A statistical approach for forecasting population density in designing sewage

treatment plant viz-a-viz groundwater contamination: A literature review study

Subject: Thesis Performance (Code: CE70301)

Semester: 4th

Presented By:

MR. PRASANTA BISWAS (Registration No. 2023RCE11)

PhD Student (Environmental Engineering)

Under the supervision of

Prof. H.K.Pandey

Supervisor

Civil Engineering Department

M.N.N.I.T Prayagraj

Allahabad India

June 2025
 CONTENT

1. Introduction

2. Urban growths of India

3. Treatment Capacity & Sewage Generation: Indian Scenario

4. Indian Standard (Sewage Discharge Effluent)

5. Literature Reviews

6. Study Area

7. Objective of study

8. Methodology

9. Workflow of methodology

10. Statistical Testing By CHI-SQUARE

11. Statistical Testing By ANOVA

12. Results & Discussion

13. Conclusions

14. Future Scopes of the study

15. References
Introduction
 What is sanitation & waste generation?

Human (Sanitation) Waste

untreated domestic sewage, faecal
sludge and septage from cities.
Sanitation waste handling facility(of
- it is the single biggest source of water
two types):

resource pollution (leading polluter of
Sewerage (treated by STPs).
water sources) in India.
Non-Sewerage (handled & managed
by on-site sanitation, OSS).

Weak Sanitation has -
Open defecation (optional).


consequences to suffering,
significant health ailmemts/costs,
OSS contains -
a host of diseases including diarrhoea,
Septic tank .

agricultural-produce contamination,
(with or without sump well)

serious pollution of water and soil
Open latrine pit.
resource, and
various other environmental degradations.
District-level spatial distribution of sanitation facility (Source: Conway, et al. 2023)
 Status of urban households (HHs) in India & sanitation
(Source: Census, 2011 & SBM)
Status of urban HHs

in India (Source: Census, 2011)
Urban towns & HHs not having toilet; opting for open
Urban population = 37.70 Crore (31% of the total population defecation

of India) ; expectation to increase to 60 Crores by 2031.

statutory towns = 4,041 nos.

households (HHs) = 79 lacs

Over 47% of urban Indian households [nearly about 79,00,000*4=3,16,00,000 (3 Crore 16 lacs)]
depend on onsite facilities, OSS (Census 2011) and this people.
proportion is increasing.

That is, 47%*37.7/4Crore=4.43 Crore HHs, That is, urban population under sewerage
or nearly about, 43*4=17.72 Crore population. =37.7-17.72-3.16=16.82 Crore

Remaining 53% of urban HHs & those not having toilets Total population under SBM=16.82+3.16=19.98 Crore
would be brought under SBM..

SWACHH BHARAT MISSION (SBM)

Nearly 80% of these 79 lacs HHs would be by newly-built individual household toilet (IHHT)

Remaining 20% (around 16 lacs HHs) under existing or newly-built community toilets.

SBM will cover those segment of urban HHs who do not have toilets & also those having without sewerage system.
 STP INVENTORY (INDIA)

 Status of STPs in India
Status of treatment capacity in India

Out of the 816 municipal sewage Treatment capacity = 37% of the

treatment plants (STPs) listed across total 62,000 MLD (million litres per
India, day) of human waste generated in

522 are operational (only 64% are urban India.

functioning), Treatment capacity = 22,940 MLD

79 STPs are Non Operational,
The human waste left as untreated =
145 STPs are under construction and
62,000 - 22,940 = 39 ,060 MLD
70 STPs are proposed


(most of it is often disposed of

(Source: Report of "Inventorization of suitably to land, surface water body

Sewage treatment plants, 2015" by or underground water resources, etc.)

Central Pollution Control Board)

 Gap & Opportunity (By STP):
Status of sewage generation & treatment capacity of STP

Census 1971
Class-I city: Population 1 lac or more
Class-II town: Population 50,000-1 lac

Sewage generation and treatment capacity in

Metropolitan Cities

Decadal trend of sewage generation and treatment capacity

(Source: CPCB, March 2021)

(Source: CPCB,
August 2013)
Gap between sewage generation & treatment
Per capita surface water availability in decreasing trend in India

(Source: IWMI, 2005)

 Sewage Treatment Market in India

(Source: India Infrastructure Research, 2021)

Urban Growths in India
The relationship between urbanization level and growth rate of
different size-class towns in India, 2011 (Source: UNDESA. (2019))
 Growth status of Indian towns & cities (Source: UNDESA. (2019))

