Si, Fe,
Ca, Na) around 20 percent. This clearly suggests that there could be
significant sources of particulate pollution from soil, and road dust. It can
also be seen that soil related fraction reduces dramatically in PM 2.5 (Figures
3.25 – 3.30).

38
 Table 3.4: Sources and Associated Signature Elements *

Sources Associated Signature Elements

Crustal Signature Al, Ca++, Ca, Si, Fe

Alluvial & Marine Signature Cl–, Na+, Na, Pd

Refuse Burning K+, K, Mn, Zn
Residual Oil V, Ni, Co
Coal Combustion As, Se, Ti
Combustion Sources OC, EC
Secondary Particulate Formulation NO 3 –, SO 4 2 –, NH 4 –

Unidentified mass

* These signature elements grouped under different source categories

were used for mass closure.

Figure 3.25: Mass Closure of PM 10 and PM 2.5 of Bangalore

39
 Figure 3.26: Mass Closure of PM 10 and PM 2.5 of Chennai

Figure 3.27: Mass Closure of PM 10 and PM 2.5 of Delhi

40
 Figure 3.28: Mass Closure of PM 10 and PM 2.5 of Kanpur

Figure 3.29: Mass Closure of PM 10 and PM 2.5 of Mumbai

41
 Figure 3.30: Mass Closure of PM 10 and PM 2.5 of Pune

3.5 Molecular Markers

Organic molecular markers are individual compounds or groups of related

compounds (homologous compounds such as n-alkanes, n-alkanoic acids,
hopanes and PAH, which at a molecular level comprise the chemical
profile or "fingerprint" for specific emission source types. An individual
molecular marker or groups of marker compounds is linked quantitatively
to major emission sources of urban fine particles. The molecular-level
technology used in the current study matches chemical fingerprints of the
PM samples collected to emission sources. Table 3.5 presents the molecular
markers and their associated sources.

Figures 3.31 to 3.36 show the molecular marker concentration in winter for
Bangalore, Chennai, Delhi, Kanpur, Mumbai and Pune. It can be seen that
not all markers are present in all cities (except for Kanpur) but markers like
hopanes those indicate gasoline and diesel burning are present in all cities.
There is description for presence of each marker underneath the figures to
suggest presence of the sources.

42
 Table 3.5: Molecular Markers and their Sources

Molecule Type Molecular Marker Major Urban Sources

Alkanes n- Hentriacontane Vegetative detritus, Cigarette
smoke
n-Tritriacontane Tyre wear debris
n- Pentatriacontane Tyre wear debris
Hopanes 22, 29, 30 – Gasoline, diesel, fuel oil
Trisnorneohopane
17α(H), 21β(H)- Gasoline, diesel, fuel oil
29Norhopane
17α(H), 21β(H) Gasoline, diesel, fuel oil
Norhopane
Alkanoic acid Hexadecanamide Biomass (Cow dung)
Octadecanamide Biomass (Cow dung)
Others Stigmasterol Biomass burning
Levoglucosan Hardwood, Softwood

Delhi emission scenario being quite complex, it is difficult to distinctly link

the presence of specific markers with different land use categories.
However, the presence of hopanes and steranes at all the sites in much
higher quantities compared to background location indicates that effect
of vehicles is prevalent at all the sites of Delhi. Higher concentration of
levoglucosan confirms contribution from biomass burning. In Kanpur, the
following molecular markers were present hentriacontane, tritriacontane,
pentatriacontane, octadecanamide, levoglucosan, stigmasterol and
PAHs. The presence of these markers suggests that possibly the following
sources are also contributing to PM 10 : vegetative detritus, tyre wear debris,
gasoline, diesel, fuel oil, biomass (cow dung, hardwood, softwood)
burning.

In Mumbai, data on molecular markers at locations with two extreme

situations provide insight to contribution of polluting activities – Dharavi, the
biggest slum area of the world, and Colaba, a cleanest location. In winter,
highest concentration of PAH was observed at Dharavi (9.9 ng/m3), which
could be due to inefficient combustions of various industrial waste,
biomass, coal burning, fuel oil consumption, etc. The concentrations of
PAH was lowest in Colaba. Similarly, the concentration of Levoglucosan at
Dharavi is 4.3 times higher than that observed in Colaba (winter), which is
mainly because of more of wood and biomass burning in Dharavi. Mulund
being a kerbside has hopanes and steranes concentration 12.7 and 11.7
times higher than what was observed in Colaba.

More information on measurement and observed concentrations at

various locations are given in the Annexure - VI.

43
 Benzo[e]pyrene concentration
600 25

Concentration (ng/m³)
500 20
400
15
300

(ng/m³)
10
200
100 5
0 0

)
)
1)

p.)

d)

nd
b1
2

)
b2
es
es

In
s
er

ou
er

Ho
(R

a(
(R

(K

gr
(K

y
lli

ck
3/
ur

ad

en
ha

es

Ba
ml

Ro

Pe
CS
na

(R
Do

ria
ma

H
cto

IC
m

IG
Ka

Vi
Benzo b Fluoranthene Benzo k Fluoranthene Coronene
Hentriacontane Hopane Indeno[1,2,3-cd] Pyrene
Octadecanamide Pentatriacontane Tritriacontane
Levoglucosan Stigmasterol Benzo(e) pyrene

Figure 3.31: Molecular Markers at various sites of Bangalore

Winter season

1000 Dodecane
Hexadecane
n-Eicosane
100
Nonacosane
Concentration

n-Tetradecane
10 Tetracosane
(ng/m3)

Tricosane
Octadecanamide
1
IITM MYL TRIP ADY SAID RKN AMBT

Figure 3.32: Molecular Markers at various sites of Chennai

Molecular markers - Delhi (Winter S eason)

50 10000
45 9000
40 8000
Levoglucosan (ng/m )
3
Mol. Marker (ng/m3)

35 7000
30 6000
25 5000
20 4000
15 3000
10 2000
5 1000
0 0
SSIGTK
ISBT

Loni Road

Mayapuri

Naraina
Ashram
Anand vihar

Prahladpur
Pitampura
Chowk

Dhaulakuan

PAHs Site s n-alkane hopane

sterane methyl-alkane branched-alkane
cycloalkane alkene Levoglucosan

Figure 3.33: Molecular Markers at various sites of Delhi

44
 Figure 3.34: Molecular Markers at various sites of Kanpur

Molecular markers - Mumbai (Winter S eason)

30 6000
28
26
5000
24
22

Levoglucosan (ng/m 3 )
Mol. Marker (ng/m3)

20 4000
18
16
3000
14
12
10 2000
8
6
1000
4
2
0 0
Colaba Dadar Dharavi Khar Andhe ri Mahul Mulund
PAHs Sites n-alkane hopane
sterane methyl-alkane branched-alkane
cycloalkane alkene Levoglucosan

Figure 3.35: Molecular Markers at various sites of Mumbai

Site wise distribution of molecular markers-

90
Pune-Winter season
80
70
60
ng/m3

50
40
30
20
10
0
COEP, Shivajinagar

Hadapsar
Shantiban

(Institutional)
Sahakarnagar

SAJ, Mundhwa
(Kerb-2)

Univ. of Pune
(Res-1)

(Res-2)

(Ind)
(Kerb-1)

n- Hentriacontane n-Tritriacontane n-Pentatriacontane

Benzo[e]pyrene Indeno[1,2,3-cd]fluoranthene Picene
Hexadecanamide Octadecanamide 17 alpha(H),21beta(H)-Hopane

Figure 3.36: Molecular Markers at various sites of Pune

45
+4

Emission Inventory

4.1 Approach for developing Emission Inventory

Emission Inventory is the first step towards understanding the sources and
their strength. These sources depending upon where they are located, at
what elevation they emit, what is their frequency and duration of emission,
etc can provide the major information about the character of a city in
terms of air pollution. Emissions from all sources, if identified and also
quantified for a particular location and time, can be effectively used for
dispersion modeling purposes. Such simulation is able to provide the
predicted levels of air pollutants in the ambient air of a city grid for a future
year based on growth rate of the sources. Emission Inventory tools and
techniques vary widely depending upon the type and quality of data
available in the city. Primary surveys combined with available information
from varied sources in the city are used for estimation of all major activities,
which are air polluting. These are used in conjunction with EF for building EI.

The study involved preparation of detailed emission inventory with

estimation of emissions from various activities such as vehicular, industrial,
residential, commercial, etc. The methodology for EI was designed with
optimization of Top-Down and Bottom-Up approaches to fulfill the following
requirements:

ƒ Identification of all major emission sources and reliable estimation of

emission quantities of significant pollutants like PM 10 , NOx, SO 2 , CO, etc.
ƒ Adequate representation of various factors influencing emissions, such
as, land use, socio-economic structure, spatial & temporal distribution
of source activities vis-à-vis pollutants.
ƒ Evaluation of time weighted emissions and their distribution for
modeling needs.

Besides using data from secondary sources of information, activity data

were also obtained, wherever necessary, through primary surveys covering,
questionnaire surveys, personal interviews, house-to-house surveys, actual
traffic counts, etc. While this approach provides reasonable quality of data
on emission estimates, resolutions with respect to time and space are
limited in view of resources and available time-frame.

46
In cities, except for a few large point sources, most of the sources being
low-level sources may have zone of maximum impact within 2x2 km2 area.
Therefore, it was reasonable to assume that in addition to large point
source, if any, air quality monitoring locations would mostly capture the
contribution of sources located within the zone of influence. As such,
greater emphasis was laid on primary surveys around monitoring locations.
In order to fulfill the needs of the project, an optimized activity framework
was followed as depicted in Figure 4.1, and with the following salient
features:

ƒ Detailed in-situ primary surveys within 2x2-km2 zone of influence around

each monitoring location were planned to identify all significant pollution
sources (e.g. construction activities, industries fuel use, domestic fuel
combustions, size and activities of DG sets, etc) and also to collect
activity data through personal interviews.
ƒ Diurnal traffic count surveys on different categories of roads along with
personal interviews at parking lots/petrol pumps with vehicle owners for
obtaining data on vintage, fuel use, vehicle kilometer traveled (VKT) per
day, etc.
ƒ Use of refined Emission Factors (EF) for vehicular exhaust emissions.
ƒ Selection of appropriate EF for non-vehicular emission sources i.e.
roadside dust, domestic fuel combustions, industries, construction
activities, etc.
ƒ Projections of city level EI based on detailed inventories prepared in 2x2-
km2 grids, and city land use plans.
ƒ Future projections of emission scenarios considering developmental
plans, changes in the land-use and activities and/or activity levels, (with
or without implementation of given pollution control plans), etc.

Steps for building EI are given in Table 4.1.

47
 Table 4.1: Steps for Developing Emission Inventory

Parameter 2x2 km2 Criteria 2007 BAU

2012 2017
Vehicles • Mapping City road network on GIS • Plot city road network in 2x2 • Refer RTO records, • Refer RTO records,
• Diurnal Vehicle count & video km2 grid in GIS future road network future road
recording on different road • Match the road type, proposal, network proposal,
categories population density, land- population & population and
• Parking lots and petrol pump survey use category of primary vehicle density, land vehicle density,
and personal interviews for vehicle 0.5x0.5 grids to calculate use maps for vehicle land use maps for
models and use details diurnal VKT in 2x2 km2grids growth and vehicle growth and
• Develop diurnal VKT profiles on of • Develop grid wise diurnal differential VKT differential VKT
vehicle as well as road categories. emission rate profiles for growth in 2012. growth in 2017.
• Use EF for respective vehicles different vehicle models, • For projected VKT • For projected VKT
type/model for diurnal emission load fuel use, vintage etc in calculate diurnal calculate diurnal
on different roads in 0.5x0.5 grids each 2x2 km2grid. emission rates emission rates
• Treat all main roads as line source • Calculate grid wise • Calculate grid wise
and feeder roads as area source for emission rates emission rates
modeling.

Road side re- • Conduct field survey for silt load on • Plot road network on GIS • Refer RTO records,
suspended dust different roads • Match land use typology, • Refer RTO records, future road
• Incorporate vehicle weight, speed as well as population future road network network proposal,
and VKT data of primary survey densities, etc. for silt load proposal, population and
• Conduct laboratory analysis of road projection population & vehicle density,
dust • Incorporate vehicle weight vehicle density, land land use maps for
• Calculate diurnal road re-suspended and speed in VKT in put to use maps for vehicle vehicle growth
dust emission rates in 0.5x0.5 grids modeling growth and and differential
• Treat all main roads as line source • Calculate re-suspended differential VKT VKT profile in 2017.
and feeder roads as area source for dust diurnal emission rates in profile in 2012. • Plot road network
modeling. 2x2 km2grids on USEPA • Plot road network on GIS

48
 Parameter 2x2 km2 Criteria 2007 BAU
2012 2017
based model on GIS • Match land use,
• Match land use population density
typology, as well as for silt load
population densities, • Incorporate
etc. for silt load vehicle weight and
projection speed in VKT in put
• Incorporate vehicle to modeling
weight and speed in • Calculate re-
VKT in put to suspended dust
modeling diurnal emission
• Calculate re- rates in 2x2 km2
suspended dust grids on USEPA
diurnal emission based model
rates in 2x2 km2grids
on USEPA based
model
Unpaved Road dust • Mark unpaved roads in road network • Refer road development Same as in 2007 Same as in 2012
• Calculate VKT for likely diurnal vehicle plan and exclude roads to
activity be paved till 2012.
• Calculate emission rates in respective • Calculate emission rates in
0.5x0.5 grids 2x2 km2 city grids

49
 Parameter 2x2 km2 Criteria 2007 BAU
2012 2017
Construction • Conduct field survey in each 0.5x0.5 • Refer land use plans for new • Refer future land use • Refer future land
grids to spot location and intensity of area and provide GIS map plan map for the use plan map for
work and map the location on GIS for the city city and mark land- the city and mark
map • Match the land use and use in GIS land-use in GIS
• Record intensity and area of population density • Match with data • Match with data
construction activity in 0.5x0.5 grids observed in 0.5x0.5 grids base developed base developed
• Use EF developed to calculate with city grids to project during field survey during field survey
diurnal PM emission rates in 0.5x0.5 construction intensity and project future and project future
grid • Develop EI for 2x2 km2 city land use activity for land use activity for
grids adopting comparable projected new projected new
construction density & population density population density
intensity • Project diurnal EI • Project diurnal EI
rates for 2012 rats for 2017

Industries • Plot industrial Estates on GIS map • Use city land use maps and • Adopt the future • Adopt the future
• Conduct field survey and personal match with land use, land use map of the land use map of
interviews in exiting IE for product, industry types/fuel of city of 2012 the city of 2o17
fuel used and emission characteristics primary survey grids within • Use data base • Use data base
• Adopt SPCBs data wherever existing industrial estates developed during developed during
available for the land use. • Use average mass emissions primary survey for primary survey for
• For Large industries use SPCB data rates developed during different different
base field survey and project industries/fuel use industries/fuel use
emission rates. • Project diurnal • Project diurnal
• For large industries use SPCB emission rates in city emission rates in
data base 2x2 km2 grid city 2x2 km2 grid
industrial estates. industrial estates.
Bakeries • Conduct primary surveys and • Match with land use and • Use future land use • Use future land use
interviews with owners in 0.5x0.5 grids population density of plan and population plan and
• Assess quantity and type of product 0.5x0.5 grids with new 2x2 density projections population density

50
 Parameter 2x2 km2 Criteria 2007 BAU
2012 2017
and fuel used km2 city grids for projecting in different city grids projection
• Use EF for respective fuel and number of bakeries for activity levels • Match with land
calculate emission rates in each locations in each city 2x2 • Match with land use use and population
0.5x0.5 grids km2 grids. and population density to project
• Calculate emission rates in density to project bakery number in
2x2 km2 grids for similar fuel bakery number in 2x2 km2 grids
configurations 2x2 km2 grids • Calculate Emission
• Calculate Emission rate in 2x2 km2 grid
rate in 2x2 km2 grid in 2017
in 2012
DG sets • Conduct primary surveys in 0.5x0.5 • Match with land use and • Use future land use • Use future land use
grids for population density to plan and population plan and
number/size/location/operation project DG sets number/size density projections population density
duration in different 2x2 km2 city grids in different city grids projections in
• Calculate emission rate for fuel use in for DG set activity different city grids
0.5x0.5 using EF developed for fuel • Calculate emission rates for levels (consider for DG set activity
and size of DG 2x2 km2 city grids power position also) levels (consider
• Match with land use power position
and population also)
density to project • Match with land
DG numbers and use and population
size in 2x2 km2 grids density to project
• Calculate Emission DG numbers and
rate in 2x2 km2 grid size in 2x2 km2 grids
in 2012 • Calculate Emission
rate in 2x2 km2 grid
in 2017
Hotels • Conduct primary surveys and • Match with land use, • Use future land use • Use future land use
interviews with hotel owners in 0.5x0.5 population density, etc. plan and population plan and

51
 Parameter 2x2 km2 Criteria 2007 BAU
2012 2017
grids of 0.5x0.5 grids with new density projections population density
• Assess quantity and type of fuel used 2x2 km2 city grids for in different city grids projections in
• Use EF for respective fuel and projecting number of for hotel rooms/beds different city grids
calculate emission rates in each hotels in each 2x2 km2 activity levels for hotel
0.5x0.5 grids grids. Large hotels should (consider tourist rooms/beds
be marked on given projections also) activity levels
locations • Match with land use (consider tourist
• Calculate emission rates and population projections also)
in 2x2 km2 grids for similar density to project • Match with land
product or fuel hotel rooms/beds in use and population
configurations 2x2 km2grids density to project
• Calculate Emission hotel rooms/beds
rate in 2x2 km2 grid in in 2x2 km2grids
2012 • Calculate Emission
rate in 2x2 km2 grid
in 2017
Open eat outs • Conduct primary surveys and • Match with land use and • Use future • Use future and
interviews with road side eat outs in population density of population density population density
0.5x0.5 grids 0.5x0.5 grids with new 2x2 projections in projections in
• Assess quantity and type of km2 city grids for projecting different city grids different city grids
production and fuel used number of eat outs with grid for quantifying the for quantifying the
• Use EF for respective fuel type and locations in each city 2x2 activity levels activity levels
quantity and calculate emission rates km2 grids. • Match with land use • Match with land
in each 0.5x0.5 grids • Calculate emission rates in and population use and population
2x2 km2 grids for similar density to project density to project
nature or fuel eat out number in eat out number in
configurations 2x2 km2 grids 2x2 km2grids
• Calculate Emission • Calculate Emission
rate in 2x2 km2grid in rate in 2x2 km2grid
2012 in 2017

52
 Parameter 2x2 km2 Criteria 2007 BAU
2012 2017
domestic • Conduct primary survey and • Match with land • Match with 2012 • Match with 2017
interviews for fuel type, consumption, use/population density and population population
monthly budget, family income etc. socio-economic variations density/socio- density/socio-
• Develop fuel use differentials and for developed for 0.5x0.5 economic economic
socioeconomic status for emission grids for calculating total profile/land use for profile/land use for
rate levels fuel consumption in 2x2 km2 projected projected
• Calculate emission rate for 0.5x0.5 grids population growth. population growth.
grids for given population character. • Calculate emission rate for • Calculate emission • Calculate emission
2x2 km2 grids rate in 2x2 km2 grid rate in 2x2 km2 grid
for 2012 for 2017
Crematoria • Mark location in GIS map • Match location of • Mark in the future • Mark in the future
• Conduct primary survey of the nature crematoria in the city and land use plan and land use plan and
of crematoria, number of bodies mark in the 2x2 km2city grid location of location of
disposed, fuel used per body etc for its location crematoria sites if crematoria sites (if
• Add emission rates for the respective • Use the disposed bodies any or use expected any) or use
0.5x0.5 grid location of crematoria counts for the unit of rate of mortality for expected rate of
and add to total emission rate from population (e.r.10,000) the religion to mortality for the
the grid having such units • Calculate emission rates distribute over religion to distribute
per day and add to projected over projected
emission load of the population. population.
respective grid location • Calculate emission • Calculate emission
rates in 2012 for the rates in 2017 for the
grid in which it is grid in which it is
located located
Open burning • Calculate domestic/garden solid • Use population density and • Use population • Use population
waste generated in 0.5x0.5 grids land use/socio economic density and land density and land
based on population and use % of it status in 2x2 km2 grids at city use/socio economic use/socio
openly burnet or for the amount of for calculating total solid status in 2x2 km2 economic status in
solid waste generated in the waste generation based on grids for 2012 for 2x2 km2 grids for

53
 Parameter 2x2 km2 Criteria 2007 BAU
2012 2017
community bench marks developed in calculating total 2017 for
• For the quantity of waste burnt use 0.5x0.5 grids solid waste calculating total
the EF (developed in the project) and generation based solid waste
calculate emission rate for 0.5x0.5 • Use percentage of total on bench marks generation based
grids. solid waste burnt in the developed in 2x2 on bench marks
community to calculate km2 grids developed in 2x2
emission rates calculations • Use percentage of km2 grids
for 2x2 km2 grids total solid waste • Use percentage of
burnt in the total solid waste
community in each burnt in the
grid to calculate community in each
emission rates grid to calculate
calculations for 2x2 emission rates
km2 grids calculations for 2x2
km2 grids

54
 Secondary Data
and primary site
survey data

Identification
of Sources Existing emissions
• Point Inventory experiences
• Area
• Line
Emission inventorization of
identified sources
• Point and Area
Collection of activity (Secondary data &
levels sources and primary site and activity
location data for each survey)
source type • Line (Primary traffic survey
and Secondary vehicular
characteristics data)

Quality Control checks

Data handling
and statistical analysis Emission characterization
• Point and Area
(Published reports/
USEPA & Indian
experience and
Source wise emission
primary emission
Inventorization & source
profiling/ monitoring)
Profiles
• Line (ARAI study and
traffic survey data)

Scenarios Analysis

GIS Mapping of total emission

inventory (grid wise) Source data input
• Point files for dispersion &
• Area receptor modeling
• Line

Figure 4.1: Framework on Emission Inventory

55
4.2 Development of Emission Factors

A properly compiled EI is the key to air quality management in a city. The

two essential inputs required for building EI are: activity data; and
corresponding EF. While activity data could be generated, non-availability
of indigenous EF for a wide range of sources including in-use vehicles was a
constraint. Therefore, development/ selection of appropriate EF were an
important component of the project, and fundamental requirement for
building meaningful EI.

EF for Vehicular Exhaust Emissions

The Automotive Research Association of India (ARAI) was responsible for

developing EF for vehicular emissions, based on mass emission tests
conducted on limited number of in-use vehicles covering different engine
technologies, types of vehicles, vintage, types of fuels, etc.

Study design was discussed with experts and by the Technical Committee
for deciding the procedure, methodology, test matrix, fuel matrix, data
reporting, etc. Important features of the study are given below:

ƒ In-use vehicles of different vintages (e.g. 1991-96, 1996-2000, Post 2000

and Post 2005 indicating technology shift) were included in the test
matrix so that effect of technology on EF gets due representation.

ƒ The study involved mass emission testing of in-use 2-wheelers, 3-

wheelers and passenger cars and light & heavy duty commercial
vehicles (LCVs and HCVs) on Chassis dynamometer.

ƒ The tests were conducted with commercial fuel. The vehicles, then,
were subjected to maintenance at authorized service stations and
again tested with commercial fuel. The tests were repeated with varied
fuel quality to understand the effect of fuels on emissions.

ƒ It was decided to adopt the Indian Driving cycle for 2-W, 3-W and Pre-
2000 4-W vehicles. Modified Indian Driving cycle was used for testing for
post-2000 4W. For comparative purpose, post -2000 4-W were also
tested on IDC. With regard to HCV, there were no standard test
procedures and driving cycle for chassis dynamometer tests as the HCV
engines are tested on the engine dynamometer for regulatory
purposes. Moreover, there was no correlation between engine test and
field emission performance of the vehicle. Therefore, a special cycle
developed by ARAI for HCV was used. This overall Bus Driving cycle is
based on the average driving pattern of HCV vehicles in the four metro
cities (Mumbai, Delhi, Kolkata and Chennai), and was the best

56
 available driving cycle for HCV category of vehicles. Test cycles used in
the study are given in Table 4.2

Table 4.2: Test Driving Cycles used for development of EF

Vehicle Category Test Cycle

2/3 Wheeler vehicles Indian Driving Cycle (IDC)
Pre 2000 Model year Four Wheeled vehicle Indian Driving Cycle
with Gross Vehicle Weight (GVW) less than or
equal to 3500 kg
Post 2000 Model year Four Wheeled vehicle Indian Driving Cycle and Modified
with GVW less than or equal to 3500 kg Indian Driving Cycle
For vehicles with GVW above 3500 kg Overall Bus Driving Cycle (OBDC)

ƒ Different inertia settings used depending on the vehicle category are as

follow:
o 2-wheelers: ULW (Un-laden Weight) + 75 kg
o 3-wheelers gasoline: 225 kg (3 passengers x 75)
o 3-wheeler diesel: GVW
o Passenger cars: ULW+225 kg (3 passengers x 75 kg)
o Multi Utility Vehicles: ULW + 450 kg (6 passengers x 75kg)
o LCV (Bus): ULW + 1500 kg (equivalent to 20 passengers of 75 kg
weight each)
o LCV (Trucks): GVW (as specified by the vehicle manufacturer)
o HCV (Bus): ULW + 4500 kg (equivalent to 60 passengers of 75 kg
each)
o HCV (Trucks): GVW (to be limited to 20 ton max. for GVW > 20tons. If
GVW is less than 20 tons, Inertia was set to the maximum specified
GVW).

ƒ Apart from measuring conventional emissions like CO, HC, NO 2 , CO 2

and PM, emissions of PAH, Benzene, 1, 3-Butadiene and Aldehydes
were also measured and expressed in mg/km.

ƒ The PM was chemically characterized into SOF (Oil and fuel fraction)
and IOF (Sulphate, Nitrate, H 2 O, Carbon Soot, and Metal Oxides). The
particulate size distribution in terms of number, size and mass was also
measured by ELPI and MOUDI instruments.

ƒ The idle and constant speed mass emissions were also measured and
expressed in g/min.
For EF development, each test vehicle was mounted with its drive
wheels on the rollers of the test bed (as depicted in Figure 4.2) whose

57
 rotational resistance was adjusted to simulate friction and
aerodynamic drag. Inertial masses were added to simulate the weight
of the test vehicle as per the category of the vehicle. A variable speed
vehicle cooling blower, mounted at a short distance in front of the
vehicle provided the necessary cooling. The test vehicle was then
soaked to ambient temperature conditions; and maintained in that
state till the commencement of the test on the chassis dynamometer.
This was required to get the vehicle and the engine conditions to the
test cell ambient conditions. Before starting the test, the chassis
dynamometer was warmed up for 30 minutes with the vehicle
mounted on the chassis dynamometer and the engine in OFF
condition. After the warm up, chassis dynamometer was calibrated to
compensate the frictional losses and then the vehicle was ready to
undergo test. The same procedure was followed for all the vehicles. A
highly skilled driver was enlisted to drive the test vehicles on the chassis
dynamometer. After ensuring the calibration of the test cell, the engine
was started and maintained at idling condition for prescribed period as
per the applicable regulatory test procedure depending upon the
vintage and category of the vehicle. Thereafter, the exhaust sampling
was started. The exhaust gases produced by the test vehicle were
diluted with fresh air using a DT (dilution tunnel) and a critical-flow
venturi-type CVS (constant volume sampler). For gaseous emission
measurement, a constant proportion of the diluted exhaust gas was
extracted for collection in one sample bag.

The gas analysis of each sample bag was done immediately after
each test. The gases in the sampling bag was analyzed for
concentrations of CO (carbon monoxide), NO 2 (nitrogen oxides), THC
(gasoline) and CO 2 (carbon dioxide), and the emissions were
expressed in g/km. For PM measurement, the flow capacity of the CVS
and DT system was such that the temperature at the particulate
sampling point was below 52°C during the test. The particulate
emissions were collected on the primary and back up filters. Before
starting the test the primary and secondary particulate filters were
conditioned as per the procedure given in EEC directives. They were
then weighed and then installed in the particulate sampling system. At
the end of the test the filters were removed and again conditioned as
per the procedure given by the EEC and weighed.

ƒ A total of 450 emission tests were performed in 89 vehicles.

ƒ The mass emission results of 96 vehicles, conducted under source

profiling study, were also used for working out EF.

58
 Figure 4.2: Schematic Test Cell Layout

An Expert Group on EF was constituted by CPCB to critically analyze the

data, identify and suggest corrective actions for anomalies in the data, if
any, and suggest emission factors for different categories. The Group, after
numerous debates & deliberations held in its five meetings, agreed on a
total of 62 EF. The EF, worked out depending on vehicle categories,
vintage and engine cubic capacities, are provided at Annexure – VII, and
detailed report is available at (http://cpcb.nic.in/DRAFTREPORT-on-
efdiv.pdf).

EF for Non-Vehicular Sources

Besides vehicles, a wide range of other emission sources exits in Indian

urban areas. There are a few typical urban sources, which were common
to all six project cities. These include different fuel combustions for domestic
and commercial use, diesel generator (DG) sets, etc. Some of the sources
such as refuse burning paved & unpaved roads, small-scale industries, etc.
were city-specific. Institutes, responsible for studies in different cities,
reviewed secondary information from local organizations such as State
Pollution Control Boards (SPCB), Municipalities, R&D Institutes, Industrial
Associations and EF used in different earlier studies. The data available
locally, particularly in respect of industries, were preferred. In order to arrive
at uniform EF for other sources, an Expert Group reviewed EF available for
developing countries as compiled by World Health Organization (WHO),
World Bank (WB), Asian Development Bank (ADB), and United State
Environment Protection Agency (USEPA). Based on the activity, type of
fuel, rate of fuel use, fuel characteristics, level of control, etc., the Group

59
arrived at consensus on best suitable EF. Since, this exercise was based on
scientific judgment rather than actual field measurements, possibility of
some error cannot be ruled out. However, developing EF for a large
number of sources was neither feasible nor desired. A set of uniform EF,
recommended by the Expert Group, was used for preparing EI in all the six
cities. These EFs are provided at Annexure – VIII.

4.3 Emission Inventory

Emission inventories are for delineating the total mass of pollutants from
vehicular, area and industrial sources. In each of these broad categories,
further sub-categories have also been detailed and their contributions
estimated. All six cities have some major sources that are common such as
vehicles, domestic and biomass burning, and re-suspended dust. Other
sources are variable and specific to a city, such as emissions from ships in
Mumbai, agriculture burning in Pune or leather burning in Kanpur. Emission
inventories demonstrate the high variance from city to city due to city
specific sources and characteristics. In area source, individual city specific
sources are highly variable and show distinct character of the city.
Industrial sector contribution is masked by power plant sources, wherever it
is present.

It is important to note that high load contribution does not necessarily lead
to high ambient contribution of a particular source at the receptor site. This
is due to the fact that emission distribution in atmosphere depends upon
multitude of factors such as local meteorology, location, height of release,
atmospheric removal processes and diurnal variation. Further, it is equally
important that the ambient fine particles which constitute higher fractions
of toxics are mostly contributed by ground level sources such as vehicles,
refuse burning, bakeries-crematoria, road side eateries, etc. Since mass
based emission inventories do not provide the complete picture of real
contributions at the levels of exposure, it is pertinent to use chemical
analysis data with appropriate receptor models such as Chemical Mass
Balance Model. Details of EI for the six cities are described below:

Particulate Matter (PM 10 )

The total emissions of PM 10 show large variations across all cities. Kanpur (9
T/d), Chennai (11 T/d) and Pune (32 T/d) have relatively lower emissions,

60
while Delhi (147 T/d) has the maximum emission load. Bangalore (54 T/d)
and Mumbai (73 T/d) lie intermediate within these extremes.

Depending on the profile of each city, the prominent sources vary across
the cities. While looking at the relative sectoral share in each of the cities, it
is important to address major consideration that in some cases a few
prominent sources in the city can mask the contribution of the other
sources. Figure 4.3 shows the prominence of sources of PM 10 in different
cities. Kanpur shows equal prominence of industries as well as area sources
almost similar to Delhi. However, Delhi, Pune and Chennai show
significantly high occurrences of road dust compared to other sources.
Bangalore is the only city with highest PM contribution coming from
vehicular sector with 41%. Pune with 61 % and Chennai with 72 % show the
highest percent contribution from road dust. Pune has the lowest industrial
contribution (1-3%) as it has mainly engineering industries and also most of
them are outside the city boundary.

80 Percent Contribution PM

Vehicles Industrial
70

60 Area Source Road (Paved & Unapved)

50

40

30

20

10

0
Kan Mum Del Ban Pun Che

Figure 4.3: Prominence of Sources of PM 10

Broadly, the major sources of PM 10 emissions across these cities are

vehicular exhaust emissions, road dust re-suspension, and industrial
emissions. The share of vehicular exhaust emissions varies considerably from
city to city: 6% in Mumbai, 7% in Delhi, 14% in Chennai, 18% in Pune, 21% in
Kanpur, and 41% in Bangalore. Despite the large fleet of vehicles in Delhi,
the share is less due to the presence of other significant sources such as
power plants, road dust re-suspension, etc. In addition to this fact, due to
the whole public transport system being on CNG the contribution is less. It
may be noted that road dust re-suspension is dependent on the road
conditions and thereby the silt content, as well as on the characteristics of
the vehicle fleet. The contribution of industrial sector to PM 10 pollution load
is highest in Kanpur (33%) owing to many small scale industries and Mumbai

61
(23%) due to a few large industries. Delhi (22%) too has a relatively large
industrial sector share, mainly due to the presence of power plants. The
share of industries to PM 10 load in Bangalore is 14% and in Chennai, it is just
2%.

In addition to the above three major sources, there are other contributors
that are significant in only some of the cities. For example, domestic
sources contribute 19% in Kanpur due to large scale burning of biomass
and coal. In Pune, the contribution of domestic sources is about 7%. DG
sets contribute 7% in Bangalore but are less than 1% in the other cities,
which could be attributed to the variation in number of DG sets and their
usage due to power cuts in each city. Other city specific sources include
landfill open burning in Mumbai (11%), bakeries in Mumbai (6%) and
garbage burning in Kanpur (5%). Construction activities too contribute
significantly (8 – 14%) to PM 10 emission load in Chennai, Mumbai, Delhi and
Bangalore. Figure 4.4 presents distribution of source contributions of PM 10
emissions in six cities.

Domestic
Combustion,
1.80, 3.31%
DG Se ts,
3.60, 6.63%

Pave d &
Industry Unpave d
Point, Rd. Dust,
Hotel & 7.80, 14.36% 10.90, 20.0%
Restaurent, To ta l
0.10, 0.18% 5 4 .3 8
TP D

Construcion, Vehicle
7.70, 14.18% Exhaust,
22.40, 41.0%

Emission Inventory PM10-Bangalore

62
 Restaurents/
Bakeries/Stre et
Industry Point,
Ve ndors,
0.40, 1.25%
1.01, 3.14% O thers,
0.83, 2.58% Domestic
Agricultural Combustion,
Pumps, 0.46, 1.43%
0.60, 1.85%
Domestic
Combustion
Slum,
1.86, 5.78%
Construcion,
1.43, 4.44% To ta l
Ve hicle 3 2 .3
Exhaust, TP D Paved &
5.91, 18.33% Unpaved
Rd. Dust,
19.75, 61.21%

Emission Inventory PM10-Pune

Figure 4.4: Distribution of Source Contributions of PM 10 Emissions in Six Cities

Oxides of Nitrogen (NO x )

Nitrogen Oxides is one of the most important gaseous pollutants in almost

all the six cities due to its increasing trend. Due to reduction of sulphur
content in the automobile fuel, SO 2 values have shown continuous decline
and, therefore, after PM, NO x appears to be next important pollutant.
Unlike PM that is also contributed by natural sources and re-suspension,
NO x is mainly contributed by man-made sources such as vehicles,
industries and other fuel combustions. The variation in cities NO x loads is
very apparent as evident from the Figure 4.5:

Percent Contribution NOx

100

90
Vehicles Industrial Area Source
80

70

60

50

40

30

20

10

0
Kan Mum Del Ban Pun Che

Figure 4.5: Prominence of Sources of NO x

As can be seen, except Mumbai and Delhi, all cities have maximum
contribution from vehicular sources. Mumbai and Delhi exhibit high
percentage of NO x from industries owing to presence of power plants
within the city boundary. The industrial contribution excluding power plants

63
is also highest in Delhi (65T/d) followed by Mumbai (23 T/d), Bangalore (17
T/d) and Kanpur (8 T/d). The least industrial contribution of 0.9 T/d is in Pune.
Delhi has about 295T/d of NO x contribution from power plant alone.
Contributions from other sources such as hotels, bakeries, open eat outs
etc. are much.

It is important to note that though in some cases a source indicates high

load within the city boundary, due to elevation of the emission as in the
case of power plants with high elevation providing high dilution and
dispersion, the exposure impacts due to this source could be lower. Source
contributions in respect of NO x emissions are presented in Figure 4.6.

64
 O thers, 0.30,
Re staure nts/ Industry Point, 0.74%
Bake rie s/Stre e t 0.89, 2.15%
Ve ndors, 0.24, Dome stic
0.58% C ombustion,
Dome stic 0.40, 0.96%
Combustion
Slum, 0.39,
0.94%

To ta l
4 1.4
TP D

Ve hicle
Exhaust,
39.19, 94.63%

Emission Inventory NOx-Pune

Figure 4.6: Distribution of Source Contributions of NO x Emissions in Six Cities

Sulphur Dioxide (SO 2 )

As explained earlier, SO 2 values have been declining continuously in

almost all the six cities due to cleaner fuel being provided for vehicles as
also use of low sulphur coal use in power plants. Other fuels, being used in
domestic sector and, in some cities for vehicles, are LPG and CNG
respectively. Figure 4.7 presents the respective percentage of contribution
from three major sources. Delhi, with 255 T/d from power plant and 9T/d
from other industries, has the highest load of SO 2 followed by Mumbai with
67T/d from power plant and 78 T/d from other industries. The least
contribution is from Chennai (less than 0.1T/d). The DG sets contribution is
highest in Bangalore with 3.35 T/d and Delhi about 0.5 T/d.

Percent Contribution SO2

120

Vehicles Industrial Area Source

100

80

60

40

20

0
Kan Mum Del Ban Pun Che

Figure 4.7: Prominence of Sources of SO 2

SO 2 contribution from vehicles is highest in Chennai (48%), followed by

much less in Bangalore (16%), Pune (13 %) and Kanpur (less than 12 %).

65
Industrial source was highest in Delhi (98%), Mumbai (93%) and Pune 73%.
Chennai showed the lowest value for SO 2 contribution.

