Integrating water quality index, GIS and multivariate statistical techniques towards a better understanding of drinking water quality
Groundwater is considered as an imperative component of the accessible water assets across the world. Due to urbanization, industrialization and intensive farming practices, the groundwater resources have been exposed to large-scale depletion and quality degradation. The prime objective of this stud...
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| Veröffentlicht in: | Environmental science and pollution research international 2022-04, Vol.29 (18), p.26860-26876 |
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| creator | Masood, Adil Aslam, Mohammad Pham, Quoc Bao Khan, Warish Masood, Sarfaraz |
| description | Groundwater is considered as an imperative component of the accessible water assets across the world. Due to urbanization, industrialization and intensive farming practices, the groundwater resources have been exposed to large-scale depletion and quality degradation. The prime objective of this study was to evaluate the groundwater quality for drinking purposes in Mewat district of Haryana, India. For this purpose, twenty-five groundwater samples were collected from hand pumps and tube wells spread over the entire district. Samples were analyzed for pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), turbidity, total alkalinity (TA), cations and anions in the laboratory using the standard methods. Two different water quality indices (weighted arithmetic water quality index and entropy weighted water quality index) were computed to characterize the groundwater quality of the study area. Ordinary Kriging technique was applied to generate spatial distribution map of the WQIs. Four semivariogram models, i.e. circular, spherical, exponential and Gaussian were used and found to be the best fit for analyzing the spatial variability in terms of weighted arithmetic index (GWQI) and entropy weighted water quality index (EWQI). Hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) were applied to provide additional scientific insights into the information content of the groundwater quality data available for this study. The interpretation of WQI analysis based on GWQI and EWQI reveals that 64% of the samples belong to the “poor” to “very poor” bracket. The result for the semivariogram modeling also shows that Gaussian model obtains the best fit for both EWQI and GWQI dataset. HCA classified 25 sampling locations into three main clusters of similar groundwater characteristics. DA validated these clusters and identified a total of three significant variables (pH, EC and Cl) by adopting stepwise method. The application of PCA resulted in three factors explaining 69.81% of the total variance. These factors reveal how processes like rock water interaction, urban waste discharge and mineral dissolution affect the groundwater quality. |
| doi_str_mv | 10.1007/s11356-021-17594-0 |
| format | Article |
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Due to urbanization, industrialization and intensive farming practices, the groundwater resources have been exposed to large-scale depletion and quality degradation. The prime objective of this study was to evaluate the groundwater quality for drinking purposes in Mewat district of Haryana, India. For this purpose, twenty-five groundwater samples were collected from hand pumps and tube wells spread over the entire district. Samples were analyzed for pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), turbidity, total alkalinity (TA), cations and anions in the laboratory using the standard methods. Two different water quality indices (weighted arithmetic water quality index and entropy weighted water quality index) were computed to characterize the groundwater quality of the study area. Ordinary Kriging technique was applied to generate spatial distribution map of the WQIs. Four semivariogram models, i.e. circular, spherical, exponential and Gaussian were used and found to be the best fit for analyzing the spatial variability in terms of weighted arithmetic index (GWQI) and entropy weighted water quality index (EWQI). Hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) were applied to provide additional scientific insights into the information content of the groundwater quality data available for this study. The interpretation of WQI analysis based on GWQI and EWQI reveals that 64% of the samples belong to the “poor” to “very poor” bracket. The result for the semivariogram modeling also shows that Gaussian model obtains the best fit for both EWQI and GWQI dataset. HCA classified 25 sampling locations into three main clusters of similar groundwater characteristics. DA validated these clusters and identified a total of three significant variables (pH, EC and Cl) by adopting stepwise method. The application of PCA resulted in three factors explaining 69.81% of the total variance. These factors reveal how processes like rock water interaction, urban waste discharge and mineral dissolution affect the groundwater quality.</description><identifier>ISSN: 0944-1344</identifier><identifier>ISSN: 1614-7499</identifier><identifier>EISSN: 1614-7499</identifier><identifier>DOI: 10.1007/s11356-021-17594-0</identifier><identifier>PMID: 34860346</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Agricultural practices ; Alkalinity ; Anions ; Aquatic Pollution ; Arithmetic ; arithmetics ; Atmospheric Protection/Air Quality Control/Air Pollution ; Cations ; Cluster analysis ; data collection ; Depletion ; Discriminant analysis ; Dissolved solids ; Drinking water ; Drinking Water - analysis ; Earth and Environmental Science ; Ecotoxicology ; Electrical conductivity ; Electrical resistivity ; Entropy ; Environment ; Environmental Chemistry ; Environmental Health ; Environmental Monitoring - methods ; Environmental science ; Geographic Information Systems ; Groundwater ; Groundwater - chemistry ; Groundwater availability ; Groundwater data ; Groundwater quality ; Hand pumps ; India ; industrialization ; Intensive farming ; kriging ; Mathematical models ; Municipal waste management ; pH effects ; principal component analysis ; Principal components analysis ; Research Article ; Spatial distribution ; Statistical analysis ; Statistical methods ; Total dissolved solids ; Turbidity ; Urbanization ; variance ; Waste Water Technology ; Water analysis ; Water discharge ; water hardness ; Water Management ; Water Pollutants, Chemical - analysis ; Water Pollution Control ; Water Quality ; Water quality standards ; Water resources ; Water sampling</subject><ispartof>Environmental science and pollution research international, 2022-04, Vol.29 (18), p.26860-26876</ispartof><rights>The Author(s) 2021</rights><rights>2021. The Author(s).