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The pollution index is a helpful tool for assessing the quality of groundwater. To assess the water quality in the southern segment of Barmer District (Rajasthan), India, we collected 20 samples of groundwater from the post-monsoon 2021 and pre-monsoon 2022 periods. Physicochemical parameters such as pH, electrical conductivity (EC), total hardness, Cl−, SO4--, F−, NO3−, total dissolved solids, Ca2+, and Mg2+ were analyzed. To better understand the spatial and temporal variations, maps were generated in the Geographic Information System (GIS) environment in association with the seasonal correlation matrix. Nemerov's index method was used for determining the pollution level of groundwater sources. The results showed that there was significant spatial and temporal variation in the concentration level of physicochemical parameters. The correlation matrix revealed that the level of positive correlation among the parameters was higher during the pre-monsoon period of 2022 compared to the post-monsoon period of 2021. The result was evaluated using the standard set by the World Health Organization and the Bureau of Indian Standards. According to the result obtained from Nemerov's index technique, most parameters were within safe conditions in both seasons except for EC and NO3-. The results indicated that the improved Nemerov index technique can represent the status of groundwater more accurately.

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  • Micro-level investigation of ions in the groundwater critical zone of western Rajasthan, India, has been studied.

  • Groundwater quality parameters have been discussed by using Nemerov's pollution index.

  • The study showed that the single-factor pollution index method and the Nemerov pollution index method can be implemented at a larger spatial extent.

groundwater quality, Nemerov's index method, physicochemical parameters, single-factor index method
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Water is an important component of all living things and constitutes a significant portion of the cellular content in both plants and animals. Water makes up between 70 and 90% of the weight of cells, highlighting its critical role in sustaining life (Hur et al. 2010). This crucial substance is not only pivotal for maintaining cellular structure and function but also serves as the basis for myriad biological reactions (Verma & Singh 2013). Groundwater is a critical resource that favors human needs, sustains ecosystems, and stabilizes various environmental processes. Therefore, water management and protection are essential for ensuring sustainable procurement and maintaining ecological balance.

Water samples mainly taken from wells, borewells, open-wells, and hand pumps from Chouhtan tehsil in Barmer district (Rajasthan) were collected and analyzed for physicochemical parameters, among which the main focus was concentrated on the analysis report of fluoride and nitrate. To assess water quality and investigate its physicochemical nature, we analyzed the major cations – Ca2+, Mg2+, Na+, K+ – and anions – ⁠, ⁠, Cl−, F−, ⁠, – and chemical parameters such as the potential of hydrogen (pH), electrical conductivity (EC), alkalinity, and total hardness (TH) (Ravikumar et al. 2010).

The presence of fluoride in groundwater is primarily a natural occurrence. Fluoride concentrations can vary widely depending on the geological and environmental factors of a region. In some areas, fluoride levels may be naturally high, while in others, they may be very low. Fluoride is beneficial in small amounts for dental health, but excessive levels in drinking water can pose health risks, such as dental and skeletal fluorosis. According to the World Health Organization and Bureau of Indian Standards (BIS), the concentration of fluoride in potable water should not be more than 1.5 mg/l (Mohan et al. 2014).

Groundwater must be suitable for human usage. However, human activity can alter the characteristics of water as well as the hydrological cycle and contaminate groundwater systems. The Nemerov pollution index (NPI) is a metric for evaluating the state of the environment that takes into account a wide range of factors, including extreme or very high values (Gummadi et al. 2015). Researchers from a variety of fields have noted that water quality is becoming an increasingly serious concern, and it is crucial to promptly and regularly assess the quality of water (Ren et al. 2023).

The water quality index is one of the commonly employed techniques in this situation. Nemerov contamination and the single-factor pollution index (SFPI) approaches are the most effective and advantageous instruments for examining and assessing the aquatic ecosystem's quality (Zhang et al. 2018). The advantages of the pollution index technique are its ease of conceptualization, capacity to measure water quality, and enhanced communication of the degree of water contamination (Gummadi et al. 2015).

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Background of the sampling area

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The Barmer District of Rajasthan is situated between 24°58′ and 26°32′N latitude and 70°05′E and 72°52′E longitude. Except on the west, the district is surrounded by districts of Rajasthan; Jaisalmer is located to the north, Jalore to the south, Pali and Jodhpur to the east, and it shares an international boundary with Pakistan to the west. The samples were collected from the south of the district (Figure 1). The present study area is located in the Arid Western Plain Zone (ACZ-II) (Kar 2014). The temperature of the region ranges between 10 and 40 °C. In recent years, the stage of groundwater exploitation has increased manifold (Kar 2014).
Figure 1
Location of Barmer on the map of Rajasthan, India.
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Location of Barmer on the map of Rajasthan, India.

