ISEIS

Journal of Environmental Informatics Letters 6(1) 10-22 (2021)

www.iseis.org/jeil

Spatio-Temporal Variation Characteristics of Water Quality and Its Response to

Climate: A Case Study in Yihe River Basin
J. Y. Ren1, 2, 3, H. M. Liu2, 3, 4, S. Y. Ding2, 3 *, and Z. H. Cao2, 3, 5
1
State Key Joint Laboratory of Environmental Simulation and Pollution Control, China-Canada Center for Energy, Environment and Ecology
Research, UR-BNU, School of Environment, Beijing Normal University, Beijing 100875, China.
2
Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng 475004, China.
3
College of Geography and Environmental Science, Henan University, Kaifeng 475004, China.
4
Civil and Economic Law College, Henan University of Economics and Law, Zhengzhou 450000, China.
5
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.

Received 17 May 2021; revised 21 June 2021; accepted 29 July 2021; published online 16 August 2021

ABSTRACT. Spatial-temporal patterns of river water quality, the identification of pollution sources and contaminated areas are crucial
to water environment protection and sustainable development of the river basin. In this study, spatial-temporal characteristics of river
water quality in the Yihe river basin were investigated through multivariate analysis methods, including principal component analysis
(PCA), cluster analysis (CA), discriminant analysis (DA), and one-way ANOVA. The water quality indicators (Hydrogen ion concentra-
tion (pH), electric conductivity (EC), dissolved oxygen (DO), turbidity, chemical oxygen demand (COD), total phosphorus (TP), and
ammonia nitrogen (NH4+-N)) were investigated at 17 sampling sites in three periods (i.e., high-, mean-, low flow period) during 2016 ~
2017. The results show that: (1) PCA served to extract and recognize the most significant indicators affecting water quality in the Yihe
river basin, i.e., pH, EC, COD, and NH4+-N. (2) CA divided the Yihe river basin into three groups with similar water quality features,
namely the upper, middle, and lower reaches. (3) DA demonstrated strong dimensionality reduction ability with the accuracy of clustering
was 94.1%, and only a few indicators (i.e., DO, EC, turbidity, NH4+-N, and TP) could reflect the spatial variations in water quality. (4)
One-way ANOVA indicated that the water quality was the worst in the lower reach of Yihe river basin during the mean-flow period, fol-
lowed by which in the upper and middle reaches during the high-flow period. (5) The spatiotemporal characteristics of water quality were
mainly restrained by human factors (e.g., the construction of highway and agricultural activities), climate change (e.g., precipitation and
temperature), and natural environments (e.g., topography).

Keywords: river water quality, spatial-temporal characteristics, multivariate statistical analysis, Yihe River Basin, climate change

1. Introduction nerable water body to contain pollution from agricultural runoff,

domestic and industrial sewage (Singh et al., 2005; Varol et al.,
Rivers are the most important water bodies on land (Oki 2012; Wang et al., 2013; Liu et al., 2019; Chaudhuri et al., 2020)
and Kanae, 2006; Vörösmarty et al., 2010), and most ancient under the multiple effects of natural processes (e.g., climate change,
civilizations in the world are originated in large river basins soil erosion, and weathering of bedrock) (Vega et al., 1998; Oke-
(Mir and Gani, 2019). Excellent water quality and adequate wa- tola et al., 2013; Shrestha and Wang, 2020), human activities
ter quantity are critical to human health (Pejman et al., 2009; (e.g., agricultural, urban and industrial activities) (Vitousek et
Zhang et al., 2012) and the sustainable development of the eco- al., 1997; Paul and Meyer, 2001; Sickman et al., 2007; Zhang
nomy in the river basin (Ravindra et al., 2003; Wu et al., 2018). et al., 2018; Yu et al., 2020), and increasing water demand
Humans have made the best use of water resources through in- (Vörösmarty et al., 2010). With the increasing degradation of
dustrialization, irrigation, and engineering measures (e.g., dams, river water quality, including eutrophication, organic matter,
reservoirs and water transfer across river basins) (Oki and Kanae, and heavy metal pollution (Vitousek et al., 1997; Carpenter et
2006; Wang et al., 2016). However, the demand for high-qual- al., 1998), its disastrous impacts on drinking water safety, indus-
ity water resources is growing consistently, nearly 80% of the trial and agricultural production, and aquatic biodiversity have
world’s population still faces serious water security threats (Oki attracted wide attention (Carpenter et al., 1998; Wang et al.,
and Kanae, 2006; Vörösmarty et al., 2010). River is the most vul- 2017). Consequently, it is particularly important to control river
pollution and prevent the degradation of river water quality.
*
Corresponding author. Tel.: 0371-23881850; Fax: 0371-23881850. Moreover, previous study revealed that water quality is appar-
E-mail address: syding@henu.edu.cn (S. Y. Ding). ently spacetime dependence, thus it is necessary to monitor and
ISSN: 2663-6859 print/2663-6867 online evaluate the river water quality in the river basin in different
© 2021 ISEIS All rights reserved. doi:10.3808/jeil.202100070. periods (Strobl et al., 2008; Pérez et al., 2017; Sinha et al.,

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 J. Y. Ren et al. / Journal of Environmental Informatics Letters 6(1) 10-22 (2021)