Die se l
Aircraft Marine Domestic
Fune ral Locomotive , Ve hicle
Ve sse ls, 0.26, Combustion, Ve hicle
Wood/C re mator 3.97, 2.57% Exhaust, 0.63,
0.17% 3.46, 2.23% Exhaust,
ia, 0.02, 0.01% 0.41% Dome stic Dome stic
0.903, 12.70%
C ombustion, C ombustion
Landfill O pe n O pe n Eatouts,
Burning, 0.30, 0.04, 0.03% O the rs, 0.088, 1.24% Slum,
Hote l & 0.449, 6.31% 0.323, 4.54%
0.19%
Re staure nt,
O pe n Burning, 0.75, 0.49% Re staure nts/
0.07, 0.05% Bake ri e s/Stre e t
Bake ries, 0.07, Ve ndors,
0.04% 0.118, 1.66%
To ta l
7 .1
To ta l TP D
15 4 .7
TP D

Industry Powe r Industry Point,

Plant, 67.05, Industry Point, 5.231, 73.55%
43.33% 78.11, 50.48%

Emission Inventory SO2-Pune

Emission Inventory SO2 - Mumbai

Figure 4.8: Distribution of Source Contributions of SO 2 Emissions in Six Cities

These emission estimates have been used later in the study for projected
loads from all these sources for the year 2012 and 2017 without control
(Business as usual) and with control (action plan components). These loads
have been used in predictive modeling with a view to project future
scenario.

66
Contribution of Different Types of Vehicles in Emissions

City wise finer distribution of source contribution within transport sector in

respect of PM 10 and NO x is presented in Figures 4.9 and 4.10 respectively.
As can be seen for transport sector, the PM 10 contribution (40% - 59%) is
mainly coming from heavy duty diesel vehicles in almost all the cities. In
case of Kanpur, contribution of 3-wheelers is highest (39%), which is
followed by heavy duty diesel vehicles (28%). With regard to NO x , again
heavy duty vehicles are major contributors (43 – 75%). It may be pertinent
to mention that these contributions depend on share in terms of number of
vehicles plying in the city and, therefore, present relative contribution
among various vehicle types.

2W,
othe rs, 0.25, 16%
2W,
0.448, 2.00% Buses &
2.912,13.00%
Trucks,
Trucks, 0.68, 43%
5.37, 24.00% 3W (LPG),
0.21, 13%
To ta l
3W, 1.5 8
To t a l
4.70, 21.00% TP D
2 2 .4
TP D

Car (Petrol), 3W (Diese l),

0.01, 1% 0.2, 13%
Bus, Car,
6.04, 27.00% 2.91,13.00% Car (Die sel), 3W (Pe trol),
0.07, 4% 0.16, 10%

Mobile Source -EI: PM10-Bangalore Mobile Source -EI: PM10-Chennai

Bu_CNG,
2W-2S,
0.0097, 2W-4S,
0.4268,
0.10% 0.6499,
4.40%
Bu_D, 6.70%
0.6014,
6.20% 3W-CNG,
0.582, 6.00%

To t a l
9 .7
Trucks, TP D
4.4426,
45.80% LCV, 2.6772,
27.60%

4W-P,
4W-D, 0.1649,
0.1455, 1.70%
1.50%

Mobile Source -EI: PM10-Delhi

67
 Bike s,
Buses, 0.84, 14%
1.13, 19%

Scooters,
0.56, 9.49%

To t a l
5.9
Trucks, TP D
1.22, 20.68% Autorick,
0.91, 15.42%

Cars,
0.17, 2.88%
3WGC ,
4WGC, 0.34, 6%
0.73, 12.37%

Mobile Source -EI: PM10-Pune

Figure 4.9: Contribution of Different Vehicle Types in PM 10 Emissions in Six

Cities

othe rs, 2W,

2.9272, 2W, 21.954, 1.4, 17.01%
Trucks, 2.00% 15.00%
29.272, 3W (LPG),
20.00% 0.03, 0.36%
3W, 2.9272,
2.00% 3W (Diesel),
0.28, 3.39%

To t a l To t a l 3W (Petrol),
14 6 . 4
C ar, 8 .2 3 0.53, 6.41%
TP D 17.5632, Buses & TP D
12.00% Trucks, Car (Diesel),
4.73, 58% 0.27, 3.26%

Car (Petrol),
Bus, 0.99, 11.97%
71.7164,
49.00%

Mobile Source -EI: NOx-Bangalore Mobile Source -EI: NOx -Chennai

2W-2S, 2W-4S,
0.1684, 7.6622,
0.20% 9.09%
3W-C NG,
Bu_C NG, 2.2734,
12.3774, 2.70%
14.69% LC V,
10.6092,
To t a l
12.59%
8 4 .2
Bu_D, 7.578, TP D
8.99% 4W-P,
10.104,
11.99%

Trucks,
32.2486, 4W-D, 0.842,
38.26% 1.00%

Mobile Source -EI: NOx-Delhi

68
 Bikes,
7.76, 19.80%
Buses,
11.74,29.96% Scooters,
1.86, 4.75%
To t a l Autorick,
3 9 . 19
2.36, 6.02%
TP D
3WGC,
0.81, 2.07%
4WGC,
Trucks,
2.75, 7.02%
8.7, 22.20%
Cars,
3.21, 8.19%

Mobile Source -EI: NOx-Pune

Figure 4.10: Contribution of Different Vehicle Types in NO x Emissions in Six

Cities

69
 5

Receptor Modeling and Source Apportionment

Two approaches were followed for quantifying contributions of pollution
sources. First approach is receptor modeling, as summarized earlier, which
is based on analyses of PM in the ambient air at a given location and
matching their characteristics with those of chemically distinct source
types. The second approach is dispersion modeling, which is relatively a
traditional approach, wherein emissions from different sources (emissions
inventory), geophysical and meteorological conditions are used to
calculate ambient concentrations at defined receptors (where ambient
concentrations are measured). Dispersion and receptor models are
complementary, and applying them to the same situation reveals
deficiencies in each one that, when remedied, lead to a better
assessment of pollution sources. Therefore, a combination of these two
approaches provides a better insight to the contribution of polluting
sources to air quality. In this study Factor Analysis and Chemical Mass
Balance (CMB 8.2) models were used for receptor modeling. Factor
Analysis (varimax rotated Principal Component Analysis) was initially
applied to assess dominance of major source groups contributing to
receptors. CMB-8.2 model was, then, used to get estimates on contribution
of different source groups to the ambient particulate concentrations.
Source dispersion modeling formed a vital component of this project. This
was used to assess impacts of different control options/ strategies for
delineating roadmap for air quality management. Ambient concentrations
are used to calibrate the models for running future scenarios.

The fundamental principle of receptor models is that mass conservation

can be assumed and a mass balance analysis can be used to identify and
apportion sources of airborne particulate matter in the atmosphere. The
approach to obtaining a data set for receptor modeling is to determine a
large number of chemical constituents such as elemental concentrations
in a number of samples. Receptor models use monitored pollutant
concentration and some information about the chemical composition of
local air pollution sources (profiles) to estimate the relative influence of
these sources on pollutant concentrations at any single monitoring
location. Receptor models are retrospective i.e. they can only assess the
impacts of air pollution source categories on pollutant concentrations that
have already been monitored.

70
5.1 Factor Analysis: Methodology

Factor analysis is a form of exploratory multivariate analysis that is used to

either reduce the number of variables in a model or to detect relationships
among variables. It replaces a large set of inter-correlated variables with a
smaller number of independent variables. Thus, the new variables (Factors)
are the linear combinations of original variables used in the analysis. The
factor analysis assumes that the total concentration of each constituent is
made up of the sum of elemental contributions from each of different
pollution source components. All variables involved in the factor analysis
need to be interval and are assumed to be normally distributed. The goal
of the analysis is to try to identify factors which underlie the variables. There
may be fewer factors than variables, but there may not be more factors
than variables. The factor analysis method is quick and requires
characterization of PM 10 collected at receptors only without the need of
obtaining chemical profiles of all the emission sources. Factor analysis is
often used in data analysis to:

ƒ Study the correlations of a large number of variables by grouping the

variables into “factors”, The variables within each factor are more
highly correlated with variables in their factor than with variables in
other factors
ƒ Interpret each factor according to the meaning of the variables
ƒ Summarize many variables by a few factors. The scores from the factors
can be used as data for tests, regression etc.

In order to reduce the dimensionality in the data set, the new variables i.e.
factors must have simple interpretations. But un-rotated principal
components are often not readily interpretable since they each attempt
to explain all remaining variance in the data set. For this reason, a limited
number of components are usually subjected to rotation that constitutes a
maximization of the variance of the communality normalized loadings
(correlations). Such rotations tend to drive variable loadings toward either
zero or one on a given factor.

Based on the above considerations, the Varimax rotated factor analysis

technique based on the principal components has been used in the
determination of the contribution of respirable particulate matter pollution
sources. The components or factors rotated had Eigen values greater than
one after rotation. It is widely used technique, because it is a simple, non-
parametric method of extracting relevant information from confusing data
sets. With minimal additional effort PCA provides a roadmap for how to

71
reduce a complex data set to a lower dimension to reveal the sometimes
hidden, simplified structure that often underlie it.

Chemical analysis data of PM 10 samples collected at each of the sites

representing different activity zones used as input to the factor analysis.
Principal Component analysis was applied to the chemical speciation
data of selected species of all the samples collected at a site in all the
seasons.

5.2 CMB Model 8.2: Methodology

A mass balance equation can be written to account for all m chemical

species in the n samples as contributions from p independent sources:

C i =∑ m j x ij a ij
j
Where, C i is the concentration of species i measured at a receptor site, x ij is
the ith elemental concentration measured in the jth sample, and m j is the
airborne mass concentration of material from the jth source contributing to
the jth sample. The term a ij is included as an adjustment for any gain or loss
of species i between the source and receptor. The term is assumed to be
unity for most of the chemical species. CMB model assumptions are:

ƒ Compositions of source emissions are constant over the period of

ambient and source sampling;
ƒ Chemical species do not react with each other (i.e., they add linearly);
ƒ All sources with a potential for contributing to the receptor have been
identified and have had their emissions characterized;
ƒ The number of sources or source categories is less than or equal to the
number of species;
ƒ The source profiles are linearly independent of each other; and
ƒ Measurement uncertainties are random, uncorrelated, and normally
distributed.

The following approach was used for CMB modeling:

ƒ Identification of the contributing source types based on primary

emission inventory data collected around the monitoring sites in the
area of 4 km2;

72
ƒ Selection of chemical species to be included in the calculation.
Following species were analyzed from the daily PM 10 samples collected
at respective sites for 20 days in three seasons.
o Carbon fractions based on temperature (Organic Carbon &
Elemental Carbon) using Thermal Optical Reflectance (TOR) Carbon
Analyzer,

o Ions (Anions- fluoride, chloride, bromide, sulphate, nitrate &

phosphate and Cations sodium, ammonium, potassium, magnesium
& calcium) using Ion Chromatography

o Elements (Na, Mg, Al, Si, P, S, Cl, Ca, Br, V, Mn, Fe, Co, Ni, Cu, Zn, As,
Ti, Ga, Rb, Y, Zr, Pd, Ag, In, Sn, La Se, Sr, Mo, Cr, Cd, Sb, Ba, and Pb)
using ED-XRF, GT-AAS or ICP-AES or ICP-MS

o Molecular Markers (Alkanes, Hopane, Alkanoic acids qualitative

analysis using GC-MS & quantitative analysis using GC-FID; PAHs
analysis using High Performance Liquid Chromatography (HPLC)).
Analysis of organic molecular markers was performed on 20 days
composite sample and the species analysed were distributed in
proportion to the organic carbon content in the respective samples.

ƒ Selection of representative source profiles with the fraction of each of

the chemical species and uncertainty. Source profiles developed for
non-vehicular sources and vehicular sources were used.
ƒ Estimation of the both ambient concentrations and uncertainty of
selected chemical species from the particulate matter collected at
respective sites; and
ƒ Solution of the chemical mass balance equations through CMB-8.2
model run.

Source contribution estimates (SCE) are the main output of the CMB
model. The sum of these concentrations approximates the total mass
concentrations. When the SCE is less than its standard error, the source
contribution is undetectable. Two or three times the standard error may be
taken as the upper limit of the SCE in this case. Assuming that the errors are
normally distributed, there is about a 66% probability that the true source
contribution is within one standard error and about a 95% probability that
the true concentration is within two standard errors of the SCE. The
reduced chi square (χ2), R2, and percent mass are goodness of fit measures
for the least-squares calculation. The χ2 is the weighted sum of squares of
the differences between calculated and measured fitting species
concentrations divided by the effective variance and the degrees of

73
freedom. The weighting is inversely proportional to the squares of the
precision in the source profiles and ambient data for each species. Ideally,
there would be no difference between calculated and measured species
concentrations and χ2 would be zero. A value of less than one indicates a
very good fit to the data. Values greater than 4 indicate that one or more
of the fitting species concentrations are not well-explained by the source
contribution estimates. R2 is determined by the linear regression of the
measured versus model-calculated values for the fitting species. R2 ranges
from 0 to 1. The closer the value is to 1.0, the better the SCEs explain the
measured concentrations. When R2 is less than 0.8, the SCEs do not explain
the observations very well with the given source profiles. Percent mass is
the percent ratio of the sum of model-calculated SCEs to the measured
mass concentration. This ratio should equal 100%, though values ranging
from 80 to 120% were considered acceptable.

5.3 Source Apportionment of PM 10 & PM 2.5

There are a large number of urban anthropogenic as well as background

sources of such a high particulate pollution. These sources include large,
medium and small-scale industries, household fuel use for cooking and
heating, refuse burning, vehicular emissions, re-suspended road dust,
construction activities, agricultural activity, naturally occurring dust and
trans-boundary migration from other regions, etc. It is accepted that the
configuration of possible contributing sources in different cities may vary
widely as different potential city-specific sources emit particles of varying
composition and sizes. However, respirable size fractions (10 microns and
finer) affect public health much more than large particles. For any
effective control strategy, it is important to have a good understanding of
not only the level of exposure to various ecological receptors, but also the
relative contributions from different sources along with the likely impacts
and cost-benefit analysis of various control options. Ambient air quality
monitoring of PM 10 and PM 2.5 , though is an important step, has limited role
in formulation of strategy, as it can merely signal the existence and extent
of problem. Ambient air monitoring at the strategic receptor needs to be
supplemented by studies to quantify the contribution made by different
sources and to assess the impacts costs (including public health
consequences) in order to prioritize the cost-effective mitigation
interventions. The receptor modeling studies, as depicted in Fig. 5.1,
provided the requisite tool/techniques for the purpose.

74
 Identification of PM 10 /PM 2.5 sources
within 2X2 km area around monitoring
stations

Sampling of PM 10 /PM 2.5 Sampling of PM 10 /PM 2.5 for each

at 7/10 locations potential source identified

Analysis of PM 10 /PM 2.5 for various

chemical elements/species

Figure 5.1: Scheme of Source Apportionment

Receptor models use monitored pollutant concentration (PM 10 and PM 2.5 )

at the receptor and apportion it to the contributions of different sources
using Chemical Mass Balance. The state-of-the- art model (CMB-8.2) was
used for this purpose. Receptor models assess the impact of air pollution
source categories on pollutant concentrations that have already been
monitored. The most important assumptions for CMB model, relevant to this
project are:

ƒ Chemical species do not react with each other (i.e. they add linearly).
ƒ All the sources with a potential to contribute to the receptor have been
identified and their emission characterized.
ƒ Measurement uncertainties are random, uncorrelated and normally
distributed.
ƒ CMB is intended to complement rather than replace diffusion modeling
tool/techniques.

Source apportionment analysis was carried out in all seasons (except rainy
season, as it does not represent the actual situation on contribution of
different sources due to wash out component) for PM 10 or PM 2.5 . The CMB-
8.2 modeling involves:

ƒ Identification of the contributing sources types;

75
ƒ Selection of chemical species or other properties to be included in the
calculation;
ƒ Estimation of the fraction of each chemical species, which is contained
in each source type (Source Profiling of PM 10 and PM 2.5 emitted);
ƒ Estimation of the uncertainty in both ambient concentrations and
source profiles; and
ƒ Analysis of modeling results.

One of the major requirements of CMB 8.2 is the chemical quantification of

different markers (specific to each source type) as fractions of total PM 10 or
PM 2.5 concentrations monitored at the respective receptors.

5.4 Chemical characterization of PM 10 and PM 2.5

PM 10 and PM 2.5 samples were subjected to detailed chemical speciation

comprising analysis of ions, elements, organic & elemental fractions of
carbon, and molecular markers. The sources identified through detailed field
visits in each city, were categorized as general and city specific sources. The
markers, specific to these sources, were identified based on detailed
literature survey and consultations with Experts. Chemical species including
molecular markers identified for analysis are given in Table 5.1.

Table 5.1: Target Physical and Chemical components (groups) for

Characterization of Particulate Matter

Components Required filter matrix Analytical

methods
PM 10 / PM 2.5 Teflon or Nylon filter Gravimetric
paper. Pre and post
exposure
conditioning of filter
paper is mandatory
Elements (Na, Mg, Al, Si, P, S, Cl, Teflon filter paper ED-XRF, GT-AAS
Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, or ICP-AES or ICP-
Zn, Ga, As, Se, Br, Rb, Sr, Y, Zr, MS
Mo, Pd, Ag, Cd, In, Sn, Sb, Ba,
La, Hg, and Pb)

76
Components Required filter matrix Analytical
methods
Ions (F-, Cl-, Br-, NO 2 -, NO 3 -, SO4- -, Nylon or Teflon filter Ion
K+, NH 4 +, Na+ , Ca++, Mg++) paper (Same Teflon chromatography
filter paper can be with conductivity
utilized if ED-XRF is detector
used for elements
analysis
Carbon Analysis (OC, EC and Quartz filter. Pre- TOR/TOT method
Carbonate Carbon) baking of quartz
filter paper at 600 ºC
is essential
Molecular markers
Alkanes n- Hentriacontane
n-Tritriacontane
n- Pentatriacontane
Hopanes 22, 29, 30 –
Trisnorneohopane
17α(H), 21β(H)-29
Norhopane The left over quartz Extraction,
17α(H),21β(H) filter paper after followed by GC-
norhopane OC/EC analysis MS analysis with
Alkanoic Hexadecanamide should be taken as and without
acid Octadecanamide composite sample derivatization
PAHs Benzo[b]fluoranthene to represent a
Benzo[k]fluoranthene location and
Benzo[e]pyrene specified duration
Indeno[1,2,3- of exposure
cd]fluoranthene
Indeno[1,2,3-
cd]pyrene
Phenylenepyrene
Picene
Coronene
Others Stigmasterol
Levoglucosan

5.5 Source Profiling of Vehicular and Non-vehicular Emission Sources

Development of chemical profiles of particulate matter for air polluting

sources is important for use as input to receptor oriented source
apportionment models like Chemical Mass Balance (CMB). The U.S

77
Environmental Protection Agency’s (EPA) SPECIATE database and several
studies carried out in other parts of the world provide an extensive collection
of source profiles. However, differences in sources, operating conditions,
geology and climate may make them unsuitable for the conditions and
sources in Indian cities. No database on profiles for sources specific to India
was available. Therefore, studies for development of such profiles for Indian
conditions, specific to each of the cities, were included as a key component
of the project.

The sources were broadly categorized under vehicular and non-vehicular

sources. PM emission samples collected from representative sources in all the
six cities were subjected to detailed chemical characterization involving
analysis of constituents similar to ambient air samples. The methodology
adopted is summarized below:

5.6 Vehicular Exhaust Emission Profiles

Chemical speciation of vehicle exhaust particulate matter (source profiles)

is required for assessment of contribution from vehicle sources using
receptor model. The available international database “SPECIATE-4.0” is the
U.S. Environmental Protection Agency’s (EPA) repository of total organic
compound (TOC) and particulate matter (PM) speciation profiles of air
pollution sources(vehicular as well as non-vehicular sources). The vehicle
categories available under Speciate for CMB application mostly fall under
LCV and HCV category. No data were available on emission profiles for
vehicles plying in Indian cities. In view of this and considering the Indian
scenario, where traffic composition includes 2-Wheeler (2-Stroke and 4-
Stroke), 3-Wheeler (Diesel, Gasoline, LPG and CNG) along with LCV and
HCV, indigenous vehicular emission profiles were developed.

To carry out detailed chemical characterization of Particulate Matter, two

mass emission tests were carried out on each vehicle - one with Teflon for
mass, ions and element analysis and another on Quartz for carbon
fractions and molecular markers analysis, following Indian Driving Cycle
(IDC) for 2/3 wheelers, Modified IDC for passenger cars and LCVs and
Overall Bus Driving Cycle for HCVs on Chassis dynamometer for collection
of particulate matter on respective filter papers. The constituents, given in
Table 3.2 and similar to analysis of ambient air samples, were analyzed.
The observed concentrations of the chemical species were determined as
percentage of total PM collected from the vehicle exhaust. Corresponding
uncertainties were also found out for the species.

78
Diesel and gasoline composite: Composite profiles for all gasoline and
diesel vehicles including different categories and vintages were
generated, which are representative of vehicle fleet including all
categories and vintages in gasoline and diesel vehicles. Distribution of
exhaust particulate matter in composite profiles for all gasoline and all
diesel vehicles in different chemical groups like organic carbon, elemental
carbon, ions, elements and other is presented below:

ƒ In all gasoline and diesel composites, organic carbon was found to

dominate with 54% and 50% respectively. Elemental carbon fraction
was found to be higher in all diesel composite (22%) than in all gasoline
composite (7%), which is a major distinguishing factor between gasoline
and diesel composite.
ƒ Ions percentage was found to be higher in gasoline composite (19%) as
compared to diesel composite (4%). Higher fraction of ions in gasoline
exhaust can be attributed to the higher sulphate, chloride, calcium
and sodium ions in exhaust due to use of lube oil.
ƒ Elements, mainly wear metal (Fe, Pb & Cu) are found to be higher in
gasoline exhaust than diesel exhaust. Although, the absolute quantities
of these metals were found to be similar from both the vehicle exhaust,
percent contribution in gasoline vehicles is higher due to less overall
mass of PM in gasoline exhaust.
ƒ Organic molecular markers fractions were found to be higher in
gasoline exhaust composite (~4%) than in diesel exhaust composite
(~1.3%). 17 alpha (H), 21 beta (H)-Hopane was found to be marginally
higher (2.1%) in gasoline than in diesel exhaust (1.6%). Hopanes are
present in lubricating oil used by gasoline and diesel powered engines
and hence, are emitted in particle phase from both the engine types.
ƒ Overall mass concentration of all the 16 PAHs is higher in diesel than
gasoline exhaust due to higher PM mass in Diesel vehicles. Qualitative
interpretation (2, 3, 4 ,5 rings PAH) reveals that mass concentration of
2,3 and 4 ring lighter PAH compounds e.g. Fluorene + Acenaphthene,
(3-ring) Fluoranthene and (4-ring) Pyrene is higher in Diesel vehicles as
compared to Gasoline vehicles. Whereas, similarly mass concentration
of 5-ring heavier PAH compounds (e.g. Benzo(a)Pyrene,
Dibenz(a,h)anthracene, Indeno(1,2,3cd) pyrene and
Benzo(ghi)perylene) is higher in diesel vehicles as compared to gasoline
vehicles. However, overall mass of 2, 3 and 4 ring lighter PAH
compounds is high as compared to 5-ring heavier PAH compounds in
Diesel as well as Gasoline vehicles.

79
Co-linearity in data: Co-linearity checks were performed to assess the
gasoline and diesel composite profiles for their distinctness. OC % found to
be close in gasoline (54%) and diesel (50%). However, diesel and gasoline
exhaust EC content was 22% and 7% respectively. Diesel exhaust particles
are known to have contained much higher fraction of elemental carbon
than gasoline exhaust particles and based on this elemental to organic
carbon ratio in gasoline and diesel exhaust, contribution from both engine
types can be differentiated. For certain elements % share was found to be
very less and similar. PAHs and ions data were not found to be collinear
and show higher % in gasoline exhaust.

Category wise distribution of species: Chemical speciation data of vehicle

exhaust PM is analyzed and grouped under different categories based on
engine technology and fuel types, with following findings:
ƒ Organic carbon was found to vary from 48 to 57 % amongst the
composite of different category gasoline vehicles. Similarly, elemental
carbon was varied from 3% to 13%. OC and EC in composite of all
gasoline vehicles was 52% and 6.6% respectively. Category wise
composites for diesel vehicles show variation in organic carbon from 46
% to 52% and variation in EC from 16% to 25%. All diesel vehicles
composite shows OC and EC % as 49% and 22% respectively. OC % in
CNG vehicles composite varied from 29% to 58% and EC % variation
was from 6% to 22%. In case of LPG category wise composite OC %
variation was 26% to 49% and EC % variation was from 7% to 14%. All
CNG vehicle composite OC and EC % are found to be 43% and 16%
respectively, whereas all LPG composite OC and EC % are 38%and 11%
respectively.
ƒ Amongst the ions, sulphate, nitrate, chloride and ammonium ions were
found to have major share. Gasoline vehicle exhaust was found to
have higher % of ions then diesel exhaust, which may be due to lower
overall PM mass in gasoline exhaust.
ƒ Elements % were found to be very less in exhaust PM of all vehicle types.
Elements from lube oil (Ba, Ca, S, Mg, Zn, P & Mo) and engine wear
metals (Fe, Cu & Pb) were found to be comparatively in higher
proportion. In terms of % mass gasoline exhaust is found to contain
higher % of these metals as compared to diesel exhaust.
ƒ PAHs % distribution shows higher fraction of Pyrene, Fluorine+
Acenaphthene and Acenaphthalene in all vehicle categories. Total
PAHs were observed to higher in gasoline vehicle composite (3.96%)
than in diesel vehicle composite (1.26%).

80
Salient features: A comprehensive data base on source profiles generated
on Indian vehicles’ exhaust includes:

ƒ Total 192 mass emission tests on 96 vehicles (2 tests on each vehicle).

The emission factors generated in this study were supplementary to the
emission factors generated in emission factor study on in use Indian
vehicles.
ƒ Vehicles selected/tested with respect to fuel type, category and
vintage are as follow:
o Gasoline, Diesel, LPG (OE/Retrofit) and CNG (OE/Retrofit)
o 2S-2W, 4S-2W, 2S-3W, 4S-3W, Cars, LCV, HCV
o 1991-96, 1996-2000, 2000-2005+ vintage
ƒ Total of 96 nos. of Individual profiles and 44 nos. of composite profiles
were developed for different vintage, category and fuel.
ƒ The profiles are reported as percentage abundances of measured
species with their corresponding uncertainties of the PM mass
collected. These source profiles were prepared in a CMB receptor
model input-file format.
ƒ Carbonaceous material accounted for a majority of the PM mass. Of
the total carbon, OC represented on average between 54 & 50% of the
mass in gasoline and diesel vehicle exhaust PM.
ƒ Higher fraction of EC was observed in Diesel Vehicles (22%) than in
Gasoline vehicle exhaust (7%).
ƒ Organic molecular markers were found to be higher in percentage in
gasoline exhaust composite (~4%) than in diesel exhaust composite
(~1.3%). 17 alpha (H), 21 beta (H)-Hopane was found to be marginally
higher (2.1%) in gasoline than in diesel exhaust (1.6%). Overall mass
concentration of all the 16 PAHs is higher in diesel than gasoline exhaust
due to higher PM mass in Diesel vehicles. Overall mass of 2, 3 and 4 ring
lighter PAH compounds is high as compared to 5-ring heavier PAH
compounds in Diesel as well as Gasoline vehicles.
ƒ Ions fraction in gasoline vehicles were found to be higher than Diesel
vehicles. Gasoline exhaust composite was found to contain high
percentage of sulphate (~4.5%), chloride (~3.5%) calcium (~3%) ions
and sodium (~2%). In diesel exhaust composite ions percentage was
found to be below 1% for all the ions.
ƒ Calcium, barium, sodium, magnesium, zinc and iron were found to be
higher as compared to other metals in gasoline and diesel exhaust. The
Zn, P, Mg, and Ca are attributed to compounds in the lubricant while
the Fe is an indication of engine wear.

81
ƒ Unidentified percentage varied from 3 to 30 % in all the vehicle
categories.
ƒ Mass fractions (%) of carbon, ions, elements, and PAH are given in Table
5.2 and 5.3.

82
 Table 5.2: Percentage distribution of Carbon, Ions and Elements in PM 10 for different categories of vehicles

Carbon
Fraction (%) Ions (%) Elements (%)

Nitrate (NO3-)

Calcium (Ca)

Molybdenum
Chloride (Cl)

Copper (Cu)

Sodium (Na)
Magnesium
Ammonium
Vehicle Category

Barium (Ba)

Phosphorus

Sulphur (S)
Lead (Pb)
Sulphate

Zinc (Zn)
Iron (Fe)
(NH4)
(SO 4 )

(Mg)

(Mo)
OC

(P)
EC
2-stroke, 2-wheeler
(Gasoline) 57.34 3.10 4.37 0.86 4.24 0.56 0.813 3.163 0.006 0.056 0.000 0.008 1.295 0.000 0.037 0.035 0.292
4-stroke, 2-wheeler
(Gasoline) 48.63 5.08 5.07 0.39 5.94 0.00 3.263 1.792 0.036 0.082 0.000 0.000 0.398 0.031 0.319 0.000 1.229
3-Wheeler(Gasoline) 54.24 4.70 2.62 0.76 4.80 0.01 2.057 0.739 0.003 0.114 0.000 0.011 0.000 0.005 0.010 0.072 1.201
Passenger
Car(Gasoline) 47.98 13.42 2.44 1.40 3.20 0.50 0.000 2.373 0.018 0.347 0.081 0.004 0.461 0.066 0.082 0.414 0.000
3-Wheeler(Diesel) 48.73 16.20 0.95 0.04 0.56 0.00 0.539 0.148 0.006 0.003 0.000 0.000 0.389 0.008 0.010 0.021 0.120
Passenger Car(Diesel) 50.26 18.59 0.10 0.17 0.74 0.01 0.573 0.507 0.014 0.026 0.000 0.000 0.000 0.016 0.021 0.324 0.303
LCV(Diesel) 46.16 26.86 0.12 0.23 0.98 0.08 0.782 0.210 0.008 0.008 0.000 0.001 0.000 0.000 0.014 0.415 0.532
HCV(Diesel) 51.93 24.62 0.20 0.28 1.06 0.00 0.782 0.210 0.008 0.008 0.000 0.001 0.000 0.000 0.014 0.415 0.532
3 Wheeler (CNG) 58.38 6.46 3.27 0.08 2.34 0.00 0.000 2.147 0.000 0.242 0.000 0.003 2.941 0.015 0.152 0.000 0.000
4 Wheeler (CNG) 28.71 18.56 3.42 2.02 3.77 0.96 0.490 1.083 0.035 0.301 0.607 0.000 0.228 0.069 0.035 0.282 0.156
HCV (CNG) 41.97 22.01 2.19 0.00 0.67 1.21 0.000 0.000 0.059 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
3 Wheeler (LPG) 49.04 7.17 2.74 0.06 1.68 0.00 0.245 1.383 0.029 0.072 0.000 0.000 0.937 0.002 0.201 0.000 0.026
4 Wheeler (LPG) 26.41 14.36 2.84 1.18 4.97 1.14 0.134 2.736 0.009 1.246 0.737 0.003 2.396 0.060 0.035 0.325 0.000
All Gasoline
Composite 52.05 6.58 3.63 0.85 4.54 0.27 1.53 2.02 0.02 0.15 0.02 0.01 0.54 0.03 0.11 0.13 0.68
All Diesel Composite 49.27 21.57 0.34 0.18 0.83 0.02 0.58 0.27 0.01 0.02 0.00 0.00 0.10 0.01 0.01 0.23 0.25
CNG Composite 43.02 15.68 2.96 0.70 2.26 0.72 0.16 1.08 0.03 0.18 0.20 0.00 1.06 0.03 0.06 0.09 0.05
LPG Composite 37.72 10.77 2.79 0.62 3.32 0.57 0.19 2.06 0.02 0.66 0.37 0.00 1.67 0.03 0.12 0.16 0.01

83
Table 5.3: Percentage distribution of PAHs in PM 10 for different categories of vehicles

PAHs (%)

Benzo(ghi) Pyrene
Acenapthalene

Fluoroanthene

Fluoroanthene
Acenapthene

Indenol(1,2,3)
Anthracene

anthracene
Dibenz(a,h)
Fluorene +

Total PAHs
Chrysene
Benzo(a)

Benzo(b)
Pyrene

Pyrene
2-stroke, 2-wheeler (Gasoline) 0.683 0.652 0.101 0.066 0.002 0.001 0.165 0.000 0.000 0.000 1.900
4-stroke, 2-wheeler (Gasoline) 0.382 1.637 0.532 1.933 0.045 0.041 0.111 0.125 0.427 0.751 8.572
3-Wheeler(Gasoline) 0.331 0.252 0.147 0.878 0.056 0.011 0.024 0.046 0.349 0.017 2.910
Passenger Car(Gasoline) 0.078 0.251 0.062 0.503 0.510 0.034 0.478 0.006 0.048 0.009 2.414
3-Wheeler(Diesel) 0.080 0.122 0.028 0.079 0.004 0.006 0.011 0.012 0.010 0.006 0.511
Passenger Car(Diesel) 0.024 0.036 0.007 0.037 0.000 0.001 0.005 0.005 0.001 0.001 0.130
LCV(Diesel) 0.020 0.056 0.013 0.056 0.004 0.000 0.002 0.006 0.002 0.001 0.181
HCV(Diesel) 0.345 0.951 0.654 0.352 0.066 0.083 0.100 0.290 0.171 0.225 4.235
3 Wheeler (CNG) 0.333 2.340 0.396 1.124 0.029 0.040 0.052 0.128 0.000 0.000 6.642
4 Wheeler (CNG) 0.092 0.324 0.060 0.109 0.002 0.001 0.001 0.000 0.000 0.000 0.666
HCV (CNG) 0.016 0.072 0.132 0.154 0.012 0.008 0.014 0.033 0.002 0.011 0.533
3 Wheeler (LPG) 0.225 1.343 0.199 0.835 0.027 0.015 0.001 0.018 0.001 0.001 3.605
4 Wheeler (LPG) 0.163 0.270 0.057 0.334 0.003 0.006 0.007 0.012 0.026 0.035 1.253
All Gasoline Composite 0.37 0.70 0.21 0.84 0.15 0.02 0.19 0.27 0.04 0.21 3.96
All Diesel Composite 0.12 0.29 0.18 0.13 0.02 0.02 0.03 0.08 0.05 0.06 1.26
CNG Composite 0.15 0.91 0.20 0.46 0.01 0.02 0.02 0.05 0.00 0.00 2.61
LPG Composite 0.19 0.81 0.13 0.58 0.01 0.01 0.00 0.01 0.01 0.02 2.43

84
5.7 Non-Vehicular Sources Emission Profiles

The stationary sources to be profiled in each city were identified based on

a list of major contributing sources compiled from the emission inventories
of respective partner institutes carrying out city-specific studies. The overall
participation framework of the present study is shown in Figure 5.2. Sources
were categorized based on their nature (combustion or non-combustion)
and occurrence (city specific or common to all cities). These categories
were combustion common (CC), combustion city specific (CCS), non-
combustion common (NCC) and non-combustion city specific (NCS). The
city specific sources were sampled in the respective city for developing
source profiles specific to that city. The common sources were sampled
either in any one of the city or in laboratory to develop source profiles. A
total of 58 PM 10 profiles and 21 PM 2.5 profiles were developed for 58 sources
in the present study. A summary of the sources and corresponding profiles
is presented in Table 5.4.

Based on the nature of sources, different methodologies were adopted for

source sampling. Sampling strategies for different sources are depicted in
Figure 5.3. Three sampling methodologies adopted in the present study
include dilution sampling for combustion sources, re-suspension sampling
for dust sources, and source dominated sampling for area sources.
Combustion sources were sampled using an iso- kinetic two stage dilution
sampler designed at IIT Bombay. Dilution sampling allowed representative
sampling of combustion sources by simulating the atmospheric dilution of
exhaust gas. Dilution ratio was varied from source to source depending
upon the source gas temperatures and the particulate matter (PM)
concentrations. The iso-kinetic sampling probe in the sampler ensured
minimum particle loss during sampling.

The re-suspended geological dust sources in each city included paved

road dust, unpaved road dust and soil dust. These dust samples were
collected from each of these cities following internationally accepted
protocols and they were sampled using a well known re-suspension
chamber approach. The sampling duration was determined based on the
characteristics of dust samples and the PM fraction. The re-suspension
chamber approach was also used for sampling other dust sources such as
cement, sand, aggregate dust and rock phosphate dust from a fertilizer
plant.

The sampling of sources such as marine aerosols, solid waste burning, paint
spray booth and tar melting was carried out by placing the sampling
probe directly into the source dominated volume. The source dominated

85
sampling of solid waste burning, paint spray booth and tar melting was
carried out under controlled laboratory conditions to ensure that these
samples are not contaminated by other sources. Marine aerosol sampling
was carried out at a beach which was far away from Mumbai city in order
to avoid influence of city pollution.