Figure 1
Location of Barmer on the map of Rajasthan, India.
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Location of Barmer on the map of Rajasthan, India.

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Sample collection and analysis

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Twenty groundwater samples were collected from the southern segment of Barmer District. For evaluation of seasonal variability in terms of the concentration of physicochemical parameters, the samples were collected during the post-monsoon 2021 and pre-monsoon 2022 periods. The sampling locations were marked using mobile Global Positioning System (GPS) and further incorporated into the Geographic Information System (GIS) environment. The samples were collected in 500 ml polyethylene bottles and marked with sampling locations. EC and total dissolved solids (TDS) were measured in the field using the digital testing meter (Table S1). For the analysis of the remaining parameters, the samples were kept at room temperature and sent to the Department of Chemistry, Jai Narain Vyas University, Jodhpur, Rajasthan, India.

The methods for the analysis of the parameters are listed in Table S1 (table placed in the Supplementary Data).

Technique for assessing the quality of water

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The SFPI method and the NPI method were utilized to properly evaluate the water quality in various seasons. The maximum membership grade premise guides the determination of the SFPI technique. The comprehensive classification of water quality is based on the category of the most affected evaluation factor (Ji et al. 2016). According to Ji et al. (2016) and Yan et al. (2015), the approach is straightforward to apply, and it can be utilized to directly comprehend the relationship between the assessment requirements and the water quality status. Table S2 (table placed in Supplementary Data) presents the standard values of water quality parameters by BIS (2012) and IS (2012).

By using the single-factor index method, also known as the category of the worst single-factor method, which is generated, the comprehensive yield of water quality can be compared and monitored to yield findings with the appropriate classification of requirements. A calculated mean of all the indicators used in the analysis was used to establish the overall water quality category of the water. Single-component analysis was also used to determine the main water pollutants and their levels of severity. The pollution index, which is created by dividing the actual measured value of each pollutant by the evaluation standard, reflects the assessment outcome. The formula for the SFPI technique is as follows:
where Pi stands for the pollution index of the single water quality index and Si is the standard value of environmental quality (mg/L), which is given in Table S3 (table placed in Supplementary Data); water standard in the Environmental Quality Standards for Surface Water (GB3838-2002) (MEE 2021). Ci is the pollutant content measurement value.

The NPI method

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Nemerov (1971) developed the proposal for the NPI on behalf of the United States Environmental Protection Agency (US EPA). Worldwide, the NPI is widely employed in water quality assessments and accounts for the effect of the SFPI (Ji et al. 2016). The following expression is used to compute the NPI:

Step-1 calculating Pi
(1)
where Pi stands for the pollution index of the single water quality index and Si is the standard value of environmental quality (mg/l);
Step-2 calculating PN
(2)

In the equation, PN stands for the sample point's overall pollution index, and Pimax for the pollutants' single-item pollution index's highest value.

Step-3 calculating P1

P1 is calculated using the following expression:
(3)
Pi is the single-factor index's mean value and n is the pollutant number. The water quality is up to the used standard, as indicated by the P1 of ≤1.

The grading system for the NPI approach to assess environmental quality is shown in Table S4 (Gummadi et al. 2015; table placed in Supplementary Data).

Mapping and plotting

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In the present study, ArcGIS software was used for mapping, and R Studio was used for the preparation of the correlation matrix using the corrplot package (Wei & Simko 2021). All the statistical diagrams were prepared in Origin software.

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Seasonal variability of physicochemical parameters

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Tables S4 and S5 show the descriptive statistics of selected groundwater parameters in both seasons. The results show that during the post-monsoon season, the mean pH value of 0.963 indicated more alkaline conditions than in the pre-monsoon season value, 0.9245. EC showed relatively lower mean 6.55 values in the post-monsoon season while it was 7.89 in the pre-monsoon season. For total hardness, there was less variation between both seasons. In the post-monsoon season, the TH value was 0.030 while in the pre-monsoon it was 0.026. The TDS value was 0.608 in the post-monsoon season and 0.49 in the pre-monsoon season. While Mg showed considerable variability that can be observed in the post-monsoon 0.115 and pre-monsoon 0.079 values, fluoride showed considerably higher concentration in terms of maximum value 0.615 during the pre-monsoon season and 0.416 in the post-monsoon season.