Regional variation in the

share of declining,
stabilizing and growing
towns in India, 2011

The growth trajectory of urban centers by size Regional variations in the growth trajectories of
class category of town/city in India, 2011 towns and cities in India, 2011
Treatment Capacity & Sewage
Generation: Indian Scenario
Decadal urban population growth upto 2021 since 1901
(Source: CPCB, March 2021)

Bar diagram showing the growth of urban

population between 1901-2021
State-wise Sewage Generation, Installed Treatment
Capacity and Actual Treatment (As on 30.06.2020)

Sewage generation, Installed treatment

capacity, Operational Capacity, Actual
Utilization and Complied Treatment
Capacity (between 2014 to 2020)
State-wise Installed Treatment Capacity and STPs

Source: CPCB, March 2021

Status-wise Break-Up of STPs Located in Different States/UTs

Source: CPCB, March 2021

Technology-wise Break-up of STPs in various States

Source: CPCB, March 2021

Technolgical distribution with respect to
number and capacity of STPs

Technology-wise capacity distribution of STPs

(Source: CPCB, March 2021)

 COMPLIANCE STATUS OF STP

Comparative Statistics on the Inventory for the years

2014 and 2020

Status of Compliance with respect to

consented norms of SPCBs / PCCs

Source: CPCB, March 2021

Indian Standard (Sewage
Discharge Effluent)
INDIIAN STANDARD OF DISCHARGE OF SEWAGE EFFLUENT

Source: CPCB, March 2021

INDIIAN STANDARD OF DISCHARGE OF SEWAGE EFFLUENT (contd...)

Source: CPCB, March 2021

Water quality standards in different countries

(Source: Edwin, et al. (2014))
Literature Reviews
Literature Review 1
 Literature Review 1
Kaczor, Grzegorz B. 2022. Analysis of the distribution of statistical concentrations of pollutants in samples of treated
wastewater from small sewage treatment plants. JOURNAL OF WATER AND LAND DEVELOPMENT, e-ISSN 2083-4535,
Polish Academy of Sciences (PAN) Institute of Technology and Life Sciences – National Research Institute (ITP – PIB),
JOURNAL OF WATER AND LAND DEVELOPMENT, 2022, No. 55 (X–XII): 115–124, DOI: 10.24425/jwld.2022.142313.

CORE AIM:
To show distribution best reflects and describes the variability of pollutant concentrations in treated sewage, discharged
from sewage treatment plants, characterised by a value below 2000 PE.

METHODOLOGY:
Three STP with PE 1530, 1280 & 1960 were studied for BOD, COD & TSS
data over 10-years (2005-2015).
Total 80 samples (8 sample per year) ateacth STP were collected.
Kolmogorov–Smirnow λ testt & the Pearson X2 test : test of fit
Preliminary box-and-whisker plot and additionally Grubbs: test of outliers

FINDINGS:
Asymmetric, right-oblique distribution of the data Best description statistics of distribution:

characteristics. BOD: Chi-square, Fisher-Tippett, Gamma and Weibull

Empirical distribution was Fisher-Tippett distribution distributions.

(best one). COD: Fisher-Tippett, Gamma distribution

Other distributions that often describe the analysed TSS: Erlang, Fisher–Tippett (+Weibull) and Gamma.

empirical data were: Gamma, log-normal, Chi-square,

and Weibull.
Literature Review 2
 Literature Review 2

D. Ramkumar, V. Jothiprakash, and B. N. Patil.Performance assessment of sewage treatment plants using compliance index.
Journal of Water, Sanitation and Hygiene for Development Vol 12 No 6, 485 doi: 10.2166/washdev.2022.055.

CORE AIM:
infer the performance assessment of sewage treatment plants (STPs) using the compliance index (CI).

METHODOLOGY: The methodology includes three steps, (1) estimation of performance

indicators from the quality test, parameter test, and conformity test, (2)
hypothesis testing of non-conformity, and (3) hypothesis testing using the non-
parametric test.

FINDINGS:

Three different methods are carried out to compute compliance: a

performance indicator system, hypothesis testing for non-
conformity and a non-parametric test

Best description statistics of distribution:

significance of studying an STP through CI as it involves all the necessary parameters for
wastewater quality, number of tests carried out, and conformity of quality with the standards to
infer the compliance of STPs
Literature Review 3
 LITERATURE REVIEW 3

Lee, Seung-Pil., Min, Sang-Yun., Kim, Jin-Sik., Park, Jong-Un., and Kim, Man-Soo. (2014). A Study on the Influence of a
Sewage Treatment Plant’s Operational Parameters using the Multiple Regression Analysis Model. Environ. Eng. Res. (pISSN
1226-1025 eISSN 2005-968X) 2014 March,19(1) : 1-6, http://dx.doi.org/10.4491/eer.2014.19.1.1.

