Entropy Weighted Water Quality Index Research Articles

Is the entropy-weighted water quality index a suitable index for evaluating the groundwater quality in Ha'il, Saudi Arabia?

This study examined the groundwater quality in Ha'il according to World Health Organization (WHO) standards using the entropy-weighted water quality index (EWQI) more accurately. The study investigated several parameters in groundwater quality and found that more than 75% of the changes in Ha'il can be attributed to four main factors (MF1, MF2, MF3, and MF4). The MF1 was found to have the biggest role in controlling more than 33% of the changes in the water quality. Due to the entropy calculations for each parameter, zinc was found to have the highest rate of influence on groundwater quality. The results of the EWQI showed that the highest number of samples (76%) had Rank 2 and good quality. Also, it was tried to couple EWQI with machine-learning techniques to improve the model performance and survey the related results in this study. The results showed that the efficiency criteria are improved noticeably. Root-mean-square error decreases by 25%, and the determination coefficient (R2) increases by 27.94%.

Evaluation of Shallow Groundwater Quality and Optimization of Monitoring Indicators in Nanchang

In the process of groundwater environmental monitoring, while ensuring the representativeness of groundwater quality evaluation, the number of monitoring indicators should be optimized as much as possible, which is of great significance to groundwater environmental management. Based on monitoring data of shallow groundwater in Nanchang in 2014 and 2019, the chemical characteristics of water and the changes in water quality were analyzed via statistical analysis, a Piper three-line diagram, and the entropy-weighted water quality index (EWQI). Furthermore, a key indicator optimization method based on water quality evaluation was constructed by coupling stepwise multiple linear regression analysis. The feasibility of this method was also evaluated. The results showed that the water chemistry type of groundwater in 2014 and 2019 was mainly HCO3-Ca, and the five abnormal indicators pH value, NO3-, I-, Fe, and Mn were the main influencing factors of water quality change. The water quality in 2019 was generally higher than that in 2014, which was considered as overall "moderate," and the average EWQI values of the two years were 53.72 and 82.34, respectively. The optimal model EWQImin-4 constructed based on the key indicator optimization method could better represent the actual EWQI; the key indicators included Mn, NO3-, TH, Fe, pH value, and I-; and the determination coefficient (R2) and percentage error (PE) values were 0.865 and 10.61%, respectively. Therefore, the optimization method of groundwater monitoring indicators based on entropy-weighted water quality evaluation could be used as an important reference for optimizing monitoring indicators and provide a method for regional groundwater environmental management.

Evaluation of Groundwater Quality for Drinking and Irrigation Purposes Using GIS-Based IWQI, EWQI and HHR Model

Groundwater pollution has emerged as a significant water crisis in various regions around the globe. Groundwater serves as a crucial source of water for human consumption and agricultural activities in the Sichuan Basin where groundwater quality has yet to be concentrated. A total of 41 groundwater samples were collected from domestic wells in Suining city of the Sichuan Basin, which were used for analyzing the hydrogeochemical processes and suitability for irrigation and drinking purposes. In the study area, groundwater samples belonged to the HCO3-Ca type. Hydrochemical compositions were dominated by carbonate and silicate mineral dissolution with positive cation exchange. Agricultural activities and urban sewage were the primary sources of NO3− pollution. The irrigation water quality index (IWQI) was calculated using electronic conductivity (EC), Na+, Cl−, HCO3−, and sodium adsorption ratio (SAR). The IWQI values showed that the suitability of groundwater irrigation was generally good and presented the decreasing trend southeastwardly. According to the entropy-weighted water quality index (EWQI), the groundwater quality for drinking purposes was generally good. However, there were some local areas with poor water quality concentrated in the southeast part. According to the human health risk (HHR) model, the groundwater was deemed safe for adults and children. However, for infants, the nitrate level in the groundwater remained high and posed potential health risks. The combined IWQI and EWQI evaluation served as a valuable reference for the utilization of the groundwater resource in the Sichuan Basin, as well as other comparable regions worldwide.