</rights><rights>The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). 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Due to urbanization, industrialization and intensive farming practices, the groundwater resources have been exposed to large-scale depletion and quality degradation. The prime objective of this study was to evaluate the groundwater quality for drinking purposes in Mewat district of Haryana, India. For this purpose, twenty-five groundwater samples were collected from hand pumps and tube wells spread over the entire district. Samples were analyzed for pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), turbidity, total alkalinity (TA), cations and anions in the laboratory using the standard methods. Two different water quality indices (weighted arithmetic water quality index and entropy weighted water quality index) were computed to characterize the groundwater quality of the study area. Ordinary Kriging technique was applied to generate spatial distribution map of the WQIs. Four semivariogram models, i.e. circular, spherical, exponential and Gaussian were used and found to be the best fit for analyzing the spatial variability in terms of weighted arithmetic index (GWQI) and entropy weighted water quality index (EWQI). Hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) were applied to provide additional scientific insights into the information content of the groundwater quality data available for this study. The interpretation of WQI analysis based on GWQI and EWQI reveals that 64% of the samples belong to the “poor” to “very poor” bracket. The result for the semivariogram modeling also shows that Gaussian model obtains the best fit for both EWQI and GWQI dataset. HCA classified 25 sampling locations into three main clusters of similar groundwater characteristics. DA validated these clusters and identified a total of three significant variables (pH, EC and Cl) by adopting stepwise method. The application of PCA resulted in three factors explaining 69.81% of the total variance. These factors reveal how processes like rock water interaction, urban waste discharge and mineral dissolution affect the groundwater quality.</description><subject>Agricultural practices</subject><subject>Alkalinity</subject><subject>Anions</subject><subject>Aquatic Pollution</subject><subject>Arithmetic</subject><subject>arithmetics</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Cations</subject><subject>Cluster analysis</subject><subject>data collection</subject><subject>Depletion</subject><subject>Discriminant analysis</subject><subject>Dissolved solids</subject><subject>Drinking water</subject><subject>Drinking Water - analysis</subject><subject>Earth and Environmental Science</subject><subject>Ecotoxicology</subject><subject>Electrical conductivity</subject><subject>Electrical resistivity</subject><subject>Entropy</subject><subject>Environment</subject><subject>Environmental Chemistry</subject><subject>Environmental Health</subject><subject>Environmental Monitoring - 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Due to urbanization, industrialization and intensive farming practices, the groundwater resources have been exposed to large-scale depletion and quality degradation. The prime objective of this study was to evaluate the groundwater quality for drinking purposes in Mewat district of Haryana, India. For this purpose, twenty-five groundwater samples were collected from hand pumps and tube wells spread over the entire district. Samples were analyzed for pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH), turbidity, total alkalinity (TA), cations and anions in the laboratory using the standard methods. Two different water quality indices (weighted arithmetic water quality index and entropy weighted water quality index) were computed to characterize the groundwater quality of the study area. Ordinary Kriging technique was applied to generate spatial distribution map of the WQIs. Four semivariogram models, i.e. circular, spherical, exponential and Gaussian were used and found to be the best fit for analyzing the spatial variability in terms of weighted arithmetic index (GWQI) and entropy weighted water quality index (EWQI). Hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) were applied to provide additional scientific insights into the information content of the groundwater quality data available for this study. The interpretation of WQI analysis based on GWQI and EWQI reveals that 64% of the samples belong to the “poor” to “very poor” bracket. The result for the semivariogram modeling also shows that Gaussian model obtains the best fit for both EWQI and GWQI dataset. HCA classified 25 sampling locations into three main clusters of similar groundwater characteristics. DA validated these clusters and identified a total of three significant variables (pH, EC and Cl) by adopting stepwise method. The application of PCA resulted in three factors explaining 69.81% of the total variance. These factors reveal how processes like rock water interaction, urban waste discharge and mineral dissolution affect the groundwater quality.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>34860346</pmid><doi>10.1007/s11356-021-17594-0</doi><tpages>17</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Agricultural practices Alkalinity Anions Aquatic Pollution Arithmetic arithmetics Atmospheric Protection/Air Quality Control/Air Pollution Cations Cluster analysis data collection Depletion Discriminant analysis Dissolved solids Drinking water Drinking Water - analysis Earth and Environmental Science Ecotoxicology Electrical conductivity Electrical resistivity Entropy Environment Environmental Chemistry Environmental Health Environmental Monitoring - methods Environmental science Geographic Information Systems Groundwater Groundwater - chemistry Groundwater availability Groundwater data Groundwater quality Hand pumps India industrialization Intensive farming kriging Mathematical models Municipal waste management pH effects principal component analysis Principal components analysis Research Article Spatial distribution Statistical analysis Statistical methods Total dissolved solids Turbidity Urbanization variance Waste Water Technology Water analysis Water discharge water hardness Water Management Water Pollutants, Chemical - analysis Water Pollution Control Water Quality Water quality standards Water resources Water sampling |
| title | Integrating water quality index, GIS and multivariate statistical techniques towards a better understanding of drinking water quality |
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