Problem solving target of study:

Results & Findings of study:
Operational variables of STP & their behavioural
For COD, accuracy of prediction & for TN, RMS error was found
controls by Regression predictions
maximum by the Regression.

Model controllingwas MLSS & FM for COD & TN respectively.

Methodology of study:

Multiple Regression Analysis (Modeling) Conclusions:

COD & TN as dependent & 30 STP operation Treatment plant workability by efficiency is best recognizable once
variables as independent variable. diagnosed with all operational variables properly & in a modeling
way.

The regressing model or method is suitable for energy proficient

treatment plant or where energy consumption is high.
 LITERATURE REVIEW 3 (Contd...) Multiple Regression Method

The Variables

Regression Analysis Results (COD)

Regression Analysis Results (TN)

 LITERATURE REVIEW 3

Regression Coefficients (COD) Standardised Regression Coefficients (COD)

Regression Coefficients (TN) Standardised Regression Coefficients (TN)

The Economic Condition

Literature Review 4
 LITERATURE REVIEW 4

Bhuvanesvari, S., and Manikandan, Dr. R.. (2023). AN EMPIRICAL ANALYSIS ON NEXUS BETWEEN POPULATION
GROW TH AN D WASTEWATER GEN ERATION IN IN DIA. EP RA In tern a tion a l J ou rn a l of Socio-Econ om ic a n d
Environmental Outlook (SEEO), ISSN: 2348-4101, Volume: 10 Issue: 6, June 2023, 55-61, 10.36713/epra0314.

Problem solving target of study: Methodology of study:

Pearson correlation
Corelation between population growth of
Linear regression with hypothesis testing
India & sewage production.

Results & Findings of study:

Strong pearson correlation (>0.95) signifying the increasing trend of the

function.

Linear regression correlation indicating the dependence of sewage

production on the population growth (independent variable).
Conclusions:

1. The study anticipating to future growth of sewage producttion, So it may act as the pathway of making up the
forecasting process.

2. The study calls for suitable actions to be taken up like SDG etc. to cope up future demands without a loss.
Literature Review 5
 LITERATURE REVIEW 5

Sarpanchal, Sourabh., Agale, Tejashri., and Jadhav, Pratika. (2022). ANALYSIS OF EFFICIENCY OF SEWAGE TREATMENT
PLANT USING DATA SCIENCE. International Journal of Engineering Applied Sciences and Technology, 2022, Vol. 7, Issue 12,
ISSN No. 2455-2143, Pages 142-146.

Methodology of study:
Problem solving target of study:
ARIMA (Auto regressive integrated moving average)

Fluctuations in inflow to STP leading to uncertain Shapiro- Wilk Normality Test - Parametric (water quality
efficiency. hypothesis testing)

Wilcoxon’ Signed Rank Test - Non-Parametric

Whether Treated water from STP reusable.

Since classification Model has certain limitatios, So

Results & Findings of study: following supervised classification models were

prescribed after testing:
Arima (1,0,1) with non-zero mean deployed.
Logistic Regression Model (Accuracy 97.61%)
Data not normally distributed & it is non-parametric.
Decision TreeAacccuracy (97.13%)
K - nearest neighbour model (93.68%)
Naïve Bayes Classifier (92.74%)

Conclusions:

1. Future sewage loads in forecasting possible by ARIMA modelling (AI).

2. Water quality testing done by hypothesis which further tested & enhanced by ML modelling.

3. Ensuring water quality & quantity management.

Literature Review 6
 LITERATURE REVIEW 6

Danelon, André F., Spolador, Humberto F.S., and Kumbhakar, Subal C.Weather and population size effects on water and
sewer treatment costs: Evidence from Brazil. Journal of Development Economics,153 (2021) 102743, pp.1-11, https:
//doi.org/10.1016/j.jdeveco.2021.102743.

Results & Findings of study:

Problem solving target of study:
Overall efficiency >12% than of full efficiency.
Variability between population size (not growth!)
Population sizing effect significant to only by its
& cost of STP
sizeable quantity to the efficiency or costing.