Figure 2 depicts the seasonal variability of the concentration of the physicochemical parameters.
Figure 2
Seasonal variation in concentration of physicochemical parameters in both monsoon seasons.
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Seasonal variation in concentration of physicochemical parameters in both monsoon seasons.

Figure 2
Seasonal variation in concentration of physicochemical parameters in both monsoon seasons.
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Seasonal variation in concentration of physicochemical parameters in both monsoon seasons.

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Spatiotemporal variation in physicochemical properties of groundwater

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The level of pH during both seasons was inclined toward slightly alkaline conditions. In the post-monsoon season, the majority of the region was associated with alkaline conditions having pH values more than 8.20. However, a few patches in the northern segment showed slightly neutral conditions, having values <7.80 in the pre-monsoon season, with more samples showing slightly alkaline conditions in the ranges between 7.8 and 8. A few patches in the western segment showed alkalinity conditions of pH value from 8.01 to 8.20 (Figure 3(a)). Conversely, during the post-monsoon season, the level of EC showed lower value ranges less than 3,000 mg/l. In the central portion of the study area, a higher level of EC was observed in both seasons ranging from 3,000 to 4,500 mg/l (Figure 3(b)). Similarly, the concentration of TH declined in the post-monsoon (range less than 10 mg/l) with a higher concentration in the southern segment (>30 mg/l) (Figure 3(c)). In the case of Cl−, in both seasons, it did not depict any significant variation. In the central part in both monsoons, it shows higher concentration values >45 mg/l (Figure 3(d)) while showed a significant decline during the post-monsoon season (values <4 mg/l), where higher concentration was observed in the southern segment in both the seasons with values >12 mg/l (Figure 3(e)). The concentration of F− has considerably declined in the post-monsoon season with values less than 1 mg/l. In the pre-monsoon, the higher concentration values >1.5 mg/l were observed in the north-western segment, while in the post-monsoon season, the higher concentration patch shifted toward the northeast (Figure 3(f)). The concentration of in both seasons showed similar spatial patterns. The lower concentration value range is <80 mg/l while higher shows >120 mg/l (Figure 3(g)), while TDS depicted a higher concentration value >600 mg/l in the post-monsoon season (Figure 3(h)). In the pre-monsoon season, Ca2+ was associated with a higher concentration value >15 mg/l in the central segment, while in the post-monsoon season, the level of concentration was mostly higher (value >15 mg/l) in the southern portion (Figure 3(i)). Mg2+ also followed a similar pattern and showed higher concentration values >4.50 mg/l in the southern portion in the post-monsoon season (Figure 3(j)). From the spatial perspective, it can be depicted that the majority of the parameters were associated with higher concentrations in the southern portion in the post-monsoon season.
Figure 3
Spatial variation of physicochemical parameters: (a) pH, (b) EC, (c) TH, (d) Cl−, (e) , (f) F−, (g) , (h) TDS, (i) Ca2+, and (j) Mg2+.
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Spatial variation of physicochemical parameters: (a) pH, (b) EC, (c) TH, (d) Cl−, (e) ⁠, (f) F−, (g) , (h) TDS, (i) Ca2+, and (j) Mg2+.

Figure 3
Spatial variation of physicochemical parameters: (a) pH, (b) EC, (c) TH, (d) Cl−, (e) , (f) F−, (g) , (h) TDS, (i) Ca2+, and (j) Mg2+.
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Spatial variation of physicochemical parameters: (a) pH, (b) EC, (c) TH, (d) Cl−, (e) ⁠, (f) F−, (g) , (h) TDS, (i) Ca2+, and (j) Mg2+.

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Correlation among the physicochemical parameters

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Figures 4 and 5 show a correlation matrix of all the selected physicochemical parameters measured in the samples from the post-monsoon 2021 and pre-monsoon 2022 seasons, respectively. During the pre-monsoon season, higher correlation positive values were observed between several parameters, such as EC and (0.99), Cl− and (0.99), TH and (0.96), and Cl− and EC (1). Conversely, negative correlations were observed between F and Ca (−0.21), and pH and TDS (−0.25). However, during the post-monsoon season, it can be observed that in most of the cases, the level of correlation between parameters tended toward lower values than during the pre-monsoon season. Some of the significant values were the correlation between pH and fluoride (−0.47) and Cl− and fluoride (−038).
Figure 4
Correlation matrix of selected physicochemical parameters of the post-monsoon season 2021.
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Correlation matrix of selected physicochemical parameters of the post-monsoon season 2021.

Figure 4
Correlation matrix of selected physicochemical parameters of the post-monsoon season 2021.
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Correlation matrix of selected physicochemical parameters of the post-monsoon season 2021.