2019). Climate change also impacts water quality directly or water periods (i.e., high-, mean-, low flow period) from 2016
indirectly, such as associated changes in precipitation patterns to 2017; (2) analyzing the spatiotemporal characteristics of wa-
(i.e., precipitation amount, intensity, and frequency) affect the ter quality through four multiple statistical methods (i.e., PCA,
mount of nitrogen transported to downstream water bodies CA, DA and ANOVA); (3) investigating the potential factors
(Sinha and Michalak, 2016; Sinha et al., 2019). To help the provin- that effecting the water quality of the Yihe River Basin (such
cial government protect the river water environment and control as human activities, climate change and natural environment).
river pollution, it is very crucial to conduct a thorough exami- The results are expected to provide a scientific basis for water
nation of the spatio-temporal characteristics of water quality re-source management, utilization, and ecological environment
and the associated influencing factors based on monitoring data. pro-tection in the Yihe River Basin.
Previously, a number of efforts about water quality in river
basins have been made in many countries and regions, through
which the spatiotemporal similarities and differences of varies
water quality indicators were analyzed at different sampling
points over multiple periods (Vitousek et al., 1997; Ravindra et
al., 2003; Bellos and Sawidis, 2005; Singh et al., 2005; Kan-
nel et al., 2007; Sickman et al., 2007; Varol et al., 2012; Wang
et al., 2013; Putro et al., 2016; Rigi et al., 2019). Multivariate
statistical analysis methods are reliable tools for water quality
assessment and water pollution problem treatment (Singh et al.,
2004, 2005; Igibah and Tanko, 2019). Due to their excellent
performance in reducing the data dimension, extracting poten-
tial information, and verifying the spatial and temporal changes
in water quality, multivariate statistical tools such as principal
component analysis (PCA), factor analysis (FA), cluster analysis
(CA), discriminant analysis (DA) and one-way analysis of
variance (ANOVA) have been widely used to handle the massive
and complex water quality data that generated through the water
environment monitoring projects. Numbers studies were devot-
ed to identify the spatiotemporal variation of river water quality
(Simeonov et al., 2003; Singh et al., 2005; Sundaray et al., 2006;
Shrestha and Kazama, 2007; Varol et al., 2012; Li et al., 2017;
Zhu et al., 2018; Mir and Gani, 2019; Sun et al., 2019) and the
water environment assessment of groundwater (Andrade et al.,
2008; Zhang et al., 2012), coastal water body (Zhou et al., 2007),
and lakes (Pejman et al., 2009; Wu et al., 2018).
The Yiluo River is the combined name of Yihe River and
Luohe River, which is the largest tributary below the Yellow
River Sanmenxia Reservoir. The water quality directly affects
the sustainable development of economy and society of the Yi-
he River Basin. However, in most of the previous studies, several
related studies in Yiluo River Basin were mainly focused on
heavy metal pollution (Yan et al., 2016), relationships between
aquatic organisms and water environment factors (Lin et al., Figure 1. Location of sampling sites and sub-basins in
2019), and effects of landscape spatial heterogeneity on water Yihe River basin.
quality (Yu et al., 2014; Liu et al., 2019). There has no report
in studying the spatiotemporal characteristics of water quality
in typical small watersheds in the Yihe River Basin. Furthermore,
2. Overview of the Study Area
only a handful studies have surveyed the combined effects of The Yihe River Basin is one of the major regions in the
land use variation and climate change on water quality, and these Yel-low River Basin over China (33°39′ ~ 34°41′N, 111°19′ ~
have only been conducted in individual river basins (Tu, 2009). 112°54′E) (Figure 1). The extent of the Yihe River Basin and
Therefore, as the extension of previous studies, the objec- 17 typical sub-basins were extracted through the Arc SWAT
tive of this study is to analyze the spatiotemporal characteris- hydrological analysis module (Ren, 2018). These extracted ba-
tics of water quality in the Yihe River Basin through multiple sins were based on the digital elevation model (DEM) data with
statistical methods, as well as the potential causes of pollution. a precision of 30 m (http://henu.geodata.cn), river network and
In detail, (1) measuring the water quality data targeting on six terrain features (e.g., slope and altitude). Yihe River Basin is a
indicators (i.e., pH, EC, DO, COD, TP and NH4+-N) that moni- transitional section of China’s topography ladder from the se-
tored in 17 typical sub-basins in Yihe River Basin during three cond step to the third one, with an elevation from 109 to 2159 m,

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Table 1. Statistical Description of Water Quality Indicators in the Yihe River Basin
Low-Flow Period Mean-Flow Period High-Flow Period
Max Min Mean Std. Max Min Mean Std. Max Min Mean Std. Units
pH 8.55 7.14 7.89 0.31 8.45 7.00 8.00 0.40 8.50 6.14 7.88 0.57 —
EC 840.50 209.50 577.32 154.44 916.00 354.50 604.82 160.92 816.00 383.00 599.51 132.04 μs•cm-1
DO 13.38 4.43 9.51 2.09 12.44 7.36 9.26 1.33 11.96 4.42 8.35 1.72 mg• L-1
Turbidity 28.35 0.42 13.17 9.57 20.00 0.77 13.69 6.88 19.91 3.33 10.36 5.15 NTU
COD 71.47 7.91 37.46 19.78 33.50 2.70 18.68 9.81 25.75 4.45 12.56 5.92 mg• L-1
NH4+-N 4.72 0.11 0.73 1.06 19.72 0.55 4.69 4.87 3.09 0.06 0.41 0.74 mg• L-1
TP 0.15 0.00 0.03 0.03 0.10 0.01 0.03 0.03 0.14 0.00 0.02 0.03 mg• L-1

Table 2. Single Sample K-S Inspection

pH EC DO TUR COD NH4+-N TP
a,b
Normal Indicators Mean 7.92 596.54 9.16 12.41 22.90 1.94 0.02
Standard deviation 0.35 111.81 1.15 5.27 8.40 1.89 0.03
Most Extreme Difference Absolute value 0.26 0.17 0.19 0.15 0.13 0.28 0.28
Positive 0.10 0.17 0.19 0.11 0.13 0.28 0.25
Negative -0.26 -0.11 -0.09 -0.15 -0.11 -0.21 -0.28
Kolmogorov-Smirnov Z 1.06 0.70 0.77 0.60 0.52 1.17 1.15
progressive significance (both sides) 0.22 0.71 0.59 0.86 0.95 0.13 0.14
a. The test distribution is normally distributed; b. Calculated based on data.