Methodology of study: Low or medium sized population will incur false

Assumption of inefficiency "technical" to functioning of responses to the efficiency.

STP.

Two types of technical inefficiency - Conclusions:

Persistent (time-invariant) inefficiency.
An urgent of policy making sighted to overcome various
Transient (time-variant) inefficiency.
constraints.
Assumption of cost function equation (cost frontier
approach). Expansion of sanitation services mentioned, this may bring

Incorporation of capital stock & population index up a mix of water supply & STP together for a benefit by break
(average /sample mean). -even & etc.
Concept of break-even arises.
A need of more study to other geographical places.
LITERATURE REVIEW
6(Contd...)

Similar literature reviews for the

efficiency analysis
Literature Review 7
 Literature Review 7
El Aatik, A.; Navarro, J.M.;
Martínez, R.; Vela, N. Estimation of
Global Water Quality in Four
Municipal Wastewater Treatment
Plants over Time Based on Statistical
Methods. Water 2023, 15, 1520.
https://doi.org/10.3390/w15081520

CORE AIM:
To develop a prediction of temporal changes in water quality by introducing a wastewater quality index
(WWQI) for four regional wastewater treatment plants (WWTPs)

METHODOLOGY: multivariate statistical analyses have been adopted to predict

the performance of WWQI

FINDINGS:

From the loadings of the PCs, the relationships between

the original parameters are analyzed. The accuracy of the developed models in terms of fit to the
training dataset ranged from 74.3% to 97.9%, with p-values < 0.05.

The techniques incorporated in

this study provided a comprehensive evaluation framework for monitoring wastewater treatment.
Literature Review 8
 LITERATURE REVIEW 8
Rahmat, S.; Altowayti,
W.A.H.; Othman, N.; Asharuddin,
S.M.; Saeed, F.; Basurra, S.; Eisa,
T.A.E.; Shahir, S. Prediction of
Wastewater Treatment Plant
Performance Using Multivariate
Statistical Analysis: A Case Study of
a Regional Sewage Treatment Plant in
Melaka, Malaysia. Water 2022, 14,
3297. https://doi.org/10.3390/ Results & Findings of study:
w14203297
For COD, accuracy of prediction & for TN, RMS error was found
maximum by the Regression.
Problem solving target of study:

Model controllingwas MLSS & FM for COD & TN respectively.

Operational variables of STP & their
behavioural controls by Regression
predictions
Conclusions:

Methodology of study: Treatment plant workability by efficiency is best recognizable once

diagnosed with all operational variables properly & in a modeling
Multiple Regression Analysis (Modeling)
way.
COD & TN as dependent & 30 STP operation
The regressing model or method is suitable for energy proficient
variables as independent variable.
treatment plant or where energy consumption is high.
Literature Review 9
 Literature Review 9

Applying the Wastewater Quality Index for Assessing the Effluent

Quality of Recently Upgraded Meet Abo El-koum Wastewater
Treatment Plant
Mohamed Ayoub1*, Ahmed El-Morsy2
Journal of Ecological Engineering 2021, 22(2), 128–133
https://doi.org/10.12911/22998993/130893
ISSN 2299-8993

CORE AIM:
suitability of the effluent quality from Meet Abo El-koum wastewater treatment plant in Egypt for safe disposal based
on the wastewater quality index approach

METHODOLOGY: Multiple linear regression analyses have been adopted to predict

the performance of WWQI

FINDINGS:

The experimental results showed that the model performed can be used to predict WWQI for each
WWTP individually and provide better achievements.
Literature Review 10
 Literature Review 10

Investigation of the Wastewater Treatment Plant

Processes Efficiency Using Statistical Tools
Dariusz Mły ´nski 1
, Anna Mły ´nska 2,*, Krzysztof Chmielowski 1 and Jan Pawełek 1

CORE AIM:
presents modelling of wastewater treatment plant (WWTP) operation work
efficiency using a two-stage method based on selected probability distributions and the Monte Carlo
method.
METHODOLOGY:The compatibility of theoretical and empirical distributions
was assessed using the Anderson–Darling
test. The best-fitted statistical distributions were selected using Akaike
criterion. Performed calculations
made it possible to state that out of all proposed methods, the Gaussian
mixture model (GMM) for
distribution proved to be the best-fitted.