Table 5.4 List of Source Profiles Developed

Name of Sources (in

No. alphabetical order) Source Code Class PM 10 PM 2.5 F L

1 Aggregate Dust 6004 NCC 1 0 0 1

2 Agricultural Waste Burning 15 CC 1 0 0 1

3 Asphalt Paving Operations 24 NCC 1 0 1 0

4 Bagasse Combustion 5 CC 1 0 0 1
Bricks and Related Clay
5 Products 40 CS 1 0 1 0
6 Cement 6002 NCC 1 0 0 1
7 Chulah (Wood)-Chennai 9 CC 1 0 0 1
8 Chulah (Wood)-Kanpur 9 CC 1 0 0 1
9 Chulah (Wood)-Mumbai 9 CC 1 1 0 1
Coal Combustion -
10 Domestic-Kanpur 8 CC 1 0 0 1
Coal Combustion -
11 Domestic -Mumbai 8 CC 1 1 0 1
Coal Combustion Power
12 Plant-Delhi 12 CS 1 0 1 0
Coal Combustion Power
13 Plant-Kanpur 12 CS 1 0 1 0
Construction and Aggregate
14 Processing 43 NCC 1 0 1 0

15 Diesel Industrial Generators 21 CC 1 1 1 0

16 Electric Arc Furnace 45 CC 1 1 1 0

17 Fertilizer Plant Stack 6007 CC 1 0 1 0

18 Fuel Oil combustion 2 CC 1 1 1 0
Fugitive Rock Phosphate
19 Emission from Fertilizer Plant 6005 NCC 1 0 0 1
20 Garden Waste Combustion 5001 CC 1 0 0 1
Kerosene Combustion-
21 Domestic 7 CC 1 1 0 1
Kerosene Generators- 80 %
22 Load 20 CC 1 1 0 1
Kerosene Generators- Full
23 Load 20 CC 1 1 0 1
Kerosene Generators-No
24 Load 20 CC 1 1 0 1

86
 Name of Sources (in
No. alphabetical order) Source Code Class PM 10 PM 2.5 F L

25 Leather Waste Burning 13 CS 1 0 0 1

Liquefied Petroleum Gas
26 Combustion 4 CC 1 0 0 1
Low Sulphur Heavy Stock-
27 Power Plant 6000 CS 1 1 1 0
28 Marine Aerosols 26 NCS 1 0 1 0
Medical Waste Incineration
29 (Controlled) 17 CC 1 0 1 0
Medical Waste Incineration
30 (Uncontrolled) 17 CC 1 0 1 0

31 Paint Spray Booth 31 NCS 1 0 0 1

32 Paved Road Dust-Bangalore 52 NCS 1 0 0 1
33 Paved Road Dust- Chennai 52 NCS 1 0 0 1

34 Paved Road Dust- Delhi 52 NCS 1 0 0 1

35 Paved Road Dust- Kanpur 52 NCS 1 0 0 1

36 Paved Road Dust- Mumbai 52 NCS 1 1 0 1

37 Paved Road Dust- Pune 52 NCS 1 1 0 1

Petroleum Refining-
38 Combustion 27 CC 1 0 1 0
Petroleum Refining-Non-
39 Combustion 28 NCC 1 0 1 0
Power Plant Natural Gas
40 based 5002 CS 1 0 1 0

41 Sand 6003 NCC 1 0 0 1

Secondary Metal (Lead)
Smelting and other
42 operations-Bangalore 46 CC 1 1 1 0
Secondary Metal (Lead)
Smelting and other
43 operations-Kanpur 46 CC 1 0 1 0

44 Soil Dust-Bangalore 54 NCS 1 0 0 1

45 Soil Dust-Chennai 54 NCS 1 0 0 1

46 Soil Dust-Delhi 54 NCS 1 0 0 1

47 Soil Dust-Kanpur 54 NCS 1 0 0 1

48 Soil Dust-Mumbai 54 NCS 1 1 0 1

49 Soil Dust-Pune 54 NCS 1 1 0 1

Solid Waste Open Burning-
50 Commercial Area 18 CC 1 1 0 1
Solid Waste Open Burning-
51 Residential Area 18 CC 1 1 0 1

52 Steel Rolling Mills 6001 CC 1 1 1 0

53 Tar Melting 6006 CC 1 1 0 1

87
 Name of Sources (in
No. alphabetical order) Source Code Class PM 10 PM 2.5 F L
Unpaved Road Dust-
54 Bangalore 53 NCS 1 0 0 1
55 Unpaved Road Dust-Delhi 53 NCS 1 0 0 1

56 Unpaved Road Dust-Kanpur 53 NCS 1 0 0 1

57 Unpaved Road Dust-Pune 53 NCS 1 1 0 1

Wood Residue Combustion
58 in Boilers 11 CS 1 1 1 0
2 3
TOTAL 58 21 0 8
F Field Sampling: 20 1 – available SPECIATE database
L Lab Sampling: 38 0 – not in SPECIATE database

CC Combustion common sources NCC Non-combustion common sources

CS Combustion city specific sources NCS Non-combustion city specific sources

88
 Identification, Selection and
Partner Institutes Categorization of Sources Existing Database
by sub-committee (US SPECIATE)
• TERI Bangalore
• IIT Madras
• NEERI Nagpur
Development of Sampling Protocol
• IIT Kanpur
• NEERI Mumbai
• ARAI Pune

Source Sampling

• Ions
Chemical Characterization • Elements
CPCB guidelines
• Elemental/Organic
Carbon
• Molecular Markers
Documentation of Source Profiles

Figure 5.2: Overall Framework for the Work Elements in the Development of Stationary Source Profiles carried out at IIT-Bombay

89
 Non vehicular
sources

Combustion sources Non Combustion

sources

Probe sampling Hood sampling Laboratory Field

Sampling/Uncategorized

1. Cast iron furnace 1. LPG –home 1. Paved roads 1. Asphalt paving

2. Fuel and oil combustion 2.Wood-stove/resturant 2. Unpaved roads operations
3.Incinerator 3.Kerosene-stove 3. Soil dust 2. Petroleum refining non-
4. Diesel generator 4.Agricultural waste 4. Fugitive dust combustion
5. Kerosene generator burning 3.Fertiliser plant packaging
6. Petroleum refining 5.Coal stove (Tandoor) 4. Hot mix asphalt plants
7. Power plant (natural 6.Cow dung 5. Glass manufacturing
gas) 7.Bagasse 6. Construction and
8. Power plant (Coal) 9.Plastic and leather aggregate processing
9. Brick and clay products waste burning 7. Iron and steel
10. Coal combustion 10. Solid waste burning production
boilers 11. Refuse combustion 8. Cupola cast-iron
9. Gray iron foundries
10. Lead oxide and
pigment production
11. Earthen port kiln
12. Carbon black
Figure 5.3: Sampling Strategies for different sources
13. Paint and warmish

90
Adequate PM deposition on the filters was ensured during all samplings.
Quartz and Teflon filters were used to collect PM for chemical analyses. Each
sample consisted one Quartz and two Teflon filters for both PM 10 and PM 2.5 .
Quartz filter was used for EC/OC and molecular marker analyses, and Teflon
filters were used for ionic and elemental analyses. All gravimetrical analyses of
filters were carried out using a microbalance having a least count of 1 µg.
Filter handling procedures were in accordance with the conceptual
guidelines and common methodology, developed for the project.

The reported source profiles contain 39 elemental species analyzed by ICP-

AES, 12 water soluble ions analyzed by Ion Chromatography (IC), organic
carbon (OC) and elemental carbon (EC) analyzed by Thermal Optical
Reflectance (TOR) method and 12 molecular marker compounds analyzed by
GC-FID. The analysis of molecular marker compounds were limited to the
qualitative identification of these compounds in the source samples. All
chemical and gravimetrical analyses were as per CPCB described protocols
and have been reported together with the detailed QA/QC documentation.

The measured concentrations of these chemical species were normalized to

PM gravimetric mass to produce source profiles as percentage abundances
and reported with their corresponding uncertainties. Figure 5.4 gives a
graphical representation of a typical source profile developed in the present
study. These source profiles were submitted in a format suitable for input in to
CMB receptor model as a soft file, and also documented as a report in two
volumes (http://www.cpcb.nic.in/). Volume 1 comprises the background
development work and Volume 2 comprises of all the source profiles data.

91
 Figure 2. Graphical Representaion of a Typical Source Profile ( Paved Road Dust ,Mumbai)

1.000E+01

1.000E+00
% Mass Fraction

1.000E-01

1.000E-02

1.000E-03
Ag

As
Al

Ba
Ca

Ce

Y
Cr

Fe
Ga

Zr

NO3

SO4

OC
EC
Hf

Na

Sc
Se

Sr
Th
Ti
Cd

Co

Cu

Hg
In

Lu

Zn

F
Ni

Pb
Pd
Sb

Sm
Si

Sn

V
Mg
Mn
Mo

Mg
W

Cl
NO2
Br

PO4

Na
NH4
K
Ca
Species

Figure 5.4: A Typical Source Profile (Paved Road Dust, Mumbai)

5.8 Contributing Sources based on Receptor Modeling

Factor analysis and Chemical mass balance (CMB8.2) models were used to
apportion contribution of source groups in ambient particulate matter (PM 10 &
PM 2.5 ). The Varimax rotated factor analysis technique based on the principal
components was initially used to determine the dominance of sources
contributing to various receptors. The information on indicative source
dominance along with data on chemical speciation of PM 10 & PM 2.5 were
subsequently, used in CMB8.2 model to get quantitative contribution of sources.
The CMB model was run for each day of sampling (at the location) for each
location and in all the three seasons. There have been seasonal as well as day-
to-day variations in the prominent sources that contribute to PM 10 and PM 2.5 .
Therefore, the source contribution estimates of all the seasons were averaged
for locations of similar land use (e.g. data for two residential locations were
pooled together). This helped in preparing overall source - receptor linkages.
The overall results of source – receptor impact relationship in terms of percent
contribution (excluding unidentified sources, which are explained in the mass
closure plots – Figures 3.25 – 3.30) of various sources at residential, kerbside,
industrial locations in all the six cities in respect of PM 10 are presented in Figures
5.5 – 5.7.

92
In residential locations, re-suspension of road dust & soil dust emerged as
prominent sources of PM 10 in the cities of Pune (57%), Bangalore (49%), Mumbai
(47%) and Delhi (15%). Vehicular sources (15 – 48%) contribute significantly in
Bangalore, Chennai, Delhi and Kanpur. Other prominent sources include DG
sets in Bangalore, Chennai and Delhi; and Garbage burning in Delhi, Kanpur
and Mumbai. Construction activities (22%) are another major source
contributing to higher PM 10 levels in Delhi.

The kerbside locations in all the cities, except Kanpur, show resuspension of
road/soil dust as the most prominent source (27- 75%). Higher contributions at
these locations clearly indicate that the dust on paved/unpaved roads get
airborne due to movements of vehicles. Transport sector, as expected, is a
major contributor in almost all the cities. Other sources show city specific
dominance (domestic – Chennai & Kanpur; garbage burning – Delhi & Kanpur;
secondary particulate (SO 4 --, NO 3 - and NH 4 +) – Bangalore & Kanpur; and
construction – Delhi).

In case of industrial locations, contributions of industries are reflected in

Bangalore (27%), Kanpur (19%) and Delhi (9%). Dominance of other sources like
re-suspension of road dust, transport, garbage burning, etc. exhibit trends more
or less similar to residential and kerbside locations.

Res. Bangalore: PM10 Paved road

& Soil dust
48.9%

Domestic
4.4%

Secondary
DG sets 9.2%
18.2% Transport
19.3%

93
 Garbage
Res. Delhi: PM10
Garbage Res. Kanpur: PM10 Burning
Burning Industries 26.0%
15.0% 6.3%
Road Dust
Road Dust
7.2%
14.5%
Secondary
Construct. 18.7%
22.0%
Domestic Industries
9.4% Domestic 2.3%
25.8%
DG Set
12.3% Transport DG Set Transport
20.5% 5.2% 14.9%

Garden W./
Res. Pune: PM10 Trash
Burning
2.9%
Re- Constr./
Susp.Dust Brick Kilns
57.4% 14.9%

Domestic
(Solid Transport
Fuel Com) DG Set (Ind.) 9.8%
10.8% 4.2%

Figure 5.5: Contribution of Sources in PM 10 in Residential Locations

Paved road
Kerb Bangalore: PM10 & Soil dust
55.6%

Domestic
2.8% Secondary
DG sets 11.1%
7.8% Transport
22.6%

94
 Garbage
Garbage Kerb Kanpur: PM10
Kerb Delhi: PM10 Burning
Burning
10.5% Industries
30.2%
9.3%
Secondary
Construct. 18.2%
Road Dust Road Dust
23.1%
29.0% 8.0%

Industries
3.1%
Domestic
Domestic Transport
17.5%
9.1% 12.1% Transport
DG Set DG Set 16.8%
6.8% 6.1%

Kerb Pune: PM10

Garden W./
Re-Susp. Trash Burn.
Dust 64.5% 4.0%

Construct./
Brick Kilns
6.5%

Domestic
(Solid Fuel Transport
Com) 8.5%
DG Set (Ind.)
13.2% 3.3%

Figure 5.6: Contribution of Sources in PM 10 in Kerbside Locations

Industries Bangalore: PM10 Industries Chennai: PM10 Road Dust

Secondary
Domestic (Paved, Soil)
Paved road 2.4% (LPG) 8.1% 16.9%
& Soil dust Kerosene
DG Set 6.8%
45.6%
Industries 13.8%
27.2% Coal
4.2%

Bakeries
4.4%
Domestic Transport Transport
6.4% DG sets
10.9% 45.8%
7.5%

95
 Industries Delhi: PM10 Industries Kanpur: PM10
Industries
Garbage Secondary
Garbage 8.8%
Burning 15.6%
Burning
24.4% 17.7%
Industries
Construct. 18.9%
23.1% Road Dust
9.4%

Road Dust Transport Domestic

25.1% 8.7%
Domestic 15.1% Transport
DG Set DG Set
2.7% 7.2% 14.7%
8.5%

Industries Pune: PM10 Constr.

Garden /Brick Kilns
W./Trash B. 27.9%
8.6%

Re-Susp.
Dust 49.2%
A
Transport
2.0%

Domestic DG Set (Ind.)

(Solid 4.1%
Fuel C.) 8.3%

Figure 5.7: Contribution of Sources in PM 10 in Industrial Locations

CMB8.2 results of PM 2.5 are presented in Figures 5.8 – 5.10. CMB 8 could not be
applied for PM 2.5 in Mumbai. The following emerge from analysis of data:

ƒ Contribution of resuspension/soil dust (mostly in coarser fraction range i.e.

PM 2.5 - 10 ) drops down (about 5% against 15 – 60% in PM 10 ) drastically at all
the locations in all the cities.
ƒ The contribution of combustion sources including transport (20 – 60%), DG
sets (8 – 28%) is much higher as compared to their contribution in PM 10 .
Domestic source contribution is quite high in Delhi (48 – 89%), Kanpur (21 –
27%) and Pune (about 15%).
ƒ Secondary particulates, which are not directly emitted but formed through
atmospheric processes, have significant contributions (14 – 60%).
ƒ While vehicles contribute significantly at all the locations, their contributions
at kerbside locations are much higher (e.g. Bangalore has 61% contribution
from vehicles at kerbside locations against 48% at residential location).

96
 ƒ Other city-specific sources include industries (at industrial monitoring sites in
Bangalore and Kanpur), Coal (Chennai) and domestic fuel combustion
Delhi).
ƒ The contribution of transport sector was observed in Delhi at residential sites
more than the kerb sites. This indicates that road network and vehicular
sources are widespread and dense, and do not exhibit typical land use
based variations. Besides, presence of molecular markers such as hopanes
and Steranes at all the locations confirm contribution of vehicular sources.

DG sets Res. Chennai: PM2.5 Road Dust

Res. Bangalore: PM2.5 28.1% Domestic (Paved,Soil) Coal
6.3% 24.4% 26.6%

Paved road
Domestic
& Soil dust
(LPG)
4.4%
13.7%

Secondary
DG Set
13.6%
8.0%
Transport Transport
47.6% 27.3%

Res. Delhi: PM2.5

Kerosene Res. Kanpur: PM2.5 Road Dust
Combustn Domestic 4.9%
17.4% 27.7%
Wood
LPG Combustn
Combustn 2.9% Secondary
49.6%
24.3%
Road Dust
4.9%

DG Set Industries
Industries
2.8% 17.6% 1.9%
Transport Transport
22.4%
23.6%

Sec.
Res. Pune: PM2.5
Particles
46.3%
Re-Susp.
Dust 3.5%

Domestic
(Solid Fuel
C.) 16.3% Transport
33.9%

Figure 5.8: Contribution of Sources in PM 2.5 in Residential Locations

97
 Kerb Bangalore: PM2.5 DG sets Kerb Chennai: PM2.5
23.2% Domestic Road Dust
2.4% (Paved, Soil)
27.9%
Paved road
& Soil dust DG Set
2.8% 8.2% Coal
18.2%

Secondary
11.0%
Transport
60.6% Transport
45.7%

Kerb Delhi: PM2.5 Wood Kerb Kanpur: PM2.5

Domestic Road Dust
Combustn Road Dust
25.6% 7.7%
Kerosene 2.4% 5.4%
Combustn Garbage
14.2% Secondary
Burning
21.2%
14.0%

LPG Industries DG Set Industries

Combustn 7.1% 10.9% 2.7%
40.5% Transport
DG Set 7.0% Transport
9.5% 31.9%

Sec.
Kerb Pune: PM2.5 Particles
57.9%

Re-Susp.
Dust
3.1% Domestic Transport
(Solid Fuel 24.7%
C.) 14.3%

Figure 5.9: Contribution of Sources in PM 2.5 in Kerbside Locations

98
 Industries Bangalore: PM2.5 Paved road Road Dust
Industries Chennai; PM2.5
& Soil dust (Paved, Soil)
2.6% 22.8%
Domestic Secondary
Kerosene
11.1% 13.1%
1.3%

Industries Domestic
DG sets (LPG) Coal
21.0%
17.2% 28.3% 18.9%

Bakeries
0.7%
DG Set
8.4% Transport
Transport
19.6%
35.1%

Industries Delhi: PM2.5 Kerosene

Combustn Wood
21.5% Combustn
3.6%

LPG Road Dust

Combustn 6.4%
61.2%
Industries
0.3%

DG Set Transport
1.1% 6.0%

Figure 5.10: Contribution of Sources in PM 2.5 in Industrial Locations

The contribution of various source categories in respect of PM 10 and PM 2.5 are

summarized in Table 5.5.

99
Table 5.5 Percent contribution of various sources at residential, kerbside,
industrial locations in all the six cities in respect of PM 10 and PM 2.5

LP Garb S Ro Secon Constru Trans D Domestic/ Co Keros Bak Indust

G age oi ad dary ction port G wood al ene ery rial
l Du Aeroso combustio
st l n
Residential
Banga PM 0 0 0 49 9 0 19 1 4 0 0 0 0
lore 10 8
PM 0 0 0 4 14 0 48 2 6 0 0 0 0
2.5 8
Chenn PM 0 0 0 6 0 0 48 1 21 5 3 4 0
ai 10 4
PM 0 0 0 24 0 0 27 8 14 27 0 0 0
2.5
Delhi PM 0 15 0 15 0 22 21 1 9 0 0 0 6
10 2
PM 50 0 0 5 0 0 22 0 3 0 17 0 3
2.5
Kanpu PM 0 26 0 7 19 0 15 5 26 0 0 0 2
r 10
PM 0 0 0 5 24 0 24 1 28 0 0 0 2
2.5 8
Mumb PM 0 0 4 0 21 0 17 0 8 0 0 0 0
ai 10 7
PM
2.5
Pune PM 0 3 0 57 0 15 10 4 11 0 0 0 0
10
PM 0 0 0 3 46 0 34 0 16 0 0 0 0
2.5
Kerbside
Banga PM 0 0 0 56 11 0 23 8 3 0 0 0 0
lore 10
PM 0 0 0 3 11 0 61 2 2 0 0 0 0
2.5 3
Chenn PM 0 0 0 27 0 0 35 1 4 7 6 5 0
ai 10 6
PM 0 0 0 28 0 0 46 8 0 18 0 0 0
2.5
Delhi PM 0 11 0 29 0 23 12 7 9 0 0 0 9
10
PM 41 14 0 5 0 0 7 9 2 0 14 0 7
2.5
Kanpu PM 0 30 0 8 18 0 17 6 18 0 0 0 3
r 10
PM 0 0 0 8 21 0 32 1 26 0 0 0 3
2.5 1
Mumb PM 2 0 2 0 10 0 26 0 16 4 0 0 1
ai 10 9
PM
2.5
Pune PM 0 4 0 64 0 6 8 3 13 0 0 0 0
10
PM 0 0 0 3 58 0 25 14 0 0 0 0
2.5
Industrial
Banga PM 0 0 0 46 2 0 11 8 6 0 0 0 27
lore 10
PM 0 0 0 3 13 0 35 1 11 0 0 0 21
2.5 7
Chenn PM 0 0 0 17 0 0 46 1 8 4 7 4 0
ai 10 4
PM 0 0 0 23 0 0 20 8 28 19 1 1
2.5
Delhi PM 0 24 0 25 0 23 9 7 3 0 0 0 9
10
PM 61 0 0 6 0 0 6 1 4 0 21 0 1
2.5
Kanpu PM 0 18 0 9 16 0 15 9 15 0 0 0 19
r 10
PM 0 0 0 6 21 0 28 7 21 0 0 0 17
2.5
Mumb PM 18 23 3 0 13 0 8 0 0 0 0 0 6
ai 10 0
PM
2.5
Pune PM 0 9 0 49 0 28 2 4 8 0 0 0 0
10
PM 0 0 0 5 43 0 21 0 17 0 0 0 0
2.5

100
 6

Dispersion Modeling

Dispersion modeling is an important component of the study that was used

for projecting air quality profiles (iso-concentration plots) of the city, under
different scenarios viz. business as usual, future projections with
implementation of control options, etc. It was also used to evaluate
efficacy of various control options for evolving city-specific action plans for
air quality improvements.

6.1 Approach and Methodology

The broad approach and methodology followed for source dispersion

modeling are summarized in Table 6.1 and given below:
ƒ With regard to choice of the model, AERMOD and ISC-3 models were
considered. It was decided to use ISC-3 due to lower meteorological
data requirement, extensive hands on experience available, general
acceptability in terms of prediction quality, etc.
ƒ A detailed and relatively reliable emission inventory is incorporated for
better prediction. Source locations and receptors were marked on GIS
based map of 02 x 02 sq. km grids. Grid wise emission rates for different
source groups were worked out for PM 10 and NO x from baseline
emission inventory of the year 2007.
ƒ In-situ micrometeorological data were collected at all sampling
locations during monitoring period. Meteorological stations were
installed at monitoring sites to capture data on wind direction, wind
velocity, ambient temperature and percent relative humidity. Relevant
data for the monitoring period were converted into daily mean hourly
parameters and used for prediction at respective sites. These site
specific data provided better predictions. Predominant meteorological
data/IMD data were used at city level, as sources from far locations are
unaffected by the local meteorology and their impact can best be
evaluated by broad meteorology for the city. Regarding mixing height
and diurnal stability pattern, the secondary data sources and/or
established calculation procedure were adopted uniformly for all cities.
For the Model calibration exercise, the correlation curves for observed
and predicted concentrations for ambient PM 10 were analyzed for
different seasons.

101
ƒ Model runs were made at grid (02 x 02 sq km around each monitoring
location) as well as city levels. Receptors were selected in Cartesian
coordinate system wherein multiple receptor networks were defined.
ƒ Predictions were made for three seasons viz. summer, post/pre
monsoon and winter.
ƒ Model performance was also examined for seasonal averages. For
each season, observed and predicted concentrations were plotted
and R-square values were determined for each of the locations.
ƒ With city-level EI (baseline 2007) and future projections of emission
growths (BAU 2012 and 2017), the Iso-concentration plots were
developed to generate air quality profiles. Plots for grid-wise emission
load projections are given in Annexure – IX.
ƒ Dispersion modeling was also carried out to determine the efficacy of
control/management options. This has led to long and short term city
specific action plans.

Table 6.1: Salient Features of Modeling Exercise at Each Grid and City level
for all Project Cities

Parameter Grid level modeling within City level modeling

zone of influence (2kmx2km)
Sources Area, Industrial, Vehicular, Area, Industrial, Vehicular,
Considered Road dust, and all sources Road dust, and all sources
together together
Pollutants PM 10 , NO x PM 10 , NO x
Monitored
Emission rate Hourly variations Hourly variations
Sources Grids 0.5x0.5 km 2 x2 km
Surface Met data Site specific IMD/predominant data
measured at sites
Upper air Met data CPCB document on mixing CPCB document on mixing
height height
Seasons Summer, Post monsoon, Summer, Post monsoon,
Winter Winter
Model used ISCST3 ISCST3
Receptors Grids 500m x 500m 2000m x 2000m
Model Output 24 hrly average 24 hrly average
concentration concentration
Ground Level Center of grids Center of grids
Concentration
(GLC) prediction
at
Ranked GLCs First 10 highest values First 10/15 grids with highest
values
Iso- concentrations --- For each pollutant in each
plots season using Surfer graphical
software

102
6.2 Modeling Results

The salient observations on grid-based modeling (predictions within 02x02 sq.

km grid around monitoring locations) are as follow:

ƒ The impact of PM 10 emissions on its GLC was found maximum due to re-
suspended road side dust. It mainly depends on silt load as well as traffic
density of the respective roads. The silt loads used for the different roads
in the six cities were based on in-situ observations.
ƒ The GLCs were maximum during critical season, which is winter for
Bangalore, Kanpur, Mumbai, Pune; and Post monsoon for Chennai and
Delhi.
ƒ Wide variations in the local meteorology were observed in different grids
depending on the geographical location and land use characteristics
(building heights, terrain, etc.). The impacts on GLC varied in the grids of
same cities because of such variations. However, in certain cities like
Bangalore, where land use and terrain are more or less uniform, such
variations were less.
ƒ The impacts of PM 10 and NO x emissions on GLC were maximum in the
grids located within the traffic dominated zones. Whereas impact of SO 2
emissions was maximum in industrial zones.
ƒ The impacts of transport sector emissions were maximum on both sides
of heavy traffic major roads depending on the wind direction. For the
health impacts on population exposure, such locations emerge as hot
spots.

Air quality profiles, in terms of iso-concentration plots, for each of the six cities
are given in Figures 6.1 – 6.18. In order to facilitate better understanding of
emissions, meteorology and resultant air quality, grid-wise emission loads,
wind rose and iso-concentration plots are provided together in the figures.
The figures provide results for the base year 2007 in respect of PM 10 & NO x for
all the three seasons. While results of grid and city level modeling are
discussed briefly in this report, the details are provided in the main reports of
the cities.

Bangalore: Modeling results for Bangalore are presented in Figures 6.1 – 6.3.
The pockets of highest concentration of PM 10 are well captured by the
contours at the city level whereby they correspond to high industrial
(Peenya industrial area) and major traffic activities close to the central hub
of the city. Likewise, the contours at the city level for predicted 24-hourly
average NO x concentration again capture well the pockets of high
concentration in terms of activity levels corresponding to high traffic and

103
DG set usage. The maximum spread of pollution in winter and least in pre-
monsoon clearly indicate that the winter is the worst season in terms of air
quality concentration. During the winter season, the contribution of
different sectors to predicted PM 10 air quality at the 6 monitoring locations
within the city indicates maximum contribution by the transport sector
(average 44%; range 13-54%) followed by road dust re-suspension
(average 22%; range 7-29%), other area sources, including domestic,
construction activities, hotels and DG sets(average 20%; range 6-35%), and
industries(average 14%; range 1-74%). Likewise, in the case of NOx, the
contribution of different sectors at the 6 monitoring locations indicates
maximum contribution by transport sector (average 50%; range 23-73%),
followed by other area sources (average 46%; range 26-77%) and industries
(average 4%; range 0-19%).

Chennai: Modeling results for Chennai are presented in Figures 6.4 – 6.6. In
industrial sites, PM 10 as well as NO x levels are higher as compared to other
sites. City specific modeling shows that there are four hotspots – (i)
Ambattur industrial area in the western zone; (ii) region comprising
Royapuram, Tondaiarpet, Washermanpet and Korrukupet area (which is
close to the Manali industrial area) in eastern zone; (iii) Alwarpet; and (iv)
K.K. Nagar area (both in southern zone). In these locations, the predicted
values are relatively high, but are within the specified limits. With regard to
PM 10, it was found that resuspension of unpaved and paved road dust
contributed close to 68%, while vehicles contributed 12% to the pollution
levels in Chennai. As far as NOx levels are concerned the contributions
from vehicles was close to 65% while the area sources (bakeries, domestic
cooking etc.) contribute around 20%.

104
 Wind Rose

BAU PM10 2007 BAU NOx 2007

13 22 63 23 ## ## 67 25 ## ## 24 21 21 13 ## ## ## ## ## ## ## ## ## ## ## ##
12 21 63 ## ## ## 35 24 ## ## 23 21 22 48 0-200 Kg/d 12 ## ## ## ## ## ## ## ## ## ## ## ##
11 26 24 26 27 31 39 24 ## ## 24 26 22 214 200-400 Kg/d 11 ## ## ## ## ## ## ## ## ## ## ## ##
10 ## ## ## ## 53 79 35 34 33 35 38 35 442 400-800 Kg/d 10 ## ## ## ## ## ## ## ## ## ## ## ## 229 0-500 Kg/d
9 ## ## ## ## ## ## ## ## 70 25 25 38 1159 800-1600 Kg/d 9 ## ## ## ## ## ## ## ## ## ## ## ## 688 500-1000 Kg/d
8 ## 67 ## ## ## ## ## ## ## 72 ## 83 1874 >1600 Kg/d 8 ## ## ## ## ## ## ## ## ## ## ## ## 1981 1000-2000 Kg/d
7 ## ## ## ## ## ## ## 82 ## ## ## ## 7 ## ## ## ## ## ## ## ## ## ## ## ## 3642 2000-4000 Kg/d
6 36 ## ## ## ## ## ## ## ## 61 45 ## Point Source : 984 Kg/d 6 ## ## ## ## ## ## ## ## ## ## ## ## 5181 >4000 Kg/d
5 ## ## ## ## ## ## ## ## ## ## ## ## 5 ## ## ## ## ## ## ## ## ## ## ## ##
4 ## 93 ## ## ## ## ## ## 23 30 58 39 4 ## ## ## ## ## ## ## ## ## ## ## ## Point Source : 13281 Kg/d
3 48 75 ## ## ## ## ## ## 42 81 30 22 3 ## ## ## ## ## ## ## ## ## ## ## ##
2 44 44 ## 81 ## ## 59 ## 40 32 25 51 2 ## ## ## ## ## ## ## ## ## ## ## ##
1 21 21 ## ## ## ## 31 ## 87 88 23 29 1 ## ## ## ## ## ## ## ## ## ## ## ##
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

PM 10 Emission Load (Kg/day) – BAU-2007 NO x Emission Load (Kg/day) – BAU-2007

26000
26000

24000
24000

22000
22000
300 µg/m3
450 µg/m3 20000
20000 425 µg/m3
280 µg/m3
400 µg/m3 260 µg/m3
18000
18000 375 µg/m3 240 µg/m3
350 µg/m3 220 µg/m3
16000
16000 325 µg/m3
200 µg/m3
300 µg/m3
275 µg/m3 14000 180 µg/m3
14000
250 µg/m3 160 µg/m3
225 µg/m3 12000 140 µg/m3
12000
200 µg/m3
120 µg/m3
175 µg/m3
10000 100 µg/m3
10000 150 µg/m3
125 µg/m3 80 µg/m3
8000 8000
100 µg/m3 60 µg/m3
75 µg/m3
40 µg/m3
6000 50 µg/m3 6000
20 µg/m3
25 µg/m3
0 µg/m3 0 µg/m3
4000 4000

2000 2000

0 0
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NOx concentration (µg/m3)

Figure 6.1: Modeling results for Bangalore (Base year 2007; PM 10 , NO x ;

Winter)

105
 Wind Rose

BAU PM10 2007 BAU NOx 2007

13 22 63 23 ## ## 67 25 ## ## 24 21 21 13 ## ## ## ## ## ## ## ## ## ## ## ##
12 21 63 ## ## ## 35 24 ## ## 23 21 22 48 0-200 Kg/d 12 ## ## ## ## ## ## ## ## ## ## ## ##
11 26 24 26 27 31 39 24 ## ## 24 26 22 214 200-400 Kg/d 11 ## ## ## ## ## ## ## ## ## ## ## ##
10 ## ## ## ## 53 79 35 34 33 35 38 35 442 400-800 Kg/d 10 ## ## ## ## ## ## ## ## ## ## ## ## 229 0-500 Kg/d
9 ## ## ## ## ## ## ## ## 70 25 25 38 1159 800-1600 Kg/d 9 ## ## ## ## ## ## ## ## ## ## ## ## 688 500-1000 Kg/d
8 ## 67 ## ## ## ## ## ## ## 72 ## 83 1874 >1600 Kg/d 8 ## ## ## ## ## ## ## ## ## ## ## ## 1981 1000-2000 Kg/d
7 ## ## ## ## ## ## ## 82 ## ## ## ## 7 ## ## ## ## ## ## ## ## ## ## ## ## 3642 2000-4000 Kg/d
6 36 ## ## ## ## ## ## ## ## 61 45 ## Point Source : 984 Kg/d 6 ## ## ## ## ## ## ## ## ## ## ## ## 5181 >4000 Kg/d
5 ## ## ## ## ## ## ## ## ## ## ## ## 5 ## ## ## ## ## ## ## ## ## ## ## ##
4 ## 93 ## ## ## ## ## ## 23 30 58 39 4 ## ## ## ## ## ## ## ## ## ## ## ## Point Source : 13281 Kg/d
3 48 75 ## ## ## ## ## ## 42 81 30 22 3 ## ## ## ## ## ## ## ## ## ## ## ##
2 44 44 ## 81 ## ## 59 ## 40 32 25 51 2 ## ## ## ## ## ## ## ## ## ## ## ##
1 21 21 ## ## ## ## 31 ## 87 88 23 29 1 ## ## ## ## ## ## ## ## ## ## ## ##
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

PM 10 Emission Load (Kg/day) – BAU-2007 NO x Emission Load (Kg/day) – BAU-2007

26000 26000

24000 24000

22000 22000

450 µg/m3 300 µg/m3

20000 425 µg/m3 20000
280 µg/m3
400 µg/m3
260 µg/m3
18000 375 µg/m3 18000
350 µg/m3 240 µg/m3

16000 325 µg/m3 16000 220 µg/m3

300 µg/m3 200 µg/m3
275 µg/m3
14000 14000 180 µg/m3
250 µg/m3
160 µg/m3
225 µg/m3
12000 12000 140 µg/m3
200 µg/m3
175 µg/m3 120 µg/m3
10000 150 µg/m3 10000 100 µg/m3
125 µg/m3 80 µg/m3
8000 100 µg/m3 8000
60 µg/m3
75 µg/m3
40 µg/m3
6000 50 µg/m3 6000
25 µg/m3 20 µg/m3
0 µg/m3 0 µg/m3
4000 4000

2000 2000

0 0
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NO x concentration (µg/m3)

Figure 6.2: Modeling results for Bangalore (Base year 2007; PM 10 , NO x ;

Summer)

106
 Wind Rose

BAU PM10 2007 BAU NOx 2007

13 22 63 23 ## ## 67 25 ## ## 24 21 21 13 ## ## ## ## ## ## ## ## ## ## ## ##
12 21 63 ## ## ## 35 24 ## ## 23 21 22 48 0-200 Kg/d 12 ## ## ## ## ## ## ## ## ## ## ## ##
11 26 24 26 27 31 39 24 ## ## 24 26 22 214 200-400 Kg/d 11 ## ## ## ## ## ## ## ## ## ## ## ##
10 ## ## ## ## 53 79 35 34 33 35 38 35 442 400-800 Kg/d 10 ## ## ## ## ## ## ## ## ## ## ## ## 229 0-500 Kg/d
9 ## ## ## ## ## ## ## ## 70 25 25 38 1159 800-1600 Kg/d 9 ## ## ## ## ## ## ## ## ## ## ## ## 688 500-1000 Kg/d
8 ## 67 ## ## ## ## ## ## ## 72 ## 83 1874 >1600 Kg/d 8 ## ## ## ## ## ## ## ## ## ## ## ## 1981 1000-2000 Kg/d
7 ## ## ## ## ## ## ## 82 ## ## ## ## 7 ## ## ## ## ## ## ## ## ## ## ## ## 3642 2000-4000 Kg/d
6 36 ## ## ## ## ## ## ## ## 61 45 ## Point Source : 984 Kg/d 6 ## ## ## ## ## ## ## ## ## ## ## ## 5181 >4000 Kg/d
5 ## ## ## ## ## ## ## ## ## ## ## ## 5 ## ## ## ## ## ## ## ## ## ## ## ##
4 ## 93 ## ## ## ## ## ## 23 30 58 39 4 ## ## ## ## ## ## ## ## ## ## ## ## Point Source : 13281 Kg/d
3 48 75 ## ## ## ## ## ## 42 81 30 22 3 ## ## ## ## ## ## ## ## ## ## ## ##
2 44 44 ## 81 ## ## 59 ## 40 32 25 51 2 ## ## ## ## ## ## ## ## ## ## ## ##
1 21 21 ## ## ## ## 31 ## 87 88 23 29 1 ## ## ## ## ## ## ## ## ## ## ## ##
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

PM 10 Emission Load (Kg/day) – BAU-2007 NO x Emission Load (Kg/day) – BAU-2007

26000
26000

24000
24000

22000
22000
450 µg/m3
20000 300 µg/m3
425 µg/m3 20000
400 µg/m3 280 µg/m3
18000 375 µg/m3 260 µg/m3
18000
350 µg/m3 240 µg/m3
16000 325 µg/m3 220 µg/m3
16000
300 µg/m3
200 µg/m3
275 µg/m3
14000 14000 180 µg/m3
250 µg/m3
225 µg/m3 160 µg/m3
12000 12000
200 µg/m3 140 µg/m3
175 µg/m3 120 µg/m3
10000 150 µg/m3 10000 100 µg/m3
125 µg/m3
80 µg/m3
8000 100 µg/m3 8000
60 µg/m3
75 µg/m3
50 µg/m3 40 µg/m3
6000 6000
25 µg/m3 20 µg/m3
0 µg/m3 0 µg/m3
4000 4000

2000 2000

0 0
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NOx concentration (µg/m3)

Figure 6.3: Modeling results for Bangalore (Base year 2007; PM 10 , NO x ; Pre-
Monsoon)

107
 Wind Rose

PM 10 Emission Load (Kg/day) – BAU-2007 NO x Emission Load (Kg/day) – BAU-2007

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NO x concentration (µg/m3)

Figure 6.4: Modeling results for Chennai (Base year 2007; PM 10 , NO x ; Winter)

108
 Wind Rose

PM 10 Emission Load (Kg/day) – BAU-2007 NO x Emission Load (Kg/day) – BAU-2007

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NO x concentration (µg/m3)

Figure 6.5: Modeling results for Chennai (Base year 2007; PM 10 , NO x ; Post
monsoon)

109
 Wind Rose

PM 10 Emission Load (Kg/day) – BAU-2007 NO x Emission Load (Kg/day) – BAU-2007

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NOx concentration (µg/m3)

Figure 6.6: Modeling results for Chennai (Base year 2007; PM 10 , NOx;
Summer)

Delhi: Air quality dispersion modeling results for Delhi are presented in
Figures 6.7 – 6.9. Maximum GLCs of PM 10 (total of all sources) during
summer, post-monsoon and winter are observed in Connaught Place-
India Gate-ITO area. Higher PM 10 concentration levels are due to the fact
that contribution of road dust is also included in the total PM 10 . Maximum

110
GLCs of NOx during summer, post-monsoon occurred in Chandni Chowk-
Chawri Bazar area (during summer) and in Mayur Vihar-Patparganj area
(during post monsoon and winter). Iso-concentration plots drawn for PM 10
and NOx for summer, post-monsoon and winter seasons indicate high
concentrations of PM through out Delhi, with higher levels confining to the
area between ISBT and Ashram Chowk. A good spread of high levels of
NOx (40 µg/m3) was observed in all the three seasons, but highest levels
are following the trend of dispersion from point sources, mainly power
plants.

Kanpur: Modeling results for Kanpur are presented in Figures 6.10 – 6.12. For
PM 10 , the industrial site (Dadanagar) showed the highest concentration
and the industries appear to contribute a significant pollution at this site (~
40 percent). There are three prominent and probably equally important
sources of PM 10 (vehicles, road dust and domestic fuel burning) that
contribute to about 80 percent of PM 10 at all sampling sites (except for the
industrial location). For NOx , 50-70 percent contribution is from vehicles at
all sampling sites. It is only at industrial area (i.e. Dadanagar), where
contribution of industries is seen and on a few occasions, the point source
(power plant) also contributes to NOx at this site. At the kerbside and
commercial sites, almost entire NOx is from vehicles. Overall city specific
modeling results follow the meteorology and emissions in each grid and
there are clear hotspots. These hotspots vary depending on the season.
PM 10 hotspot in summer is in the industrial area and in winter season there
are two equally important hotspots – industrial area and city centre. NOx
levels show two consistent hotspots, where concentrations can really be
very high: (i) industrial area; and (ii) the city centre. It is seen that emission
are also high in these two areas.