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Figure 5
Correlation matrix of selected physicochemical parameters of the pre-monsoon season 2022.
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Correlation matrix of selected physicochemical parameters of the pre-monsoon season 2022.

Figure 5
Correlation matrix of selected physicochemical parameters of the pre-monsoon season 2022.
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Correlation matrix of selected physicochemical parameters of the pre-monsoon season 2022.

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Seasonal variability of the NPI

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The pH NPI values ranged from 0.91 to 1.01 in post-monsoon conditions and 0.89 to 0.98 under pre-monsoon conditions and they were within the permitted range. Results showed the various ranges in the sample stations. The EC value varied from 2.26 to 17.5 in post-monsoon conditions 2021 and from 2.17 to 42.8 in pre-monsoon conditions 2022 in all sample sites. In all study locations, the total hardness varies from 0.01 to 0.13 in the post-monsoon and from 0.01 to 0.15 in the pre-monsoon. The graphical depiction illustrates the variance in sample stations for the Mg2+ ranging from 0.02 to 0.48 in post-monsoon 2021 and 0.02 to 0.22 in pre-monsoon 2022. The Cl− NPI ranges fluctuated from 0 to 0.24 in the post-monsoon and 0 to 0.61 in the pre-monsoon season. NPI of F− at all survey locations ranged from 0.06 to 1.06 in post-monsoon conditions and from 0.20 to 2.10 in pre-monsoon conditions. Furthermore, in all sample stations, Ca2+ NPI levels ranged from 0.02 to 0.34 in post-monsoon conditions and 0.03–0.53 in pre-monsoon conditions, respectively. The small range of total dissolved solids NPI values (0.2–1.74 in post- and 0.02–0.22 in pre-monsoon seasons) exceeded their levels between these ranges. Only four sample stations – S2, S3, S4, and S9 in post-monsoon and only two in pre-monsoon – showed NPI values below the NPI limit, whereas the other sampling stations had higher values and were unfit for drinking. NPI values ranged from 0.72 to 9.95 and 0.2 to 1.7 in the post-monsoon and pre-monsoon seasons, respectively. The NPI value was from 0 to 0.6 in the post- and 0 to 0.20 in the pre-monsoon seasons. The results are depicted in Tables S7 and S8 and illustrated in Figures 6 and 7 for the post-monsoon 2021 and pre-monsoon 2022 seasons, respectively.
Figure 6
The NPI values of the post-monsoon season 2021.
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The NPI values of the post-monsoon season 2021.

Figure 6
The NPI values of the post-monsoon season 2021.
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The NPI values of the post-monsoon season 2021.

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Figure 7
The NPI values of the pre-monsoon season 2022.
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The NPI values of the pre-monsoon season 2022.

Figure 7
The NPI values of the pre-monsoon season 2022.
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The NPI values of the pre-monsoon season 2022.

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In Table S9 (table placed in supplementary data), the NPI of post-monsoon 2021 and pre-monsoon 2022 seasons are incorporated and it can be observed that except EC and all the parameters indicate suitable conditions. The present result is also confirmed by the work of several scholars (Ghosh & Kanchan 2014; Rahman et al. 2021) where an elevated level of is mostly associated with anthropogenic effects.

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The NPI was used to evaluate groundwater quality in the southern Barmer district of Rajasthan. The study analyzed physicochemical parameters that influence water quality, applying the Nemerov contamination index method to categorize them. The analysis revealed that, in the pre-monsoon season of 2022, there were generally more positive correlations among the parameters, although significant seasonal variations were observed. The NPI indicated that most parameters were within acceptable limits, except for EC and nitrate (⁠⁠), which were problematic and likely due to human activities. This suggests an urgent need for measures to ensure the safety of drinking water. This report indicates that the overall water of this region should be treated for well-being, better health, and survival. The report also highlighted the need for further studies involving additional samples and hydrogeochemical factors to extend the applicability of the NPI method. If the pollution sources are susceptible to releasing other parameters such as heavy metals, then further study needs to be done to detect the heavy metals in the water. Enhanced water management is crucial for meeting agricultural, industrial, and domestic needs. Regular analysis of water samples is essential to improve awareness and knowledge about groundwater quality.

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The authors are thankful to the Department of Chemistry, Jai Narain Vyas University, Jodhpur, Rajasthan, India as well as the Researchers Supporting Project number (RSP2024R496), King Saud University, Riyadh, Saudi Arabia.

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All authors have read, understood, and complied as applicable with the statement on ‘Ethical responsibilities of Authors’.