Assessing lake water quality during COVID-19 era using geospatial techniques and artificial neural network model.

The present study evaluates the impact of the COVID-19 lockdown on the water quality of a tropical lake (East Kolkata Wetland or EKW, India) along with seasonal change using Landsat 8 and 9 images of the Google Earth Engine (GEE) cloud computing platform. The research focuses on detecting, monitoring, and predicting water quality in the EKW region using eight parameters-normalized suspended material index (NSMI), suspended particular matter (SPM), total phosphorus (TP), electrical conductivity (EC), chlorophyll-α, floating algae index (FAI), turbidity, Secchi disk depth (SDD), and two water quality indices such as Carlson tropic state index (CTSI) and entropy‑weighted water quality index (EWQI). The results demonstrate that SPM, turbidity, EC, TP, and SDD improved while the FAI and chlorophyll-α increased during the lockdown period due to the stagnation of water as well as a reduction in industrial and anthropogenic pollution. Moreover, the prediction of EWQI using an artificial neural network indicates that the overall water quality will improve more if the lockdown period is sustained for another 3years. The outcomes of the study will help the stakeholders develop effective regulations and strategies for the timely restoration of lake water quality.

Evaluation of Human Health Risks Associated with Groundwater Contamination and Groundwater Pollution Prediction in a Landfill and Surrounding Area in Kaifeng City, China

Landfill accumulation can cause its leachate to seep into groundwater, which can lower the quality of local groundwater. Exploring the risks of groundwater contamination to human health in the area around a landfill can offer a clear understanding of the current situation of regional groundwater and provide a theoretical basis for groundwater remediation and governance. By taking a landfill in Kaifeng City, China as the research object, this study explored the chemical types and sources of groundwater in the study area, used the entropy-weighted water quality index (EWQI) to evaluate the groundwater quality and assessed human health risks in the study area. The results show that the groundwater in the study area is neutral (7.14 ≤ pH ≤ 7.86), and the water chemical type is HCO3−—Ca·Na. The EWQI results indicated that the overall water quality in the study area ranges from 48.4 to 250.26, which is above the medium level, and that the local water quality is poor. The deterioration of groundwater quality in the study area is mainly influenced by NH4+-N, Mn, As, F− and Pb. According to the human health risk assessment model, the non-carcinogenic risk to humans through oral and dermal exposure can be assessed. In this paper, five ions, NH4+-N, Mn, As, F− and Pb in groundwater, were selected for the analysis of groundwater in the study area to assess non-carcinogenic risk to humans through oral administration. The results showed that the hazard quotient (HQ) values for NH4+-N, Mn, As, F− and Pb varied in the following ranges: 9.14 × 10−4—0.03; 0.07—0.22; 0.02–0.07; 0.16—0.23; and 0.01—0.13, respectively (all of these are less than 1, and so the potential risks to human health can be ignored). The characteristic pollutant Pb was selected as a predictor to study the influence on groundwater quality in eastern fish ponds and farmlands under continuous leakage. The leakage can be detected timeously to reduce the effects downstream by using enhanced monitoring measures.

Hydrochemical evolution characteristics, controlling factors, and high nitrate hazards of shallow groundwater in a typical agricultural area of Nansi Lake Basin, North China

Anthropogenic nitrate contamination in groundwater could not be neglected, which has been a global issue threatening public health, especially in agricultural fields where fertilizers were used intensively. The present study focused on evaluating the groundwater evolution process, quality, and associated health risks from nitrate pollution in Nansi Lake Basin (NLB), a typical intensive agricultural region of North China. For this purpose, fifty-two shallow groundwater samples were collected and analyzed major chemical parameters in June 2022. The groundwater samples are found to be mainly dominated by HCO3–Ca·Mg and SO4·Cl–Ca·Mg types. Water-rock interactions like minerals dissolution/precipitation and ion exchange were found to be the important processes influencing hydrochemistry. Nitrate content in groundwater fluctuated from 1.9 to 750.0 mg/L (average:148.7 mg/L), with about 75% of samples surprisingly exceeding the permissible limit (50 mg/L) set by the World Health Organization (WHO). Anthropogenic activities can be classified as excessive nitrogen fertilizer application, livestock manure, and industrial/domestic sewage, coupled with irrigation return flow, which brought significant hazards to human health. The calculation results of entropy weighted water quality index (EWQI) showed that about half of groundwater samples are unfit for drinking purposes. Most importantly, 88.5%, 88.5%, 73.1%, and 71.2% of the water samples had considerable NO3− health risks (HQ > 1) for infants, children, females, and males, respectively. It is suggested that the groundwater should be chemical and biological denitrification for nitrate removal before being used for drinking purposes. The findings of this work can help policymakers to solve groundwater pollution problems and ensure healthy drinking water in such intensive agricultural basins and other similar regions worldwide.