111
 Wind Rose

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
C D E F G H J K L M N O P Q R S
C D E F G H J K L M N O P Q R S
Prah
1 SSI 1
1 Prah 1
SSI
2 PP 2 2 2

Loni PP
3 3 3 3

4 ISBT 4 Loni
4 4

5 5 5 5
ISBT
6 AV 6 6 6
Nar
7 7 7 7

MP AV
8 8 8 8

DK Nar
9 9 9 9

AC
10 10 10 10
MP

11 11 11 11
DK
12 12 12 12
AC
13 13 13 13

14 14 14 14

15 15 15 15

C D E F G H J K L M N O P Q R S C D E F G H J K L M N O P Q R S

0-300 300-600 600-900 900-1200 >1200 0-300 300-600 600-900 900-1200 >1200

PM 10 Emission Load (Kg/day) – BAU-2007 NOx Emission Load (Kg/day) – BAU-2007

PM_Sum_PAL_2007 NOx_Sum_PAL_2007

28 28

26 26

24 24

22 22

20 20
4000
Distance along North (km)

Distance along North (km)

18 3700 18 520

16 3400
16
3100 440
14 14
2800
12 2500 12 360

10 2200
10
1900 280
8 8
1600
6 1300 6 200

4 1000
4
700 120
2 2
400
0 100 0 40
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Distance alonh East (km) Distance along East (km)

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NOx concentration (µg/m3)

Figure 6.7: Modeling results for Delhi (Base year 2007; PM 10 , NOx; Summer)

112
 Wind Rose

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
C D E F G H J K L M N O P Q R S
C D E F G H J K L M N O P Q R S
Prah
1 SSI 1
1 Prah 1
SSI
2 PP 2 2 2

Loni PP
3 3 3 3

4 ISBT 4 Loni
4 4

5 5 5 5
ISBT
6 AV 6 6 6
Nar
7 7 7 7

MP AV
8 8 8 8

DK Nar
9 9 9 9

AC
10 10 10 10
MP

11 11 11 11
DK
12 12 12 12
AC
13 13 13 13

14 14 14 14

15 15 15 15

C D E F G H J K L M N O P Q R S C D E F G H J K L M N O P Q R S

0-300 300-600 600-900 900-1200 >1200 0-300 300-600 600-900 900-1200 >1200

PM 10 Emission Load (Kg/day) – BAU-2007 NOx Emission Load (Kg/day) – BAU-2007

NOx_PoMon_PAL_2007
PM_PoMon_PAL_2007

28
28
26
26
24
24

22
22

20 20
6100
Distance along North (km)
Distance along North (km)

18 600
18 5600

16 5100 16
520
4600
14 14
4100 440
12 12
3600
10 360
10 3100

8 2600 8 280
2100
6 6
1600 200
4 4
1100
120
2 600 2

0 100 0 40
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Disatance along East (km) Distance along East (km)

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NOx concentration (µg/m3)

Figure 6.8: Modeling results for Delhi (Base year 2007; PM 10 , NOx; Post
monsoon)

113
 Wind Rose

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
C D E F G H J K L M N O P Q R S
C D E F G H J K L M N O P Q R S
Prah
1 SSI 1
1 Prah 1
PP
SSI
2 2
2 2

3 Loni 3 PP
3 3

4 ISBT 4
4 Loni 4

5 5
5 ISBT 5

6 AV 6
6 6
Nar
7 7
7 7
MP
8 8 8 8
AV

DK Nar
9 9 9 9

AC
10 10 10 10
MP
11 11 11 11
DK
12 12 12 12
AC
13 13 13 13

14 14 14 14

15 15 15 15

C D E F G H J K L M N O P Q R S C D E F G H J K L M N O P Q R S

0-300 300-600 600-900 900-1200 >1200 0-300 300-600 600-900 900-1200 >1200

PM 10 Emission Load (Kg/day) – BAU-2007 NOx Emission Load (Kg/day) – BAU-2007

PM_Win_PAL_2007 NOx_Win_PAL_2007

28 28

26 26

24 24

22 22

20 7100 20
1040
Distance along North (km)

Distance alonh North (km)

6600
18 18
6100 940
16 5600 16
840
14 5100 14
740
4600
12 12
4100 640
10 3600 10 540
3100
8 8 440
2600
6 2100 6 340
1600
4 4 240
1100
2 600 2 140

0 100 0 40
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Distance along East (km) Distance along East (km)

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NOx concentration (µg/m3)

Figure 6.9: Modeling results for Delhi (Base year 2007; PM 10 , NOx; Winter)

114
 Wind Rose

PM 10 Emission Load (Kg/day) – BAU-2007 NOx Emission Load (Kg/day) – BAU-2007

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NOx concentration (µg/m3)

Figure 6.10: Modeling results for Kanpur (Base year 2007; PM 10 , NOx; Winter)

115
 Wind Rose

PM 10 Emission Load (Kg/day) – BAU-2007 NOx Emission Load (Kg/day) – BAU-2007

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NOx concentration (µg/m3)

Figure 6.11: Modeling results for Kanpur (Base year 2007; PM 10 , NOx;
Summer)

116
 Wind Rose

PM 10

Emission Load (Kg/day) – BAU-2007 NOx Emission Load (Kg/day) – BAU-2007

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NOx concentration (µg/m3)

Figure 6.12: Modeling results for Kanpur (Base year 2007; PM 10 , NOx; post
monsoon)

117
Mumbai: The results of modeling for Mumbai are given in Fig 6.13 to 6.15.
Seasonal changes in Mumbai are not significant, except in monsoon, when
due to high precipitation; the air pollutants levels are very low. The critical
winter season for a limited period can be considered important. The PM
dispersion modeling for the whole city shows that PM 10 hot spots are
around Dharavi, Dadar and Mahul. The measured PM 10 concentrations are
highest at Dharavi followed by Mulund and Mahul. The model results for
average of ten highest concentrations in different grids in the city indicate
that percent contribution of PM 10 is 5% for vehicles, 16% for industries, 1% for
area sources and rest from the re-suspended dust. With regard to NOx,
industries contribute the highest, with 53% followed by 46% for vehicles and
rest from area sources. The model results are dominated by few industrial
sources, however, they are located in a very limited area of the city.

Pune: Modeling results for Pune are given in Figures 6.13 – 6.15. Winter
season was found to be critical with respect to ambient concentration
levels. Re-suspended dust was found to be the major source with a
contribution of around 58%. Mobile source and other area sources
contribute around 22% and 19% respectively. Average contribution of all
the grids in Pune of Industrial sources was found to be very less (0.1%). Site-
specific dispersion modeling results show higher contribution of about 30-
40% from mobile sources at all sites, especially kerbside locations. However,
re-suspended dust was found to be the highest contributor at all sites with
contribution ranging from 40 to 60%. Other area sources contributed in the
range from 8 to 19%. Mobile source was found to be largest contributor
towards the NOx concentration with average contribution more than 95%
from dispersion modeling for Pune city and at various sites. Area sources,
including hotels, bakeries, residential fuels, contributed for about 3% of the
NOx concentrations. Contribution from industry was about 1%. Hot-spots
are found to be present at the central part of Pune with higher population
as well as road densities.

118
 Wind Rose

Mu
Mu

A
A

K Legend
C =Colaba
K
D =Dadar
Dh =Dh aravi
Dh K =Khar Dh L egen d
A =Andher i C =Co laba
Mh Mh=Mahu l
D =Dad ar
Mu=Mulun d Mh Dh=Dh ara vi
D
D K =Kh ar
Con. In Kg/d A =And heri
M h=M ahu l
M u=M ulund
Con. In Kg/ d

C
C

PM 10 Emission Load (Kg/day) – BAU-2007 NOx Emission Load (Kg/day) – BAU-2007

Fig. Iso-concentration Plots for PM - Existing Scenario – Summer 2007 :All Sources (Mumbai) Fig. Iso-concentration Plots for NOx– Existing Scenario- Summer 2007 : All Sources (Mumbai)
Distance along North, (Km)
Distance along North, (Km)

Conc.
In (µg/m3)

Conc.
In (µg/m3)

Distance along East, (Km)

Distance along East, (Km)

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NOx concentration (µg/m3)

Figure 6.13: Modeling results for Mumbai (Base year 2007; PM 10 , NOx;
Summer)

119
 Wind Rose

Mu
Mu

A
A

K Legend
C =Colaba
K
D =Dadar
Dh =Dh aravi
Dh K =Khar Dh L egen d
A =Andher i C =Colaba
Mh Mh=Mahu l
D =Dad ar
Mu=Mulun d Mh Dh=Dhara vi
D
D K =Khar
Con. In Kg/d A =And heri
M h=M ahu l
M u=M ulund
Con. In Kg/ d

C
C

PM 10 Emission Load (Kg/day) – BAU-2007 NOx Emission Load (Kg/day) – BAU-2007

Fig. Iso-concentration Plots for PM - Existing Scenario – Post Monsoon 2007 :All Sources (Mumbai) Fig. Iso-concentration Plots for NOx– Existing Scenario- Post Monsoon 2007 : All Sources (Mumbai)
Distance along North, (Km)

Distance along North, (Km)

Conc.
Conc. In (µg/m3)
In (µg/m3)

Distance along East, (Km) Distance along East, (Km)

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NOx concentration (µg/m3)

Figure 6.14: Modeling results for Mumbai (Base year 2007; PM 10 , NOx; Post
monsoon)

120
 Wind Rose

Mu
Mu

A
A

K Legend
C =Colaba
K
D =Dadar
Dh =Dh aravi
Dh K =Khar Dh L egen d
A =Andher i C =Co laba
Mh Mh=Mahu l
D =Dad ar
Mu=Mulun d Mh Dh=Dh ara vi
D
D K =Kh ar
Con. In Kg/d A =And heri
M h=M ahu l
M u=M ulund
Con. In Kg/ d

C
C

PM 10 Emission Load (Kg/day) – BAU-2007 NOx Emission Load (Kg/day) – BAU-2007

Fig. Iso-concentration Plots for PM - Existing Scenario – Winter 2007 :All Sources (Mumbai) Fig. Iso-concentration Plots for NOx– Existing Scenario- Winter 2007 : All Sources (Mumbai)

)
Distance along North, (Km)

m
(K
,
h
rto Conc.
N In (µg/m3)
g
n
o Conc.
l
a In (µg/m3)
e
c
n
a
t
si
D

Distance along East, (Km)

Distance along East, (Km)

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NOx concentration (µg/m3)

Figure 6.15: Modeling results for Mumbai (Base year 2007; PM 10 , NOx; Winter)

121
 Wind Rose

less than 150 kg/d less than 150 kg/d

150 to 300 kg/d 150 to 300 kg/d

300 to 450 kg/d 300 to 450 kg/d

450 to 600 kg/d 450 to 600 kg/d

Above 600 kg/d Above 600 kg/d

PM 10 Emission Load (Kg/day) – BAU-2007 NOx Emission Load (Kg/day) – BAU-2007

20000 20000

18000 18000

16000 16000

150ug/m3 14000
200ug/m3
14000

12000
125ug/m3 12000 160ug/m3
Y Co-ord
Y co-o rd

10000
100ug/m3 10000
120ug/m3
8000 75ug/m3 8000
80ug/m3
6000 50ug/m3 6000

40ug/m3
4000
4000 25ug/m3
2000 0ug/m3
2000

0
0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000

0
0

2 000

4 000

6 000

8 000

10 000

12 000

14 000

16 000

18 000

20 000

22 000

X-Co-ord
X co-ord

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NOx concentration (µg/m3)

Figure 6.16: Modeling results for Pune (Base year 2007; PM 10 , NOx; Summer)

122
 Wind Rose

less than 150 kg/d less than 150 kg/d

150 to 300 kg/d 150 to 300 kg/d
300 to 450 kg/d
300 to 450 kg/d
450 to 600 kg/d
450 to 600 kg/d
Above 600 kg/d
Above 600 kg/d

PM 10 Emission Load (Kg/day) – BAU-2007 NOx Emission Load (Kg/day) – BAU-2007

20000 20000

18000 18000

16000 16000

14000
200ug/m3
14000 150ug/m3
12000
125ug/m3 12000 160ug/m3
Y Co-ord
Y C o -o rd

10000 10000
100ug/m3 120ug/m3
8000 8000
75ug/m3
80ug/m3
6000 6000
50ug/m3
40ug/m3
4000 4000
25ug/m3
2000 2000 0ug/m3

0 0
0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000
0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000

X Co-ord X-Co-ord

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NOx concentration (µg/m3)

Figure 6.17: Modeling results for Pune (Base year 2007; PM 10 , NOx; Post
monsoon)

123
 Wind Rose

less than 150 kg/d

150 to 300 kg/d less than 150 kg/d

300 to 450 kg/d 150 to 300 kg/d
450 to 600 kg/d
300 to 450 kg/d
Above 600 kg/d
450 to 600 kg/d
Above 600 kg/d

PM 10 Emission Load (Kg/day) – BAU-2007 NOx Emission Load (Kg/day) – BAU-2007

20000
20000

18000
18000

16000
16000
150ug/m3
200ug/m3
14000 14000
125ug/m3
12000 12000 160ug/m3
100ug/m3
Y Co-ord
X C o -ord

10000 10000 120ug/m3

75ug/m3 8000
8000 80ug/m3
50ug/m3 6000
6000
40ug/m3
25ug/m3 4000
4000
0ug/m3
2000
2000
0
0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000

0
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000
X Co-ord
X Co-ord

Isopleths for 24-hourly average– BAU-2007

PM 10 concentration (µg/m3) NOx concentration (µg/m3)

Figure 6.18: Modeling results for Pune (Base year 2007; PM 10 , NOx; Winter)

124
6.3 Model Performance and Calibration

The predicted and observed concentrations were compared to assess the

performance of the model. For PM, the predicted values, at times, do not
fall within the acceptable modeling criteria because of extraneous factors
(e.g. background sources) not accounted while modeling. It is recognized
that the term ‘NO x ’ at the source is largely NO (about 90%) and at
receptor location (far from sources), the major component of NO x is NO 2 .
As the Gaussian model does not account for chemical reactions, it is not
reconcilable that the model estimated NO x be compared with observed
NO 2 in an absolute sense. However, it remains a fact that model
computed NO x and observed NO 2 will be linearly associated. Therefore,
the model interpretation in terms of source-specific contribution of NO x
remains valid. In general the model performance was found to be
reasonably good and adequate (within a factor of 2 for NO x ), though
there have been variations across the cities. The city specific observations
are given below:

Bangalore: Dispersion modeling of PM 10 and NOx at city level using

calibrated model suggests that in general the predicted concentrations lie
within a factor of 2 as compared against observed concentrations.

Chennai: There is a general match between the concentrations predicted in

the 2x2 km and the concentration measured for PM 10 and NOx, i.e. the
concentration measured falls within the minimum and maximum
concentrations predicted within the 2x2 km grid. However, there is no one-
to-one correspondence between the values predicted exactly at the
monitoring site and the values measured. There is no consistent over
prediction or under prediction of the concentrations for PM 10 or NOx.

Delhi: It is observed that the predicted values of PM 10 are much lower as

compared to the measured levels, at all the sites and during all seasons.
Very high levels of PM are prevalent, which can be attributed to
windblown dust, which are not accounted in emission inventory and many
other unaccounted infrequent sources prevailing in the study zones.
Analysis of measured and predicted PM concentration levels reveal
dominance of non-anthropogenic sources almost at all the study zones.
The predicted NOx values are either lower or equal to the observed values.
However, concentrations are over-predicted at kerbside locations. This can
be attributed to the fact that the vehicular emission factors are for NO x
giving emission rates in terms of NO x , whereas the monitored observations
confined to NO 2 . Due to higher vehicular activities, the difference

125
between measured and modeled values becomes much prominent at
kerside locations.

Kanpur: Model performance was found adequate (R-square 0.88 – 0.44 for
observed and predicted values). Observed levels are generally higher
than predicted levels both for PM 10 and NO x . For PM 10 , there is a significant
part as background level.

Mumbai: Comparison of measured and predicted concentrations shows

that site specific average concentrations predicted using primary survey
based emission inventory are close to measured values to a large extent.
NOx values across the city in both cases are much closer to measured
values.

Pune: The model was found to under predict the PM 10 concentrations. The
ratio of observed to predicted concentrations at different sites revealed
the variation in the range of 1.1 to 2.3. Average ratio, of all sites, of
observed and predicted ground level concentrations was found to be 1.7.
In case of NOx, variation in ratio of observed to predicted ground level
concentrations at different sites was found in the range 0.3 to 0.8 with
overall average ratio of observed to predicted concentrations of 0.6. Thus
the model was found to over predict the concentrations to some extent,
which may be attributed to the fact that the observed concentrations are
representative of NO 2 while the emission inventory data used for
predictions include all oxides of nitrogen. However, comparisons between
observed and predicted values are within the factor of 2 and considered
reasonably good.

126
 7

Evaluation of Control/Management Options and

City Specific Action Plans

The results of air quality measurements, emission inventory, source

apportionment based on receptor modeling, and dispersion modeling
provide vital information in terms of status of air quality and sources
contributing to it, in each of the six project cities. List of prioritized sources
based on EI, receptor modeling (factor analysis and CMB8) is given in Table
7.1. The levels of PM 10 are high, and there are multiple contributing sources.
Therefore, controlling a single source type may not yield desired results, and
it is necessary to evolve a comprehensive action plan comprising a
combination of control/management options. These options would vary
from city to city depending on extent of problem and source configuration
in the city.

There is no single method which provides the complete idea about the
urban air pollution sources, their strength, exposure assessment at the
receptor, probability of high concentrations, seasonal variations, predicted
values etc. In view of such high probable variation in large data sets about
the air pollution levels that city has to grapple with, dispersion models are
needed for evaluating effectiveness of action plans. In order to evaluate
various control options and suggest an appropriate action plan, the
following approach and methodology were adopted:

7.1 Approach and Methodology

The broad approach included identification of critical season from air

quality perspective, EI projections for BAU for the years 2012 and 2017,
preparation of comprehensive list of possible control/management options
under each source category (e.g. vehicle, industries, fugitive area sources,
etc.), analysis of their efficacies, development of alternate scenarios with
different combination of options using dispersion modeling, and selecting
most preferred scenario as action plan. A schematic view of approach for
developing action plan is given in Figure 7.1, and broad methodology
adopted is summarized below:

ƒ The results of dispersion modeling for existing scenario (please refer

Figures 6.1 – 6.18 in Chapter 6) indicated that PM 10 and NOx levels were

127
 of major concerns requiring priority attention. Besides, levels during
winter (post monsoon, in case of Chennai & Delhi) were higher as in
other seasons. Therefore, detailed analysis was carried out for PM 10 and
NOx for the critical season in respective cities.
ƒ Baseline EI for 2007 was used to estimate emission loads for BAU
scenarios for the years 2012 and 2017. While working out future
projections for a city, growth pattern of the city including growth in
population, vehicles, industries, construction activities, Diesel Generator
(DG) sets, etc. as well as proposed future land use/urban development
plans was considered. BAU does not account for any intervention to
abate air pollution levels. However, the roadmap already planned is
considered as BAU e.g. BS-III regulations for 2&3 wheeler vehicles and
BS-IV regulations for all other categories of vehicles from the year 2010.
Grid based emission rates were worked out.
ƒ A comprehensive list of various control options was compiled for
different source groups (Table 7.2). This included technological as well
as management based interventions. In order to assess (wherever
possible) effectiveness of each of these options, the dispersion model
was run twice with and without the control option (keeping other
sources being absent). By this, it was possible to determine impact of
each control option for improving air quality from vehicles, industries,
and area sources. While the model simulations were performed for the
whole city, the greater focus was on improving the air quality in the city
specific hot spots. The air quality in other grids will automatically improve
concurrently along with critical grids.
ƒ Based on the potential impacts of each of these options, three or four
alternate plans with different combination of options were evaluated.
While deciding a set of options, other factors like ease in
implementation from administrative point of view, technical feasibility,
financial viability, short & long term impacts, co-benefits in terms of
reduction in other pollutants, etc. were considered. Analysis of these
factors for various control options is given in Table 7.3.
ƒ The most preferred scenario was determined to formulate action plan,
which can result in GLC of PM 10 and NOx within the permissible levels as
per National Ambient Air Quality Standards in 2012 as well as 2017.

128
 Table 7.1: List of Prioritized Sources

Contributing Factor Analysis Emission Inventory Receptor Modeling/CMB Receptor

sources PM 10 Modeling/CMB
PM 2.5
Bangalore Vehicle exhaust, road dust, Vehicle exhaust, road dust, Road & soil dust, vehicle Vehicle exhaust, DG
secondary particulates, construction activities, exhaust, DG sets, sets, Secondary
construction activities industries secondary particulate particulate, domestic
combustion, road dust,
industries
Chennai Vehicle exhaust, Road dust, vehicle exhaust, Domestic, vehicle exhaust, Vehicle exhaust, road
construction, DG sets, construction, industries road dust, DG sets dust, domestic, DG sets
bakeries
Delhi Combustion, road & soil Road dust, power plant, Road dust, construction, Domestic, vehicle
dust, vehicle exhaust, construction, domestic vehicle exhaust, garbage exhaust, road dust,
industries combustion, vehicle burning industries, garbage
exhaust burning
Kanpur Road dust, secondary Industry, vehicle exhaust, Garbage burning, Vehicle exhaust,
particulates, oil burning domestic combustion, road domestic combustion, domestic combustion,
(e.g. diesel or heavy oil), dust secondary particulate, secondary particulate,
biomass burning vehicle exhaust DG sets, road dust,
industries.
Mumbai Road & soil dust, vehicle Road dust, power plant, Domestic, Soil & road dust, Not done
exhaust, coal combustion, landfill open burning, garbage burning, industries,
kerosene combustion construction vehicle exhaust

Pune Road dust, construction, Road dust, vehicle exhaust, Road dust, Secondary particulates,
combustion/vehicles, domestic combustion, construction/brick kilns, vehicle exhaust,
secondary particulates construction domestic combustion, domestic combustion,
vehicle exhaust road dust

129
 Table 7.2: List of Potential Control Options

Source Control Options Expected % Reduction in Scenario for Scenario for Remarks
Category Emissions (Factor) 2012 2017
Vehicles Technology based
1.Implementation of BS Difference between BS – III BS – IV from 2010 BS – IV from 2010 Technically
– IV norms and BS – IV (as currently BS (adopt (adopt feasible,
– III is in use): progressive progressive involves
increment) increment) huge
Gasoline – NOx: 47% investments

Diesel – PM: 45%, NOx: 50%

2. Implementation of Difference between BS – IV BS – IV from 2010 BS – IV from 2010 Technically
BS – V norms and BS – V: (adopt (adopt feasible,
progressive progressive involves huge
Gasoline – NOx: 25% increment) increment) investments

Diesel – PM: 90%, NOx: 28% BS – V from 2015

(adopt
progressive
increment)
3. Implementation of Difference between BS – V BS – IV from 2010 BS – IV from 2010 Technically
BS – VI norms and BS – VI: (adopt (adopt feasible,
progressive progressive involves huge
Diesel – NOx: 55% increment) increment) investments

BS – VI from 2015
(adopt
progressive
increment)
4. Electric Vehicles NOx and PM: 100% (Zero Share of Electric Share of Electric Technically
emissions) vehicles in total vehicles in total feasible,
city fleet – city fleet – Infrastructure
Two wheeler: Two wheeler: and power
1%, 2%, requirement

130
Source Control Options Expected % Reduction in Scenario for Scenario for Remarks
Category Emissions (Factor) 2012 2017
Auto Riksha and Auto Riksha and to be
Taxi: 5% Taxi: 10% assessed.
Public buses: 5% Public buses:
10% With regard to
public utility
vehicles, %
share of
electric
vehicles may
be city-
specific and
changed, if
required.
5. Hybrid vehicles NOx: 50% Share of Hybrid Share of Hybrid
vehicles in total vehicles in total
city fleet city fleet
(Gasoline (Gasoline
powered four- powered four-
wheelers only) – wheelers only) –
1% 2%
6. CNG/LPG to Public Transport (Buses) – 25% conversion 100% conversion Technically
commercial (all 3 and feasible,
4-wheelers) PM: 75% Supply of
NOx: 12.5% CNG/LPG
(as compared to BS – II and and required
BS – III vehicles) infrastructure
to be
assessed
7. Ethanol blending NOx: 5% Share of Ethanol Share of Ethanol Technically
(E10 – 10% blend) blended fuel – blended fuel – feasible,
10% 10% Availability of
Ethanol to be
assessed
8. Bio-diesel (B5/B10: 5 PM: 10% Share of Bio- Share of Bio- Estimation, as
– 10% blend) NOx: + 2.5% (increase) diesel fuel – 5% diesel fuel – 10% no data

131
Source Control Options Expected % Reduction in Scenario for Scenario for Remarks
Category Emissions (Factor) 2012 2017
(current)
available on
B5/B10
9. Hydrogen – CNG NOx: 10% Share of Techno-
blend (H10/H20: 10 – Hydrogen economic
20% blend) blended feasibility to
(H%)fuel - 10% be
(for vehicles on established.
CNG)

9. Retrofitment of Diesel PM: 22.5 % (as compared to 50% conversion 100% conversion Technically
Oxidation Catalyst BS – II vehicles) feasible but
(DOC) in 4-wheeler compliance
public transport (BS – II) to be ensured
10. Retrofitment of PM: 70 % (as compared to 50% conversion 100% conversion Technically
Diesel Particulate Filter BS – III vehicles) feasible but
in 4-wheeler public compliance
transport(BS – III city to be ensured
buses)
Management based
1. Inspection/ BS – II & BS – III public New I&M Strict Strict
maintenance transport vehicles – PM: regulation compliance by compliance
12.5% introduced and 100% mechanisms
NOx: 12.5% compliance by to be worked
50% anticipated out
2 and 3-wheelers (gasoline)
– NOx: 10%

3-wheelers (diesel) – NOx:

5%, PM: 12.5%

4-wheelers (gasoline) –
NOx: 7.5%

4-wheelers (diesel) – NOx:

132
Source Control Options Expected % Reduction in Scenario for Scenario for Remarks
Category Emissions (Factor) 2012 2017
7.5%, PM: 7.5%
2. Banning of 10 year 100% reduction of off-road Old vehicles (10 Old vehicles (10 Regulatory
old commercial vehicles years +): nos. to years +): nos. to provision
vehicles be worked out be worked out required
3. Banning of 15 year 100% reduction of off-road Old vehicles (10 Old vehicles (10
old private vehicle vehicles years +): nos. to years +): nos. to
be worked out be worked out
4. Synchronization of 20% reduction in pollution Effective Effective
traffic signals load for the roads on which synchronization synchronization
it is implemented. on all major on all major &
roads (or about minor roads,
10% of the prime excluding
roads) feeder roads (or
about 20% of
the prime roads)
5(a). Improvement of Refer DPR or EIA reports or Incorporate city Incorporate city
public transport: as per any other suitable specific specific
existing plan for the document for percentage proposals on proposals on
city shift in VKT and off road public transport public transport
personal transport vehicles with respect to with respect to
for calculating reduction in Metro/mono Metro/mono
PM & NOx emissions. rail, BRT, large rail, BRT, large
buses buses
contingent etc contingent etc.

5(b). Improvement of For percentage shift in VKT 10% shift in VKT 20% shift in VKT
public transport: % calculate off-road personal
share (VKT of cars, 2- transport vehicles for
wheelers and buses) calculating reduction in PM
and NOx emissions.

6. Fiscal Quantification
incentives/disincentives may be
like increased parking difficult but

133
Source Control Options Expected % Reduction in Scenario for Scenario for Remarks
Category Emissions (Factor) 2012 2017
fee, proper fuel pricing could be a
policy, incentives for good option
car pool, etc
7. Scattered business Quantification
timings may be
difficult but
could be a
good option
8. Banning odd/even Zero emissions from the
vehicles on particular vehicles off the roads
roads
Industries
1. Fuel change Appropriate EF to be used All solid fuel fired All solid fuel or Strict
combustion HSD fired compliance
Zero PM emissions in case of converted to combustion required
NG LSHS converted to
NG
2. Cleaner technology Based on data on type of Clean Clean Incentives are
change industrial units and possible production production necessary
conversion to cleaner option option and need to
technology and implemented in implemented in be defined
information on expected all feasible all feasible
emission reduction due to industries industries
use of cleaner technology
3. Fugitive emission 80% PM reduction in fugitive 50% of the 100% of the Will also
control emissions for the industries in industries having industries having improve
which implemented. effective control effective control occupational
implementations implementations health

4. Particulate control Bag Filters to have 95% Bag Filters Bag Filters
system (cyclone, BF collection efficiency of PM adopted for adopted for
etc) combustion combustion
emissions emissions
5. Shifting of air Deduct emission load from 50% air polluting 100% air

134
Source Control Options Expected % Reduction in Scenario for Scenario for Remarks
Category Emissions (Factor) 2012 2017
polluting industries shifted industries industries shifted polluting
out industries shifted
out
6. Ban of new industries No addition of No addition of
in existing city limit industries industries
7. Voluntary measures Quantification of impact - 100% industries
like ISO 14000, ISO may not be possible. Could with ISO 14000
18000 make some assumption
8. Compliance Quantification of impact 100% 100%
monitoring may not be possible. Could compliance compliance
make some assumption
Area 1. Use of Natural Appropriate EF to be used 50% of solid fuel, 75% of solid fuel,
sources Gas/LPG kerosene for kerosene for
(Combustion Zero PM emissions in case of domestic use to domestic use to
- Domestic, NG be shifted to be shifted to
Bakeries, LPG/NG LPG/NG
Open Eat
outs, Hotels, 100% of other 100% of other
etc.) sources to sources to
NG/LPG NG/LPG
DG sets 1. Inspection & Data required
Maintenance of large for
DG sets quantification
of impact is
not available
but this could
be a good
option. Some
assumptions
(e.g. 15%
change or
improvement
similar to
vehicle I & M)
could be

135
Source Control Options Expected % Reduction in Scenario for Scenario for Remarks
Category Emissions (Factor) 2012 2017
made.
2. Adequate supply of Zero emissions from DG sets No use of DG No use of DG
grid power sets sets
Construction 1. Better construction PM: 50% 50% reduction 50% reduction
practices including from from
proper loading construction construction
/unloading, activities in the activities in the
transportation of BAU 2012 BAU 2017
material, water
spraying, etc.

Road side 1. Converting unpaved Use appropriate EF for 50% of all 100% of all
dust roads to paved roads emissions from respective unpaved roads unpaved roads
roads to paved to paved
2. Wall to wall paving Use appropriate EF [leads All major roads All major roads; Use of bricks
(brick) to 15% reduction on paved and minor roads or tiles that do
roads, 40% on unpaved with heavy not restrict
roads for SPM] traffic excluding ground water
feeder roads percolation
3. Sweeping and Use appropriate EF All major roads All major roads;
watering (mechanized) and minor roads
with heavy
traffic excluding
feeder roads
Open Strict compliance to 50% compliance 100%
Burning ban of open burning compliance

All sources 1. Zero polluting Certain areas

in hotspots activities in hotspots in the city with
high pollution
levels could
be declared
zero polluting
activity/no

136
Source Control Options Expected % Reduction in Scenario for Scenario for Remarks
Category Emissions (Factor) 2012 2017
vehicles zone

Table 7.3: Factors considered in formulating control strategies

A) Framework for Selecting Measures to Address Urban Air Pollution – Vehicles

Action Category (a) Technical (b) Administrative / Regulatory (c) Economic

/ fiscal
(1) Strategy: Reducing Emissions per Unit of Fuel
Fuel Quality Sulphur Reduction Delineating tighter diesel fuel standards Phasing out fuel subsidies, uniform pricing
Improvement all over the state followed by country

Installation of after Fitment of Diesel Tighter diesel fuel standards particularly for Differential taxation to those with and
treatment devices Oxidation Catalyst, Sulphur to bring down its level up to 50 without after treatment device
catalytic converter in ppm.
older vehicles Emission test frequency to be more for
those without the after treatment devices
Tackle fuel Markers for detection Better specification of fuel quality for Oil companies to finance the setting up of
adulteration detection as well as booking the a laboratory, Fines and cancellation of
offenders. Monitoring fuel quality in a license
specified laboratory, making companies
accountable
Use of alternative CNG, LPG and Promote its use in private sector as well as Differential taxation for older vehicles
fuels Bio fuels organized sector through administrative changing to CNG/LPG, Incentive for new
orders owners to buy CNG/LPG vehicles. Low
cost bio fuels
Renewal of vehicle Phase out vehicles Scraping of older vehicles Older vehicles to remain on road if it
fleet above a certain age passes the fitness test as well as emission
test, however higher tax to be paid as
the vehicle gets older
Improve traffic flow Synchronized signal Coordination with other institutions to Congestion pricing
corridors check indiscriminate parking , and Higher parking fees
enforce one way system at peak hours
(2) Strategy: Reducing Fuel Consumption per unit Distance
Change to better 4 stroke engines for two Standards for fuel economy need to be Tax break for older vehicles changing to

137
technology engines –three wheelers, Bharat specified new engine with DOC or DPF
stage III engines with Useful age of the vehicles to be specified
DOC for older diesel by the manufacturer
vehicles, All new diesel
vehicles to be Bharat
stage III and above
Improve vehicle I&M I&M programs that are Strict enforcement Better infrastructure, manpower
difficult to cheat; augmentation, Strict fines
computerized data
capture and control of
tests
Better road Investment in better Standards for road construction specified in Financial incentives for contractors using
maintenance road maintenance terms of guaranteed life of the road better technology for road construction
technology to avoid
frequent relaying
(3) Strategy: Reduce Vehicle Distance Traveled
Increase private Encourage car pooling Congestion pricing
vehicle occupancy
Promote better and Dedicated bus lanes; Reform of public transport – competition, Subsidize public transport by taxing
more public transport user friendly MRTS privatization etc private car users

Demand 1. Limit parking - High one time tax on purchase of

management 2. Limit the use of vehicles in congested a new vehicle
areas - High parking fees
3. Allow odd /even no. private vehicles on - Road user charges
specified days. - Allow to ply a vehicle (odd/even) with
charges
Encourage non Pedestrian friendly Protection of pedestrian facilities Financial incentives for pedestrian friendly
motorized transport walkways / subways design

Reduce dust re- Road paving /cleaning Coordination with all institution working in Steep fines to agencies leaving the debris-
suspension the area of road and pavement dust on the roads after the completion of
maintenance, digging for utilities etc. One jobs.
agency to monitor the working practices.

138
B) Framework for Selecting Measures to Address Urban Air Pollution – Industries

Fuel change For power plant the fuel change SPCB can make the rule High cost initially. However, in longer run
leads to technology change as well. stringent and link with City more cost effective
However, newer technologies are Action Plan
more efficient and long term cost
effective. Other industries may
experience lower level of
technology issues.
Industrial Policy • Specifying technology needs Detail feasibility study for Financial incentive to burn cleaner fuel or
policy review. technology as well as land use of cleaner technology
• Area specific location policy use based policy issues.

C) Framework for Selecting Measures to Address Urban Air Pollution – Area Sources

Action Category (a) Technical (b) Administrative / Regulatory (c) Economic

/ fiscal
Fuel change - Domestic No major technical issue Adequate administrative measures No major cost involved. Facilitation
in place for awareness needed. Low cost fuel
to slums
Fuel change - Need for technical Standards to be specified to drive Medium cost
Bakeries/Crematoria evaluation technical changes

Resuspended dust • Pavement to be wall to Regulatory push and enforcement • Minor cost
wall required for better road paving • Fines for poor road surfaces
• Better sweeping system technologies.
• Better road paving
technology issue
Construction Improved construction Regulatory push and surveillance Minor costs of regulation and
practices, no technology needed for better compliance surveillance
issue
DG Sets • Revision of emission Revision of emission standards have been initiated by CPCB; and I&M needs
standards to be taken up. As such, technical/administrative/economic issues need
• Inspection & evaluation. Issue of non-emission/industrial engines being delivered for DG
maintenance sets is an issue which needs to be taken.

139
7.2 Evaluation of Efficacy of Control Options and Development of City-
Specific Action Plans

In each of the cities, depending on dominance of source contributions,

local situations, various options were short listed. Efficacy of each of these
options was worked out using dispersion model. Alternate scenarios with
combination of selected options were developed. These scenarios were
developed for the year 2012 and 2017 for PM 10 as well as NOx. The best
scenario, in terms of air quality improvement, was chosen as appropriate
Action Plan. While details of alternate scenarios including analysis of
expected improvement in air quality are given in the base reports of the
cities, best scenario that led to formulation of Action Plan is presented in this
report. Model predictions were made considering implementation of the
suggested Action Plan, to assess the expected improvement in the air
quality. The expected emission reductions expected after implementation of
Action Plan are given in Table 7.4.

Table 7.4: Emission Reductions with Implementation of Action Plan

Pollutants Emission Load (T/day) % increase % reduction w.r.t.

over Baseline BAU
2007
Baseline- BAU- BAU- Action Action BAU- BAU- Action Action
2007 2012 2017 Plan 2012 Plan 2017 2012 2017 Plan Plan
2012 2017

54 72 96 33 35 32% 76% 54% 64%

PM 10
Bangalore

11 17 26 10 9 16% 25% 41% 66%

Chennai

147 175 203 101 146 19% 38% 42% 28%

Delhi

9 11. 14 7 7 22% 48% 40% 51%

Kanpur

73 105 132 37 22 43.% 79% 65% 83%

Mumbai

140
 32. 48 78 25 39 50% 140% 49% 50%

Pune
NOx 217 321 460 131 122 48% 112% 59% 73%

Bangalore
12 17 25 11 12 41% 108% 34% 53%

Chennai

460 489 611 83 202 6% 32% 83% 67%

Delhi

22 33 44 20 30 49% 97% 40% 33%

Kanpur

215 278 341 185 212 29% 58% 33% 37%

Mumbai

41 71 112 39 49 71% 170% 45% 56%

Pune

City-wise details are given below:

Bangalore:

Based on emission inventory and receptor modeling approach, the major

common sources of PM 10 are transport and road dust re-suspension. DG
sets and industry show significant contributions in different approaches. In
addition, due to major construction activities ongoing in Bangalore,
construction sector also contributes to the emission load. Therefore, in the
case of Bangalore, control strategies need to be devised for transport,
road dust re-suspension, industry, DG sets, and soil dust/construction. In
addition, CMB8.2 quantification shows secondary particulates as an
additional source. The control strategies for primary pollutant like SO 2 and
NOx would results in the reduction of the secondary particulates as well.

141
For prioritizing the list of management/control options, an analysis is made
of the percentage reduction in the overall emission load as compared to
the BAU total emission load in the respective years i.e. 2012 and 2017. Four
alternate scenarios were generated that were a mix of different control
strategies and management options. The prioritized list of key individual
interventions/control measures that were considered under different
alternate scenarios in terms of percent reduction in the PM 10 emission load
in 2017 as compared to the respective BAU emission load; and the best
scenario for air quality improvement (alternate IV) is discussed below:

ƒ By-passing of trucks through the proposed peripheral ring road around

Bangalore (14%); Installation of DOC and DPF devices in all pre-2010
diesel vehicles (13%);No power cuts leading to zero usage of DG sets
(13%);Ban on 10 year old commercial vehicles in 2012 and 2017 (12%);
Ban on any new industries in city limits(6%) and fuel shift towards
cleaner fuel CNG (5%) in existing industries (11%); Installation of DOC
and DPF devices in DG sets (8%); Wall to wall paving for reduction of
road dust (6%); Better construction practices (5%);Conversion of public
transport (commercial 3 & 4 w) to CNG (25% in 2012 and 100 % in
2017)(4%); Improvement in inspection and maintenance for vehicles
(2%); Inspection and maintenance for DG sets (2%); Conversion of
public transport (commercial 3 & 4 w) to CNG (25% in 2012 and 100 % in
2017) (2%);Enhancement of public transport system based on diesel
(shift of PKT from private vehicles to public transport i.e. 10% in 2012 and
20% in 2017) (1%).