Table 3. Principal Component Loading Matrix of Low-, Mean-, and High-Flow in Yihe River Basin
Indicators Low-Flow Period Mean-Flow Period High-Flow Period
PC1 PC2 PC3 PC1 PC2 PC1 PC2 PC3
pH -0.803 0.179 0.164 0.894 0.060 0.939 -0.051 -0.006
EC 0.849 0.304 -0.046 0.379 0.707 0.840 -0.084 0.238
DO -0.458 -0.078 0.393 0.142 0.633 -0.123 -0.030 0.864
Turbidity 0.048 0.262 0.836 0.320 -0.696 0.939 -0.070 -0.188
COD 0.022 0.897 0.170 0.322 0.755 0.078 0.889 0.010
NH4+-N 0.731 0.045 0.099 0.857 0.235 -0.253 0.806 -0.123
TP 0.163 0.608 -0.654 0.741 0.113 0.493 -0.122 0.563
Eigenvalue 2.363 1.327 1.173 2.989 1.492 2.947 1.426 1.076
Variance contribution rate (%) 30.569 19.644 19.266 35.042 28.970 40.137 20.989 16.710
Accumulating contribution rate (%) 30.569 50.213 69.479 35.042 64.012 40.137 61.126 77.837

with an area of 5.54 ×103 km2. The mountainous area accounts 2.1. Water Quality and Climate Change Data
for 53.4%, the hilly area accounts for 35%, and plains and the Given the operability of sampling and the representative-
river valley account for 11.6% (Ren, 2018). Therefore, the main ness of the output of sampling points in the sub-basin, the exits
landscape of the Yihe River Basin is the mountain landscape.
of these 17 sub-basins are exactly the planned sampling sites.
Yihe River originates from Menton Ridge, Sanhe Village, Tao-
A total of six times samples were taken from December 2016
wan Town, Luanchuan County, south of Xiong’er Mountain,
to August 2017 according to the hydrological regularity and the
and locates in Luoyang City, Henan Province, China. The river
characteristics of multi-year precipitation in the Yihe River Ba-
flows from the southwest to the northeast, and through Song
sin: Twice during the low-flow period (December 2016 to Jan-
Country, Yichuan country, and Luoyang City from the source,
and finally injects into Luohe River in Yang Village, Yanshi uary 2017), twice during the mean-flow period (April to May
Country. The length of the main river is 2.68 ×102 km, and the 2017), and twice during high-flow period (July to August 2017).
annual average run-off volume is 1.27 ×109 m3. Yihe River Ba- These samples were taken once a month. Following previous
sin locates in a temperate continental monsoon climate zone, studies (GonzalesInca et al., 2015; Li et al., 2017; Zhang et al.,
which have cold-dry winters and hot-humid summers. The an- 2018) and the main landuse types, seven river water quality
nual average temperature varies from 12.4 °C (southwest) to indicators were selected in the Yihe River Basin, including pH,
15.2 °C (northeast) (Ren et al., 2017). The annual average pre- EC, DO, turbidity, COD, NH4+-N, and TP, Table 1 presents their
cipitation ranges 700 (northeast) to 900 mm (southwestern), specific statistical descriptions. Water samples at each sampling
approximately 50% of which is concentrated in July to Septem- point in the sub-basin were collected in a pretreated polyethy-
ber. Rainstorms were often occurred during summer, which lene bottle and stored in a sampling box at 4 °C. The values of
would trigger disasters such as floods and mudslides (Liu et al., pH, EC and DO were measured on-site with the SX713 por-
2019). Cinnamon soil, brown soil, adamic earth and skeletal soil table measuring instrument (Runsun Instruments Inc., Chengdu,
are the main soil types of Yihe River Basin. China). Turbidity was measured on-site using the portable GZ-

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20B turbidimeter (Fenglin Technology Inc., Shanghai, China). River Basin. Multivariate statistical analysis requires that the
The location of the sampling point and the peripheral environ- pollution indicators have a normal or near-normal distribution
ment were also recorded at the same time. The residual three (Sun et al., 2013; Mavukkandy et al., 2014). The single-sample
indicators (i.e., COD, NH4+-N and TP) were examined through Kolmogorov-Smirnov test method (Liu et al., 2003; Rigi et al.,
the 5B-6 (C) triple-parameter measuring instrument (Lianhua 2019) was used to analyze the distribution characteristics of
Technology Inc., Beijing, China) as soon as possible after all pollutant concentrations in the water samples. When α = 0.05
water samples have been brought back to the laboratory within and α = 0.01, the cutoff values of Kolmogorov-Smirnov (K-S)
24 hours. test statistic Z (Kolmogorov-Smirnov Z) were 1.36 and 1.63,
respectively. When the bilateral asymptotic significance proba-
(a) Cluster dendrogram based on hierarchical cluster analysis bility Sig < α, reject the null hypothesis, otherwise accept.
The PCA performance well on the dimensionality reduc-
tion of complex data. Several comprehensive factors that re-
flect the majority of original data could be examined through
linear transformation. It also has a significant ability in identi-
fying the types and sources of major pollutants in different
study periods (Vega et al., 1998; Simeonov et al., 2003). There
are six key steps: normalizing the original data, constructing
the correlation coefficient matrix, acquiring the eigenvalues
and eigen-vectors, identifying the principal component contri-
bution rate and cumulative contribution ratio, calculating the
(b) Spatial distribution map of cluster groups in Yihe River Basin principal component load, and examining the score of each prin-
cipal component (Ma et al., 2015).
CA is an unsupervised pattern recognition technology, which
can quantitatively determine the kinship relationship between
a batch of samples without prior assumptions (Vega et al., 1998;
Varol et al., 2012). Hierarchical cluster analysis (HCA) is one
of the most general CA to classify water quality indicators into
cluster groups according to their similarity or nearness (Igibah
and Tanko, 2019). HCA has two forms, there are Q-type clus-
tering (i.e., classifying samples) and R-type clustering (i.e., clas-
sifying observed variables of the research object). In this study,
Q-type clustering method was used to measure the distance be-
tween samples and generate the clustering tree diagram. The
method is based on the squared Euclidean distance and the Ward
algorithm (Strobl et al., 2008).
DA can be used to distinguish cluster analysis results and
determine primary pollution indicators. There are three main
types: standard, forward and backward. Among them, the back-
ward discriminant analysis method has a better ability to reduce
Figure 2. Cluster dendrogram of the sampling sites based on the dimension and discriminate of the indicators. As a result,
water quality indicators. the backward discriminant analysis method was used to ana-
lyze the spatial difference of water quality in the Yihe River Basin,
and the cross-validation method was chosen to test its discrim-
From our previous studies (Liu et al., 2017), climate change
inative ability.
had close direct or indirect associations with the investigated
water quality. This study focused on analyzing the spatio-tem- ANOVA is a very useful tool for analyzing datasets, which
poral associations between river water quality and potential in- can be classified into two categories one-way and multiway
fluencing factors of water pollution, such as climate changes ANOVA. The controlling factors of water quality changes in
(i.e., precipitation and temperature) in Yihe River Basin. To do the Yihe River Basin are mainly time and space. A one-way anal-
this, the observed monthly precipitation and temperature data ysis of variance can be performed. The significance test formula
at four weather stations (i.e., Luanchuan Country, Song Country, as follows:
Yichuan Country, and Yanshi city) from 1998 to 2017 were col-
lected in the Statistical Yearbook (Luoyang Statistical Yearbook). SSA /  k  1
F (1)
SSE /  n  k 
2.2. Multivariate Statistical Analysis Methods
In this study, PCA, CA, DA and ANOVA were used to eval- where k is the number of levels, n is the number of samples, SSA
uate the spatiotemporal characteristics of water quality in Yihe is the sum of squared spreads between groups, and SSE is the