An approach based on multivariate distribution and Gaussian copulas to predict groundwater quality using DNN models in a data scarce environment

Machine Learning models have become a fruitful tool in water resources modelling. However, it requires a significant amount of datasets for training and validation, which poses challenges in the analysis of data scarce environments, particularly for poorly monitored basins. In such scenarios, using Virtual Sample Generation (VSG) method is valuable to overcome this challenge in developing ML models. The main aim of this manuscript is to introduce a novel VSG based on multivariate distribution and Gaussian Copula called MVD-VSG whereby appropriate virtual combinations of groundwater quality parameters can be generated to train Deep Neural Network (DNN) for predicting Entropy Weighted Water Quality Index (EWQI) of aquifers even with small datasets. The MVD-VSG is original and was validated for its initial application using sufficient observed datasets collected from two aquifers. The validation results showed that from only 20 original samples, the MVD-VSG provided enough accuracy to predict EWQI with an NSE of 0.87. However the companion publication of this Method paper is El Bilali et al. [1].•Development of MVD-VSG to generate virtual combinations of groundwater parameters in data scarce environment.•Training deep neural network to predict groundwater quality.•Validation of the method with sufficient observed datasets and sensitivity analysis.

Coupling Hydrochemistry and Stable Isotopes (δ2H, δ18O and 87Sr/86Sr) to Identify the Major Factors Affecting the Hydrochemical Process of Groundwater and Surface Water in the Lower Reaches of the Yarlung-Zangbo River, Southern Tibet, Southwestern China

In Tibet, water resource has been less studied due to remote and harsh conditions. In this study, hydrochemistry and stable isotopes (δ2H, δ18O and 87Sr/86Sr) were employed to investigate the major factors affecting the hydrochemical process of groundwater and surface water in the lower reaches of the Yarlung-Zangbo River, southern Tibet. Groundwater and surface water were weakly alkaline and very soft to moderately hard water. The hydrochemical type of surface water is mainly Ca-HCO3 (mainstream) and Ca-SO4-HCO3 (tributary), while the hydrochemical type of groundwater was mainly Ca-SO4-HCO3. Multivariate statistical analysis and Gibbs analysis proposed hydrochemical components were dominated by water-rock interaction. Ion ratio, saturation index, and Sr isotope revealed calcite dissolution and silicate weathering with local sulfide oxidation were involved in water–rock interaction. D-O isotopes indicated the recharge source was mainly derived from atmosphere precipitation. The entropy-weighted water quality index indicated surface water and groundwater reach the standard of drinking purpose in the lower reaches of the Yarlung-Zangbo River. The hydrochemical type varied regularly along the Yarlung-Zangbo River. The dissolution of carbonate rocks and local silicate weather and evaporate dissolution are the primary hydrochemical process along the Yarlung-Zangbo River. This study would provide a preliminary insight for hydrochemical process in the Yarlung-Zangbo River.