ƒ Besides the above strategies, introduction of electric vehicles and

synchronization of traffic signal also lead to reduction in emission loads.
Other options such as staggered business timings and no vehicle zones in
hot spots would also be helpful in improving the air quality. Fiscal measures
such as congestion charges, enhanced parking charges, etc. would be
helpful in reducing the usage of private vehicles. More importantly,
rationalization of excise duty on vehicles and appropriate fuel pricing
policies could play an important role in curbing the growth of more
polluting private vehicles. Other measures such as appropriate land use
planning to curb travel demand, enhancing virtual mobility, car pooling,
etc would contribute to air quality improvements. However, in order to
implement many of these strategies, the basic requirement is to have an
efficient mass public transport system in place.

142
ƒ Selection of various control options shows an impact in terms of reduction
in emission loads eventually translating into reduction of PM 10 ambient
concentrations. The benefits are anticipated in terms of improvements in
the ambient air quality at the six ambient air quality stations as well as at
the city level thereby leading to improved health and ecological benefits.

ƒ The suggested Action Plan for Bangalore is given in Table 7.5. Air quality
profiles (isopleths) of the city for BAU 2012 and 2017 as well as expected
after implementation of proposed Action Plan in respect of PM 10 and NOx
are given in Figures 7.1 and 7.2 (yellow plots for PM 10 and Blue plots for
NO x ).

143
 Table 7.5: Action Plan for Bangalore
S. Sector Strategy Impact* Responsible Agency / Time Remarks
No. agencies frame
1 Transport Strengthening of Govt of India, State Medium Leveraging the JNNURM funding
Public transport Government, BMRCL term mechanism for public transportation
system High (Bangalore Metro rail improvement
Corporation Ltd.),
• Metro Transport Department- Public-private partnership models to be
implementation Bangalore, BMTC explored
on schedule (Bangalore
• Enhance share Metropolitan Transport The metro network needs to be
of public mass Corporation), GAIL progressively expanded.
transport system
on diesel Bangalore currently does not have a
• Conversion/ CNG network. There are plans to set up
enhancement such a network in future. ULSD would also
of public be available by April 2010 in Bangalore.
transport to
CNG Retro-fitted 2-stroke three wheelers on
LPG in Bangalore have higher PM
emissions compared to OE 2-stroke/ 4-
stroke LPG/Petrol. Thus retro-fitment of 2-
stroke 3-wheelers is not an effective
control option.
Ban on old High Transport department - Short-term Fiscal incentives/ subsidies for new
commercial Bangalore vehicle buyers
vehicles (10 year) A plan should be devised for gradual
in the city phase out with due advance notice.
Careful evaluation of socio-economic
impact of banning required.
In the long run, a ban/ higher tax on
private vehicles (> 15 years) could be
looked into.
By-passing of trucks High Traffic Police, Transport Short-term Has high potential in reducing the
through the department pollutant load in the city
proposed
peripheral ring
road around
Bangalore
Progressive Low MoRTH, MoPNG, Ministry Medium Auto-fuel road map should be
improvement of of Heavy Industry and to Long developed well in advance to plan the
vehicular emissions Public Enterprises, MoEF, term progressive improvement of emissions
norms (BS-V, BS-VI) Oil companies, norms and corresponding fuel quality

144
S. Sector Strategy Impact* Responsible Agency / Time Remarks
No. agencies frame
Automobile norms.
manufacturers Though the impact is low, its potential is
high in the long term when gradually
fleet renewal takes place.
Installation of High Transport department Medium Technical feasibility and implementation
pollution control plan of this strategy needs to be carefully
devices evaluated, though it has potential for
(DOC/DPF) in all emission load reduction. Retro-fitment of
pre-2010 diesel DOC in BS-II buses and DPF in BS-III buses
vehicles is technically feasible.
Introduction of fuel Low BEE, Ministry of Power, Medium Impact is low since it is applied only to
efficiency Ministry of Heavy new vehicles registered after 2012.
standards Industry and Public However, its potential is high in the long
Enterprises, Automobile term when gradually fleet renewal takes
manufacturers, Ministry place.
of Road Transport
Introduction of Low - Ministry of Finance, Short- Appropriate fiscal incentives need to be
hybrid vehicles/ medium Ministry of Heavy Medium provided; Electric vehicles would be
electric vehicles Industry and Public especially effective in high pollution
Enterprises, Automobile zones. Impact determined by the extent
manufacturers, State of switchover to hybrid/ electric vehicles.
government,
Effective Medium Transport Department, Short to Initial focus could be on commercial
Inspection and Traffic police Medium vehicles; Capacity development in terms
maintenance of infrastructure for fully computerized
regime for vehicles testing/certification and training of
personnel. Linkage of all PUC centers for
better data capture.
Alternative fuels Low MNRE, MoRD, MoPNG, ongoing There are operational issues regarding
such as ethanol, MoA, Oil companies, availability and pricing that need to be
bio-diesel sorted.
Reduction in Min. of Finance, State Medium A pre-requisite for curbing the growth of
private vehicle Government term private vehicles is the provision of an
usage/ ownership effective mass based transport system.
NGOs Strategies such as costlier parking, higher
General public excise duties/sales tax on private
vehicles, car pooling would be helpful.
Improve traffic flow Medium Traffic police, Short Synchronization of signals, one way
Bangalore roads, flyovers, widening of roads,
Development Authority removal of encroachments, staggering
(BDA), Bruhat of office timings to reduce peak flow and
Bengaluru Mahanagara congestion. Application of IT tools for

145
S. Sector Strategy Impact* Responsible Agency / Time Remarks
No. agencies frame
Palike (BBMP), traffic management (Intelligent transport
system)
Fuel adulteration NA Govt. of India, Oil Short Re-assess subsidy on kerosene, strict
companies, Food and vigilance and surveillance actions, better
civil supplies infrastructure in terms of testing
department- Bangalore laboratories
2 Road dust • Construction of NA Bangalore Short - Effective enforcement of road quality
better quality Development Authority Medium norms is required. Landscaping/ greening
roads (BDA), Bruhat Bengaluru term of areas adjacent to roads
• Regular Mahanagara Palike
maintenance (BBMP), NHAI
and
cleaning/sweep
ing of roads
• Reduction in
vehicular fleet
and trips
Wall to wall paving High Bangalore Short term Interlocking tiles may be used so that
for reduction of Development Authority water percolation takes place.
road dust (BDA), Bruhat
Bengaluru Mahanagara
Palike (BBMP)

3 Industries Fuel shift towards High KSPCB, Directorate of Short- Shift from solid fuels to liquid fuels (LSHS)
cleaner fuels Industries and Medium and subsequently to gaseous fuels (CNG)
Commerce, Industry term
associations, GAIL, Oil
companies
Ban on any new air High KSPCB, Department of Short term Industrial estates/zones may be
polluting industry in Forest, Ecology and developed well outside the city
city limits Environment,
Department of
Industries and
Commerce, Karnataka
Industrial Area
Development Board
Strengthening of NA KSPCB, Industry Short term This would ensure greater compliance
enforcement associations, with standards. In addition, cleaner
mechanism for technology options need to be
pollution control promoted and appropriate incentives to
be defined. Voluntary measures such as
ISO certifications to be encouraged.

146
S. Sector Strategy Impact* Responsible Agency / Time Remarks
No. agencies frame
4 Power/ DG No power cuts High Bangalore Electricity Medium Adequate tie-ups need to be ensured
sets leading to zero Supply Company, term
usage of DG sets Karnataka Power
Corporation Ltd.
Installation of High KSPCB, DG set Medium Technical feasibility and implementation
pollution control manufacturers plan of this strategy needs to be carefully
devices evaluated, though it has potential for
(DOC/DPF) in DG emission load reduction
sets
Effective Medium KSPCB, Chief Electrical Short to
Inspection and inspectorate Medium
maintenance
regime for large
DG sets
5 Construction Better High KSPCB, SEAC (State Short term
enforcement of expert appraisal
construction committee), Bruhat
guidelines (which Bengaluru Mahanagara
should reflect Palike (BBMP),
Green Building
concepts)
6 Other Integrated land- NA Bangalore Metropolitan Medium Holistic development of the entire region
sectors use development Region Development term including peripheral areas.
of Bangalore Authority, Bangalore
taking Development Authority,
environmental Bruhat Bengaluru
factors into Mahanagara Palike
consideration (BBMP)
Open burning/ NA Bruhat Bengaluru Short term Organic matter could be used for
Waste burning to Mahanagara Palike compost formation and methane gas
be discouraged (BBMP), KSPCB generation
Domestic sector – Low Food and civil supplies Medium Rural areas should be encouraged to
biomass burning to department, Oil shift to cleaner fuels
be reduced companies
Virtual mobility- NA Department of Short- Reduced number of trips.
using ICT Information Medium
information and Technology& term
communication Biotechnology,
technology Government of
Karnataka;

Strengthening of NA KSPCB Short Good quality data is an important input

147
S. Sector Strategy Impact* Responsible Agency / Time Remarks
No. agencies frame
air quality in assessing the change in air quality and
monitoring the impact of policy interventions.
mechanism in Continuous monitoring stations to be
terms of number of promoted.
stations as well as
pollutants
monitored.
Capacity building
of KSPCB staff.
Environmental NA Education department, Short Also, sensitization programmes for policy
education and Schools/Colleges, makers.
awareness CBOs, NGOs
activities

* Impact is determined in terms of percent reduction in total emission load for PM 10 for the study period up to 2017 subject to the assumptions listed in
chapter 6 (High impact > 5% reduction; medium impact 1-5% reduction; low impact < 1% reduction; NA = not quantified or not quantifiable). Time
frame: Short (up to 2012), Medium (2012-2017)

148
 26000
26000

24000
24000

22000
22000
450 µg/m3
20000 425 µg/m3 300 µg/m3
20000
400 µg/m3 280 µg/m3
18000 375 µg/m3 260 µg/m3
18000
350 µg/m3 240 µg/m3
16000 325 µg/m3
16000 220 µg/m3
300 µg/m3
200 µg/m3
275 µg/m3
14000
250 µg/m3 14000 180 µg/m3
225 µg/m3 160 µg/m3
12000
200 µg/m3 12000 140 µg/m3
175 µg/m3 120 µg/m3
10000 150 µg/m3 10000 100 µg/m3
125 µg/m3
80 µg/m3
8000 100 µg/m3
8000
75 µg/m3 60 µg/m3

6000 50 µg/m3 40 µg/m3

6000
25 µg/m3 20 µg/m3
0 µg/m3 0 µg/m3
4000 4000

2000 2000

0 0
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3) for winter:
2012 BAU

26000 26000

24000
24000

22000
22000
450 µg/m3
20000 300 µg/m3
425 µg/m3 20000
400 µg/m3 280 µg/m3
18000 375 µg/m3 260 µg/m3
18000
350 µg/m3 240 µg/m3
16000 325 µg/m3
16000 220 µg/m3
300 µg/m3
200 µg/m3
275 µg/m3
14000
250 µg/m3 14000 180 µg/m3
225 µg/m3 160 µg/m3
12000
200 µg/m3 12000 140 µg/m3
175 µg/m3
10000 120 µg/m3
150 µg/m3
10000 100 µg/m3
125 µg/m3
8000 100 µg/m3 80 µg/m3
75 µg/m3 8000
60 µg/m3
6000 50 µg/m3
40 µg/m3
25 µg/m3 6000
20 µg/m3
0 µg/m3
4000
0 µg/m3
4000

2000
2000

0
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000 0
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3)

respectively for winter:
Control Scenario 2012

Figure 7.1: Air Quality Profiles for BAU 2012 and with Implementation of
Action Plan in Bangalore

149
 26000 26000

24000 24000

22000 22000
450 µg/m3
300 µg/m3
20000 425 µg/m3 20000
280 µg/m3
400 µg/m3
18000 375 µg/m3 260 µg/m3
18000
350 µg/m3 240 µg/m3
16000 325 µg/m3 220 µg/m3
16000
300 µg/m3
200 µg/m3
275 µg/m3
14000
250 µg/m3 14000 180 µg/m3
225 µg/m3 160 µg/m3
12000
200 µg/m3 12000 140 µg/m3
175 µg/m3
10000
120 µg/m3
150 µg/m3
10000 100 µg/m3
125 µg/m3
8000 100 µg/m3 80 µg/m3
75 µg/m3 8000
60 µg/m3
6000 50 µg/m3
40 µg/m3
25 µg/m3 6000
20 µg/m3
0 µg/m3
4000
0 µg/m3
4000
2000
2000
0
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000 0
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3) for winter:
2017 BAU
26000

26000
24000

24000
22000

450 µg/m3 22000

20000 425 µg/m3
300 µg/m3
400 µg/m3 20000
280 µg/m3
18000 375 µg/m3
260 µg/m3
350 µg/m3 18000
325 µg/m3 240 µg/m3
16000
300 µg/m3 16000 220 µg/m3
275 µg/m3 200 µg/m3
14000
250 µg/m3 14000 180 µg/m3
225 µg/m3 160 µg/m3
12000
200 µg/m3 12000 140 µg/m3
175 µg/m3
10000 120 µg/m3
150 µg/m3
10000 100 µg/m3
125 µg/m3
8000 100 µg/m3 80 µg/m3
8000
75 µg/m3 60 µg/m3

6000 50 µg/m3 40 µg/m3

6000
25 µg/m3 20 µg/m3
0 µg/m3 0 µg/m3
4000 4000

2000 2000

0 0
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000 24000

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3)

respectively for winter:
Control Scenario 2017

Figure 7.2: Air Quality Profiles for BAU 2017 and with Implementation of
Action Plan in Bangalore

Chennai:

The primary conclusions are that the PM can be abated by reducing the
silt loading and that the NOx can be abated by controlling the vehicular
emissions. Improving public transport would help control both to a
significant extent. However, the effectiveness of the individual options was
different. Implementing silt loading reduction would reduce the PM by 23%
in 2012 and by 48% in 2017. Improving public transport would reduce the
PM by 9% in 2012 and by 19% in 2017. Implementation of BS IV option would
decrease the NOx load by 6% in 2012 and by 20% in 2012. Implementation

150
of BS V would decrease the NOx by 22% in 2012 and the implementation of
BS VI norms would decrease NOx by 24% in 2012. Banning 10 year old
commercial vehicles would reduce the NOx by 13% in 2012 and by 14% in
2017. Banning 15 year old private vehicles would reduce the NOx by 15% in
2012 and by 17% in 2017. Improving public transport would reduce the
NOx by 8% in 2012 and by 17% in 2017. Thus it is clear that a judicious
combination of these options have to be exercised to control the pollution
level in the future.

The control options which resulted in significant reduction in the PM 10 and

NOx concentrations would be of higher priority for implementation.
Combinations of these control options were considered to determine the
total effect. On the basis of the effect on emission loads as well as on the
concentration levels of PM 10 and NOx, the control options which are most
effective are:

ƒ Control of emissions from road dust by reducing silt loading. This has a
significant impact on the emissions and concentration levels of PM 10 .
This however does not include the effect on NOx levels.

ƒ Control of emissions from vehicles has significant improvement on NOx

emissions and NOx levels, but not much influence on emissions and
ambient levels of PM.

ƒ Improving public transport has significant reduction in both PM and

NOx.

ƒ Other control options such as action on point sources and open

burning have a more local impact since their contribution to the air
quality in the city level is low.

ƒ The banning of commercial vehicles and private vehicles of a critical

age has a significant impact on the NOx emission load and pollution
levels.

ƒ The suggested Action Plan for Chennai is given in Table 7.6. Air quality
profiles (isopleths) of the city for BAU 2012 and 2017 as well as expected
after implementation of proposed Action Plan in respect of PM 10 and
NOx are given in Figures 7.3 and 7.4.

151
 Table 7.6: Action Plan for Chennai

S. Sector Strategy Impact* Responsible Agency / Time frame Remarks

No. agencies
1 Transport Strengthening of Public Govt of India, State Medium The metro network is being
transport system Government, CMRL term expanded. On Jan 2009, GOI
High (Chennai Metro Rail approved plans for two phase
• Metro implementation Limited), Transport expansion, with first phase
on schedule Department- Chennai, , expected to complete by
• Enhance share of MTC (Metropolitan 2014-15.
public mass transport Transportation
system Corporation Chennai), Chennai does not have a
• Conversion of public GAIL CNG network and has a
transport to CNG limited LPG network. There are
plans to set up CNG network
in future, but no timeframe is
available. ULSD would also be
available by April 2010 in
Chennai.
Retro-fitted 2-stroke three
wheelers on LPG have higher
PM emissions compared to OE
2-stroke/ 4-stroke LPG/Petrol.
Thus retro-fitment of 2-stroke 3-
wheelers is not an effective
control option.
Ban on old commercial High Transport department - Short-term Fiscal incentives/ subsidies for
vehicles (10 year) and Chennai new vehicle buyers
private vehicles (15 years) A plan should be devised for
in the city gradual phase out with due
advance notice. Careful
evaluation of socio-economic
impact of banning required.
Progressive improvement High MoRTH, MoPNG, Ministry Medium to Auto-fuel road map should be
of vehicular emissions of Heavy Industry and Long term developed well in advance to
norms (BS-V, BS-VI) Public Enterprises, MoEF, plan the progressive
Oil companies, improvement of emissions
Automobile norms and corresponding fuel
manufacturers quality norms.
Though the impact is low, its
potential is high in the long
term when gradually fleet

152
S. Sector Strategy Impact* Responsible Agency / Time frame Remarks
No. agencies
renewal takes place.
Installation of pollution Low Transport department Medium Technical feasibility and
control devices implementation plan of this
(DOC/DPF) in all pre-2010 strategy needs to be carefully
diesel vehicles evaluated, though it has
potential for emission load
reduction. Retro-fitment of
DOC in BS-II buses and DPF in
BS-III buses is technically
feasible.
Effective Inspection and Medium Transport Department, Short to Initial focus could be on
maintenance regime for Traffic police Medium commercial vehicles;
vehicles Capacity development in
terms of infrastructure for fully
computerized
testing/certification and
training of personnel. Linkage
of all PUC centers for better
data capture.
Improve traffic flow Low Traffic police, CMDA Short Synchronization of signals, one
(Chennai Metropolitan way roads, flyovers, widening
Development Authority) of roads, removal of
encroachments, staggering of
office timings to reduce peak
flow and congestion.
Application of IT tools for traffic
management (Intelligent
transport system)
2 Road dust • Construction of better High CMDA, NHAI Short - Effective enforcement of road
quality roads Medium quality norms is required.
• Regular maintenance term Landscaping/ greening of
and areas adjacent to roads
cleaning/sweeping of
roads
• Reduction in vehicular
fleet and trips
Wall to wall paving for High CMDA Short term Interlocking tiles may be used
reduction of road dust so that water percolation
takes place.
3 Industries Fuel shift towards cleaner Low TNPCB, Directorate of Short- Shift from solid fuels to liquid
fuels Industries and Medium fuels (LSHS) and subsequently

153
S. Sector Strategy Impact* Responsible Agency / Time frame Remarks
No. agencies
Commerce, Industry term to gaseous fuels (CNG). Local
associations, GAIL, Oil pollution load will decrease
companies significantly. However, impact
on overall level in the city will
be low
Ban on any new air Low TNPCB, Department of Short term Real estate prices will inhibit
polluting industry in city Forest, Ecology and growth of new large scale
limits Environment, industry within city limits and
Department of Industries hence the impact is low.
and Commerce, Tamil Industrial estates/zones may
Nadu Industrial be developed well outside the
Development city
Corporation
Strengthening of Low TNPCB, Industry Short term This would ensure greater
enforcement mechanism associations, compliance with standards. In
for pollution control addition, cleaner technology
options need to be promoted
and appropriate incentives to
be defined. Voluntary
measures such as ISO
certifications to be
encouraged.
4 Power/ DG No power cuts leading to Medium TNEB (Tamil Nadu Medium Adequate tie-ups need to be
sets zero usage of DG sets Electricity Board) term ensured
Installation of pollution Medium TNPCB, DG set Medium Technical feasibility and
control devices manufacturers implementation plan of this
(DOC/DPF) in DG sets strategy needs to be carefully
evaluated, though it has
potential for emission load
reduction
Effective Inspection and Medium TNPCB, Electrical Short to
maintenance regime for inspectorate Medium
large DG sets
5 Construction Strict enforcement of Low TNPCB, CMDA Short term Pollution load due to
construction guidelines construction is limited to short
(which should reflect time and small locality.
Green Building concepts)
6 Other Integrated land-use Low CMDA Medium Holistic development of the
sectors development of Chennai term entire region including
taking environmental peripheral areas.
factors into consideration

154
S. Sector Strategy Impact* Responsible Agency / Time frame Remarks
No. agencies
Open burning/ Waste Low CMDA, TNPCB Short term Sorting solid waste is
burning to be important. Organic matter
discouraged could be used for compost
formation and methane gas
generation
Strengthening of air Low TNPCB, CPCB Short Good quality data is an
quality monitoring important input in assessing
mechanism in terms of the change in air quality and
number of stations as well the impact of policy
as pollutants monitored. interventions. Continuous
Capacity building of monitoring stations to be
TNPCB staff. promoted.
Environmental education Medium Education department, Medium/ Sensitization programmes for
and awareness activities Schools/Colleges, CBOs, Long Term policy makers.
NGOs

* Impact is determined in terms of percent reduction in total emission load for PM 10 for the study period up to 2017 subject
to the assumptions listed in chapter 6 (High impact > 5% reduction; medium impact 1-5% reduction; low impact < 1%
reduction; NA = not quantified or not quantifiable). Time frame: Short (up to 2012), Medium (2012-2017)

155
 Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3)
respectively for Post Monsoon: 2012 BAU

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3)

respectively for Post Monsoon:
Control Scenario 2012

Figure 7.3: Air Quality Profiles for BAU 2012and with Implementation of
Action Plan in Chennai

156
 Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3)
respectively for Post Monsoon: 2017 BAU

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3)

respectively for Post Monsoon:
Control Scenario 2017

Figure 7.4: Air Quality Profiles for BAU 2017 and with Implementation of Action Plan
in Chennai

Delhi:

Analysis of various technology and management based options indicated

that implementation of any one of the above strategies would not be able
to achieve significant reduction Significant reductions are not observed
because several vehicular pollution control measures like introduction of BS-
II for 2 Wheelers and BS-III for all other vehicles, introduction of CNG for 3
Wheelers, taxis and buses, less sulfur content in diesel, ban on more than 8
years old buses, etc. have already been implemented in Delhi during the
last decade. To get further incremental reductions, the efforts required
would be much more, whereas the expected benefits would not be
commensurate with the efforts towards technology based pollution control
systems. Therefore, the control scenario of vehicular sector particularly must
look at managerial options, which can provide the right reductions.

157
The control scenario for vehicular emissions includes implementation of
next stage emission norms for new vehicles, retro-fitment of diesel
particulate filter for in-use commercial diesel vehicles, mandatory
inspection and maintenance in automobile manufacture company
owned service centers, improvement in public transport system,
synchronization of traffic signals, introduction of hybrid vehicles with
improvement in fuel quality (no adulteration) is expected to yield about
47% reduction by 2012 and 82% reduction in PM emissions by 2017 as
compared to estimated emissions under BAU for those years. Reduction in
NOx emissions is expected to the tune of 30% by 2012 and 43% by 2017, as
compared to respective years BAU scenario emission levels.

Preferred scenario delineation involves critical examination of the

constraints (technical, fiscal, administrative and others) with a view to
understand the applicability of the solution for the city. It also examines the
benefits and co-benefits of each of the actions. Impact of control options
in the improvement of air quality was assessed using three types of
scenario generation. The first scenario is for only PM emissions reduction,
whereas the second scenario is formulated for NOx emissions reduction.
However, the final scenario considered is for the reduction of both PM and
NOx emissions.

Percentage reductions in emissions of PM, SO 2 and NOx have been

estimated for different control options with respect to the corresponding
year BAUs (2012 and 2017) emission loads. Area coverage for PM and NOx
reduces substantially by 2012 and continues till 2017 as compared to the
respective year BAU scenarios. With the suggested management plans,
road dust re-suspension is expected to be reduced to a large extent by
2012 and 2017. Based on series of evaluation aiming at emission load
reduction leading to better air quality, a list of most important options have
been prepared as Action Plan and the same is given in Table 7.7. The
resultant air quality is given in Figure 7.5 – 7.6.

158
 Table 7.7: Action Plan for Delhi

S. Sector Strategy Impact Responsible Time Frame Remarks

No. Agency/ Agencies
1 Transport Augmentation of High Govt of Delhi, Medium • Dedicated bus lane, better buses,
city public MCD, NCR Board, low cost of travel, faster travel etc.
transport system Metro Rail and • Finances for better buses.
public transport • Measures to reduce the cost of
companies DTC travel by way of cross subsidizing.
etc. • Dedicated funds for public
transport

Traffic restrain, Moderate SIAM, MORTH,CPCB Medium • Improvement of roads, new roads,
and congestion Delhi Government, scientifically planned traffic
related taxes – MCD, NCR Board, management, mass transit systems,
Financial aid to parking on roads
Transport police,
public transport • Concretization of road may be the
other utilities.
solution. New road planning and
Traffic management are being
taken as integral part of the road
and flyovers construction.
• Better planning and training in
traffic management. Mass Rapid
Transit System (Metro and High
Capacity Bus system)
• It will reduce traffic junction hotspot
of all the pollutants
• It will also reduce continuous source
of dust
Transport Development of Moderate Vehicle Medium • No technical issue with new
fuel based manufacturer, vehicles.

159
S. Sector Strategy Impact Responsible Time Frame Remarks
No. Agency/ Agencies
emission norms (Marginal GoI, CPCB, • For in use old vehicles, technical
for all category improvement SIAM feasibility needs to be established
of vehicles from newer • The process of in-use vehicles
vehicles except standards may take time as they
when need to be revised at central level.
implementation Inadequate infrastructure and
is for Euro V & VI manpower at local; levels could
In-use vehicles other major barriers.
emission • After the legislation is in place,
reduction can be provision of strict penalty leading to
substantial) cancellation of vehicle registration.
• As the old vehicle population is
substantial, the standards will bring
in the much needed control on
emissions of all types
Fuel Quality Moderate Oil companies, Medium • The S reduction will not only reduce
Improvement GoI, Vehicle the PM but also lead to
(S reduction in (Reduction in S Manufacturer, correspondingly lower SO 2 emission
Diesel) leads to 2.5 – 13 leading to lower ambient SO 2 and
Ministry of
% reduction in sulphate.
Petroleum,
PM #) • High cost. Being planned by
Refineries as per the Auto Fuel
Policy.
• Improvement in emission standards
as well as legislation for stringent
fuel standards for S
• Phasing out the subsidies on diesel.
Bringing diesel cost at par in a
state/centre

160
S. Sector Strategy Impact Responsible Time Frame Remarks
No. Agency/ Agencies
Stringent system Moderate Oil Companies, Medium • One of biggest advantage of non-
for checking for Anti Adulteration adulteration shall be longer engine
adulteration in Cell, life besides the emission reduction
fuels Reduced PM Vehicle owners for PM as well as CO and HC. The
emissions catalytic converter shall be active
for its lifetime. Better quality fuel by
(difficult to
adopting stricter fuel supply and
quantify).
dispensing system (e.g. Pure for Sure
Effectiveness is etc.)
moderate as • Chemical marker system
marker system • Present system of Anti Adulteration
has not been cell function needs major
seen as a primary improvement in terms of higher
means to reduce manpower and spread.
PM • Finer fuel specifications are needed
for implementation.
Alternative fuels High GOI, SIAM, Medium • Will lead to substantial reduction in
Oil Companies CO and HC emission, however,
*More than 90 % NOx values may go up
* Technical
reduction in PM • Can be applicable mainly for
infrastructure in
can be achieved vehicles, which are supposed to ply
Mumbai for
compared to within the city. Applicable to only
dispensing
diesel # local public transport, taxies etc.
CNG/LPG is fairly
good and is
improving

* Biofuels can be * Similar to diesel * Low SO 2 emission

used up to 5-10% but low SO 2 and
without any low PM

161
S. Sector Strategy Impact Responsible Time Frame Remarks
No. Agency/ Agencies
major technical
issue
Inspection and High DPCC, CPCB Medium • On use of alternative fuel,
Maintenance MCD, GoD, Inspection and certification,
(I&M) System for (May lead to adulteration of fuels, use of public
Transport Dept,
all category of 5-10% reduction transport, less usage of private
of emission). Other institutions vehicles
vehicles in
involved in • Resources for awareness and
Automobile
awareness training, bringing the different
Manufacture
campaign groups together
Company
Owned Service • Savings by way of improved vehicle
Centers maintenance and operation
(AMCOSC)
Introduction of High Vehicle Medium • New technology based vehicles
new technology Companies, emit less per unit distance travelled
vehicles Transport Office • Electric vehicles for grossly polluting
GoD, , MNRE high VKT vehicles are a good
option. It needs regulatory push
• It will lead to better compliance
from on-road emission test and
overall improvement in emission of
all the pollutants.
Retro-fitment of Moderate DPCC, SIAM Medium • Experience of other countries
DPF in LCVs, Govt. of Delhi, suggests that it can be feasible.
Trucks and (Engine Vehicle However, in Indian scenario, a pilot
Diesel-Buses replacement Manufacturer, retrofit programme to evaluate the
could lead to efficacy needs to be undertaken. A
Vehicle Fleet
major reduction small pilot project is on in Pune with
Owners
of PM. Emission USEPA, USTDA and NEERI

162
S. Sector Strategy Impact Responsible Time Frame Remarks
No. Agency/ Agencies
control devices • Availability of new engines for
available (DPF, retrofit. Vehicle manufacturers
DOC) can need to come forward.
remove PM upto • For Emission control devices, there
90% are innumerable agencies.
• Presently no legislation
• Short time frame, high levels of
compliance expected for all the in-
use older vehicles.
Phase out of High MORTH, DPCC, Medium • New legislation may require
older grossly CPCB, Transport changes in Motor Vehicles Act
polluting vehicles (Estimate suggest Commissioner • Poor Inspection system both for
25% of these office, emission as well as vehicle.
vehicle may • Better compliance will lead to
contribute 75% of reduction of other pollutants as
total emission) well. It will also lead to less pressure
on complying vehicles
2 Industries Alternate Fuel High DPCC, Govt of Medium • Large no of industries are using NG
India, and LPG
Power Plants Power companies, • More allocation of NG/LPG to the
(Fuel shift - coal industrial sector by Govt. of India
to NG) • Better air quality in terms of SO 2 , CO
Medium Scale and HC will be achieved.
industries (Fuel
shift)
Combustion Moderate DPCC, Power Medium • Change in combustion technology
Processes companies and will be needed for shifting from
Industries, coal/oil to natural gas
CPCB • Finances to change the process

163
S. Sector Strategy Impact Responsible Time Frame Remarks
No. Agency/ Agencies
technology
• It will lead to lower emission of CO
and HC
Promoting Moderate CII, MoEF, CPCB Medium • MoEF can provide incentives to
Cleaner carry out the necessary change
Industries (Large scale shift • It will lead to sustainable existence
shall result in of industries within the city. It will
major PM /NOx also lead to other pollutants
reduction) reduction
Location Specific Medium Govt of Delhi, Medium • State as well as central
emission DPCC government can provide the
Reduction CPCB and GoI necessary incentive on use of
advance technology by the power
plant and other industries
• Lower NOx and other emissions
Fugitive Emission Moderate DPCC, Industries, Medium • Monitored data is scarce and
control CPCB therefore how and where to
undertake the action will be limited
• Local area air quality improvement
could be highly effective.
3 Area Improve fuel Moderate State Govt., Central Medium • LPG/PNG major domestic fuel,
Source used for Govt and MoPNG however kerosene is still a major
domestic source in low income group/ better
purposes stoves or change in fuel to LPG
• Lack of finance to low income
group, particularly in slums
• It would alleviate large section of
population with high indoor
pollution of other sources leading to

164
S. Sector Strategy Impact Responsible Time Frame Remarks
No. Agency/ Agencies
lower disease burden and better
quality of life
Bakeries High MCD, DPCC Medium • Electric/LPG source based bakeries
/crematoria Local grid based needing changes in design
PM can be • Awareness to bakeries that the
reduced. quality can still be maintained with
electric or LPG ovens.
• Many crematoria have electric
system, but need to convert all the
other into electric system
• Similarly, despite electric
crematoria being available, people
prefer using wood based pyres
• Reduction in PM as well as odour
will take place and is likely to
improve the local air quality
Biomass/trash Moderate MCD, DPCC Medium • Better control on collection and
burning, landfill (Local area can disposal at the respective sites.
waste burning have substantial Landfill waste burning needs
reduction in PM. proper technology driven site
Very high management
effectiveness to • Awareness and local control.
adjoining grids) Apathy to take urgent action. No
burning day vow to be taken by
BMC
• High level improvement in local
area ambient air quality not only
for PM but other pollutants
Resuspension Moderate MCD, DPCC Medium • Vehicle movement related

165
S. Sector Strategy Impact Responsible Time Frame Remarks
No. Agency/ Agencies
(Highly effective resuspension can be reduced by
for kerb-side air having better paved roads, regular
quality) sweeping and spray of water.
• Norms for road construction to be
framed and implemented
• Roadside as well population within
the distance of about 200-300 m
from the road will have low
exposure of PM leading to better
sense of well being
Illegal SSI Moderate MCD, DPCC, DIC Medium • Level of problem not well known.
(Local area Need to understand what are the
improvement levels of operation and their
can be contribution in each of the grids in
moderately the city
good) • Need for strict rules of such units
and identification by DPCC/DIC
and MCD
• It will lead to large scale reduction
of fire accidents as well as
minimization of wastewater
problem
Construction Moderate MCD, DPCC, Short Term • Construction activities which
(Large scale Builders Association involve demolition, digging,
improvement in construction, vehicle movement
local area is etc.
expected.) • Emphasis on better construction
practices and management plan
for air emission and its control by
the implementing agencies

166
S. Sector Strategy Impact Responsible Time Frame Remarks
No. Agency/ Agencies
• Spillage on road and further re-
suspension of dust can be
minimized

167
 2012BAU_PM_Win_PAL 2012BAU_NO_Win_PAL

28 28

26 26

24 24

22 22

20 9100 20
1040

Distance along North (km)

Distance along North (km) 8600
18 18
8100 940
7600
16 16
7100 840
6600 14
14
6100 740
12 5600 12
640
5100
10 4600 10
540
4100
8 3600 8 440
3100
6 6 340
2600
2100
4 4 240
1600
2 1100 2 140
600
0 100 0 40
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Distance along East (km) Distance along East (km)

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3)

respectively for Post Monsoon: 2012 BAU
Scenario 3, 2012, Particulate Matter Scenario 3, 2012, NOx

28 28

26 26

24 24

22 22

20 2300 20
490
Distance along North (km)

Distance along North (km)

18 2100 18
440
16 1900 16
390
1700
14 14
1500 340
12 12
1300 290
10 10
1100
240
8 8
900
190
6 700 6
140
4 500 4

2 300 2 90

0 100 0 40
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Distance along East (km) Distance along East (km)

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3) for

respectively Post Monsoon:
Control Scenario 2012

Figure 7.5: Air Quality Profiles for BAU 2012 and with Implementation of
Action Plan in Delhi

168
 BAU17_NO_Win_PAL
BAU17_PM_Win_PAL

28
28
26
26
24
24
22
22
20 1340
20
11100

Distance along North (km)

Distance along North (km)
1240
18
18 10100
1140
16 9100 16
1040

14 8100 14 940

7100 840
12 12
6100 740
10 10
640
5100
8 8 540
4100
440
6 6
3100
340
4 2100 4
240
2 1100 2 140

100 40
0 0
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Distance along East (km) Distance alonh East (km)

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3)

respectively for Post Monsoon:
BAU 2017
Scenario 3, 2017, Particulate Matter Scenario 3, 2017, NOx

28 28

26 26

24 24

22 22

20 20

Distance along North (km)

Distance along North (km)

1150 740
18 18

16 1000 16 640

14 14
850 540
12 12

700 440
10 10

8 550 8 340

6 6
400 240
4 4
250 140
2 2

0 100 0 40
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Distance along East (km) Distance along East (km)

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3) for

respectively Post Monsoon:
Control Scenario 2017

Figure 7.6: Air Quality Profiles for BAU 2017, and with Implementation of
Action Plan in Delhi

Kanpur:

Some 24 control options were considered and evaluated for their impact in
terms of emission reduction and air quality improvements for PM 10 and NOx
using ISCST3 modeling. Table 3 presents ten most promising options those
showed the significant average air quality improvements in all critical grids.
Further, scenarios were developed as a combination of various control
options to examine as to which control options, if implemented as a group,
will give the best improvements in air quality.

If no action is taken to reduce PM 10 and NOx emissions, in 2012, entire city

will totter under high air pollution when standards for PM 10 will exceed over

169
the entire city with very high concentration of over 500 µg/m3 (max 24-
hour) in 16 km2 area. If no action is taken up to 2017 and city will have
unbridled growth, not only the entire city will exceed the air quality
standards, nearly 50 km2 (nearly 1 /5th of city) areas may have air quality
much above 500 µg/m3 (max 24- hour) for PM 10 . NOx standard will be met
in the year 2012 with control options however about 1/5th of the area will
still exceed air quality standard for NOx.

It is recommended that the following control options, found most effective

in improving the air quality, must be implemented in a progressive manner:
(i) Implementation of BS – VI norms; (ii) CNG/LPG for commercial Vehicles;
(iii) Banning of 15 year old private vehicles; (iv) Particulate control systems
in industry; (v) Domestic-Use of Natural Gas/LPG; (vi) Converting unpaved
roads to paved roads; (vii) Sweeping and watering (mechanized); and (ix)
Strict compliance to ban of open burning.

By implementing the above options, air quality will improve dramatically

but will fall short of achieving 24-hour air quality standards for PM 10 in 1/4
the part of the city and by the year 2017, almost 2/3 area will still be below
the desired air quality; this represents worst case scenario by considering
that standard should be attained on each day. It is necessary that
emissions in certain grids (area) should reduce to 40 percent of controlled
emission of 2012 and 2017 (as recommended above). This will require
additional efforts to reduce the emission. Vehicles, road dust and domestic
cooking are the important sources both in 2012 and 2017. It may be noted
that that if vehicular pollution is reduced further by 50 percent which can
best be done by restricting entry of vehicles in critical areas by 50 percent
of projected number of vehicles (e.g. by allowing odd number of vehicles
the first day and the next day even number and the repeating the cycle).
This needs to be done during winter months (November to January) only.
The reduction in vehicles will also reduce the road dust by 50 percent. In
addition, there should be a total ban on any refuse or garbage burning
from 2012.

The decision to restrict the public transport can put the general public to
inconvenience. It is emphasized that a network of public transport in terms
of metro or elevated railway needs to be planned. Further, construction of
flyovers at all railway intersections (15 Nos.) will help in about 20 percent
time saving that will result in 20 percent lowering of vehicular emissions and
corresponding improvements in air quality. The overall action plan that will
ensure compliance with air quality standards both for PM 10 and NOx is
presented in Table 7.8. The resultant air quality is given in Figures 7. 7 – 7.8.