FINDINGS:

Obtained simulation results proved that the statistical tools

used in this paper describe the changes of pollutant indicators correctly. The calculations allowed
us to state that the proposed calculation method can be an effective tool for predicting the course of
subsequent sewage treatment stages. Modelling results can be used to make a reliable assessment of
sewage susceptibility to biodegradability expressed by the BOD5/COD, BOD5/TN and BOD5/TP ratios.
Literature Review 11
 Global sensitivity analysis in Literature Review 11
wastewater treatment plant
model applications: Prioritizing
sources of uncertainty
Gu
¨ rkan Sin a,
*, Krist V. Gernaey b
, Marc B. Neumann c,d
, Mark C.M. van Loosdrecht e
,
Willi Gujer

CORE AIM:
To explain uncertainties in predicted plant performance

METHODOLOGY:

Global sensitivity analysis was performed by linear

regression on MonteeCarlo simulation model output

FINDINGS:

Sensitivity analysis can only be interpreted within the context of the analysis

Both the process knowledge and global sensitivity analysis

methods identify correctly the most significant parameters
driving the plant performance.
Study Area
Loaction: Kolkata City, West Bengal

19 STP Locations across Kolkata,

West Bengal has been

considered in the study

 - A Brief

Sewage Generation & Treatment

Capacity (MLD) - West Bengal

(Source: CPCB, March 2021)
Kolkata - Fighting over urbane
Kolkata - Natural environment &

pollution
Objectives of the study
 Objectives of the study

To understand the sanitation facility scenario by sewerage at a given location.

To find out dependency level of population density witth treatment plant efficiency.

To incorporate sewerage facility to counteract groundwater contamination.

Methodology
Workflow of Methodology
 PATHWAY TO DATA STAISTIC TO MODELING

STP DATA (KOLKATA Data processing via

LOCATION) hypothesis

Secondary data analysis by using:

Chi-Square Hypothesis

Anova Hypothesis

Data to be statisticallly
Final Output distributed & justified
 Status of STPs of West Bengal

Disposal of Process of
Installed Designed Actual Cap.
Sl. STP Treated Sewage
Cap. MLD MLD
Sewage Treatment

1 Baidyabati 6 6 River Ganga OP INPUT: GIVEN

2 Kannogar 22 NA OP

3
Chandan Nagar,
18 18 River Ganga OP RAW DATA
Khalisani

Irrigation
4 4.5 ASP
Titagarh &Fishery

Irrigation
5 4.5 OP
Titagarh &Fishery

6 Bandipur 14 14 Irrigation OP

7 Panihati 12 12 Irrigation OP

8 Serampore 19 19 River Ganga TF

9 Chakapara 30 30 River Ganga OP

10 Arupara 45 45 River Ganga TF

Location map of
11 Bansberia 0.3 0.3 WSP
study
12 Garden Reach 48 Trial Phase River Ganga ASP

13 Mahestala, Nungi 4 4 Not Known WSP

Aerated
14 8 8 River Ganga
Bhadreshwar Lagoon

15 Cossipore, Chitpur 45 Trial Phase River Ganga ASP

16 Naihati 12 River Ganga ASP

17 Kamarhati 40 40 River Ganga TF

Jagaddal, Bhatpara
18 10 10 River Ganga ASP
(New)

19 Nabadwip 10 2.5 Nothing OP

 TREATMENT EFFICIENCIES & POPULATION EQUIVALENT

RAW DATA CALCULATED DATA

Installed BOD mg/l COD mg/l Population

BOD Removal COD Removal
Sl. STP Designed Actual Cap. MLD Equivalent
Efficiency (%) Efficiency (%)
Cap. MLD Inlet Outlet Inlet Outlet (PE)

1 Baidyabati 6 6 14 1 59 20 92.86 66.10 1050

2 Kannogar 22 21 12 82 43 42.86 47.56 5775

3 18 18 82 8 260 71 90.24 72.69 18450

Chandan Nagar, Khalisani

4 Titagarh 4.5 110 58 216 130 47.27 39.81 6188

5 Titagarh 4.5 110 67 216 146 39.09 32.41 6188

6 Bandipur 14 14 14 5 47 35 64.29 25.53 2450

7 Panihati 12 12 23 8 126 55 65.22 56.35 3450

8 Serampore 19 19 51 15 137 59 70.59 56.93 12113

9 Chakapara 30 30 56 11 312 55 80.36 82.37 21000

10 Arupara 45 45 110 27 549 67 75.45 87.80 61875

11 Bansberia 0.3 0.3 17 16 59 51 5.88 13.56 64

12 Garden Reach 48 Trial Phase 13 8 51 7 38.46 86.27 7800

13 Mahestala, Nungi 4 4 13 2 51 23 84.62 54.90 650

14 Bhadreshwar 8 8 103 4 335 39 96.12 88.36 10300

15 Cossipore, Chitpur 45 Trial Phase 7 7 148 45 100.00 69.59 3938

16 Naihati 12 55 8 125 39 85.45 68.80 8250

17 Kamarhati 40 40 66 6 250 41 90.91 83.60 33000

18 10 10 126 66 392 165 47.62 57.91 15750

Jagaddal, Bhatpara (New)