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S.P., R.J., S.K.P., T.G., and V.S.S. performed material preparation, data collection, and analysis. S.S.A., V.S.S., K.K.Y., and N.A. contributed toward the visualization, investigation, and presentation. The first draft of the manuscript was written by S.P., R.J., S.K.P, and T.G., and all authors commented on previous versions of the paper. All authors read and approved the final paper.

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All the authors have given their consent to publish this paper.

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All relevant data are included in the paper or its Supplementary Information.

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The authors declare there is no conflict.

Ghosh
T.
 &
Kanchan
R.
 (
2014
)
Geoenvironmental appraisal of groundwater quality in Bengal alluvial tract, India: A geochemical and statistical approach
,
Environmental Earth Sciences
,
72
(
7
),
2475
–
2488
.
https://doi.org/10.1007/s12665-014-3155-3
.
Google Scholar
Crossref
Search ADS
 
Gummadi
S.
,
Latha
G. S.
,
Vijayakumar
G.
,
Rao
P.
 &
Venkatarathnamma
V.
 (
2015
)
Application of Nemerov's pollution index (NPI) for groundwater quality assessment of Bapatla mandal west region, coastal Andhra Pradesh, India
,
International Journal of Applied Science and Engineering Research
,
4
(
4
),
500
–
506
.
Google Scholar
 
Ji
X.
,
Dahlgren
R. A.
 &
Zhang
M.
 (
2016
)
Comparison of seven water quality assessment methods for the characterization and management of highly impaired river systems
,
Environmental Monitoring and Assessment
,
188
(
1
),
15
.
https://doi.org/10.1007/s10661-015-5016-2
.
Google Scholar
Crossref
Search ADS
PubMed
 
Kar
A.
 (
2014
)
Agricultural land use in arid Western Rajasthan: Resource exploitation and emerging issues
,
Agropedology
,
24
(
02
),
179
–
196
.
Google Scholar
 
MEE
(
2021
)
Report on the State of the Ecology and Environment in China 1986–2020; Ministry of Ecology and Environment (MEE): Beijing, China
.
Mohan
D.
,
Kumar
S.
 &
Srivastava
A.
 (
2014
)
Fluoride removal from groundwater using magnetic and nonmagnetic corn stover biochars
.
Ecological Engineering, 73 (214), 798–808
.
Google Scholar
 
Nemerow
N. L.
 (
1971
)
Benefits of Water Quality Enhancement
, Vol.
16110
.
Environmental Protection Agency, Water Quality Office
,
Washington, DC
.
Google Scholar
 
Rahman
A.
,
Mondal
N. C.
 &
Tiwari
K. K.
 (
2021
)
Anthropogenic nitrate in groundwater and its health risks in the view of background concentration in a semi arid area of Rajasthan, India
,
Scientific Reports
,
11
(
1
),
9279
.
https://doi.org/10.1038/s41598-021-88600-1
.
Google Scholar
Crossref
Search ADS
PubMed
 
Ravikumar
P.
,
Venkatesharaju
K.
 &
Somashekar
R. K.
 (
2010
)
Major ion chemistry and hydrochemical studies of groundwater of Banglore South Taluk, India
.
Environmental Monitoring and Assessment
,
163
,
643
–
653
.
Google Scholar
Crossref
Search ADS
PubMed
 
Verma
A. K.
 &
Singh
T. N.
 (
2013
)
Prediction of water quality from simple field parameters
,
Environmental Earth Sciences
,
69
(
3
),
821
–
829
.
Google Scholar
Crossref
Search ADS
 
Wei
T.
 &
Simko
V.
 (
2021
)
R Package ‘Corrplot’: Visualization of A Correlation Matrix (0.92)[R]
.
Available at: https://github.com/taiyun/corrplot.
Yan
C.-A.
,
Zhang
W.
,
Zhang
Z.
,
Liu
Y.
,
Deng
C.
 &
Nie
N.
 (
2015
)
Assessment of water quality and identification of polluted risky regions based on field observations & GIS in the Honghe River Watershed, China
,
PLoS One
,
10
(
3
),
e0119130
.
https://doi.org/10.1371/journal.pone.0119130
.
Google Scholar
Crossref
Search ADS
PubMed
 
Zhang
Q.
,
Feng
M.
 &
Hao
X.
 (
2018
)
Application of Nemerow index method and integrated water quality index method in water quality assessment of Zhangze Reservoir
,
IOP Conference Series: Earth and Environmental Science
,
128
,
012160
.
doi:10.1088/1755-1315/128/1/012160
.
Google Scholar
 
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Supplementary data

Supplementary Information- docx file
Supplementary Information- docx file