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sum of squared deviations within groups. The F statistic obeys weakly alkaline, which is mainly affected by external pollution
the F distribution with degrees of freedom (k – 1, n – k). When sources and aquatic biological activities, and the pH can control
the corresponding associated probability value is less than the the redox reaction of river water to a certain extent (Li et al.,
significance level α (usually 0.05), the null hypothesis is reject- 2017). The pH load value was negative, indicating that it was
ed, indicating that the population means have significant dif- lower during the low-flow period. The content of DO is gener-
ferences at different levels of the control variable. Otherwise, ally saturated in natural water, but it will decrease rapidly with
there are no significant differences. The above analysis was done the increase of biomass (Chang, 2005). When DO is less than
collaboratively using Microsoft Excel 2007 and SPSS 20.0. 2 mg·L-1, most fish cannot survive (Russo et al., 1981). The
water quality indicator that strongly related to the PC2 was
COD, while TP was generally weaker associated. COD char-
3. Results
acterizes the level of organic pollution in water bodies and is
3.1. Evaluation of Water Quality closely associated with wastewater discharge from urban, in-
dustrial, and agricultural activities (Bellos and Sawidis, 2005;
Table 2 shows the result of the sample K-S test for each
Li et al., 2017). TP related to the input of pesticides, industrial
water quality indicator. Progressive significance values (both
wastewater, and agricultural activities (Kannel et al., 2007).
sides) for each indicator are greater than 0.05, indicating that
The high values of COD and TP in the water would reduce the
overall river water quality indicators in 17 typical sub-basins
DO concentration and induce the deterioration of surface water
over the Yihe River Basin conforms to the normal distribution.
quality (Kannel et al., 2007; Zhang et al., 2012; Li et al., 2017).
Thus, the monitored river water quality data can be used for
Fang et al. (2013) have emphasized that agricultural non-point
multivariate statistical analysis.
source pollution was relatively small during the low-flow peri-
od compared to point source pollution. Therefore, during the
3.2. Temporal Variation Characteristics of River Water low-flow period, various pollutants that entered the Yihe River
Quality in Yihe River Basin have formed the organic pollution, such as domestic sewage
Temporal variation characteristics of seven river water qual- and industrial wastewater discharge. The decomposition pro-
ity indicators in 17 typical sub-basins were analyzed through the cess may consume a large amount of oxygen (the value of DO
PCA (Table 3). KMO and Bartlett Spheroid tests showed that the is negative, Table 3) and generate ammonia (higher NH4+-N val-
principal component analysis was effective (df = 21, P < 0.01). ue, Table 3), organic acids and carbon dioxide. The hydrolysis
The extracted principal components could reflect the basic of these acidic substances leaded to a decrease in pH, which
situation of the original data (Liu et al., 2003; Simeonov et al., was consistent with previous findings (Vega et al., 1998; Shrestha
2003). Three principal components (PCs) were extracted dur- and Kazama, 2007). Consequently, the PC1 and PC2 can be clas-
ing the low- and high-flow period, and two PCs were extracted sified as the combined effects of natural changes in the water
during the mean-flow period according to the principle (i.e., eigen- environment and human activities (e.g., domestic and industrial
value is greater than 1). Based on previous studies (Liu et al., wastewater discharge). Turbidity indicates the degree of resis-
2003; Hussain et al., 2021), the absolute value of PCs that above tance of suspended matter and colloids in water (such as soil,
0.70 in this study was used as a criterion for determining high- silt, and plankton) to light transmission (Wu et al., 2018). Tur-
load values (Table 3, Bold font). Then, the water quality varia- bidity has a strong positive correlation with PC3, which may
tions during different water periods in the Yihe River Basin can be related to a large number of sand mining operations in the
be explained and discussed as follows. The absolute values of basin during the low-flow period.
the PCs load of COD, NH4+-N, EC and pH were greater than When it comes to the mean-flow period, the variance con-
0.7 in the three periods (Table 3). It indicated that COD, NH4+- tribution rate of the PC1 was 35.04%, of which the larger factor
N, EC and pH were the most notable factors which affecting loads were mainly occupied by pH, NH4+-N and TP. River wa-
water quality throughout the study period in the Yihe River Basin. ter quality at sub-basin scale was largely affected by the dual
However, during low- and high-flow period, turbidity has a re- effects of natural processes and human activities (Oketola et al.,
latively higher PC load value. TP and DO have a strong correla- 2013; Zhang et al., 2018; Mir and Gani, 2019). For example,
tion with the PC load value during the mean- and high-flow pe- short-term surface runoff from rainfall will bring non-point
riod, respectively. source pollution and domestic sewage that generated through
During the low-flow period, the PC1 explained 30.57% of agricultural activities into rivers, causing nitrogen and phos-
the water quality variation, which was much larger than the phorus pollution in the water body. Field investigations have
contribution rates of the variance of PC2 (19.64%) and PC3 found that spring cultivation in the Yihe River Basin was car-
(19.27%). EC and NH4+-N showed a strong positive correlation ried out from March to April every year. The chemical fertile-
(correlation coefficient > 0.7) with the PC1, while pH and DO izers (e.g., phosphorus and organic fertilizers) that were used
have a negative correlation with the PC1. EC is a comprehen- in the cultivation of crops (e.g., peanuts, cotton, tobacco, and
sive indicator of the degree of ion activity in water and the eval- vegetables) would directly pollute rivers due to the erosion of
uation of ion quality (Zhang et al., 2012), which mainly reflects rainfall and runoff. In addition, the denitrification of patho-
the impact of human activities and geological sediments in the genic microorganisms such as E. coli carried in organic fertile-
water environment (Bellos and Sawidis, 2005; Yang et al., 2007; izers (e.g., livestock and poultry manure) and bacteria pro-
Xie et al., 2020). Generally, the value of pH in river water is duced in the process of returning straw to the field also have