Solute Geochemistry and Water Quality Assessment of Groundwater in an Arid Endorheic Watershed on Tibetan Plateau

Understanding groundwater geochemistry is crucial for water supply in arid regions. The present research was conducted in the arid Mo river watershed on the Tibetan plateau to gain insights into the geochemical characteristics, governing processes and quality of groundwater in arid endorheic watersheds. A total of 28 groundwater samples were collected from the phreatic and confined aquifers for hydrochemical analysis. The results showed that the groundwater was slightly alkaline in all aquifers of the watershed. The phreatic groundwater samples (PGs) and confined groundwater samples (CGs) had the TDS value in the ranges of 609.19–56,715.34 mg/L and 811.86–2509.51 mg/L, respectively. PGs were salter than CGs, especially in the lower reaches. Both the PGs and CGs were dominated by the Cl-Na type, followed by the mixed Cl-Mg·Ca type. The toxic elements of NO2− (0.00–0.20 mg/L for PGs and 0.00–0.60 mg/L for CGs), NH4+ (0.00–0.02 mg/L for PGs and 0.00–0.02 mg/L for CGs) and F− (0.00–4.00 mg/L for PGs and 1.00–1.60 mg/L for CGs) exceeded the permissible limits of the Chinese guidelines at some sporadic sites. Water–rock interactions, including silicates weathering, mineral dissolution (halite and sulfates) and ion exchange, were the main contributions to the groundwater chemistry of all aquifers. The geochemistry of PGs in the lower reach was also greatly influenced by evaporation. Agricultural sulfate fertilizer input was responsible for the nitrogen pollutants and salinity of PGs. All CGs and 73.91% of PGs were within the Entropy-weighted water quality index (EWQI) of below 100 and were suitable for direct drinking purposes. Precisely 8.70 and 17.39% of PGs were within the EWQI value in the range of 100–150 (medium quality and suitable for domestic usage) and beyond 200 (extremely poor quality and not suitable for domestic usage), respectively. The electrical conductivity, sodium adsorption ratio, sodium percentage and permeability index indicated that groundwater in most parts of the watershed was suitable for irrigation, and only a small portion might cause salinity, sodium or permeability hazards. Groundwater with poor quality was mainly distributed in the lower reaches. CGs and PGs in the middle-upper reaches could be considered as the primary water resources for water supply. Agricultural pollution should be paid more attention to safeguard the quality of groundwater.

Levels, origins and probabilistic health risk appraisal for trace elements in drinking water from Lhasa, Tibet.

Due to the lack of monitoring systems and water purification facilities, residents in western China may face the risk of drinking water pollution. Therefore, 673 samples were collected from Lhasa's agricultural and pastoral areas to reveal the status quo of drinking water. We used inductively coupled plasma-mass spectrometry to determine trace elements concentrations for water quality appraisal, source apportionment, and health risk assessment. The results indicate that concentrations of V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd, Ba, and Pb are below the guidelines, while As concentrations in a few samples exceed the standard. All samples were classified into "excellent water" for drinking purpose based on Entropy-weighted water quality index. Thereafter by principal component analysis, three potential sources of trace elements were extracted, including natural, anthropogenic, and mining activities. It is worth noting that geotherm and mining exploitation does not threaten drinking water safety. Finally, health risks were assessed using Monte Carlo technique. We found that the 95th percentiles of hazard index are 1.80, 0.80, and 0.79 for children, teenagers, and adults, indicating a non-carcinogenic risk for children, but no risks for the latter two age groups. In contrast, the probabilities of unacceptable cautionary risk are 7.15, 2.95 and 0.69% through exposure to Cr, Ni, As, and Cd for adults, children, and teenagers. Sensitivity analyses reveal As concentration and ingestion rate are most influential factors to health risk. Hence, local governments should pay more attention to monitoring and removal of As in the drinking water.