19 Nabadwip 10 2.5 88 8 232 43 90.91 81.47 11000

 Variability profile of Efficiency Vs. Influent BOD

100

80
)lavomeR DOB f o( .ffE t nemtaerT %

60

40

20
Exponential(Series1)

Polynomial(Series1)
0
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Influent BOD
 Variability profile of Efficiency Vs. Influent COD

120

100
)lavome R DOC fo( .ffE tnemtaerT %

80

60

40

20
Exponential(Series1)

Polynomial(Series1)

0
0 50 100 150 200 250 300 350 400 450 500 550 600

Influent COD
 Variability profile of Treatment Efficiency (BOD) vs. PE

150
)l avo meR DOB fo( .ffE t ne mt aerT %

120

90

60

30

Exponential(Series1)

0 Polynomial(Series1)
0 10000 20000 30000 40000 50000 60000 70000

Population Equivalent
 Variability profile of Treatment Efficiency (COD) vs. PE

100

80
)l avo meR DOC fo( .ffE t ne mt aerT %

60

40

20

Exponential(Series1)

0 Polynomial(Series1)
0 10000 20000 30000 40000 50000 60000 70000

Population Equivalent
Statistical Testing By CHI-SQUARE

(Data Distribution & Justification)

 Analysis by Chi-Square Distribution

Combined
Given Expected Total CHI-SQUARE TABULATION
ITERATIONS % Decrease Proportion
(Ao) (Ae) Sampled
for Ao for Ae Ao Ae Ao-Ae (Ao-Ae)^2 (Ao-Ae)^2/Ae

40 47.27 87.27 40.00 47.27 -7.27 52.80 1.12

50 59.08 109.08 50.00 59.08 -9.08 82.50 1.40

ITERATION 1 1.18 45.84 54.16
60 70.90 130.90 60.00 70.90 -10.90 118.79 1.68

70 82.72 152.72 70.00 82.72 -12.72 161.69 1.95

SUM 220 259.96 479.96 45.84 54.16 -8.33 69.33 1.28

Chi-Square Value (Total Sum) For Iteration 1 (for 5% C.L) 7.42

Ao &Ae: With respect to BOD (Influent) mg/l or Treatment Plant Capacity (%)

Chi-Square (Standard) for an Accuracy Limit of

Confidence
Chi-Square Value (Standard) Value
Limit (C.L)
-5% 5% -10% 10%

Chi-Square value (standard) at

7.815 5% C.L 7.42 8.21 7.03 8.60
given C.L of 5%

Chi-Square value (standard) at

11.345 1% C.L 10.78 11.91 10.21 12.48
given C.L of 1%
 Some Iterations
Combined
Expected Total CHI-SQUARE TABULATION
ITERATIONS Given (Ao) % Decrease Proportion
(Ae) Sampled
for Ao for Ae Ao Ae Ao-Ae (Ao-Ae)^2 (Ao-Ae)^2/Ae