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contributed to nitrogen and phosphorus pollution in water (Jia slightly alkaline, and the change range during the entire study
and Zhang, 2015; Li et al., 2018; Wang et al., 2020; Yao et al., period from 6.13 to 8.9, which was basically within the range
2020). The variance contribution rate of PC2 is 28.97%, which (6.5 ~ 9.0) which aquatic organisms can maintain their natural
is primarily related to EC and COD and can be classified as the processes (Chang et al., 2012). The lowest value of pH (6.14)
common impact of natural and human activities. was appeared in the high-flow period, which might becaused
In terms of the high-flow period, pH, turbidity and EC by the decomposition of organic matter in wastewater discharge.
were the main influence factors of the PC1. In the process of Seasonal changes in pH did not show much difference. The max-
high-intensity rainfall during the high-flow period, a large number imum values of DO and COD were appeared in the low-flow pe-
of terrestrial materials that produced by agricultural activities riod, while these of EC, turbidity and NH4+-N were distributed
and soil erosion will migrate into the river water body with rain- in the mean-flow period. The mean value of TP was higher in the
water and the surface runoff, polluting the surface water. Turbidity low-flow and the mean-flow period relative to these in the high-
represents pollution that induced by rainfall events during the flow period, which is consistent with previous research (Li et al.,
high-flow period, such as Erosion. The variance contribution 2018). These might be due to the decreased of precipitation and
rate of the PC1 was 40.14% (Table 3), which is much larger than runoff in these two periods, and the hydraulic erosion which af-
the variance contribution rates of PC2 (0.99%) and PC3 (16.71%), fected the concentration of TP in river water to a certain extent.
indicating that the river water quality during the high-flow peri-
od was greatly affected by the combined effects of non-point 3.3. Spatial Distribution Characteristics of River Water
source pollution and rainfall events. The load values of COD, Quality in Yihe River Basin
NH4+-N of the PC2 was increased obviously in the high-flow
period than the other two periods. During the high-flow period, 3.3.1. Spatial Cluster Analysis
not only the precipitation increased significantly, but also the Based on the monitored water quality indicators of the Yi-
farmers applied large amounts of fertilizer to crops, especially he River Basin from 2016 to 2017, hierarchical cluster analysis
corn. Nitrogen that cannot be effectively used by the plants would was performed on the 17 typical sub-basins. After recalibrated
finally enter river with rainfall-runoff or groundwater infiltra- the distance based on the cluster, the threshold 13 was selected
tion, resulting in an increasing of nitrogen content in river water to divide the sample points into three groups (Figure 2). The
bodies. Besides, the NH4+-N content that discharged into surface spatial similarity analysis of the sample points was operated to
water was also much higher, which usually came from the manure capture the spatial distribution characteristics of river water qual-
of free ranges livestock and poultry in rural areas, domestic and ity in the Yihe River Basin. The samples that included in each
industrial sewage. Thus, with the increase of water temperature group were as follows: Cluster 1 (i.e., C1) {4, 16, and 17}; Clus-
in summer, bacteria and other microorganisms in the river will ter 2 (i.e., C2) {9, 10, 11, 12, 13, 14, and 15}; Cluster 3 (i.e.,
increase nitrification, reflecting the degree of nitrogen and organic C3) {1, 2, 3, 5, 6, 7, and 8}.
pollution in water. The PC3 has a strong positive correlation According to the river network map, sub-basins division
with DO (Table 3). In addition, the average value of DO during map, field investigation, and sampling in the Yihe River Basin,
the high-flow period was as low as 8.35 (Table 1). This might it can be inferred that the water quality of the same cluster group
be due to the fact that the water body received large amounts was affected by similar pollution sources and natural backgrounds,
of untreated domestic, industrial wastewater, and other sources and thus has similar characteristics: in detail, samples 16 and
of pollution, resulting in higher concentrations of pollutants in 17 included in C1 were mainly distributed in the lower plain
the water and higher microbial activity. These biological pro- rivers. The rivers in this group (C1) flow through agriculture
cesses may further consume lots of oxygen in the water, such area, are close to the urban area, and are more scattered in space
as the metabolic activities of microorganisms and the decay of than the other two groups. In recent years, with the reduction
aquatic organisms. of cultivated land, the continuous increase of construction land
Water quality indicators that have a significant impact on (Ren et al., 2017), and the accumulation of pollution in the upper
river water quality in one period may have reduced influence and middle reaches of rivers, the water quality in C1 has been
in another period. In general, the main water quality indicators seriously polluted by agriculture, industry, and domestic discharge.
of the three water periods in the Yihe River Basin reflect the Affected by the domestic sewage and industrial wastewater of
dual influences of human activities and the natural environ- Luanchuan County and its tributaries, the water quality of sam-
ment characteristics on water quality: the main polluted factors ple 4 (C1) located in upstream was also poor. The C2 samples
in the low-flow period were organic pollution and the varia- mainly flows through farmlands and towns in the middle reach-
tions of natural environment, followed by nitrogen pollution. es of the Yihe river. Along with the increase in construction and
Nitrogen was the leading factor in river water pollution during forest land in C2, and the decrease of cultivated land and grass-
the mean-flow period, followed by organic and phosphorus pol- land (Ren et al., 2017), the water quality in this region was af-
lution. Organic pollution and nitrogen were the main factors af- fected by both urbanization and agricultural activities. The sam-
fecting river water quality in the high-flow period. At the same ple points of C3 were belong to the upper reaches of the forest
time, comparing the statistical description of the water quality area and located at the southern foot of the Xiong’er Mountain.
indicators of the Yihe River Basin during the three periods (i.e., There was less human disturbance in this area, so the river was
low-, mean- and high-flow period) (Table 1), it can be found clean. The water quality of the above three groups (i.e., C1, C2,
that: the average values of pH in the three water periods were and C3) was all subject to human interference to varying degrees.