170
 Table 7.8: Action Plan for Kanpur

S. Sector Strategy Impact* Responsible Agency / Time frame Remarks

No agencies
1 Transport Strengthening of Public Govt of India, State Medium term The metro network is being
transport system Government, KMC (Kanpur expanded. On Jan 2009, GOI
High Municipal Corporation0 approved plans for two phase
• Metro implementation on and KDA (Kanpur expansion, with first phase
schedule Development Authority), expected to complete by 2014-
• Enhance share of public GAIL 15.
mass transport system
• Conversion of public Kanpur does not have a CNG
transport to CNG network and has a limited LPG
network. There are plans to set
up CNG network in future, but no
timeframe is available. ULSD
would also be available by April
2010 in Chennai.
Retro-fitted 2-stroke three
wheelers on LPG have higher PM
emissions compared to OE 2-
stroke/ 4-stroke LPG/Petrol. Thus
retro-fitment of 2-stroke 3-
wheelers is not an effective
control option.
Ban on old commercial High Transport department - Short-term Fiscal incentives/ subsidies for
vehicles (10 year) and Kanpur new vehicle buyers
private vehicles (15 years) A plan should be devised for
in the city gradual phase out with due
advance notice. Careful
evaluation of socio-economic
impact of banning required.
Progressive improvement of High MoRTH, MoPNG, Ministry of Medium to Auto-fuel road map should be
vehicular emissions norms Heavy Industry and Public Long term developed well in advance to
(BS-V, BS-VI) Enterprises, MoEF, Oil plan the progressive
companies, Automobile improvement of emissions norms
manufacturers and corresponding fuel quality

171
S. Sector Strategy Impact* Responsible Agency / Time frame Remarks
No agencies
norms.
Though the impact is low, its
potential is high in the long term
when gradually fleet renewal
takes place.
Installation of pollution Low Transport department Medium Technical feasibility and
control devices (DOC/DPF) implementation plan of this
in all pre-2010 diesel strategy needs to be carefully
vehicles evaluated, though it has
potential for emission load
reduction. Retro-fitment of DOC
in BS-II buses and DPF in BS-III
buses is technically feasible.
Effective Inspection and Medium Transport Department, Short to Initial focus could be on
maintenance regime for Traffic police Medium commercial vehicles; Capacity
vehicles development in terms of
infrastructure for fully
computerized
testing/certification and training
of personnel. Linkage of all PUC
centers for better data capture.
Improve traffic flow Low Traffic police, KMC and Short Synchronization of signals, one
KDA way roads, flyovers, widening of
roads, removal of
encroachments, staggering of
office timings to reduce peak
flow and congestion. Application
of IT tools for traffic management
(Intelligent transport system)
2 Road dust • Construction of better High KMC, KDA, NHAI Short - Effective enforcement of road
quality roads Medium term quality norms is required.
• Regular maintenance Landscaping/ greening of areas
and cleaning/sweeping adjacent to roads
of roads
• Reduction in vehicular
fleet and trips

172
S. Sector Strategy Impact* Responsible Agency / Time frame Remarks
No agencies
Wall to wall paving for High KMC, KDA Short term Interlocking tiles may be used so
reduction of road dust that water percolation takes
place.
3 Industries Fuel shift towards cleaner Low UP PCB, Directorate of Short-Medium Shift from solid fuels to liquid fuels
fuels Industries and Commerce, term (LSHS) and subsequently to
Industry associations, GAIL, gaseous fuels (CNG). Local
Oil companies pollution load will decrease
significantly. However, impact on
overall level in the city will be low
Ban on any new air Low UP PCB, Department of Short term Real estate prices will inhibit
polluting industry in city Forest, Ecology and growth of new large scale
limits Environment, Department industry within city limits and
of Industries and hence the impact is low.
Commerce, UP Industrial Industrial estates/zones may be
Development Corporation developed well outside the city
Strengthening of Low UPPCB, Industry Short term This would ensure greater
enforcement mechanism associations, compliance with standards. In
for pollution control addition, cleaner technology
options need to be promoted
and appropriate incentives to be
defined. Voluntary measures
such as ISO certifications to be
encouraged.
4 Power/ DG No power cuts leading to Medium UPSEB (UP State Electricity Medium term Adequate tie-ups need to be
sets zero usage of DG sets Board) ensured
Installation of pollution Medium UPPCB, DG set Medium Technical feasibility and
control devices (DOC/DPF) manufacturers implementation plan of this
in DG sets strategy needs to be carefully
evaluated, though it has
potential for emission load
reduction
Effective Inspection and Medium UP PCB Short to
maintenance regime for Medium
large DG sets
5 Construction Strict enforcement of Low UPPCB, KMC, KDA Short term Pollution load due to
construction guidelines construction is limited to short

173
S. Sector Strategy Impact* Responsible Agency / Time frame Remarks
No agencies
(which should reflect Green time and small locality.
Building concepts)
6 Other sectors Integrated land-use Low KMC and KDA Medium term Holistic development of the
development of Chennai entire region including peripheral
taking environmental areas.
factors into consideration
Open burning/ Waste Low KMC, UPPCB Short term Sorting solid waste is important.
burning to be discouraged Organic matter could be used
for compost formation and
methane gas generation
Strengthening of air quality Low CPCB, UPPCB Short Good quality data is an
monitoring mechanism in important input in assessing the
terms of number of stations change in air quality and the
as well as pollutants impact of policy interventions.
monitored. Capacity Continuous monitoring stations to
building of TNPCB staff. be promoted.
Environmental education Medium Education department, Medium/ Sensitization programmes for
and awareness activities Schools/Colleges, CBOs, Long Term policy makers.
NGOs

174
Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3) respectively for
winter: 2012 BAU

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3) for respectively
winter: Control Scenario 2012

Figure 7.7: Air Quality Profiles for BAU 2012, and with Implementation of Action Plan in
Kanpur

175
Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3) respectively for
winter: 2017 BAU

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3) for winter:
Control Scenario 2017

Figure 7.8: Air Quality Profiles for BAU 2017 and with Implementation of Action Plan in
Kanpur

Mumbai:

Management options for each sector need to be prioritized with a view to

understand the issue of implementation. Implementations are highly influenced
not only by the idea of the improvement alone but also by the nature of the
recommendations, fiscal and administrative barriers, effectiveness,

176
implementing agencies and acceptance from large group of stakeholders. The
Prioritization also takes into account the earlier recommendations made by Lal
Committee under the order of Hon’ble High Court, Mumbai, 2000. Many of the
recommendations have been implemented; however few which are still
relevant and applicable in current scenario have been included in the
recommendations made for control options. Prioritization issues are also driven
by the comparative account of short term and long term implementation
dilemma. Low cost with high effectiveness and low cost with shorter
implementation period shall be a better option, when compared with high
effectiveness with high costs or long implementation period. Though some of the
options were selected on the basis of PM reduction potential, their possible co-
benefits in reducing NOx and other pollutants were also considered during the
process of prioritizing. The prioritized control options are based on high
effectiveness in pollution load reduction leading to improvement in ambient air
quality.

The predicted ground level concentrations show improvement all across the
city. Results shown for the seven sampling locations indicate that though NOx
levels would be met most of the time, PM levels will exceed at many locations,
except at Mahul and Mulund. The levels would become highly un-acceptable
with regard to the PM and NOx especially if no control scenario continues till
2012 and 2017.

The suggested Action Plan is given in Table 7.9 and expected air quality is given
in Figures 7.9 – 7.10.

177
 Table 7.9: Action Plan for Mumbai

S. Sector Strategy Impact Responsible Time Frame Remarks

No. Agency/ Agencies
1 Transport S reduction in Medium Oil companies, Medium • Improvement in emission standards
diesel (Reduction in S Ministry of as well as legislation for stringent
leads to 2.5–13 % Petroleum, fuel standards for S,
reduction in PM) Vehicle • Phasing out the subsidies on diesel.
manufacturer bringing diesel cost at par in a
state/centre
• The S reduction will not only reduce
the PM but also lead to
correspondingly lower SO 2 emission
leading to lower ambient SO 2 and
sulphate
•
Reduce fuel Low Anti-Adulteration Medium • One of biggest advantage of non-
adulteration (Reduced cell, adulteration shall be longer engine
adulteration will Oil Companies, life besides the emission reduction
lead to reduced Vehicle owners for PM as well as CO and HC. The
PM (difficult to catalytic converter shall be active
quantify). for its entire lifetime.
• Better quality fuel by adopting
Effectiveness is
stricter fuel supply and dispensing
moderate as
system (e.g. Pure for Sure etc.)
marker system
• Chemical marker system
has not been
• Finer fuel specifications are needed
seen as a primary
for implementation.
means to reduce
• Present system of Anti Adulteration
PM)
cell function needs major
improvement in terms of higher
manpower and spread.
Alternative fuels • Will lead to substantial reduction in

178
S. Sector Strategy Impact Responsible Time Frame Remarks
No. Agency/ Agencies
-- Technical -- High, more --Local --Medium CO and HC emission, however, NOx
infrastructure in than 90 % Government values may go up
Mumbai for reduction in PM Mahanagar Gas, • Can be applicable mainly for
dispensing can be achieved Oil Companies vehicles, which are supposed to ply
CNG/LPG is fairly compared to marketing LPG, within the city.
good and is diesel # • Applicable to only local public
improving transport, taxies etc.

* Biofuels can be * High * Medium - Similar to diesel but low SO 2

used upto 5-10% Similar to diesel , * Ministry of and low PM
without any low SO 2 and low Petroleum - Can be easily implemented
major technical PM
issue.
Phase out of High Ministry of Road Medium • High, Estimate suggest 25% of these
grossly polluting (No major Transport and vehicle may contribute 75% of total
vehicles technical Highway emission $
problem) Transport • Poor Inspection system both for
Commissioner emission as well as vehicle.
office, • Need for improved inspection
certification system, better testing
facility.
• New legislation may require
changes in Motor Vehicles Act
• Better compliance will lead to
reduction of other pollutants as well.
It will also lead to less pressure on
complying vehicles
Congestion - High State Govt, BMC, Medium • High emission due to fuel burning at
reduction (Improvement of MMRDA, Transport idle or slow moving traffic
roads, new police, other • Road quality improvement is a
roads, utilities. matter of technology and quality of
scientifically work carried out.

179
S. Sector Strategy Impact Responsible Time Frame Remarks
No. Agency/ Agencies
planned traffic • Concretization of road may be the
management) solution. New road planning and
Traffic management are being
taken as integral part of the MUTP.
• It will reduce traffic junction hotspot
of all the pollutants
• It will also reduce continuous source
of dust
Standards for - Medium Transport Office Medium • No technical issue with new
new and In-use (Marginal Govt. Maharashtra vehicles.
vehicles improvement MoRTH, • For inuse old vehicles, technical
from newer feasibility needs to be established
vehicles except • The process of in-use vehicles
when standards may take time as they
implementation need to be revised at central level.
is for Euro V & VI. • Inadequate infrastructure and
In-use vehicles manpower at local levels could be
emission other major barriers.
reduction can be • After the legislation is in place,
substantial) provision of strict penalty leading to
cancellation of vehicle registration.
• As the old vehicle population is
substantial, the standards will bring
in the much needed control on
emissions of all types
Introduction of High Transport Office Medium • New technology based vehicles
new technology Govt. Maharashtra, emit less per unit distance travelled
vehicles (Moderate with MNRE Electric vehicles
respect to in use MoRTH, • Emphasis to allow only a type of
vehicles) technology to be permitted may
meet with resistance from
manufacturer as well as buyer. (e.g.

180
S. Sector Strategy Impact Responsible Time Frame Remarks
No. Agency/ Agencies
rule to allow only 4 stroke vehicle to
be registered)
• Proper legislation else charge higher
registration fee or subject them to
carry out more frequent I&C test.
Electric vehicles for grossly polluting
high VKT vehicles are a good
option. It needs regulatory push
• It will lead to better compliance
from on-road emission test and
overall improvement in emission of
all the pollutants. Electric vehicles
provide localized benefits of no air
pollution
Retrofitment of High Transport Office Medium • Experience of other countries
new engine/ (Engine Govt. Maharashtra, suggests that it can be feasible.
Emission control replacement vehicle However, in Indian scenario, a pilot
device could lead to manufacturer, retrofit programme to evaluate the
major reduction vehicle fleet owners efficacy needs to be undertaken. A
of PM. Emission small pilot project is on in Pune with
control devices Short time frame, USEPA, USTDA and NEERI
available (DPF, high levels of • Availability of new engines for
DOC) can compliance retrofit. Vehicle manufacturers need
remove PM upto expected for all the to come forward.
90%) in-use older • Presently no legislation. Need to
vehicles. frame one including a mechanism
by which the system can be
evaluated by an appropriate
agency.

Higher usage of High BMC, MMRDA, Medium • Dedicated bus lane, better buses,
Public Transport (Effectiveness is MSRDC, low cost of travel, faster travel etc.

181
S. Sector Strategy Impact Responsible Time Frame Remarks
No. Agency/ Agencies
high as less and BEST, • Feasibility to be established for bus
less road space lane. Finances for better buses
will be occupied • Local level planning in coordination
by private with all the authorities involved in
vehicles, faster MUTP.
movement of • Future growth of the city will entirely
public transport depend upon the levels of public
in comfort shall transport availability. Cheaper and
lead to low faster mode of public transport will
emissions) lead to higher per capita efficiency.
Decrease Private Low Private vehicles Medium • Awareness matched with better
vehicles on owners should must public transport.
Road own their own • Higher parking charges, high
garages, less registration fees, higher car user
parking on the charges, sale linked with parking
roads, less availability
congestion • Less private vehicles on road, high
BMC, MMRDA, RTO, road space utilization
Training and Medium Savings by way of Short Term • On use of alternative fuel,
Awareness (May lead to 5- improved vehicle Inspection and certification,
programme for 10% reduction of maintenance and adulteration of fuels, use of public
car owners, emission). operation transport, less usage of private
public transport vehicles
operators, drivers MMRDA, Transport • Resources for awareness and
and mechanics Department, Other training, bringing the different
institutions involved groups together
in awareness • Structure for such programme
campaign should be developed and
integrated into legislation.
2 Industry S reduction in Moderate MPCB, Industries Medium • This process is currently on, however,
fuel (Many industries the fuel S reduction is mainly for
in Mumbai region vehicular sector

182
S. Sector Strategy Impact Responsible Time Frame Remarks
No. Agency/ Agencies
use coal, HSD, • As the industrial growth is negative
LSHS, and FO) in Mumbai, the need of S reduction
in conventional fuel is not being
pressed upon
• S levels in fuel have been very
strictly controlled for Tata Power. An
example of this can be extended to
other industries
Combustion Moderate MPCB Medium • It will lead to lower emission of CO
Processes and HC
• Change in combustion technology
will be needed for shifting from
coal/oil to natural gas
• Finances to change the process
technology
Alternate Fuel High Mahanagar Gas, Medium • Large no of industries are using NG
MPCB and LPG
Use of cleaner • More allocation of NG/LPG to the
fuel has already industrial sector through MGL/GAIL/
resulted in better Govt. of India
air quality in the • Better air quality in terms of SO 2 , CO
city and HC will be achieved.
Promoting High MPCB, BCCI, CII, Medium • Use of cleaner production
Cleaner Large scale shift MoEF, CPCB processes
Industries shall result in • Finances to carry out these
major PM changes
reduction • It will lead to sustainable existence
of industries within the city. Also lead
to other pollutants reduction
Location Specific High GoM, MPCB, Medium • Specification of site specific
emission GoI, CPCB emission standards
Reduction • Higher allocation of NG/LNG at

183
S. Sector Strategy Impact Responsible Time Frame Remarks
No. Agency/ Agencies
lower cost is needed
• High level emission shall have lower
PM and other gaseous pollutants
Fugitive Emission Moderate MPCB, Industries, Medium • Industrial process improvement
control CPCB better operation and maintenance
• Monitored data is scarce and
therefore how and where to
undertake the action will be limited
• MPCB can work on the standards
for fugitive emission and develop
compliance system
• Local area air quality improvement
could be highly effective.
3 Area Improve fuel Moderate State Govt., Medium • LPG/PNG major domestic fuel,
Source used for Central Govt however kerosene is still a major
domestic MoPNG source in low income group/ better
purposes stoves or change in fuel to LPG
• Lack of finance to low income group,
particularly in slums
• It would alleviate large section of
population with high indoor pollution
of other sources leading to lower
disease burden and better quality of
life
Bakeries High MMRDA, BMC and Short Term to • Electric/LPG source based bakeries
/crematoria (Local grid based MPCB Long Term needing changes in design.
PM can be • Many crematoria have electric
reduced) system, but need to convert all the
other into electric system
• Awareness to bakeries that the
quality can still be maintained with
electric or LPG ovens. Similarly,

184
S. Sector Strategy Impact Responsible Time Frame Remarks
No. Agency/ Agencies
despite electric crematoria being
available, people prefer using wood
based pyres
• Reduction in PM as well as odour will
take place and is likely to improve
the local air quality
Biomass/trash High BMC, MMRDA Medium • Better control on collection and
burning, landfill (Local area can MPCB disposal at the respective sites.
waste burning have substantial Landfill waste burning needs proper
reduction in PM. technology driven site management
Very high • Awareness and local control.
effectiveness to • MPCB needs to address this issues
adjoining grids) • High level improvement in local area
ambient air quality not only for PM
but other pollutants
Resuspension Moderate BMC, MMRDA Long Term • Vehicle movement related
(Highly effective resuspension can be reduced by
for kerb-side air having better paved roads, regular
quality) sweeping and spray of water.
• Awareness and will to implement
• Norms for road construction to be
framed and implemented
• Roadside as well population within
the distance of about 200-300 m
from the road will have low exposure
of PM leading to better sense of well
being
Illegal SSI High MPCB, BMC, DIC Medium • Level of problem not well known.
(Local area Need to understand what are the
improvement levels of operation and their
can be contribution in each of the grids in
moderately the city

185
S. Sector Strategy Impact Responsible Time Frame Remarks
No. Agency/ Agencies
good) • Poor rules and guidelines of such
units and it will lead to large scale
reduction of fire accidents as well as
minimization of wastewater problem
Construction Moderate BMC, MMRDA, Short Term • Construction activities which involve
Large scale Builders Association demolition, digging, construction,
improvement in vehicle movement etc. need
local area is information on how to minimize the
expected. dust
• Use best construction practices
• Spillage on road and further re-
suspension of dust can be minimized
Railways Moderate CR, WR, MRVC Medium • All trains are being change to
Emissions Low, as the electric. Limited use of diesel
extent of locomotive. Resuspension due to
problem is not in train can be minimize by platform
large areas. cleaning.
• Awareness to railways
• Practices and norms to be framed
• Exposure to population will reduce

186
 Fig. Iso-concentration Plots for PM - Existing Scenario – Winter 2012 : All Sources (Mumbai) Fig. Iso-concentration Plots for NOx– Existing Scenario- Winter 2012 : All Sources (Mumbai)

D is ta n c e a lo n g N o r th , ( K m )

D is ta n c e a lo n g N o r th , ( K m )
Conc.
In (µg/m3)

Conc.
In (µg/m3)

Distance along East, (Km) Distance along East, (Km)

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3) respectively

for winter: 2012 BAU

Fig. Iso-concentration Plots for PM – Preferred Scenario- II – Winter 2012 : All Sources (Mumbai)
Fig. Iso-concentration Plots for NOx– Preferred Scenario-II- Winter 2012 : All Sources (Mumbai)
D is ta n c e a lo n g N o r th , ( K m )

D is ta n c e a lo n g N o r th , ( K m )

Conc.
In (µg/m3)
Conc.
In (µg/m3)

Distance along East, (Km) Distance along East, (Km)

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3) respectively

for winter: Control Scenario 2012

Figure 7.9: Air Quality Profiles for BAU 2012, and with Implementation of Action Plan in
Mumbai

187
 Fig. Iso-concentration Plots for PM - Existing Scenario – Winter 2017 : All Sources (Mumbai) Fig. Iso-concentration Plots for NOx– Existing Scenario- Winter 2017 : All Sources (Mumbai)

D is ta n c e a lo n g N o rth , ( K m )

D is ta n c e a lo n g N o r th , ( K m )
Conc.
In (µg/m3) Conc.
In (µg/m3)

Distance along East, (Km) Distance along East, (Km)

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3) respectively

for winter: 2017 BAU

Fig. Iso-concentration Plots for PM – Preferred Scenario- II – Winter 2017 : All Sources (Mumbai) Fig. Iso-concentration Plots for NOx– Preferred Scenario-II- Winter 2017 : All Sources (Mumbai)
D is ta n c e a lo n g N o r th , ( K m )

D is ta n c e a lo n g N o r th , ( K m )

Conc.
In (µg/m3)
Conc.
In (µg/m3)

Distance along East, (Km) Distance along East, (Km)

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3) respectively

for winter:
Control Scenario 2017

Figure 7.10: Air Quality Profiles for BAU 2017 and with Implementation of Action Plan in
Mumbai

188
Pune:

Based on the evaluation of the impact of various individual control options

and their feasibility in implementation for both management and technology
based options, a list of control options considered is prepared for generating
controlled scenarios. Percent reduction in PM 10 and NOx concentration levels
with respect to BAU scenario, were worked out using ISCST3 dispersion
modeling. The top10 grids were found to be present at the central part of
Pune where population and road densities are higher.

In case of PM 10 control scenario, the ambient concentrations were found to

be reduced by 30- 50% in the year 2012 and 2017 with respect to BAU
scenario in that year. Similarly, NOx concentrations were found to be reduced
by 40-50% in top 10 grids with respect to BAU scenario.

As the major source of PM 10 was found to be re-suspended road dust from

paved roads (48%), benefits for PM 10 control options could be higher if only
the silt-loading of the road is reduced i. e. if the road quality itself is improved.
It is, therefore, evident that, road conditions need to be improved. With the
reduction in silt loading factor by 50% of the existing value, total PM 10
reduction benefit of about 35% can be achieved. However, silt loading
improvements will depend upon the methods used for road quality
improvement and subsequent reduction in silt loading. Though Mechanized
sweeping and watering shows higher benefits, the implementation is difficult.
Wall to wall pavement can yield around 10% and 16 % benefits if
implemented on all major roads and all major and minor roads respectively.
Therefore, road infrastructure needs to be set up and maintained as per
national/inter-national standards. Guidelines should be made available for
the quality of the roads based on traffic patterns.

In Pune, public transportation system is inadequate and not in pace with the
transportation requirement. This also increases the use of personalized
vehicles which intern contributes to road dust as well as vehicle tail pipe
emissions. Effective Mass transport system must be established to reduce the
rising tendency of owning personal vehicles. In Pune, the average occupancy
of 2 Wheelers is 1 and for cars around 1.3. The 10% & 20% shift of 2W & cars to
public transport in 2012 & 2017 respectively, gives benefit of upto 3 % for PM 10
& NOx. However, the reduction in VKT also reduces the road dust by 3.5 % and
12% respectively for year 2012 &2017.

189
Progressive tightening of emission norms must be implemented and vehicle
emission regulation road map should be ready for next 10 years, which need
to be updated on continuous basis. Progressive tightening of emission
regulations since 1991 to BS-III regulations for 2& 3 wheelers and BS-IV
regulations for all other categories of vehicles (in line EURO-IV) scheduled to
be implemented in 2010; have given an edge, by curbing the emission to
some extent, over the multifold growth of cities and Mega cities of India and
in turn the increase in number of vehicles. The cities like Pune have been
showing the growth of vehicles around 10% continuously for more than last 10
years.

Continuous power supply must be ensured to avoid use of non-industrial

generators, as it has remarkable benefits in terms of emission reduction.
Reduction in use of non-industrial generators by ensuring continuous power
supply can give benefits as reduction in PM and NOx emissions by 6% and 83%
respectively in year 2012 and by 6% and 83% respectively in year 2017.

Banning of 10 years old commercial vehicles yields highest benefits in terms of

emission reduction. However, the socio economic impacts of banning of older
vehicles need to be evaluated. Entry of these vehicles in the city area must be
restricted as an immediate measure to curb major portion of vehicle pollution.
Higher NOx reduction benefits, 45% and 56% for year 2012 and 2017
respectively, are observed mainly due to banning of old vehicles. Table 7.10
provides list of control options required for improving air quality in Pune.
Expected benefits, in terms of predicted air quality, are given in Figures 7.11 –
12.

Table 7.10: Action Plan for Pune

Technology based control options for line sources

Control Option Scenario 2012 Scenario 2017 Remarks

Considered

Implementation of BS Same as BAU. BS-III for 2-3 W, Progressive tightening

– V norms BS-III for 2-3 W, BS-IV for rest all of emission regulations
BS-IV for rest all from 2010 to for new vehicles
from 2010 2015
onwards.

190
 -- BS-IV for 2-3 W,
BS-V for rest all
from 2015
Electric / Hybrid Share of Electric Share of Electric Though electric /
Vehicles vehicles in total vehicles in total hybrid vehicles are very
city fleet – Two city fleet – Two effective in city
wheeler: 1%, wheeler: 2%, pollution curbing, the
Auto-Rickshaw Auto-Rickshaw infrastructure and initial
and Taxi: 5%, and Taxi:10%, cost may limit the
Public buses: 5% Public buses: penetration.
10%
OE-CNG for new 47.5% fleet 90% of fleet Retro fitment has may
Public transport buses issues like effectiveness
and durability along
with safety related
problems
Ethanol blending (E10 E-10 all petrol E-10 all petrol Considering proposed
– 10% blend) vehicles vehicles implementation in near
future
Bio-diesel (B5/B10: 5 – -- B-10 all diesel Effective in vehicle PM
10% blend) vehicles reduction, but
availability by 2012 is
questionable.
Retro-fitment of Diesel BS-II & III buses BS-II & III buses Feasibility and
Oxidation Catalyst retro-fitment - retro-fitment 50% effectiveness along
(DOC) in 4-wheeler 20% with durability issues,
public transport (BS–II will limit the
and BS-III) penetration.
Retro-fitment of Diesel BS-III buses BS-III buses retro- Feasibility,
Particulate Filter in 4- retro- 10% 20% effectiveness,
wheeler public regeneration and
transport(BS – III city durability issues will limit
buses) the penetration.
Management based control options for line sources
Control Option Scenario 2012 Scenario 2017 Remarks
Considered

Banning of 10 year old 100% compliance 100% compliance – Most effective

commercial vehicles –pre 2002 3W, pre 2007 3W, GC, control option.
GC, buses and buses and trucks
trucks
Inspection/ 50% compliance 100% compliance I&M is the option
maintenance to all for identifying and
BSII & BSIII controlling
commercial vehicles emissions from high
polluters.
Improvement of 10% shift in VKT 30% shift in VKT Mass
public transport: % transportation like
share metro, etc. is
feasible by next 10
years. % shift will

191
 depend upon the
option considered
and its capacity
and connectivity.
Synchronization of All highways & All major & minor Very effective and
traffic signals major roads roads excluding relatively simpler
feeder roads for
implementation.
Restrict commercial 50% Trucks & 20% 70% Trucks & 30% Expected to have
vehicles entering city LCVs -diversion LCVs -diversion ring roads in place
by having ring roads from 2012-2015.
Area source control options
Control Option Considered Scenario 2012 Scenario 2017

Shift to LPG from solid fuel 50% compliance 100% compliance

&kerosene for domestic
applications
Shift to LPG from solid fuel 100% compliance 100% compliance
&kerosene for commercial
applications (bakeries, open
eat outs etc)
Better construction practices 50% compliance 100% compliance
with PM reduction of 50%
Banning of operation of brick 100% compliance 100% compliance
kilns in city area
Strict compliance of ban on 50% compliance 100% compliance
open burning, including open
eat outs
Reduction in DG set operation/ 50% reduction in power 100% reduction in power
Un-interrupted power supply cut cut
Control options for road dust
Control Option Considered Scenario 2012 Scenario 2017
Wall to wall paving (brick) All major roads All major & minor roads
excluding feeder roads
Point source control options
Control Option Considered Scenario 2012 Scenario 2017

Banning of new industries in 100% compliance 100% compliance

existing city limit

192
 20000 20000

18000 18000

16000 16000
350ug/m3 450ug/m3
14000 14000

280ug/m3
12000
12000 360ug/m3
Y C o -o rd

10000 210ug/m3 10000

270ug/m3
8000
140ug/m3 8000
180ug/m3
6000
6000
70ug/m3
4000 90ug/m3
4000
0ug/m3
2000
2000
(b
0ug/m3
0
0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000
0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000
X Co-ord

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3) respectively

for winter: 2012BAU

20000 20000

18000 18000

16000 16000

500ug/m3 450ug/m3
14000 14000

400ug/m3 360ug/m3
12000 12000

10000 300ug/m3 10000 270ug/m3

8000 8000
200ug/m3 180ug/m3
6000 6000

100ug/m3 90ug/m3
4000 4000

0ug/m3 0ug/m3
2000 2000

0 0
0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000
0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000

X Co-ord

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3) respectively

for winter:
Control Scenario 2012

Figure 7.11: Air Quality Profiles for BAU 2012 and with Implementation of Action
Plan in Pune

193
 20000 20000

18000 18000

16000 16000
500ug/m3 800ug/m3
14000 14000

400ug/m3 640ug/m3
12000 12000

10000 300ug/m3 10000 480ug/m3

8000 200ug/m3 8000

320ug/m3
6000 6000
100ug/m3 160ug/m3
4000 4000

(a
0ug/m3 0ug/m3
2000 2000

0 0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000
0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3) respectively

for winter: 2017 BAU

20000 20000

18000 18000

16000 16000

500ug/m3 800ug/m3
14000 14000

12000 400ug/m3 12000 640ug/m3

Y Co-ord

10000 300ug/m3 10000 480ug/m3

8000 8000
200ug/m3 320ug/m3
6000 6000

100ug/m3 160ug/m3
4000 4000

0ug/m3 0ug/m3
2000
2000

0
0
0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000
0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000

X Co-ord

Isopleths for 24-hourly avg. PM 10 and NOx concentration (µg/m3) for winter:
Control Scenario 2017

Figure 7.12: Air Quality Profiles for BAU 2017 and with Implementation of Action
Plan in Pune

194
 8

Conclusion

Based on the Source Apportionment Studies carried out in six cities, the
following broad conclusions emerge, which provide guidance, with adequate
scientific evidence, to plan strategies for improving air quality in urban areas:

1. Levels of PM 10 and PM 2.5 in the ambient air are significantly high

irrespective of the type of locations. Even background locations indicate
presence of considerable levels of particulates, which could be occurring
naturally and/or due to transport of finer dust from other settlements
surrounding the cities. The concentrations of these pollutants are relatively
higher at kerbside/roadside locations. While vehicles contribute
significantly at all the locations, their contributions at kerbside locations
are comparatively higher.

2. Winter and post monsoon seasons had been found most critical when
standard exceedence rates are higher than in the summer months.

3. PM pollution problem is severe and NO 2 is the emerging pollutant. These

two pollutants require immediate attention to control their emissions.

4. O 3 concentrations in all cities did not exceed the proposed hourly

standard of 180 µg/m3 at any of the locations, where sampling was done.
However, in case of Mumbai and Pune, the peak hourly concentration
observed is very close to 180 µg/m3 (90 ppb). Although higher ozone
concentrations are expected around 1 – 3 pm, but it appears that good
dilution and high speed winds (in afternoon) bring the concentration
down. As such, O 3 does not seem to be of much concern. Similarly, CO
levels may exceed marginally the hourly standard of 4000 µg/m3 in at a
few kerbside locations. In all cities, there are morning and evening peaks
in CO levels corresponding to vehicular movement.

5. With regard to air toxics, Benzene levels are higher in Bangalore, Pune
and Kanpur. The values of formaldehyde are also matter of concern in
Mumbai, Pune and Bangalore.

195
6. High Elemental Carbon (EC) to Organic Carbon (OC) ratio (EC/OC)
represents freshly contributed diesel/coal combustion particles biomass
and garbage burning. Many cities have shown this ratio to be high at
kerbside and industrial locations. EC and OC contribution to PM 2.5 is even
more than what it is to PM 10 ; and have high (25 – 75%) values in all the
cities. It signifies an important point that PM 2.5 has much higher
component of toxic EC and OC that mostly come from combustion
sources like vehicles and others.

7. Higher fraction of PM 2.5 in PM 10 , and higher values of EC and OC (which

have more severe health impacts) at kerbside locations indicate that
control of vehicular exhaust would be an important element of any
strategy or action plan for improving air quality and minimizing adverse
effects on the health of people.

8. Elemental and ion analysis show abundance of soil constituents (e.g. Si,
Fe, Ca, Na). This clearly suggests that there could be significant sources of
particulate pollution from soil, and road dust. The soil related fraction
drops down drastically (about 5% against 15 – 60% in PM 10 ) in PM 2.5 . The
re-suspension of road dust due to vehicular movements on
paved/unpaved roads and construction activities, emerging as
prominent sources, would largely be contributing to coarser fraction of
PM 10 and combustion sources including vehicles, DG sets, refuse burning,
etc. would emit particles in the finer size (< PM 2.5 ). Hence, strategies for
reduction of PM 10 and PM 2.5 would involve different categories of sources.

9. There are significant quantities of SO 4 2– and NO 3 –, (10-15% in most cities

and 20-30% in Kanpur) in PM 10 indicating an important contribution of
secondary particles. These contributions are even high at the
background upwind direction in all cities. It signifies long-range transport
of particles in the city as well as formation of secondary particles in the
city. Any control strategy for reduction of particulate will have to
consider control of SO 2 , NO 2 and NH 3 .

10. The presence of molecular markers like hopanes and steranes in much
higher quantities compared to background location indicates that effect
of vehicles is prevalent. Higher concentration of levoglucosan confirms
contribution from biomass burning.

196
11. Within the transport sector, the PM 10 contribution in terms of emission load
is mainly from heavy duty diesel vehicles (40 – 59%) in almost all the cities.
With regard to NO x emissions, again heavy duty vehicles are major
contributors (43 – 75%).

12. Though, there are city-specific variations among the dominance of

sources, re-suspension of road dust and combustion sources including
vehicles, refuse burning & DG sets; emerge as prominent sources in all the
cities for PM.

13. Several epidemiological studies have linked PM 10 and especially PM 2.5

with significant health problems. PM 2.5 is of specific concern because it
contains a high proportion of toxins, and aerodynamically it can
penetrate deeper into the lungs. Therefore, while planning control
strategies greater emphasis is to be given on reduction of PM 2.5 and toxic
constitutes of particulates.

14. An effective control strategy would require combination of engineering

as well as non-engineering solutions. Prioritization of these solutions, in
addition to their effectiveness, should also be driven by the comparative
account of short and long term implementation dilemma. Low cost with
high effectiveness and low cost with shorter implementation period shall
be a better option, when compared with high effectiveness with high
costs or long implementation period.

15. Based on the findings of the study, some of the important steps required
for improving the air quality in urban areas are given below.

(i) Better maintenance of roads, paving of unpaved roads, footpaths or

low-elevation concreting of unpaved surfaces along major roads
with high traffic. Use of fly ash bricks could be considered as an
option for pavement that would also help in utilization of fly ash.

(ii) Agencies responsible for road construction & maintenance (MoRTH,

City Development Authority) should prepare guidelines for reducing
silt load on roads.

(iii) With regard to minimizing vehicular emissions, following actions are

required:

197
 ƒ Implementation of progressive norms:

o Progressive tightening of emission regulations since 1991 to

BS-III regulations for 2& 3 wheelers and BS-IV regulations for all
other categories of vehicles (in line EURO-IV) implemented in
2010; have given an edge over the multifold growth of cities
and Mega cities of India and in turn the number of vehicles.

o The implementation roadmap for emission regulations for all

categories of vehicles in the short and medium run need to
be prepared and has to be updated on the continuous
basis.

o Progressive tightening of emission regulations may be

implemented. As a next stage universalization of BS IV norms
throughout the country and subsequently introduction of BS –
V regulations, taking into account environmental and
economic factors may be considered.

o New vehicles to be introduced in future also have to be

compliant to the auto fuel norms that may be prescribed.

ƒ Road map for fuel quality improvement

o Since year 2000, differential norms are implemented in metros

and rest of the country due to non-availability of uniform
quality fuel across the country. Due to non-availability of
appropriate quality fuel, the vehicles of advance technology
registered in metros and major cities are deteriorating fast
defeating the purpose.

o BS-III regulations except for 2&3 wheelers are implemented in

12 cities of India since 2005. However, there will be vehicles
plying in these cities which are not registered in these cities
and such numbers are also high due to local tax structures.
Similarly, BS-III fuel is available only in the city and not even
20-30km away from city boundary. Considering the
circumferential growth of these cities, the number of city
vehicles traveling out of the city boundary is much higher
and tend to refuel the vehicles outside the city because of
the lesser cost of the fuel. Thus not using the required fuel,

198
 particularly low sulfur content fuel, deteriorates the emission
performance of these vehicles and in turn increases the in-
use vehicle emissions.

o Ensuring nationwide same quality of fuel will definitely

improve the conditions of in-use vehicle pollution noticeably
due to the fact that the after-treatment devices and other
newer technologies are very susceptible to the quality of fuel
used. Very short distance exposure to low grade fuel quality
may damage these devices permanently and thus making
newer generation of in-use vehicles not effective or even
worse than those of earlier generation vehicles due to the
failures of emission control devices. With this background, it is
desirable to have the policy of ‘One country One fuel quality
and One regulation’.

ƒ Restricting entry of polluting trucks and heavy duty goods

vehicles, and banning of old commercial vehicles in the cities.
ƒ As old vehicles emit more, a comprehensive vehicle scarp policy
needs to be evolved.
ƒ In place of existing PUC scheme, mandatory periodical
inspection and maintenance requirements may be considered.
Authorized service stations may issue certificates, after servicing
of the vehicles, with details of inspections/maintenance jobs
carried out.
ƒ Management options like synchronizing traffic signals, staggering
business hours, restricting vehicular movements in certain areas
with high pollution levels (particularly during peak hours and/or
critical season), fiscal incentives/disincentives (e.g. increased
parking fee, proper fuel pricing policy), banning odd/even
vehicles on major roads, etc. may be considered.
ƒ Development of mass rapid transportation system. This will
reduce traffic congestion, smaller personalized VKT, and reduce
soil and road dust re-suspension.
ƒ Financial incentives on non-polluting vehicles like electric- hybrid
will also increase the penetration of these vehicles in public as
well as in personal vehicles category.

199
 (iv) Certain highly polluting areas (hotspots) can be identified as low
emission zone and very specific norms are applied including
restrictions on certain activities.

(v) Guidelines to be prepared for better construction practices and strict

compliance of the same is to be ensured.

(vi) Garbage/refuse burning should be strictly banned and efforts should

be made to minimize biomass use for domestic purposes.

(vii) A time-bound action plan for reduction in use of biomass for cooking
may be prepared.

(viii) Reduction in use of DG sets by ensuring adequate power supply, and

have stricter norms for DG set emissions.

(ix) Use of cleaner fuels, stricter emission norms for industries located in
and around the cities.

As multiple agencies are involved, a high power Inter-ministerial Committee

may be set up to implement recommendations of the report. The Committee,
considering various factors, may decide and monitor implementation plan.