40 47.27 87.27 40.00 47.27 -7.27 52.80 1.12

50 59.08 109.08 50.00 59.08 -9.08 82.50 1.40

ITERATION 1 1.18 45.84 54.16
60 70.90 130.90 60.00 70.90 -10.90 118.79 1.68

70 82.72 152.72 70.00 82.72 -12.72 161.69 1.95

SUM 220 259.96 479.96 45.84 54.16 -8.33 69.33 1.28

Chi-Square Value (Total Sum) For Iteration 1 (for 5% C.L) 7.42

40 47.76 87.76 40.00 47.76 -7.76 60.16 1.26

50 59.70 109.70 50.00 59.70 -9.70 94.00 1.57

ITERATION 2 1.19 45.58 53.86
60 71.63 131.63 60.00 71.63 -11.63 135.35 1.89

70 83.57 153.57 70.00 83.57 -13.57 184.23 2.20

SUM 220 259.96 482.66 45.58 53.86 -8.28 68.56 1.27

Chi-Square Value (Total Sum) For Iteration 2 (for 5% C.L) 8.20

40 47.06 87.06 40.00 47.06 -7.06 49.78 1.06

50 58.82 108.82 50.00 58.82 -8.82 77.78 1.32

ITERATION 3 1.18 45.95 54.05
60 70.58 130.58 60.00 70.58 -10.58 112.00 1.59

70 82.35 152.35 70.00 82.35 -12.35 152.44 1.85

SUM 220 258.80 478.80 45.95 54.05 -8.10 65.68 1.22

Chi-Square Value (Total Sum) For Iteration 3 (for 5% C.L) 7.03

40 47.88 87.88 40.00 47.88 -7.88 62.03 1.30

50 59.84 109.84 50.00 59.84 -9.84 96.91 1.62

ITERATION 4 1.20 45.52 54.48
60 71.81 131.81 60.00 71.81 -11.81 139.56 1.94

70 83.78 153.78 70.00 83.78 -13.78 189.95 2.27

SUM 220 263.32 483.32 45.52 54.48 -8.96 80.32 1.47

Chi-Square Value (Total Sum) For Iteration 4 (for 5% C.L) 8.60

 Determination of "Design" PE (By Chi-Square)

Population Equivalent (PE) per MLD of flow

ITERATIONS Given (Ao) Expected (Ae) Change of PE

Based on Ao (Present) Based on Ae (Future)

40 47.27 500 591 91

50 59.08 625 739 114

ITERATION 1
60 70.90 750 886 136

70 82.72 875 1034 159

SUM 220 259.96 2750 3250 500

40 47.76 500 597 97

50 59.70 625 746 121

ITERATION 2
60 71.63 750 895 145

70 83.57 875 1045 170

SUM 220 259.96 2750 3250 500

40 47.06 500 588 88

50 58.82 625 735 110

ITERATION 3
60 70.58 750 882 132

70 82.35 875 1029 154

SUM 220 258.80 2750 3235 485

40 47.88 500 598 98

50 59.84 625 748 123

ITERATION 4
60 71.81 750 898 148

70 83.78 875 1047 172

SUM 220 263.32 2750 3291 541

 Statistical Testing By ANOVA

(Data Distribution & Justification)

 Observation data set for Anova

Observation value
Observation Set of efficiency or Mean of efficiency
BOD or COD
30
40
Set 1 50 40
60 50
70 60

Efficiency: Treatment Efficiency

 DETERMINATION OF BETWEEN COLUMN VARIANCE

n=Number Grand Sample Mean - (Sample Mean - n*(Sample Mean -

Level of Sample
Sl. of sample Sample Grand Sample Grand Sample Grand Sample
Efficiency Mean
size Mean Mean Mean)^2 Mean)^2

1 E1 40 3 50 -10 100 300

2 E2 50 3 50 0 0 0

3 E3 60 3 50 10 100 300

Sum 150 9 600

Variance between samples (=First Population Variance) = Sum/(k-1) 300

 DETERMINATION OF WITHIN COLUMN VARIANCE

(For Efficiency Level: E1)

Level of Efficiency: E1

Sl. Sample Variance

Sample
Sample (Sample Observation- (=Sum[(Observation
Sample Mean Observation-
Observation Sample Mean)^2 Mean - Sample
Sample Mean
Mean)^2]/(n-1))

1 30 40 -10 100 50

2 40 40 0 0 0

3 50 40 10 100 50
 DETERMINATION OF WITHIN COLUMN VARIANCE

(For Efficiency Level: E2)

Level of Efficiency: E2

Sl.
Sample Sample Variance
Sample Sample (Sample Observation-
Observation- (=Sum[(Observation Mean -
Observation Mean Sample Mean)^2
Sample Mean Sample Mean)^2]/(n-1))

1 40 50 -10 100 50

2 50 50 0 0 0

3 60 50 10 100 50
 DETERMINATION OF WITHIN COLUMN VARIANCE

(For Efficiency Level: E3)

Level of Efficiency: E3

Sl.
Sample Variance
Sample Sample Sample Observation- (Sample Observation-
(=Sum[(Observation Mean -
Observation Mean Sample Mean Sample Mean)^2
Sample Mean)^2]/(n-1))

1 50 60 -10 100 50

2 60 60 0 0 0

3 70 60 10 100 50
 F- value statistic by Anova

Value for Value for Value for

Determination of kinetics, the variance & F-value statistic efficiency efficiency efficiency level
level E1 level E2 E3