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Table 4. Eigenvalues of the Spatial Discriminant Analysis

Typical Discriminant Function Eigenvalues Variance (%) Accumulation (%) Canonical Correlation Coefficient
1 22.817 93.4 93.4 0.979
2 1.606 6.6 100.0 0.785

Table 5. The Structure Matrix of the Characteristic Function of the Spatial Discriminant Analysis
Discriminant Function
Water Quality Indicators
1 2
TP 0.274* -0.172
DO 0.134* -0.074
NH4+-N 0.123* -0.094
COD (a) 0.061* 0.008
EC 0.248 0.525*
Turbidity 0.085 0.415*
pH (a) 0.037 -0.085*
Note: * represents the largest absolute correlation between a variable and a discriminant function; (a) indicates that the variable was not utilized in the analysis.

Table 6. Wilks’ λ, Chi-Sqr, and Test of the Spatial Discriminant Analysis

Function Test Wilks’ λ Chi-Sqr df Significant
1 to 2 0.016 45.410 14 0.000
2 0.384 10.535 6 0.104

3.3.2. Discriminant Analysis better ability to reduce the dimension of the indicators, but also
To identify the water quality indicators that induced sig- showed that they have significant variations between there clu-
nificant variations between groups, and to further verified the ster groups (i.e., C1, C2, and C3). Therefore, the above five wa-
results of the above-mentioned spatial cluster analysis, the spa- ter quality indicators and discriminant functions could be used
tial characteristics of the three groups of water quality indica- to characterize the spatial differences of water quality in the
tors were analyzed through DA (Tables 4, 5, and 6). The eigen- Yihe River Basin. The monitoring of such indicators needs to
values of the spatial discriminant analysis (Table 4) showed be strengthened in the future.
that the first discriminant function could explain almost all the
variables (93.4%). TP, DO, NH4+-N, and COD contribute higher Table 7. Validation Matrix of the Classification by Means of
to discriminant function 1 than other indicators (i.e., EC, turbidity Discriminatory Analysis
and pH). However, EC, turbidity, and pH contributed higher for Forecast Group
Primitive
the discriminant function 2 (Table 5). The value of Wilks’ λ and Group C1 C2 C3
chi-square coefficient were 0.016 ~ 0.384 and 10.535 ~ 45.41 C1 3 (100%) 0 0
(Table 6), respectively. The significance test values (0.000) of
C2 0 7 (100%) 0
two discriminant functions were both less than 0.01, indicating
C3 0 1 (14.3%) 6 (85.7%)
that the spatial clustering analysis was reliable; the Wilks’ λ
“C1” represents the sample points 4, 16, and 17; “C2” represents the sample
significance test value of discriminant function 2 is 0.104 > points 9, 10, 11, 12, 13, 14, and 15; “C3” represents the sample points 1, 2,
0.05, which also showed the validity of discriminant functions 3, 5, 6, 7, and 8.
of 1 and 2.
Table 7 exhibited the verification matrix of the discrimi- Table 8. Classification Function Coefficients of DA
nant analysis classification. The diagonal represented the num- Clustering Groups
ber and proportion of samples that were predicted correctly, 1 2 3
while the remainder denoted the number and proportion of sam-
DO 56.816 41.795 44.387
ples which were incorrectly predicted. The overall accuracy of
EC 1.591 1.392 1.358
the grouping results of the clustering analysis was 94.1%, indi-
Turbidity -7.758 -6.375 -6.782
cating that it was reasonable to explore the characteristics of the
NH4+_N 14.764 14.837 15.416
spatial variation of water quality based on the clustering groups.
TP 1552.138 260.377 471.095
Table 8 showed the classification function coefficients of (Constant) -2265.599 -1723.768 -1690.270
the linear discriminant function of Fisher. Based on Tables 4
and 8, it can be inferred that the construction of the discriminant
function involved five water quality indicators, including DO, 3.4. Spatiotemporal Variation Characteristics of Water
EC, turbidity, NH4+-N, and TP. These five water quality in- Quality in Yihe River Basin
dicators not only implied that the discriminant function have a The one-factor ANOVA analysis was performed for the