200
 9

Major Accomplishments

The source apportionment study with integrated approach has been a

milestone work in air quality management in India. The study of this nature and
extent has probably been done for the first time in the world. Some of the
major accomplishments of the study are as follow:

ƒ A standard methodology for dealing with air quality management in

Indian cities was established.
ƒ It provided the most needed scientific basis, evidence and insight into
urban air quality issues.
ƒ Useful database on various air quality parameters including some of air
toxics has been developed.
ƒ Technical competence, experience and capacity building in terms of
infrastructure as well as trained manpower to conduct comprehensive air
quality studies are now available in the country.
ƒ Refined Emission Factors (EF) for vehicular exhaust emissions, based on
mass emission tests of in-use vehicles, was evolved that provide better
assessment of vehicular pollution.
ƒ More reliable emission inventories were built up for the six cities on the basis
of primary data.
ƒ Source emission profiles for vehicular as well as non-vehicular sources were
developed. This would provide more reliable inputs to receptor modeling
based source apportionment studies in future.

201
 10

Way Forward

Source apportionment study in six cities was a comprehensive set of works

involving all major factors influencing urban air quality management viz. air
quality measurements, meteorological measurements, building up emission
inventories, receptor modeling for apportioning the source contribution,
dispersion modeling to evaluate efficacies of various interventions, and
delineating appropriate action plans for improving air quality to desired levels.
Based on the experiences gained and outcomes of the study, following are
suggested as future course of action:

1. At national level, different working groups may be set up to deal with the
sectoral recommendations of the study. These may be housed in the
respective thematic Ministries:

(i) Group for working on road quality improvement and minimizing re-
suspension of road dust – can take up studies on silt load
measurements in different cities; and prepare guidelines for quality of
road, silt content, paving of unpaved roads, concreting of unpaved
surface along road side for various traffic volume and road types.

(ii) Group on improvement of fuel quality & vehicle exhaust norms –

roadmap beyond 2010 for progressive implementation of BS – IV/V
norms.

(iii) Group to deal with old vehicles – retrofitment of pollution control

devices, scrap policy, inspection & maintenance issues, etc.

(iv) Group on traffic management – use of IT in traffic management,

guidelines for minimizing/synchronization traffic signals, providing
adequate parking, parking fee structure, etc.

(v) Group on construction activities – prepare and supervise

implementation of guidelines on cleaner construction practices.

202
 (vi) Group on industrial activities: industrial action plan implementation.

2. In case of six cities, local Implementation Committee comprising various

stakeholders viz. municipal corporation, development authorities, RTO,
SPCB, etc. may be set up to oversee implementation of city-specific action
plans. Wherever such Committees or Authorities are functional, the study
findings could supplement their efforts. The local Committees may also
address biomass. garbage/refuse burning and other city-specific sources.

3. Since a comprehensive source apportionment is resource intensive, a

simpler and quicker methodology may be worked out for application in
other cities (e.g. measurement for critical season, chemical speciation with
regard to limited key constituents, etc.).

4. With regard to dispersion modeling, better available models such as

AERMOD can be used with new met processor, which can convert IMD
data into a usable met input file for AEROMOD.

5. Technical competence, experience and capacity (infrastructure and

human resources) built through the project should be gainfully utilized. The
institutes participated in the project should become focal point in the
respective region, and help in expanding the institutional network by
providing necessary training to other institutes through partnership in the
future work.

6. Stock taking of air quality trends in major cities needs to be continued. In

all the six cities, air quality measurements including chemical speciation of
PM 10 and PM 2.5 (only key parameters) should continue for at least next five
years. This would not only help in building database on key air quality
measurements but also provide scientific means to assess the effect of
implementation and take mid-term corrections on action plan.

7. Emission inventory, which is an important constituent of air quality

management, has been a weak link. More reliable inventories need to be
developed in all major cities, particularly for non-attainment cities. A
computerized database would be essential for updating inventories at
regular intervals.

203
8. Molecular markers analysis is a highly skilled task that needs to be
strengthened. The project institutes should focus on developing necessary
expertise in such analysis.

9. The emission factors for vehicles need to be improved at regular intervals,

as automotive industry in Indian is expanding at a very rapid rate and
more and more numbers of vehicle models are introduced. More number
of tests on in-use vehicles should be carried out in future. Similarly, city-
specific driving cycles need to be evolved/updated, as there is
continuous change in the road traffic pattern such as synchronization of
traffic signals, construction of flyovers, one way traffic, restriction of entries
of HCV in city areas and continuous increase in density of vehicles. These
steps will lead to more refined EF and subsequently, better estimation of
vehicular exhaust. More comprehensive work on non exhaust emission for
vehicles may be undertaken.

10. Developing source profiles for non-vehicular sources should be extended

to cover a few more sources. Similarly, more research, for vehicular and
non-vehicular sources, to deal with issues on co-linearity of sources,
molecular markers analysis, etc. need to be taken up.

11. Source apportionment of PM 2.5 was on limited measurements. PM 2.5 being

more critical from health point of view, more focus should be given on this
parameter in future.

12. As and when new studies are commissioned, other emerging parameters
like NO 2 , PM 2.5 and Benzene may be looked at apart from PM 10 .

13. Public health impact and related issues should also be studied in future.
This would help in better understanding of linkages between air Quality
and exposure assessment and health impacts

14. More scientific studies should be planned to understand formation of

secondary particles in Indian condition with presence of high OH radical
concentration and moisture. This will require modeling efforts and scientific
measurements of SO 2 , NO x , HNO 3 , SO 4 --, NO 3 - , NH 3 and NH 4 + in the
atmosphere. This exercise can also look into long range transport of
pollutants and formation of haze in winter months.

204
15. A series of Guidebooks drawing on the work done may be developed.
Some of the titles include Planning and Operating Urban Air Quality
Monitoring Networks, Statistical Analyses, Presentation and Interpretation of
Air Quality Data, Approaches to Conduct Source Apportionment,
Preparation of Urban Emission Inventories, Urban Air Quality Dispersion
Modeling, and Development of City Level Air Quality Action Plans.

205
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 ANNEXURE – I

Steering Committee Finance Sub-Committee

1. Secretary, MoEF – Chairman 1. Director (CP), MoEF – Chairperson

2. Additional Secretary (PA Division), MoEF – Vice- 2. Dr. R. K. Malhotra, Executive Director, IOC (R&D) –
Chairman Member
3. Joint Secretary (PA), MoEF – Member 3. Member Secretary, CPCB – Member
4. Chairman, CPCB – Member 4. Shri Nainsingh, Accounts Officer, CPCB – Member
5. Representative of Oil Companies (Dir. Level) – 5. Dr. Prashant Gargava, SEE, CPCB – Member
Member Convener
6. Representative of SIAM – Member
7. Representative of MoP&NG (JS level) – Member Expert Group on Emission Factors
8. Representative of MoRT&HW (JS level) – Member
9. Representatives of Project executing agencies – 1. Prof. H. B. Mathur, Rtd. Professor, IITD – Chairman
Member 2. Prof. B. P. Pundir, Professor, IIT, Kanpur
10. Member-Secretary, CPCB – Member 3. Dr. R. K. Malhotra, Executive Director/ Shri G. K.
11. Director (PA), MoEF – Member Secretary Acharya, Chief Manager, IOC (R&D)
4. Dr. A. L. Aggarwal, Consultant, ASEM-GTZ
5. Dr. S. A. Dutta, Divisional Manager, Tata Motors Ltd.,
Technical Committee Pune
6. Shri K. K. Gandhi, Executive Director, SIAM
1. Chairman, CPCB – Chairman 7. Shri. M. K. Chaudhari, Sr. Deputy Director, ARAI
2. Dr. (Mrs.) Nalini Bhat, Adviser/Shri R. N. Jindal, 8. Dr. Prashant Gargava, SEE, CPCB
Additional Director, MoEF – Member
3. Member Secretary, CPCB – Member Expert Group on QA/QC
4. Prof. H.B. Mathur, Retd. Professor, IIT, Delhi –
Member 1. Dr. V. K. Kondawar, Retd. Scientist, NEERI, Nagpur
5. Shri G. K. Acharya, Chief Manager, IOC (R&D) 2. Dr. A. L. Aggarwal, Consultant, ASEM-GTZ
(representative of oil companies) – Member 3. Dr. D.P. Mukherjee, Scientist C, CPCB, ZO Kolkata
6. Shri M. Kannan, Head (Env), RIL, (representative of 4. Dr. Prashant Gargava, SEE, CPCB
oil companies) – Member 5. Shri Abhijeet Pathak, Scientist B, CPCB
7. Shri K. K. Gandhi, Executive Director, SIAM
(representative of SIAM) – Member
8. Shri M. K. Chaudhari, Senior Deputy Director, ARAI
– Member
9. Prof. Mukesh Sharma, IIT, Kanpur – Member
10. Dr. Rakesh Kumar, Head, NEERI, ZO, Mumbai –
Member
11. Dr. T. S. Panwar, Senior Fellow, TERI – Member
12. Dr. C. V. Chalapati Rao, Deputy Director, NEERI –
Member
13. Prof. S. Pushpavanam, IIT, Chennai – Member
14. Prof. Virendra Sethi/Prof. (Mrs.) R. S. Patil, IIT,
Mumbai – Member
15. Dr. S. A. Dutta, Tata Motors Ltd., Pune – Member
16. Dr. B. Mukhopadhyay, Director, IMD – Member
17. Representative of concerned SPCBs – Member
18. Dr. Prashant Gargava, SEE, CPCB – Member
Secretary

I
 ANNEXURE – II
National Ambient Air Quality Standards, prevailing in 2007

Pollutants Time Concentration in ambient air Method of measurement

weighted
average Industrial Residential, Sensitive
areas rural & other areas
areas
Sulphur Annual 80 μg/m3 60 μg/m3 15 μg/m3 Improved West and Geake
Dioxide (SO 2 ) Average∗
method
24 hours∗∗ 120 μg/m3 80 μg/m3 30 μg/m3

Ultraviolet Fluorescence
Oxides of Annual 80 μg/m3 60 μg/m3 15 μg/m3 Jacob & Hochheiser
Nitrogen as Average∗ modified (Na-Arsenite)
NO 2 method Gas Phase
24 hours∗∗ 120 μg/m3 80 μg/m3 30 μg/m3 Chemiluminescence
Suspended Annual 360 μg/m3 140 μg/m3 70 μg/m3 High volume sampling
Particulate Average∗ (average flow rate not less
Matter (SPM) 24hours∗∗ 500 μg/m3 200 μg/m3 100 μg/m3 than 1.1 m3/minute)

Respirable Annual 120 μg/m3 60 μg/m3 50 μg/m3 Respirable Particulate Matter

Particulate Average∗ Sampler
Matter (RPM)
(Size less than 24 hours∗∗ 150 μg/m3 100 μg/m3 75 μg/m3
10 microns)

Lead (Pb) Annual 1.0 μg/m3 0.75 μg/m3 0.50 μg/m3 ASS method after sampling
Average∗ using EPM 2000 or equivalent
filter paper
24 hours∗∗ 1.5 μg/m3 1.0 μg/m3 0.75 μg/m3
Ammonia1 Annual 0.1 mg/m3 0.1 mg/m3 0.1 mg/m3
Average∗
24 hours∗∗ 0.4 mg/m3 0.4 mg/m3 0.4 mg/m3
Carbon 8 hours∗∗ 5.0 mg/m3 2.0 mg/m3 1.0 μg/m3 Non Dispersive Infrared
Monoxide (NDR) Spectroscopy
(CO) 1 hour 10.0 mg/m3 4.0 mg/m3 2.0 μg/m3
* Annual Arithmetic mean of minimum 104 measurements in a year taken twice a week 24
hourly at uniform interval.
** 24 hourly/8 hourly values should be met 98% of the time in a year. However, 2% of the
time, it may exceed but not on two consecutive days.

II
 ANNEXURE – III
Revised National Ambient Air Quality Standards

S. Pollutant Time Concentration in Ambient Air

No. Weighted
Average Industrial, Ecologically Methods of Measurement
Residential, Sensitive Area
Rural and (notified by
Other Area Central
Government)
(1) (2) (3) (4) (5) (6)
1 Sulphur Dioxide Annual* 50 20 - Improved West and
(SO 2 ), µg/m3 Gaeke
24 hours** 80 80 - Ultraviolet fluorescence

2 Nitrogen Dioxide Annual* 40 30 - Modified Jacob &

(NO 2 ), µg/m3 Hochheiser (Na-
24 hours** 80 80 Arsenite)
- Chemiluminescence
3 Particulate Matter Annual* 60 60 - Gravimetric
(size less than - TOEM
10µm) or PM 10 24 hours** 100 100 - Beta attenuation
µg/m3
4 Particulate Matter Annual* 40 40 - Gravimetric
(size less than - TOEM
2.5µm) or PM 2.5 24 hours** 60 60 - Beta attenuation
µg/m3
5 Ozone (O 3 ) 8 hours** 100 100 - UV photometric
µg/m3 - Chemilminescence
1 hour** 180 180 - Chemical Method

6 Lead (Pb) Annual* 0.50 0.50 - AAS /ICP method after

µg/m3 sampling on EPM 2000
24 hours** 1.0 1.0 or equivalent filter
paper
- ED-XRF using Teflon filter
7 Carbon 8 hours** 02 02 - Non Dispersive Infra Red
Monoxide (CO) (NDIR) spectroscopy
mg/m3 1 hour** 04 04
8 Ammonia (NH 3 ) Annual* 100 100 - Chemiluminescence
µg/m3 24 hours** 400 400 - Indophenol blue
method
- Gas chromatography
9 Benzene (C 6 H 6 ) Annual* 05 05 based continuous
µg/m3 analyzer
- Adsorption and
Desorption followed by
GC analysis

10 Benzo(a)Pyrene - Solvent extraction

(BaP) - particulate Annual* 01 01 followed by HPLC/GC
phase only, ng/m3 analysis
- AAS /ICP method after
11 Arsenic (As), ng/m3 Annual* 06 06 sampling on EPM 2000
or equivalent filter
paper

III
S. Pollutant Time Concentration in Ambient Air
No. Weighted
Average Industrial, Ecologically Methods of Measurement
Residential, Sensitive Area
Rural and (notified by
Other Area Central
Government)
(1) (2) (3) (4) (5) (6)
- AAS /ICP method after
12 Nickel (Ni), ng/m3 Annual* 20 20 sampling on EPM 2000
or equivalent filter
paper

∗ Annual arithmetic mean of minimum 104 measurements in a year at a particular site taken twice a
week 24 hourly at uniform intervals.

** 24 hourly or 08 hourly or 01 hourly monitored values, as applicable, shall be complied with 98% of the
time in a year. 2% of the time, they may exceed the limits but not on two consecutive days of
monitoring.

Note. ― Whenever and wherever monitoring results on two consecutive days of monitoring exceed the
limits specified above for the respective category, it shall be considered adequate reason to institute
regular or continuous monitoring and further investigation.

IV
 ANNEXURE – IV
Description of Monitoring Sites

S. Monitoring Type Site Description

No. Stations
Bangalore
1 Kanamangala Background Bio fuel usage for cooking, movement
of very few public transport buses
and HDD vehicles.
2 Domlur Residential1 DG sets, light vehicles, construction

3 Kammanhalli Residential2 DG sets, light vehicles, heavy

construction activities
4 Peenya Industrial1 DG sets, heavy vehicles, few
construction activities
5 Victoria Kerbside1 DG sets, vehicles, heavy construction
activities
6 CSB Kerbside2 DG sets, vehicles, heavy construction
activities
7 Other Hospital DG sets, vehicles, few construction
activities
Chennai
1 IIT Madras (IITM) Background area Forest area, residential, minor roads
2 Mylapore Residential Residential, minor roads, minor
commercial
3 Triplicane Residential Residential, minor roads, commercial
4 Adyar Kerbside Site Commercial, major roads, residential
5 Saidapet Kerbside Site Commercial, major roads, residential
6 R K Nagar Industrial Industries, roads, commercial.
7 Ambattur Industrial Industries, roads, commercial.
Delhi
1 Prahladpur Background Close to agricultural field, Main
Bawana road about 800m from the
site.
2 Pitampura Residential Residential area, commercial activity,
Ring road at 600m distance from
station
3 Naraina Mixed Use SSI units like electrical, electronics,
hospital equipments, plastics,
garments, printing press, engineering
machinery etc. Fly over construction
in progress, residential area,
commercial activity, slum population
4 SSI- GTK Road Industrial National Highway and outer Ring
Road, Jahangirpur Industrial area,
High traffic volume, Slum population,
construction work for Metro Rail

V
 S. Monitoring Type Site Description
No. Stations
5 Ashram Chowk Kerb Side High traffic volume, Ring roads,
National Highway, Residential,
commercial activity
6 Dhaula kuan Kerb Side High traffic volume, Ring road and
(DK) two National Highways, Cantonment
area, Relatively less commercial and
residential activity
7 Mayapuri Kerb Side Industries, Heavy traffic on Ring Road,
Limited residential area, Tihar Jail
area
8 Anand Vihar Kerb Side Proximity to ISBT, Sahibabad and
(Road Patparganj Industrial area,
No. 56) commercial activity

9 ISBT Ring Road Kerb Side Traffic connecting to Northern States,

River Yamuna on east side, Metro rail
station, Road side eateries and
moderate commercial activities
10 Loni Road Kerb Side National and State highway, High
traffic volume, residential
and commercial activities
Kanpur
1. IIT Kanpur (IITK) Background area Domestic cooking, light vehicles

2 Vikash nagar Commercial Domestic cooking, DG sets,

(VN) cum residential vehicles, road dust, garbage burning,
area restaurants

3 Govind nagar Residential Domestic cooking, vehicles, road

(GN) area dust

4 Dada nagar Industrial area Industries, Domestic cooking, DG

(DN) sets, vehicles, road dust, garbage
burning
5 Colonel ganj Kerb site Vehicles, Domestic cooking, DG
(CG) sets, road dust, garbage burning

6 A.H.M. Hospital Commercial Domestic cooking, DG sets,

(AHM) area vehicles, road dust, garbage burning,
restaurants
7 Ramadevi Traffic site, Domestic cooking, DG sets,
Square away from kerb vehicles, road dust, garbage burning,
(RD) restaurants

Mumbai
1 Colaba Control Protected area under Indian Navy,
minimum traffic, one side surrounded
by sea, residential area

VI
 S. Monitoring Type Site Description
No. Stations
2 Dharavi Residential, Slum Slums, poor sanitations, small scale
industries viz. Glassworks, Leathers,
Plastic /Pellets Recycling,
Earthenware, Eat outs, Small scale
Food Industries, welding operations,
truck terminal, bakeries, Unpaved
roads, refuse and biomass burning
3 Khar Residential, upper Residential area, vehicles , Hotels/
income group Restaurants, public transport & Pvt.
Buses, Auto rickshaw, construction
activity
4 Mahul Industrial Refineries, Thermal power plant,
Unpaved roads, fertilizer/ chemical
industries, Heavy duty vehicles,
tankers, residential areas, sea coast,
salt pans
5 Dadar Commercial Commercial activity, open eatouts,
flyovers, petrol pump, inter city bus
terminals, railway cart shed, traffic
junction, Multiple lane with traffic
6 Andheri Kerb Western Express Highway, MIDC
(Industries), Hotels/ Restaurants,
Airport, BEST & Pvt. Buses, Auto
rickshaw, trucks, construction activity
7 Mulund Kerb Heavy traffic, industries, buses, trucks,
Auto rickshaw, city limit- toll naka,

Pune
1 CWPRS Guest Background Site Away from the main city area, very
House, low vehicle traffic activity, 40% of the
Khadakwasla land is agricultural land, about 20%
bare land, 16 % forest and hilly area
and about 11% residential area.
2 Shantiban, Residential site-1 Residential site with the residential
Kothrud bungalows around the monitoring
sites. Considerable vehicular traffic
activity and new constructions.
Around the site, 40% of the area is
residential area, 20% of bare land
and 15% of hilly area.
3 Sahakarnagar Residential site-2 Mixed residential and commercial site
Colony, with many street-vendors near the
Sahakarnagar site. Construction activity and solid
fuel burning is observed around the
site. Surrounded by residential area
40%, Slum 8%, bare land 25% and Hilly
area 11%.

VII
 S. Monitoring Type Site Description
No. Stations
4 College of Kerbside site-1 This site is along a major road with
Engineering continuous traffic flow from the old
Ground, Mumbai highway. Residential area
Shivajinagar (31%) with commercial activities
(17%), slum (12%) and 16% agricultural
land.
5 Hadapsar Kerbside site-2 Alongside a state-highway with
square, continuous traffic flow and heavy
Hadapsar commercial activities. Surrounded by
27% agricultural land, 21 % bare land,
23% residential area, 14% slum area.
Significant construction activities
were around the site.
6 SAJ Test Plant Industrial Site Major industries nearby include metal
Pvt. Ltd., industries like Bharat Forge Ltd.,
Mundhwa Kalyani Carpenter, etc.; ceramic and
clay products industries like Siporex
India Ltd., B G Shirke Construction
Tech Ltd. Surrounded by 37%
agricultural land, 13% bare land and
about 10% residential area.
7 Geography Other (Institute) site No major air pollution sources in the
Dept., University impact zone other than the vehicular
of Pune activity. Surrounded by Forest
plantation (59%), bare land (16%),
Residential area (10%) and
commercial (9%).

VIII
 ANNEXURE – V
Air Quality Monitoring: Sampling Period

S. No. City Sampling Period

Winter Pre/Post monsoon Summer

1 Bangalore Dec. 2006 – March July 2007 – Sep. 2007 April 2007 – July
2007 2007
2 Chennai Oct. 2007 – Jan. May 2007 – Oct. 2007 Feb 2007 – Aug.
2008; March 2008 2007
3 Delhi Dec. 2007 – Feb. Sep. 2007 – Dec. 2007 April 2007 – June
2008 2007
4 Kanpur Dec. 2006 – Feb Oct. 2007 – Dec. 2007 April 2007 – June
2008. 2007
5 Mumbai Dec. 2007 – March Oct. 2007 – Dec. 2007 March 2007 -
2008 June 2007
6 Pune Dec. 2007 – Feb Sep. 2007 – Oct 2007 April 2007 – June
2008 2007

IX
 ANNEXURE – VI
Seasonal Variation in Concentration of Different Pollutants

SPM PM 10 PM 2.5
Winter Post Monsoon* Summer Winter Post Monsoon Summer Winter Post Monsoon Summer
City Site Type Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg
Bangalore Background 36 183 110 30 146 83 42 160 82 16 91 47 17 227 66 54 255 105 15 45 27 22 33 27 15 34 23
(Kanamangala)
Residential1 68 192 126 38 107 71 81 345 152 22 108 69 28 126 63 32 211 93 24 26 25 16 22 19 16 31 25
(Domlur)
Residential2 207 387 294 79 304 177 211 443 301 79 211 133 28 107 53 25 242 92 23 42 34 15 48 29 9 81 41
(Kammanhalli)
Industrial 167 331 262 76 410 171 168 315 245 131 212 171 23 228 69 86 346 171 21 46 30 15 32 22 20 22 21
(Peenyal)
Kerbside1 217 394 306 263 505 369 191 423 287 111 257 199 33 221 109 33 566 184 48 83 64 22 40 27 17 78 43
(Victoria)
Kerbside2 94 259 181 256 537 411 54 431 191 52 171 98 34 113 73 40 184 96 38 85 62 33 46 38 29 34 31
(CSB)
Hospital 143 272 197 35 125 79 32 74 52 47 134 85 16 178 69 16 74 34 31 44 36 18 25 22 11 19 15

Chennai Background 40 411 117 29 200 75 49 1200 178 7 87 56 37 138 88 2 143 70 17 71 35 24 49 38 15 82 34

(IIT Madras)
Residential1 60 642 164 56 372 147 66 544 174 29 168 77 40 295 122 14 131 73 19 61 39 18 34 28 27 48 34
(Mylopore)
Residential2 39 594 106 45 986 173 34 1221 168 30 166 82 15 953 200 12 416 86 49 110 78 31 36 34 13 32 22
(Triplicate)
Industrial1 95 572 311 51 554 298 80 524 319 39 246 108 37 191 113 26 736 117 38 77 57 26 64 41 35 218 79
(R.K. Nagar)
Industrial2 99 668 297 124 971 348 91 716 295 59 365 138 25 326 147 52 469 141 48 87 67 21 65 40 49 81 67
(Ambattur) )
Kerbside1 92 661 241 65 748 242 24 342 161 36 261 111 37 212 128 13 622 144 29 94 64 29 54 44 36 49 42
(Saidapet)
Kerbside2 115 1587 350 79 638 207 49 971 211 48 158 87 7 299 127 2 1172 271 41 110 73 37 94 56 30 66 51
(Adyar)

Delhi Background 284 888 574 400 833 558 251 967 558 156 571 333 249 515 365 108 479 253 163 194 183 170 205 188 87 233 143
(Prahladpur)
Residential 654 1133 839 640 1427 990 174 358 289 284 901 527 308 1119 719 25 230 92 297 304 300 NA NA NA 30 30 30
(Pritampura)
Industrial1 710 1463 990 268 824 481 385 972 637 279 725 446 172 492 268 124 359 216 186 209 198 56 89 75 50 53 51
(Naraina)
Industrial2 692 1359 929 762 1776 1263 169 685 423 397 726 553 389 1425 815 94 418 251 74 320 212 146 699 383 6 46 20
(SSI-GTK)
Kerbside1 501 861 682 678 1097 890 577 985 800 262 585 393 138 798 453 153 383 297 104 203 166 148 226 182 56 102 80
(Ashram Chowk)
Kerbside2 364 971 566 179 444 306 409 764 529 188 536 347 96 353 195 NA NA NA 137 143 140 43 55 49 NA NA NA
(Dhaulakuan)
Kerbside3 656 1464 1111 258 697 422 338 922. 604 233 688 453 84 499 251 137 518 344 129 327 209 112 176 141 54 55 54
(Mayapuri)
Kerbside4 429 988 628 459 1285 878 717 1944 1272 198 678 380 177 842 508 163 486 307 176 224 198 146 268 208 56 102 80
(Anand Vihar)
Kerbside5 681 1382 984 670 1569 1264 311 650 460 212 838 487 240 1053 675 92 218 149 122 160 137 103 199 149 102 114 107
(ISBT)
Kerbside6 654 1903 1123 1058 3877 2765 541 2568 1339 243 895 456 341 2023 1051 131 868 375 276 350 307 347 375 361 46 47 46
(Loni Road)

X
 SPM PM 10 PM 2.5
Winter Post Monsoon* Summer Winter Post Monsoon Summer Winter Post Monsoon Summer
City Site Type Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg
Kanpur Background 269 506 362 164 555 329 180 664 342 90 313 205 91 285 169 89 400 187 97 236 172 104 173 132 104 178 136
(IITK)
Residential1 210 781 429 197 657 373 217 812 422 135 365 226 143 283 195 106 478 216 135 299 207 106 221 161 87 294 190
(Vikas Nagar)
Residential2 217 688 445 185 636 417 237 595 437 122 349 240 114 346 212 111 471 234 153 269 185 108 204 154 105 206 159
(Govind Nagar)
Commercial 298 894 550 250 823 511 233 779 537 142 444 276 115 423 239 114 468 255 165 247 198 123 209 169 121 223 172
(AHM Hospital)
Industrial 345 857 603 288 782 576 258 891 591 220 631 396 167 629 371 183 614 388 220 471 305 103 391 273 151 356 232
(Dada Nagar)
Kerbside1 339 928 564 326 884 532 198 941 561 152 476 291 137 400 260 139 514 272 160 303 216 153 278 226 145 310 218
(Colonelganj)
Kerbside2 360 700 515 315 871 508 330 773 507 106 377 234 139 367 221 108 334 190 149 345 207 127 284 197 108 274 170
(Ramadevi)

Mumbai Background 115 512 246 125 343 205 77 438 159 45 382 174 64 225 140 50 151 91 54 113 92 52 71 60 16 41 29
(Colaba)
Residential1 289 859 552 304 642 501 186 698 401 143 468 272 186 321 245 91 299 177 67 122 92 84 96 91 43 120 74
(Dharvi)
Residential2 288 812 495 276 588 400 85 204 146 151 440 263 119 306 229 41 94 62 91 110 102 66 107 83 14 15 14
(Kher)
Industrial 241 502 395 237 497 392 116 640 239 178 400 271 100 374 226 55 181 98 116 134 127 85 89 87 13 23 17
(Mahul)
Commercial 177 423 291 204 503 351 197 758 335 116 388 248 105 417 212 49 204 116 75 137 106 98 134 112 27 47 37
(Dadar)
Kerbside1 208 570 396 253 742 399 116 676 255 103 433 237 173 287 223 48 144 84 103 140 121 84 113 99 25 29 28
(Andheri)
Kerbside2 310 689 463 269 566 391 230 626 352 143 549 279 158 361 234 90 254 163 89 165 131 159 220 189 60 64 62
(Mulund)

Pune Background 123 333 225 41 119 76 90 210 142 44 200 100 25 85 60 37 108 78 29 34 32 17 26 23 22 22 22
(Khadakwasla)
Residential1 160 567 328 48 285 107 157 304 210 27 286 130 20 202 64 72 162 106 51 63 58 23 28 26 26 31 28
(Kothrud)
Residential2 383 821 511 135 728 384 121 200 173 99 373 178 80 261 137 34 116 67 38 55 48 26 42 35 19 26 22
(Sahakarnagar)
Institutional 200 361 259 89 285 212 47 197 121 75 225 133 18 165 71 11 117 59 43 48 45 26 39 32 13 17 14
(Shivajinagar)
Industrial 272 567 412 83 393 188 196 321 272 91 498 237 22 316 86 91 167 124 48 84 64 21 31 26 29 44 37
(Mundhwa)
Kerbside1 279 670 466 136 457 282 375 714 517 112 413 266 71 290 140 86 262 143 113 136 124 29 55 45 42 48 46
(Shivajinagar)
Kerbside2 439 888 663 362 878 599 178 503 321 111 506 237 59 484 212 44 184 103 106 143 120 59 65 62 27 32 30
(Pune-Solapur Highway,
Hadapsar)

* In case of Bangalore – Pre-monsoon

XI
 NO 2 SO 2
Winter Post Monsoon* Summer Winter Post Monsoon Summer
City Site Type Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg Min Max Avg
Bangalore Background 7 60 18 BDL 280 91 BDL 151 45 BDL 10 6 BDL 23 9 5 42 14
(Kanamangala)
Residential1 17 63 46 7 38 22 10 53 29 5 13 9 BDL 37 15 5 30 15
(Domlur)
Residential2 15 32 26 24 163 49 8 31 19 BDL 12 5 3 19 11 10 12 10
(Kammanhalli)
Industrial1 22 156 53 7 234 89 6 75 30 BDL 21 9 6 14 10 6 22 10
(Peenya)
Kerbside1 42 78 60 15 125 66 30 242 105 BDL 12 7 BDL 37 12 4 39 19
(Victoria)
Kerbside2 29 182 94 23 72 47 8 178 58 5 17 10 4 33 10 3 26 14
(CSB)
Hospital 15 37 23 10 191 90 4 48 18 4 11 6 3 15 8 4 31 13

Chennai Background 9 41 27 BDL 15 8 5 27 14 BDL 10 3 BDL BDL BDL BDL 9 5

(IIT Madras)
Residential1 12 42 28 BDL 28 13 5 32 12 BDL 8 3 BDL 7 3 BDL 9 5
(Mylapore)
Residential2 13 58 32 7 34 17 3 60 28 BDL 36 4 BDL 5 3 BDL 13 3
(Triplicane)
Industrial1 20 71 39 12 37 20 19 63 36 BDL 22 5 BDL 3 BDL BDL 13 3
(R.K. Nagar)
Industrial2 25 70 45 6 47 17 22 60 42 BDL 14 6 BDL 6 4 BDL 18 6
(Ambattur)
Kerbside1 2 73 42 20 50 33 28 71 43 BDL 7 3 BDL 6 BDL BDL 5 3
( Saidapet)
Kerbside2 24 61 45 13 46 25 8 43 25 BDL 24 6 BDL 4 BDL BDL 10 4
( Adyar)

Delhi Background 14 54 35 14 53 33 15 43 26 6 32 17 5 20 9 5 21 9
(Prahladpur)
Residential 57 94 74 59 120 90 19 44 30 3 24 15 10 34 19 3 11 8
(Pitampura)
Industrial1 36 97 60 13 43 31 23 62 41 5 21 12 3 12 6 4 23 10
(Naraina)
Industrial2 98 228 162 81 216 146 34 93 63 48 134 90 34 144 83 4 20 12
(SSI-GTK)

XII
 Kerbside1 52 150 94 67 172 114 27 61 48 10 20 15 7 25 14 3 13 6
(Ashram Chowk)
Kerbside2 42 98 75 22 60 40 41 93 68 6 35 15 6 9 7 6 13 8
(Dhaulakuan)
Kerbside3 38 90 65 26 46 33 31 76 47 5 16 10 4 7 5 4 10 7
Mayapuri
Kerbside4 44 102 72 41 125 85 18 67 37 8 33 16 7 49 23 6 34 14
Anand Vihar
Kerbside5 60 149 112 69 183 124 35 61 48 6 28 18 7 32 19 3 14 7
(ISBT)
Kerbside6 47 93 74 36 67 44 9 57 33 11 33 21 6 9 7 4 22 10
(Loni Road)

Kanpur Background 12 28 23 8 58 20 8 38 20 3 22 8 3 19 8 BDL 13 4

(IITK)
Residential1 19 77 49 12 55 32 6 37 19 5 29 14 4 23 8 BDL 14 4
(Vikas Nagar)
Residential2 14 60 40 8 61 36 20 72 37 5 24 9 4 26 10 BDL 36 6
(Govind Nagar)
Commercial 14 74 38 16 50 32 10 60 32 4 25 12 3 17 8 BDL 29 7
(AHM Hospital)
Industrial 22 66 35 10 37 24 7 70 27 9 68 26 10 30 19 BDL 31 15
(Dada Nagar)
Kerbside1 13 79.9 45 16 98 42 16 76 37 7 25 15 3 27 9 BDL 17 8
(Colonelganj)
Kerbside2 25 54 39 14 58 35 14 56 30 7 58 17 4 37 15 BDL 27 12
(Ramadevi)

Background
Mumbai 14 113 53 13 76 38 9 94 18 5 45 15 4 27 13 4 9 5
(Colaba)
Residential1
28 119 70 33 75 53 12 76 36 7 21 13 5 53 16 4 24 6
(Dharvi)
Residential2
39 129 75 31 94 66 6 34 14 6 41 12 4 23 11 4 8 5
(Kher)
Industrial
29 97 72 35 100 57 10 38 20 7 35 18 4 25 15 4 28 7
(Mahul)
Commercial
47 148 99 32 92 63 11 60 31 6 31 16 5 30 15 4 15 6
(Dadar)
Kerbside1
29 130 79 57 97 79 9 39 17 5 26 11 5 34 14 4 57 8
(Andheri)
Kerbside2
38 139 71 30 100 53 24 155 51 5 33 15 4 41 17 4 10 5
(Mulund)

XIII
 Background
Pune 6 33 17 9 20 10 9 14 10 4 39 14 4 17 9 4 14 5.6
(Khadakwasla)
Residential1
8 65 27 9 16 10 9 30 15 5 60 14 4 17 9 4 19 7.2
(Kothrud)
Residential2
9 90 45 27 74 45 9 14 10 9 30 19 5 31 12 4 11 5.1
(Sahakarnagar)
Institutional 6.0
(Shivajinagar) 21 61 38 23 52 35 9 19 10 17 36 24 6 17 10 4 14

Industrial
27 90 57 7 38 18 10 47 23 11 103 47 9 34 18 7 49 24.5
(Mundhwa)
Kerbside1
22 96 74 9 75 33 30 133 65 7 80 21 4 44 13 4 16 72
(Shivajinagar)
Kerbside2
(Pune-Solapur Highway, 37 99 68 25 66 44 9 43 26 18 99 41 8 40 13 4 11 6
Hadapsar)

* In case of Bangalore – Pre-monsoon

XIV
 ANNEXURE-VII
Emission Factors for Vehicular Exhaust

Vehicle Type
Model PM CO HC NO 2

Year g/km g/km g/km g/km

Scooters-2s
1991-1995 0.073 6 3.68 0.02
Scooters-2s
1996-2000 0.073 5.1 2.46 0.01
Scooters-2s
2001-2005 0.049 2.37 2.05 0.03
Scooters-4s
2001-2005 0.015 0.93 0.65 0.35
Scooters-4s
2006-2010 0.015 0.4 0.15 0.25
(4 Stroke) Motorcycles
1991-1995 0.01 3.12 0.78 0.23
(4 Stroke) Motorcycles
1996-2000 0.015 1.58 0.74 0.3
(4 Stroke) Motorcycles
2001-2005 0.035 1.65 0.61 0.27
(4 Stroke) Motorcycles
2006-2010 0.013 0.72 0.52 0.15
3-Wheeler - CNG- 4S OEM
2006-2010 0.015 1 0.26 0.5
3-Wheeler - Auto rickshaw-Petrol 2S
Post 2000 0.045 1.37 2.53 0.2
3-Wheeler - Auto rickshaw-LPG 2S
Ret-pre 2000 0.721 4.39 3.6 0.08
Ret-post-2000 0.13 1.7 1.03 0.04
3-Wheeler - Auto rickshaw-D
Post 2000 0.347 2.09 0.16 0.69

Post 2005 0.091 0.41 0.14 0.51

4 Wheeler - Petrol
1991-1995 0.008 4.75 0.84 0.95
4 Wheeler - Petrol
1996-2000 0.008 4.53 0.66 0.75
4 Wheeler - Petrol
2001-2005 0.004 1.3 0.24 0.2
4 Wheeler - Petrol
2006-2010 0.002 0.84 0.12 0.09
4 Wheeler - Diesel
1996-2000 0.145 0.87 0.22 0.45
4 Wheeler - Diesel
2001-2003 0.19 0.72 0.14 0.84
4 Wheeler - Diesel
2003-05 0.06 0.3 0.26 0.49
4 Wheeler - Diesel
2006-2010 0.015 0.06 0.08 0.28
4 Wheeler - LPG
1996-2000 0.001 6.46 1.78 0.44
2001-2005 0.002 2.72 0.23 0.2

2006-2010 0.002 2.72 0.23 0.2

4 Wheeler - CNG
2006-2010 0.006 0.06 0.46 0.74
LCVs
1991-1995 0.998 3.07 2.28 3.03
(Light Commercial Vehicles)
4 Wheeler GC 1996-2000 0.655 3 1.28 2.48
2001-2005 0.475 3.66 1.35 2.12

2006-2010 0.475 3.66 1.35 2.12

XV
Vehicle Type
Model PM CO HC NO 2
Year g/km g/km g/km g/km
Large Trucks + MAV
1991-1995 1.965 19.3 2.63 13.84
1996-2000 1.965 19.3 2.63 13.84
2001-2005 1.24 6 0.37 9.3

2006-2010 0.42 4.13 0.28 8.63

Buses-Diesel
1991-1995 2.013 13.06 2.4 11.24
1996-2000 1.213 4.48 1.46 15.25
2001-2005 1.075 3.97 0.26 6.77