Weighted Sample Variance=WSV= Sample Variance * Proponent ; where,

33.33 33.33 33.33
Proponent=(n-1)/(N-k). For the problem, the proponent=(3-1)/(9-3)=2/6=1/3

Within Column Variance=Sum (=Second Population Variance) of WSVs 100

F value=Between Column Variance/Within Column Variance 3.00

 Anova F-statistic at different accuracy limits

Anova F-Statistic (Standard) for an Accuracy Limit of

Anova F-Value Confidence
Value
Statistic (Standard) Limit (C.L)
-5% 5% -10% 10%

Col.(1) Col.(2) Col.(3) Col.(4) Col.(5) Col.(5) Col.(6)

Anova F-value
statistic (standard) 5.14 5% C.L 4.88 5.40 4.63 5.65
at given C.L of 5%

Anova F-value
statistic (standard) 10.9 1% C.L 10.36 11.45 9.81 11.99
at given C.L of 5%
 Determination of

"Design" PE

(By Anova)

Observation value of Design BOD (mg/l), after adjusting

Design PE per Change of
BOD (mg/l), given with F-statistic (standard), PE per MLD of flow
MLD of flow PE
(assumed) assumed

30 35.4 375 443 68

40 47.2 500 590 90

50 59 625 738 113

60 70.8 750 885 135

70 82.6 875 1033 158

considering per head BOD generation per day=80gram.

Results &
Discussion
 Discussion

PE an approximate evakuation of Population Density.

Socio-demography barriers to sanitation facilities like STP, OSS, etc.

Chi-Square statistic:

Directly designing available; flexible to use; useful for less capacity of STP.

Anova statistic:

Indirectly facilitative designing; quite more incorporative to use; useful for any suitable capacity of STP.

Statistically repaired designing - balanced & competitive possibility to cutting edge outputs.

Better with a combination with OSS.

 Discussion (contd...)

Limiting use of OSS: - significant scope to STP additions

There are various Issues for which OSS might be in avoidance to or amongst the people in need. These include -

1. Accessibility: space crunch problems to construction; social belief systems or acceptance (cultural & social barriers) to go defecationthan ising toilet;
inability to value recognitions (Source: Swachhta Status Report - NSSO).

2. Collection & conveyance of septage: this includes -

Illegal Manual scavenging

No / Limited access to tanks

Inappropriate tank sizing & design

Lack of infrastructure, and a regulated schedule for cleaning

Lack of formal private players

3. Traditional monotone design of use by aesthetic, sobreness, etc.

4. Treatment and Disposal

5. Poor Awareness

6. Lack of an Integrated City-wide Approach

7. Limited Technology Choices

8. Gender Sensitive Gap

9. Fragmented Institutional Roles and Responsibilities

Conclusion
 Conclusions

More data, more precisive the modelling.

Other "might-be" potential attributes like bacteriological property, etc.

Suitable effectiveness possible - with respect to standard statistic.

A well distributed set of data is more prone to future sustainability to work with.

The present study confirms inter-connective assertainment between rational & statistical hypothesis. It indicates
that a practical prototype system can be regulated as responsive one with a hypothetical statistic.

The statistical output or treatment efficiency of STP would better function once with an accuracy limit of 5% or 10%
as such.

Which statistical hypothesis is better must be judged for with respect to several practical conditions of STP
management & system. It includes capacity, design period, treatment technologies, etc.

The study explains the PE (design PE) to be variable with plant capacity, plant efficiency & desludging of septic
tanks which may be considered for future work of the study.

Once again, all future efforts like SDG, SBM, Cliamte change conventions, etc. can be applied to, once behaviour of
STP is built-up with a statistical output.
 FUTURE SCOPES OF THE STUDY

Physical interpretation feasibility.

Technological accessibility to make physicality advancing, competitive &

long-lasting.

Correctly forecasting ability to several "smart" urbane locations.

Accurately advance accountability to input & output.

Vast research scope for AI & ML.

More better an integration of location be, more solidifying be the data

congruence & designing sustainability.

REFERENCES
REFERENCES (Contd...)
REFERENCES (Contd...)
REFERENCES (Contd...)
REFERENCES (Contd...)
REFERENCES (Contd...)
REFERENCES (Contd...)
REFERENCES (Contd...)
REFERENCES (Contd...)
REFERENCES
THANK YOU