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water quality indicators during three time periods (i.e., low-, ty was better. Therefore, the regions with relatively severe pol-
mean-, and high-flow period) and different space scales (i.e., lution could be identified based on the average concentration
C1, C2, and C3). Figure 3 presented the average value of water of the above five water quality indicators and their spatial dif-
quality during the three periods under the three clustering groups ferrence. For instance, the averaged values of EC, TP, DO and
in the Yihe River Basin. It can be found that EC, DO, turbidity, turbidity were much higher in C1, and they also have signifi-
NH4+-N, and TP revealed significant spatial and temporal dif- cant differences with the water quality in the middle and upper
ferences, except for the two indicators of pH and COD (Figure reaches (C2, C3). The poor water quality in C1 may be induced
3). This verified the results of discriminant analysis to some ex- by industrial pollution and agriculture non-point source pollu-
tent, which also further confirmed that there were significant tion in the area, as well as the accumulation pollution from the
differences among clusters. middle and upper reaches (C2, C3).
According to the variations of the water quality indicators
during the three monitoring periods, it was interesting to find 4. Discussion
that: DO and NH4+-N were significantly different in the high-
flow period, respectively; turbidity presented notable differ- 4.1. Temporal Variation Characteristics and Its Response
ence in the mean-flow period; EC revealed an apparent differ- to Climate Change
ence between the low-flow period and mean-flow period; TP Generally, river water quality was the worst in the low-
showed significant differences in the three periods of low-, flow period and was better in the high-flow period (Yang et al.,
mean-, and high-flow. The averaged values of water quality in- 2007). However, the overall water quality of the Yihe River
dicators in most clusters (C1: EC, turbidity and TP; C2: DO Basin was the worst in the mean-flow period, followed by the
and NH4+-N, C3: turbidity, NH4+-N and TP) showed a trend of low-flow period, while it was the best in the high-flow period.
mean-flow period > low-flow period > high-flow period; the The river water quality may be significantly affected when the
averaged values of DO-C1, DO-C3, turbidity-C2, (NH4+-N)- peripheral land-use type changed from natural to urban land
C1 and TP-C2 presented a trend of low-flow period > mean- (Tu and Xia, 2006; Shen et al., 2011; Putro et al., 2016). The
flow period > high-flow period; the averaged value of EC-C3 construction of the highway from Luanchuan Country to Lushi
showed a trend in the low-flow period > high-flow period > Country highway in the upstream reaches was started during
mean-flow period; the averaged value of EC-C2 revealed a trend the low-flow period (Henan Government, 2017), and the land-
of high-flow period > mean-flow period > low-flow period. use changes caused by which might be one of the main influ-
Among the seven monitoring river water indicators, the maxi- encing factors of the poor water quality during this period.
mum averaged value of four indicators (i.e., EC, turbidity, NH4+-N, Luanchuan County is a mountainous landform, the roads, farm-
and TP) were appeared in the mean-flow period; and these of land, and residential areas of which were mainly distributed
two indicators (COD and DO) were distributed in the low-flow along the river valley. The development of the highway con-
period; only the maximum value of the averaged pH was moni- struction project has promoted the conversion of land-use types
tored during the high-flow period. In general, river water pollu- such as cultivated land and residential areas along with the riv-
tion was the most serious during the meanflow period, followed er reserves to roads. This process would not only cause soil ero-
by the low-flow period, and the water quality was best in the sion but also directly affect the water quality of the adjacent river.
high-flow period. The bare soil produced by this project would be washed into
The variation of the mean values of the seven water quality the river water body through surface runoff. Besides, the high-
indicators (i.e., pH, EC, DO, turbidity, COD, NH4+-N and TP) way in this area mainly constructed in the form of high sub-
during the same period in different spatial clusters (i.e., C1, C2 grades. Construction waste (e.g., waste slag, magma, and silt),
and C3) indicated that the water quality of the Yihe River Basin road runoff, and flying dust that generated in the process of sub-
displayed a gradual decline trend from upstream to downstream. grades filling would easily enter the river. Thus, the turbidity
Moreover, the water quality characteristics of the three cluster- of the river water was apparently increased, and finally induce
ing groups were closely related to their spatial location. The the reduction of the quality in river water. At the same time, the
maximum averaged values of the EC, TP, DO and turbidity aquatic ecological control project was carried out in the Song
were occurred in C1, which flows through agriculture land and Country urban area and the Yichuan Country in the middle reach-
urban area in the lower reaches of the river basin. Besides, EC, es during the mean-flow period. The project promoted the input
TP in C1 showed significant differences with the other two groups. of pollutants into the Yihe River, which made the concentration
The averaged value of TP in C1 was higher than the other two values of turbidity, NH4+-N and TP of C3 and C1 were much
groups, and the difference between C2 and C3 was not signifi- higher during the mean-flow period than those in low- and
cant. The DO and turbidity in C1 were significantly different high-flow period and affected the river water quality.
from the other two groups in the high-flow and low-flow peri- During the low-flow period, the water quality of the Yihe
ods, respectively. The minimum value of the mean concentra- River Basin was also relatively poor. Previously, precipitation
tion of NH4+-N was appeared in C2, which was significantly was widely regarded as one of the most powerful meteorologi-
different from C1 in the high-flow period. The water quality in- cal inputs in hydrological and water quality (Sajjad et al., 2018;
dicators (except TP) in the upper reaches of the forest area (C3) Solakian et al., 2019). Figure 4 shows the averaged values of
were lower than those of the other two groups, indicating that the monthly precipitation and temperature at four weather sta-
C3 was less subjected to human interference and the water quali- tions (i.e., Luanchuan Country, Song Conntry, Yichuan Country

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Figure 3. Spatial variations of discriminant indicators from spatial DA. Different letters (i.e., a, b, and c) of each water quality
indicator in the same period represent significant differences among groups, P < 0.05.

and Yanshi city) from 1998 to 2017. The results showed that lutants. Thus, a large amount of pollution load would stay in the
during the low-flow period (i.e., December and January), the river water, resulting in pollution enrichment. In addition, dur-
upper reaches (i.e., Luanchuan Country) only received 15.9 mm ing the investigation, we also noted that the domestic sewerage
precipitation, much less than that in other regions. The river flow was not fully integrated into the sewerage treatment system in
would also decrease owning to the decrease in precipitation dur- the upper reaches of the Yihe River Basin. Therefore, the direct
ing the low-flow period. Moreover, some river tributaries of the discharge of domestic sewage was another major fac-tor of se-
upper reaches (C3) were restricted by the mountainous terrain, vere pollution during the low-flow period in the area. Finally,
and rivers were small and narrow, which increases the difficulty in the low-flow period, active sand mining activities were also
of pollutants the diffusing in the river water. The lowest values one of the causes of river water pollution. Most of the precipita-
of temperature were presented in the low-flow period, ranging tion in the Yihe River Basin was concentrated in summer and
from –0.17 to 2.98 °C (Figure 4). The temperature of the river has a higher river runoff. Thus, pollutants (e.g., NH4+-N and TP)
water was also relatively low during this period, which affects were diluted, and resulting in lower values of water quality moni-
the metabolism rate of the microorganisms that decompose pol- tored indicators during the high-flow period.