2006-2010 0.3 3.92 0.16 6.53

Buses - CNG
2001-2005 NA 3.72 3.75 6.21

2006-2010 - 3.72 3.75 6.21

Emission Factors for Vehicle Exhaust Future Scenario Generation

Vehicle Model PM (g/km) % red. Remarks NO 2 % red. Remarks

Type Year (g/km)

BS-II to BSIII:
HC+ NOx
2006-2010 0.057 0.02 limit
0.333
2 Wheelers- 2011-2015 0.0456 20.0% 0.0134 33.0%
2S 20% reduction In absence
assumed with of road map,
technology 20%
changes(Direct reduction
2015-2017 0.0365 20.0% Injection etc) 0.0107 20.0% assumed
BS-II to BSIII:
HC+ NOx
2006-2010 0.015 0.25 limit
0.333
2 Wheelers
(4-Stroke) 2011-2015 0.012 20.0% 0.1675 33.0%
Scooters 20% reduction In absence
assumed with of road map,
technology 20%
changes(Fuel reduction
2015-2017 0.0096 20.0% Injection etc) 0.134 20.0% assumed
0.15 BS-II to BSIII:
HC+ NOx
2006-2010 0.013 limit
2 Wheelers 0.333
2011-2015 0.0104 20.0% 0.1005 33.0%
(4 Stroke)
20% reduction In absence
Motorcycles
assumed with of road map,
technology 20%
changes(Fuel reduction
2015-2017 0.0083 20.0% Injection etc) 0.0804 20.0% assumed
3-Wheeler- 20% reduction 0.5 BS-II to BSIII:
OE - 4S assumed with HC+ NOx
CNG/ LPG/ 2006-2010 0.015 technology limit
Petrol changes( 0.375
2011-2015 0.012 20.0% 0.3125 37.5%

XVI
 Injection etc) In absence
of road map,
20%
reduction
2015-2017 0.0096 20.0% 0.2500 20.0% assumed
BS-II to BSIII 0.51 BS-II to BSIII:
HC+ NOx
2006-2010 0.091 limit
0.176
3-Wheeler - 2011-2015 0.0455 50.0% 0.5 0.4202 17.6%
Diesel In absence of In absence
road map, 20% of road map,
reduction 20%
assumed reduction
2015-2017 0.0364 20.0% 0.3362 20.0% assumed
0.2 BS-II to BSIII:
HC+ NOx
2006-2010 0.045 limit
3-Wheeler- 0.375
OE - 2S 2011-2015 0.0360 20.0% 0.125 37.5%
CNG/ LPG/ 20% reduction In absence
Petrol assumed with of road map,
technology 20%
changes( reduction
2015-2017 0.0288 20.0% Injection etc) 0.100 20.0% assumed
BS-II to BSIII:
HC+ NOx
2006-2010 0.13 0.04 limit
0.375
3-Wheeler - 2011-2015 0.104 20.0% 0.025 37.5%
LPG 2S- retro 20% reduction In absence
assumed with of road map,
technology 20%
changes( reduction
2015-2017 0.0832 20.0% Injection etc) 0.02 20.0% assumed
BS-II to BSIII:
HC+ NOx
2006-2010 0.118 0.19 limit
0.375
3-Wheeler -
2011-2015 0.0944 20.0% 0.1188 37.50%
CNG 2S-
20% reduction In absence
retro
assumed with of road map,
technology 20%
changes( reduction
2015-2017 0.0755 20.0% Injection etc) 0.095 20.0% assumed
0.09
2006-2010 0.002

2011-2015 0.0016 20.0% 0.0477 47.0%

EURO-V, PM
4 Wheeler - norm instruction
Petrol in line with diesel
2015-2017 0.0013 20.0% values 0.0358 25.0% EURO-V

EURO-VI, no EURO-VI, no
change in norms change in
from EURO-V to norms from
2015-2017 0.0013 0.0% VI 0.0358 0.0% EURO-V to VI
4 Wheeler - 0.28
2006-2010 0.0150

XVII
Diesel
2011-2015 0.0083 45.0% 0.14 50.0%
28.00%
2015-2017 0.0008 90.0% EURO-V 0.1008 EURO-V

2015-2017 0.0008 0.0% EURO-VI 0.0454 55.0% EURO-VI

0.74
2006-2010 0.006
In line with
4 Wheeler - 2011-2015 0.0048 20.0% 0.3922 47.0% petrol
CNG
2015-2017 0.0038 20.00% 0.2942 25.00%

2015-2017 0.0038 0.0% 0.2942 0.0%

0.2
2006-2010 0.002
In line with
4 Wheeler - 2011-2015 0.0016 20.0% 0.106 47.0% petrol
LPG
2015-2017 0.0013 20.0% 0.0795 25.0%

2015-2017 0.0013 0.0% 0.0795 0.0%

2.12
2006-2010 0.475
LCVs (Light
Commercial 2011-2015 0.0808 83.0% 1.484 30.0%
Vehicles)- 43.0%
diesel 2015-2017 0.0808 0.0% EURO-V 0.8459 EURO-V

2015-2017 0.0339 58.0% EURO-VI 0.1692 80.0% EURO-VI

5.7 data taken
from
vehicle
data taken from emissions
LCVs (Light vehicle emissions source
Commercial 2006-2010 0.058 source profile profile
Vehicles) - In line with
CNG 2011-2015 0.0464 20.0% 3.99 30.0% diesel
43.0%
2015-2017 0.0371 20.0% 2.2743

2015-2017 0.0297 20.0% 0.4549 80.0%

data taken from

vehicle emissions
Large Trucks 2006-2010 0.42 source profile 8.63
+ MAV
2011-2015 0.0714 83.0% 6.041 30.0%
43.0%
2015-2017 0.0714 0.0% EURO-V 3.4434 EURO-V

2015-2017 0.03 58.0% EURO-VI 0.6887 80.0% EURO-VI

data taken from

Large Trucks vehicle emissions
+ MAV 2006-2010 0.032 source profile 3.92
CNG In line with
2011-2015 0.0256 20.0% 2.744 30.0% diesel
43.0%
2015-2017 0.0205 20.0% 1.5641

2015-2017 0.0164 20.0% 0.3128 80.0%

XVIII
 2006-2010 0.3 6.53

2011-2015 0.051 83.0% 4.5710 30.0%

Buses-Diesel
43.0%
2015-2017 0.051 0.0% EURO-V 2.6055 EURO-V

2015-2017 0.0214 58.0% EURO-VI 0.5211 80.0% EURO-VI

6.21

taken from TERI-

2006-2010 0.044 ARAI report
Buses - CNG- In line with
OE 2011-2015 0.0352 20.0% 4.347 30.0% diesel
43.0%
2015-2017 0.0282 20.0% 2.4778

2015-2017 0.0225 20.0% 0.4956 80.0%

3.92

data taken from

Buses - CNG- vehicle emissions
Retro 2006-2010 0.032 source profile
In line with
2011-2015 0.0256 20.0% 2.744 30.0% diesel
43.0%
2015-2017 0.0205 20.0% 1.5641

XIX
 ANNEXURE – VIII
Non-Vehicular Emission Factors

S. Source/Activity Common Emission Factor Reference/Remarks

No.
1 Fuel Oil TSP = {9.19(S) + 3.22} * 0.120 TSP may be considered PM 10 .
Combustion SO 2 = 18.84S
NOx = 6.6 TOC is Total Organic Compound including VOC.
CO = 0.6
CH 4 = 0.0336 EPA-42: Table 1.3 – 1 And Table 1.3 – 3; S – Sulphur Content In Fuel (For 1%
TOC = 0.1248 Sulphur S=1); Gm/Lit Oil, Fuel Oil Combustion, Normal Firing.
NMTOC = 0.091

(Unit: kg/103 L)
2 Natural Gas TSP = 121.6 TSP may be considered PM 10 .
Combustion SO 2 = 9.6
NOx = 1600 http://www.epa.gov/ttn/chief/ap42/ch01/Final/C01s04.Pdf
CO = 1344
CO2 =1,920,000
CH4 = 36.8
VOC =88
TOC = 176
NMTOC = 0.091

(Unit: kg/106 M3)

3 Liquefied PM= 2.1 Reddy And Venkatraman
Petroleum Gas SO 2 = 0.4
Combustion Http://www.Epa.Gov/Ttn/Chief/Ap42/Ch01/Final/CO1s05.Pdf
(Unit: Gm/kg) (Commercial Boilers)

NO x = 1.8
CO = 0.252
S. Source/Activity Common Emission Factor Reference/Remarks
No.
CO 2 =1716
CH4 = 0.024
VOC =88
TOC = 0.072
NMTOC = 0.091

(Unit: kg/106 M3)

4 Bagasse TSP = 7.8 EPA-AP42: Table 1.8-1,
Combustion NO x = 0.6 Uncontrolled Emission Factors
CO 2 = 780

(Unit: kg/Ton)
5 Residential Wood PM 10 =15.3 Table 1.10-1 Conventional AP-42
Stoves/Restaurants CO=115.4
NOx=1.4
SOx= 0.2
TOC=41.5
CH 4 =15
TNMOC=26.5

(Unit: kg/Mg)
6 Kerosene PM=1.95 PM & SO 2 – Reddy And Venkatraman
Combustion SO 2 =4
Domestic
(Unit: G/Lit)
TSP May Be Considered as PM 10 .
TSP=0.61 USEPA 2000
CO=62
NOx=2.5

XXI
S. Source/Activity Common Emission Factor Reference/Remarks
No.
CH 4 =1
TNMOC=19

(Unit: G/kg)

7 Coal Combustion TSP=20 TERI Report

- Tandoor / CO=24.92 Uncontrolled wherever controlled use efficiency.
Domestic NOx=3.99
SO 2 = 13.3

(Unit: kg/Mg)
8 Coal Combustion Stoker Fired Boilers S= Weight Percent Sulphur
Boilers CO=0.3 A= Ash content (weight %)
CO 2 =2840 AP-42 1.2-1,2,3
SOx=19.5S Use suitable EF pertinent to the city & 2x2 grid
NOx=4.5
PM=0.04 A

FBC Boilers
CO=0.3
CO 2 =ND
SOx=1.45
NOx=0.9

Pulverized Coal Boilers

SOx=19.5S
NOx=9.0

(Unit: kg/Mg)

XXII
S. Source/Activity Common Emission Factor Reference/Remarks
No.
9 Chulha (Dung, PM=6.3 Reddy And Venkatraman - (PM 10 , SO 2 , PM)
Wood) PM 10 =5.04
SO 2 = 0.48

(Unit: G/kg) TSP may be Considered as PM 10 .

USEPA 2000
TSP=1.9
CO=31 Use suitable EF pertinent to the city & 2x2 grid
NOx=1.4

TNMOC=29.8
CH 4 =3

(Unit: G/kg)
10 Agricultural Waste PM=11 EPA-AP42: Table 2.5-5 Emission Factors For Open Burning Of Agricultural
(From Pune And PM 10 =11 Materials, kg/Ton; Unspecified Field Crop Burning Emission Factor Is
Kanpur) CO=58 Considered. Particulate Matter From Most Agricultural Refuse Burning Has
CO 2 =207 Been Found To Be In The Sub micrometer Size Range.
SO 2 =0.12 For SO 2 And NO 2 : M. S. Reddy And C. Venkatraman (2002), Inventory Of
NO X =0.49 Aerosol And Sulphur Dioxide Emissions From India. Part II - Biomass
Combustion, Atm. Envt., Vol 36, Issue 4, Pp 699-712
(Unit: kg/Ton) Manish Shrivastava, Gazala Habib, Venkatraman C, Jeffery W. Sterh, Russell
R. Dickerson(Sept.8 2003) Emissions From Biomass Burning In India : II - Sulfur
Dioxide And Nitrogen Dioxide, Global Biogeochemical Cycles, Pp15

11 Garden Waste (Same as under 10)

Combustion
12 Medical Waste PM=2.33 EPA-AP42: Table 2.3.2;
Incineration SO 2 =1.09 Apply Emission Factors for uncontrolled Emission

XXIII
S. Source/Activity Common Emission Factor Reference/Remarks
No.
CO=2.95
NOx=1.78

(Unit: kg/Ton)
13 Solid Waste PM 10 = 8 A Guide To Rapid Source Inventory Techniques And Their Use In Formulating
Burning (Landfill PM 2.5 =5.44 Environmental Control Strategies – Part One – Rapid Inventory Techniques In
Sites) CO=42 Environmental Pollution By A.P. Economopolous, WHO, Geneva, 1993
SOx=0.5000
NOx=3
VOC= 21.5

(Unit: kg/MT)
14 Kerosene Apply same EF as for item
Generators no. 6: domestic Kerosene
Domestic combustions
15 Diesel Industrial PM 10 = 1.33 10-3 AP-42 (Table 3.3-1) EF For Uncontrolled Gasoline & Diesel Industrial Engines.
Generators Large CO 2 = 0.69
Stationary Diesel CO=4.06 10-3
And All Stationary SOx= 1.24 10-3
Dual - Fuel NOx=0.0188
Engines(Film Aldehydes= 2.81 10-4
Shooting) TOC
Exhaust = 1.50 10-3
Evaporative =0
Crankcase = 2.68 10-3
Refueling =0

(Unit: kg/Kw-Hr)

XXIV
S. Source/Activity Common Emission Factor Reference/Remarks
No.
16 Petroleum Refining Boilers & Process Heaters AP-42 (Table 5.1-1 To 5.1-3).
Fuel Oil – EF Used For Fuel Calculate EF inclusive for all the processes for each ton of product.
Oil Combustion (Sec 1.3
AP-42)
Natural Gas - EF Used For
Natural Gas Combustion
(Sec 1.4 AP-42)

Fluid Catalytic Cracking

Units
Uncontrolled
PM=0.695
SO 2 =1.413
CO=39.2
Total Hydrocarbons=0.630
NO 2 =0.204
Aldehydes=0.054
Ammonia=0.155

Electrostatic Precipitator
and CO Boiler
PM=0.128
SO 2 =1.413
CO=Negligible
Total Hydrocarbons=
Negligible
NO 2 =0.204
Aldehydes= Negligible
Ammonia= Negligible

XXV
S. Source/Activity Common Emission Factor Reference/Remarks
No.
Moving Bed Catalytic
Cracking Units
PM=0.049
SO 2 =0.171
CO=10.8
Total Hydrocarbons=0.250
NO 2 =0.014
Aldehydes=0.034
Ammonia=0.017

Fluid Coking Units

Uncontrolled
PM=1.50
SO 2 =ND
CO= ND
Total Hydrocarbons= ND
NO 2 = ND
Aldehydes= ND
Ammonia= ND

Electrostatic
Precipitatorand CO Boiler
PM=0.0196
SO 2 =ND
CO= Negligible
Total Hydrocarbons=
Negligible
NO 2 =ND
Aldehydes= Negligible

XXVI
S. Source/Activity Common Emission Factor Reference/Remarks
No.
Ammonia= Negligible

(Unit: kg/103 L Fresh Feed)

Vapor Recovery System

and Flaring
PM= Negligible
SO 2 =0.077
CO=0.012
Total Hydrocarbons=0.002
NO 2 =0.054
Aldehydes= Negligible
Ammonia= Negligible

(Unit: kg/103 L Refinery

Feed)

Vacuum Distillation
Column Condensers
Uncontrolled
PM=Negligible
SO 2 =Negligible
CO=Negligible
Total Hydrocarbons=0.14
NO 2 =Negligible
Aldehydes=Negligible
Ammonia=Negligible
Controlled (Vented To

XXVII
S. Source/Activity Common Emission Factor Reference/Remarks
No.
Heater Or Incinerator)

PM=Negligible
SO 2 =Negligible
CO=Negligible
Total
Hydrocarbons=Negligible
NO 2 =Negligible
Aldehydes=Negligible
Ammonia=Negligible

(Unit: kg/103 L Refinery

Feed)

Claus Plant And Tail Gas

Treatment (See Sec 8.13-
“Sulphur Recovery” AP-42)

Cooling Towers
(Uncontrolled Emissions)
PM=0.7

(Unit: kg/106 L Cooling

Water)

Oil Water Separators

(Uncontrolled Emissions)
PM=0.6

XXVIII
S. Source/Activity Common Emission Factor Reference/Remarks
No.
(Unit: kg/103 L Waste Water)

Storage (Uncontrolled
Emissions)
(See Chapter 7-Liquid
Storage Tanks AP-42)

Loading (Uncontrolled
Emissions)
(See Section 5.2 –
Transportation And
Marketing Of Petroleum
Liquids AP-42)

Fugitive VOC Emissions

(Uncontrolled Oil Refinery
Of 52,500 M3/Day)
Total =20,500

(Unit: kg/Day)
17 Electric Arc TSP=6.3 WHO 1993, Rapid Techniques In Environmental Pollution Part 1 By Alexander
Welding SO 2 =ND P. Economopoulos
NO x =0.16 EF Are Cited Without Control Equipments
CO=9.75
VOC=0.09

(Unit: kg/Ton)
18 Secondary Metal Lead AP-42 12.11 For Lead; 12.13 For Steel Foundries; 12.4 For Zinc
Smelting (Lead) Sweating

XXIX
S. Source/Activity Common Emission Factor Reference/Remarks
No.
And Other PM=16-35
Operations Pb=4-8
(Foundries) SO 2 =ND

Reverberatory Smelting
PM=162
Pb=32
SO 2 =40

Blast Smelting Cupola

PM=153
Pb=52
SO 2 =27

Kettle Refining
PM=0.02
Pb=0.006
SO 2 =ND

Kettle Oxidation
PM=< 20
Pb=ND
SO 2 =ND
Casting
PM=0.02
Pb=0.007
SO 2 =ND

XXX
S. Source/Activity Common Emission Factor Reference/Remarks
No.
(Unit: kg/Mg)

Steel Foundries
Melting
3 Electric Arc
TSP=6.5 (2 To 20)
NOx=0.1
PM 10 =ND
Open Hearth
TSP =5.5 (1 To 10)
NOx=0.005
PM 10 =ND
Open Hearth Oxygen
Lanced
TSP =5.5 (1 To 10)
NOx=0.005
PM 10 =ND
Electric Induction
TSP =0.05
NOx=ND
PM 10 =0.045
Sand Grinding/Handling In

Mold And Core Making

TSP =ND
NOx=NA
PM 10 =0.27 3.0

XXXI
S. Source/Activity Common Emission Factor Reference/Remarks
No.
Core Ovens
TSP =ND
NOx=ND
PM 10 =1.11 0.45
Pouring and Casting
TSP =ND
NOx=NA
PM 10 =1.4
Casting Cleaning
TSP =ND
NOx=NA
PM 10 =0.85
Charge Handling
TSP =ND
NOx=NA
PM 10 =0.18
Casting Cooling
TSP =ND
NOx=NA
PM 10 =0.7

(Unit: kg/Mg)

Zinc
Reverberatory Sweating
Clean Metallic Scrap
PM= Negligible
General Metallic Scrap

XXXII
S. Source/Activity Common Emission Factor Reference/Remarks
No.
PM=6.5
Residual Scrap
PM=16

(Unit: Mg/Mg Of Feed)

Rotary Sweating
PM=5.5-12.5
Muffle Seating
PM=5.4-16
Kettle Sweating
Clean Metallic Scrap
PM= Negligible
General Metallic Scrap
PM=5.5
Residual Scrap
PM=12.5
Electric Resistance
Sweating
PM=<5
Sodium Carbonate
Leaching Calcining
PM=44.5

(Unit: kg/Mg Of Zinc Used)

Kettle Pot
PM=0.05

XXXIII
S. Source/Activity Common Emission Factor Reference/Remarks
No.
(Unit: Mg/Mg)

Crucible Melting
PM=ND
Reverberatory Melting
PM=ND
Electric Induction Melting
PM=ND
Alloying
PM=ND
Retort and Muffle
Distillation
Pouring
PM=0.2 – 0.4
Casting
PM=0.1-0.2
Muffle Distillation
PM=22.5

(Unit: kg/Mg Of Product)

Graphite Rod Distillation

PM-Negligible
Retort
Distillation/Oxidation
PM=10-20
Muffle
Distillation/Oxidation
PM=10-20

XXXIV
S. Source/Activity Common Emission Factor Reference/Remarks
No.
Retort Reduction
PM=23.5
Galvanizing
PM=2.5

(Unit: kg/Mg Of Zinc Used)

19 Cast Iron Furnace Cupola AP-42 (Table 12.10-2)
Uncontrolled Use suitable EF pertinent to the city & 2x2 grid
PM=6.9

Electric Arc Furnace

Uncontrolled
PM=6.3

(Unit: kg Of Pollutant/Mg Of
Grey Iron Produced)
20 Power Plant - CO 2 =1920000 AP-42 Table (1.4-1-2)
Natural Gas Pb=0.008 Use suitable EF pertinent to the city & 2x2 grid
PM(Total)=121.6
NOx=4480
CO=1344
SO 2 =9.6
TOC=176
CH 4 = 36.8
VOC=88

(Unit: kg /106 M3)

21 Wood Residue PM 10 =17.3 AP42 (Sec. 1.9, Pp. 1.10.4, Table 1.9.1)
Combustion In CO=126.3 Use suitable EF pertinent to the city & 2x2 grid

XXXV
S. Source/Activity Common Emission Factor Reference/Remarks
No.
Boilers / Bakeries SOx=0.2
NOx=1.3
CO 2 =1700
Total VOC=114.5

(Unit: kg /Mg)
22 Coal Combustion PC, Dry Bottom, Wall-Fired, AP-42 (Table 1.1-3-4)
- Power Plant Sub-Bituminous Pre-NSPS Use suitable EF pertinent to the city & 2x2 grid
SOx=19S
NOx=11 (5.5 With Low NOx
Burners)
CO=0.25 Particulate Is Expressed In Terms Of Coal Ash Content, A, Factor Is
Filterable PM=5A Determined By Multiplying Weight % Ash Content of Coal (as Fired) By The
Filterable PM 10 =1.15 Numerical Value Preceding The A.

(Unit: kg /Mg)
23 Plastic And SO 2 =0.5 AP 42/(Table 2.4-7)
Leather Waste NOx=3
Burning CO=42
CH 4 =6.5
TSP=8

(Unit: kg /Mg Of Waste)

24 Bricks And Related TSP=0.9 EPA-AP42 (Table: 11.3-2)
Clay Products PM 10 =0.7 Apply for uncontrolled Emissions for Coal Fired Kiln unless a different fuel is
(Earthen Pot Kiln) SO 2 =0.6 used.
NOx=0.255
CO=0.4
CO 2 =150

XXXVI
S. Source/Activity Common Emission Factor Reference/Remarks
No.

(Unit: kg /Tons Of Bricks)

25 Cupola Cast Iron TSP=6.9 WHO 1993, Rapid Techniques In Environmental Pollution Part 1 By Alexander
SO 2 =0.6S P. Economopoulos
NOx=ND
CO=73
VOC=ND
Pb=0.32

(Unit: kg /Tons)
26 Municipal Solid PM 10 = 8 A Guide To Rapid Source Inventory Techniques And Their Use In Formulating
Waste Landfills PM 2.5 =5.44 Environmental Control Strategies – Part One – Rapid Inventory Techniques In
CO=42 Environmental Pollution By A.P. Economopolous, WHO, Geneva, 1993
SOx=0.5000
NOx=3 Divide under different types of emissions such as vehicular movement on
VOC= 21.5 unpaved roads, combustion of organic content, loading and unloading etc.
Determine the activity levels for each category and apply suitable factors
(Unit: kg/MT) given.
27 Manufacture Of PM=17.5 AP-42 (Table 6.6.1-1)
Rubber Products / Use suitable EF pertinent to the city & 2x2 grid
Plastics Small (Unit: kg /Mg)
Scale
28 Fertilizer And Solution Formation and AP-42 8.2-1 – Chapter – 8
Inorganic Concentration Use suitable EF pertinent to the city & 2x2 grid
Chemical Industry PM=0.0105
NH 4 =9.23
Non Fluidized Bed Prilling
Agricultural Grade
PM=1.9

XXXVII
S. Source/Activity Common Emission Factor Reference/Remarks
No.
NH 4 =0.43
Feed Grade
PM=1.8
NH 4 =ND
Fluidized Bed Prilling
Agricultural Grade
PM=3.1
NH 4 =1.46
Feed Grade
PM=1.8
NH 4 =2.07
Drum Granulation
PM=120
NH 4 =1.07
Rotary Drum Cooler
PM=3.89
NH 4 =0.0256
Bagging
PM=0.095
NH 4 =NA

(Unit: kg /Mg Of Product)

29 Hot Mix Asphalt Batch HMP AP-42 11.1-1, 5 & 6
Plants PM=16 Use suitable EF pertinent to the city & 2x2 grid
PM 10 =2.25
CO=0.2(Natural Gas-Fired
Dryer, Hot Screens
and Mixer)
0.2 (Fuel Oil-Fired

XXXVIII
S. Source/Activity Common Emission Factor Reference/Remarks
No.
Dryer, Hot Screens
and Mixer)
0.2 (Waste Oil-Fired
Dryer, Hot
Screens and
Mixer)
CO 2 =18.5(For All Type O
Process)
NOx=0.0125(Natural Gas-
Fired Dryer,
Hot Screens
And Mixer)
0.06 (Fuel Oil-Fired
Dryer And
Waste Oil-
Fired Dryer,
Hot Screens
And Mixer)
SO 2 =0.0023(Natural Gas-
Fired Dryer
,Hot Screens
And Mixer)
0.044(Fuel Oil-Fired

Dryer, And

Waste Oil-

XXXIX
S. Source/Activity Common Emission Factor Reference/Remarks
No.
Fired Dryer Hot

Screens And

Mixer)

0.0215 (Coal-Fired
Dryer, Hot
Screens And
Mixer)
TOC =0.0075 (Natural Gas-
Fired Dryer,
Hot
Screens
And Mixer)
0.0075 (No.2 Fuel Oil-
Fired Dryer,
Hot
Screens
And Mixer)
0.0215(No.6 Fuel Oil-
Fired Dryer,
Hot Screens
And Mixer)
CH 4 = 0.0037(For All Type O
Process)
VOC=0.0041(Natural Gas-
Fired Dryer,
Hot Screens

XL
S. Source/Activity Common Emission Factor Reference/Remarks
No.
And Mixer)
0.0041(No.2 Fuel Oil-
Fired Dryer,
Hot Screens
And Mixer)
0.018(No.6 Fuel Oil-
Fired Dryer, Hot
Screens And
Mixer)

(Unit: kg /Mg)

Drum Mix HMP

PM=14
PM 10 =3.25
CO=0.065 (For All Process
Type)
CO 2 = 16.5(For All Process
Type)
NOx=0.013 (Natural Gas
Fired Dryer)
0.0275(No.2 Fuel

Oil And Waste Oil Fired

Boiler)

XLI
S. Source/Activity Common Emission Factor Reference/Remarks
No.
TOC=0.022 (For All Process
Type)
CH 4 =0.006(For All Process
Type)
VOC=0.016(For All Process
Type)

(Unit: kg /Mg)
30 Glass TSP=0.7 WHO 1993, Rapid Techniques In Environmental Pollution Part 1 By Alexander
Manufacturing SO 2 =1.7 P. Economopoulos
NO X =3.1
CO=0.1
VOC=0.1

(Unit: kg /Ton)
31 Lead Oxide And TSP=7 WHO 1993, Rapid Techniques In Environmental Pollution Part 1 By Alexander
Pigment SO 2 =NA P. Economopoulos
Production NO X = NA
CO= NA
VOC= NA
Pb=7

(Unit: kg /Ton)
32 Construction TSP=1.2 For Details Refer AP-42 Section 13.2.3.3
(Building) Use suitable EF pertinent to the city & 2x2 grid depending upon construction
(Unit: Tons/Acre/ Month Of activity
Activity)
33 Construction TSP=1.2 For Details Refer AP-42 Section 13.2.3.3
Roads (A) (Unit: Tons/Acre/ Month Of Use suitable EF pertinent to the city & 2x2 grid depending upon construction

XLII
S. Source/Activity Common Emission Factor Reference/Remarks
No.
Aggregate Laying Activity) activity
And (B) Asphalt
34 Construction Of TSP=1.2 For Details Refer AP-42 Section 13.2.3.3
Flyovers Use suitable EF pertinent to the city & 2x2 grid depending upon construction
(Unit: Tons/Acre/ Month Of activity
Activity)
35 Carbon Black Oil Furnace Process AP 42 Table 6.1-3
Main Process Vent Use suitable EF pertinent to the city & 2x2 grid
PM=3.27
CO=1400
NO=0.28
SO=0
CH 4 =25
Non CH 4 VOC=50
Flare
PM=1.35
CO=122
NO=ND
SO=25
Non CH 4 VOC=1.85
CO Boiler And Incinerator
PM=1.04
CO=0.88
NO=4.65
SO=17.5
Non CH 4 VOC=0.99

Oil Storage Tank Vent

Non CH 4 VOC=0.72

XLIII
S. Source/Activity Common Emission Factor Reference/Remarks
No.

Fugitive Emissions
PM=0.10

(Unit: Weight Of Emissions

/Weight Of Carbon Black
Produced)
36 Paint Applications Prime Coat (Solventborne AP 42 Table 4.2.2.8-1
(Auto) Spray) Based on the number of vehicles being painted in each location
6.61
Guide Coat (Solventborne
Spray)
1.89
Top Coat (Enamel)
7.08

(Unit: Automobile kg Of
VOC/Vehicle)
37 Paved Roads Refer Section 13.2.1.3 Of AP 42 (13.2.1.3)
AP-42 Given equation has to be used and respective parameters shall vary for
each city and/or grid

38 Unpaved Roads Refer Section 13.2.2 Of AP- AP 42 (13.2.2)

42 Given equation has to be used and respective parameters shall vary for
each city and/or grid

39 Soil Dust PM=0.263 Pune EI Study Conducted By ARB, In kg/Acre/Year,

(Wind Erosion) PM 10 =0.1315 1. Emission Factor For TSP Is 0.001052 Tons Per Acre Per Year, Which Is The

XLIV
S. Source/Activity Common Emission Factor Reference/Remarks
No.
Default Emission Factor For Default San Joaquin Valley, California, Averaged
Over All The Counties. Multiplication Factor Of 0.5 For Deriving PM10 Is Used.
(Unit: kg/Acre/Year) 2. Assumed A Longer Vegetative Coverage In India After The Harvest, Hence
Multiply The Above Emission Factor By A Factor Of 1/4.
40 Stone Pulverization Primary And Secondary AP 42 Table (11.19.2-1)
Industry, Quarries Crushing Use EF of uncontrolled Emission
Total PM=ND
Total PM 10 = ND
Total PM 2.5 = ND

Tertiary Cushing
Total PM=0.0027
Total PM 10 = 0.0012
Total PM 2.5 = ND
Fines Crushing
Total PM=0.0195
Total PM 10 = 0.0075
Total PM 2.5 = ND
Screening
Total PM=0.0125
Total PM 10 = 0.0043
Total PM 2.5 = ND
Fines Screening
Total PM=0.15
Total PM 10 = 0.036
Total PM 2.5 = ND
Conveyor Or Transfer
Point
Total PM=0.0015

XLV
S. Source/Activity Common Emission Factor Reference/Remarks
No.
Total PM 10 = 0.00055
Total PM 2.5 = ND
Wet Drilling Unfragmanted
Stone
Total PM=ND
Total PM 10 = 4.0 * 10-5
Total PM 2.5 = ND
Truck Unloading –
Fragmanted Stone
Total PM=ND
Total PM 10 = 8.0 * 10-6
Total PM 2.5 = ND
Truck Unloading –
Conveyor Crushed Stone
Total PM=ND
Total PM 10 = 5.0 * 10-5
Total PM 2.5 = ND

(Unit: kg/Mg)
NOTE:

ƒ The proposed EFs were involved through a consultative process wherein the proposed EFs along with the feed backs provided by each respective
organization (s) were studied by an expert group and best possible option was selected based on the references available.
ƒ The proposed EF(s) are applicable for this study only and are proposed with the sole objective of using common EFs so that inter-city comparison between the
results obtained can be made.
ƒ The participating organizations are advised that they should also study the whole documents referred here so that any city specific deviations can be suitably
incorporated. However, in such a case the changes incorporated should be documented with justification.

XLVI
 ANNEXURE-IX
Projections of grid-wise emission load for 2012 and 2017

Bangalore

BAU-2012 BAU-2017

BAU PM10 2012

BAU PM10 2017

32 83 33 1351 555 91 35 385 405 34 31 31

13 48 106 50 1800 767 123 52 460 483 51 121 48
31 83 165 150 160 48 34 386 404 33 31 31 48 0-200 Kg/d 12 48 106 213 195 200 67 51 462 482 50 48 48
37 35 36 38 100 54 35 387 398 34 36 32 214 200-400 Kg/d
11 56 52 54 56 68 76 52 463 475 52 54 49 48 0-200 Kg/d
589 141 337 324 71 114 48 47 45 47 52 48 442 400-800 Kg/d
10 795 205 453 458 98 172 68 67 64 66 71 67 214 200-400 Kg/d
1159 800-1600 Kg/d
524 443 232 541 1460 442 1159 214 91 36 35 52
9 786 689 329 718 1873 581 1490 299 124 54 53 72 442 400-800 Kg/d
1874 >1600 Kg/d 8 115 480 405 720 275 135 196 154 1159 800-1600 Kg/d
2847 86 618 368 276 1086 549 1757 199 96 141 112
3836 819 1390 2251
2591 1992 837 904 1836 2117 591 108 338 223 333 220 7 3450 2564 1104 1198 2368 2730 807 148 532 325 465 325 1874 >1600 Kg/d
49 913 1062 742 831 741 1413 1874 1349 83 65 187 Point Source : 1307 Kg/ 6 71 1208 1405 984 941 1204 1891 2417 1876 119 96 269
160 2080 973 704 452 2006 471 833 322 220 762 527 5 201 2662 1286 933 596 2705 618 1439 438 313 971 667
139 128 1844 783 933 606 1651 145 33 44 84 53 4 180 177 2360 1033 1359 910 2217 201 50 69 122 73 Point Source : 1737 Kg/
65 101 1395 726 672 828 1558 1545 56 122 41 32 3 89 140 1793 966 905 1236 2026 1996 77 188 60 48
60 60 210 106 1439 1414 79 298 54 44 35 68 2 80 80 295 146 1885 1874 110 404 75 63 52 90
31 31 416 503 396 469 43 262 116 119 33 41 1 48 48 495 625 474 581 63 353 157 164 50 58
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

PM 10 Emission Load (kg/day)- All Sources

BAU NOx 2012 BAU NOx 2017

13 249 648 257 18001 1724 490 277 265 422 266 242 242 13 456.1738 974.0601 462.4628 24038.45 2447.461 781.888 488.8874 472.3685 678.036 474.1611 442.4931 442.4931
12 242 648 765 639 695 375 268 280 417 259 242 246 12 442.4931 974.0601 1126.874 961.8239 1044.55 616.821 476.9915 493.0716 671.9679 464.9417 442.4931 447.2009
11 300 275 288 302 394 446 274 289 371 271 289 253 11 521.0742 486.7424 503.0846 521.9397 686.0722 733.519 484.5836 504.0154 611.8367 481.388 504.0393 456.8881
10 757 655 868 1368 561 1066 380 379 353 373 408 378 10 1195.759 1066.845 1277.831 2004.96 904.5825 1824.678 622.6077 628.2076 588.2651 613.6874 659.5396 620.1867 229 0-500 Kg/d
9 2721 3042 915 2678 6222 2067 4860 903 416 286 280 414 229 0-500 Kg/d 9 4755.464 5294.021 1375.427 3636.163 7839.577 2789.094 6155.761 1292.602 670.7205 500.634 492.8999 669.8225 688 500-1000 Kg/d
8 2045 428 2914 1660 1611 4589 2596 7477 848 547 618 374 688 500-1000 Kg/d 8 3218.843 710.0273 3917.64 2347.333 2567.161 5799.016 3450.632 9361.107 1215.372 913.5131 926.9171 628.4934 1981 1000-2000Kg/d
7 974 7848 4157 4565 8084 8484 3366 465 2576 1297 963 1167 1981 1000-2000Kg/d 7 1477.895 9919.892 5447.309 6070.065 10350.3 11326.87 4881.722 725.025 4464.772 2076.219 1447.778 1951.485 3642 2000-4000Kg/d
6 409 3568 4365 3730 2645 7524 7093 8103 5662 534 594 850 3642 2000-4000Kg/d 6 665.9193 4696.118 5775.346 5026.254 4244.365 13320.72 9993.994 10298.16 7131.139 945.2746 1005.969 1321.032 5181 >4000 Kg/d
5 692 8987 3861 3532 2284 10662 2217 9518 645 929 883 1007 5181 >4000 Kg/d 5 1039.191 11248.19 5140.699 4770.232 3342.972 15244.77 2966.66 17212.25 990.8128 1399.177 1289.675 1554.626
4 569 1014 7879 3858 6813 4971 8719 618 260 399 784 423 4 860.5978 1599.181 9857.708 5112.237 10788.23 8379.045 12424.87 924.1682 466.326 714.3081 1306.779 682.8301 Point Source : 23449 K
3 537 312 6031 3712 3617 6551 6791 6574 447 1286 325 248 Point Source : 17647 K 3 854.1285 534.4551 7664.663 5019.027 5048.438 10717.21 8811.404 8369.609 711.9729 2203.637 551.7845 451.0966
2 467 467 911 556 6729 7125 458 1149 432 350 275 533 2 736.3191 736.3191 1313.105 904.4464 9026.333 797.0234
9880.022 1642.063 691.617 584.4573 485.9065 823.7318
1 242 242 510 857 367 704 350 924 527 281 262 318 1 442.4931 442.4931 796.476 1399.876 612.7683 1104.268 599.3256 1353.134 830.1774 493.3216 468.5818 541.7068
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

NOx Emission Load (kg/day)- All Sources

XLVII
Chennai

BAU -2012 BAU- 2017

PM 10 Emission Load (kg/day)- All Sources

NOx Emission Load (kg/day)- All Sources

XLVIII
Kanpur

BAU - 2012 BAU – 2017

PM 10 Emission Load (kg/day)- All Sources

NOx Emission Load (kg/day)- All Sources

L
Mumbai

BAU - 2012 BAU – 2017

Mu
Mu

A
Legend A
C =Colaba
K D =Dadar
Dh=Dharavi K
K =Khar
A =Andheri
Dh Mh=Mahul Legend
Mu=Mulund Dh
C =Colaba
Mh D =Dadar
Mh Dh=Dharavi
D
D K =Khar
Con. In Kg/d A =Andheri
Mh=Mahul
Mu=Mulund
Con. In Kg/d

C C

Figure 3 : Grid Wise PM Emission Load for 2017, Mumbai

PM 10 Emission Load (kg/day)- All Sources

NOx Emission Load (kg/day)- All Sources

LI
Pune

BAU - 2012 BAU – 2017

less than 150 kg/d

150 to 300 kg/d

300 to 450 kg/d
450 to 600 kg/d
Above 600 kg/d

BAU 2012- PM BAU 2017-PM

PM 10 Emission Load (kg/day)- All Sources

less than 150 kg/d

150 to 300 kg/d

300 to 450 kg/d
450 to 600 kg/d
Above 600 kg/d

BAU 2012-NOx BAU 2017-NOx

NOx Emission Load (kg/day)- All Sources

LII