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 J. Y. Ren et al. / Journal of Environmental Informatics Letters 6(1) 10-22 (2021)

4.2. Spatial Variation Characteristics of Water Quality 2013; Wu et al., 2018). However, methods such as spatial inter-
and Potential Pollute Source polation were rarely used to draw contour maps on a two-di-
The gradient of urbanization is increasing from the upstream mensional plane to simulate the spatial difference in water qual-
to the downstream in the Yihe River Basin. Land-use variations ity (Wang et al., 2013). And little or no studies have used three-
and land management methods that caused by human activities dimensional (i.e., 3D) surface maps to describe the quantity,
and urbanization development have significant effects on water quality, density, and correlation of river water quality data at
quality changes (Varol et al., 2012; Wu et al., 2018). The water different sampling points. Rivers are 3D geographical things
quality of the middle and upstream reaches of the Yihe river (Liu et al., 2019). Three-dimensional simulation of the spatial
was superior to that of downstream reaches. Forest land has a characteristics of river water quality data at different sampling
pretty good weakening effect on water quality deterioration, points will be more visual, intuitive and more in line with peo-
and the roots and litter of vegetation have a strong retention and ple’s habits of daily observation. The 3D kernel density surface
absorption effect of pollutants (Ji et al., 2015). Most of the main- graph can appropriately exaggerate the difference between the
stream (C2) belongs to hilly landforms. More than half of the high and low values and can effectively highlight the differences
rivers, canals and reservoirs in the study area were distributed between hot spots and regions, thereby making the display of
on C2. Due to their strong capacity to purify pollutants, most thematic information more intuitive and diverse (Lu et al., 2017).
of the water quality indicators monitored at C2 showed smaller In the future, related theories and techniques that expressed in
averaged concentration values during the low- and high-flow three-dimensional such as kernel density estimation are desired
periods. The lower reaches of the Yihe river flows through Yi- to be explored and applied to the spatial mapping of river water
chuan Country and Yanshi Country (C1), its urbanization level quality. These are expected to provide more intoitive and scientific
was relatively higher than the middle and upper reaches. There- tools for river water environmental protection and water quality
fore, in addition to the increase in point source pollution lead- improvement.
ing to the deterioration of river water quality, an increase in the
proportion of impervious surfaces in the sub-basins would also
endanger aquatic ecosystems and river water quality, which was
consistent with previous study (Paul and Meyer, 2001). In urban
areas, short-term heavy rainfall can promote contaminates matters
to enter rivers through impervious surfaces. The underground
drainage system in the city would further increase the peak
flood flow and shorten the time for pollutants to enter the river.
Besides, the accumulation of upstream pollutants leads to the
deterioration of the water quality of downstream rivers after
passing through the city. These reflect the impact of human so-
cioeconomic activities and natural processes (e.g., precipitation)
on river water quality. Therefore, in addition to the high pollu- Figure 4. The monthly average precipitation (i.e., P) and tem-
tion load of TP and NH4+-N in the water body due to the con- perature (i.e., T) of four weather stations (i.e., Luanchuan Country,
struction of the high-way in the upper reaches of the Yihe river Song Conntry, Yichuan Country, and Yanshi city) in Yihe
during the mean-flow period, the water quality in the upper reaches River Basin from 1998 to 2017.
(C3) and the midstream (e.g., Luhun Reservoir in C2) of the
Yihe River Basin was better than the lower reaches (C1) during
the low-flow and high-flow period (Figures 2b and 3). 5. Conclusions
Consequently, the temporal and spatial changes in water In this study, the spatiotemporal characteristics of river
quality in the Yihe River Basin were affected by both human water quality were analyzed in 17 sub-basins in Yihe River Basin
activities and natural factors. In addition to the effects of the during low-, mean- and high-flow period based on the multi-
construction of the high-way, the aquatic ecological control pro- variate analysis methods. The potential pollution sources and
ject, and cultivation, the temporal variation in water quality was contaminated areas were also identified according to the socio-
also affected by climate change (e.g., precipitation and tempe- economic activities, meteorological data and the field investi-
rature), the variation in space was also restricted by natural en- gations data in the Yihe River Basin in China. The results showed
vironment characteristics such as topography. that: (1) PCA shows that the water quality in the Yihe River Basin
was dominated by organic pollution during the low-flow period,
followed by nitrogen pollution; nitrogen pollution was the influ-
4.3. Representations of Spatial-Temporal Changes of the
ential indicators during mean-flow period, followed by phos-
River Water Quality
phorus and organic pollution; nitrogen and organic was the main
In terms of the representation of spatiotemporal changes pollution during the high-flow period, respectively. (2) CA di-
in water quality in a river basin, the quantity and quality of vides the sampling points of the Yihe River Basin from upstream
monitoring water data in space were mostly presented through to downstream into three categories (i.e., C1, C2 and C3). The
the discrete graphics (e.g., histogram, scatter diagram, and line results of DA verified that the accuracy of the CA was 94.1%.
chart) (Ravindra et al., 2003; Sundaray et al., 2006; Sun et al., It also showed good dimensionality reduction ability, that is,

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 J. Y. Ren et al. / Journal of Environmental Informatics Letters 6(1) 10-22 (2021)

only five key water quality indicators (i.e., DO, EC, turbidity, http://www.henan.gov.cn/2017/04-11/248859.html (accessed July
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