Major concerns of surface water quality in south-west coastal regions of Vietnamese Mekong Delta

This study aimed to appraise seasonal variations in surface water quality on the coasts of Southwestern Vietnam using entropy-weighted water quality index (EWQI) and multivariate statistics: cluster analysis (CA), principal component analysis (PCA), and discriminant analysis (DA). Forty-nine samples monitored in Kien Giang province during the rainy and dry seasons were analysed for 16 physiochemical and biological parameters. Compared to the Vietnamese standard, surface water quality in the study areas was contaminated with organic matter (high biological oxygen demand and chemical oxygen demand), nutrients (high ammonium (NH4+), nitrite, and orthophosphate), total suspended solids (TSS), iron (Fe), and coliform. Seasonal variations in surface water quality in the coastal regions were observed. TSS, organic matter and microbial problems in water bodies tend to be more serious in the rainy seasons due to an increase in water flow containing pollutants from upstream and wastes from regional human activities. Meanwhile, the salinity in the dry season (0–32‰) was greatly higher, which caused only 10% of samples to be suitable for irrigation. CA extracted 11 and 13 clusters from 49 locations in the dry and rainy seasons, respectively. Five principal components obtained from PCA can explain 74 and 70% of total water quality variations in dry and rainy seasons, respectively. Moreover, the results of PCA suggested that natural factors (hydrological regimes, temperature, rainfall, sea-level rise) and human sources (domestic, agriculture, industry, and tourism) are accountable for these fluctuations. DA extracted 7 parameters (pH, TSS, salinity, Fe, nitrate, NH4+, and chloride) for leading the difference in water quality, with 88% of correct assignation. EWQI revealed that about 66% of total samples were classified as a very bad quality for drinking in the dry season. However, this ratio declined to 59% in the rainy season. Although the surface water quality was slightly improved during the rainy season, organic matter and microbial pollution need to be concerned. The findings of this study can provide insights into seasonal variations in surface water with the application of multivariate statistics and EWQI, which could support policymakers in developing water management strategies.

Bottled water quality assessment through entropy-weighted water quality index (EWQI) and pollution index of groundwater (PIG): a case study in a fast-growing metropolitan area in Türkiye

ABSTRACT The aim of the study is to evaluate the quality of water collected during wet and dry seasons of Şanlıurfa and to expand and improve understanding of the pollution status and drinking quality of bottled water used for drinking purposes. To do so, an entropy-weighted water quality index (EWQI) and a groundwater pollution index (PIG) were used to evaluate data on physicochemical parameters related to drinking water quality standards. The parameters related to bottled water quality were evaluated with Piper and Ternary diagrams. The calculated EWQI values ranged from 7.78 to 29.74 in wet season whereas 11.63 to 32.20 in dry season. Overall, EWQI data showed that all of water samples were suitable for drinking. Similarly, the values of PIG varied from 0.09 to 0.3 in wet season but 0.1 to 0.26 in dry season, which also showed that all water samples from the study area were suitable.

Water quality variation and source apportionment using multivariate statistical analysis

Rivers are the primary source of potable water and other residential uses. Since wastewater from the nearby industries and urban areas degrades the quality of the rivers, it is essential to monitor their water quality and quantity continuously. This study determines the pollution level in a river in accordance with drinking water standards. The entropy-weighted water quality index (EWQI) indicates the level of river pollution, and the principal component analysis (PCA) and Pearson correlation matrix were used to identify pollution sources. Twenty parameters were considered for the study, which includes alkalinity, biochemical oxygen demand (BOD), calcium (Ca+2), cadmium (Cd), chloride (Cl-), copper (Cu), dissolved oxygen (DO), electrical conductivity (EC), fluoride (F-), hardness, iron (Fe), lead (Pb), magnesium (Mg+2), manganese (Mn), nitrate (NO3 -), pH, potassium (K+), sodium (Na+), sulphate (SO4 2-), total dissolved solids (TDS) and zinc (Zn). The EWQI varied between 68.93 (good) to 259.91 (extremely poor). Post-monsoon EWQI values are higher due to the fact that monsoon-filled sewers continue to drain into the river even after the rainy or monsoon season has ended. Winter, pre-monsoon, and monsoon all produced five components (PCs) that explained 79%, 74%, and 80% of the overall variance, respectively and post-monsoon produced 6 PCs that explained 83% of the overall variance. PCA and Pearson correlation matrix indicate pollution from runoff sources during the pre-monsoon and monsoon season along with the pollution sources existing in the monsoon and post-monsoon season, domestic and metal sources.

Understanding the factors contributing to groundwater salinity in the coastal region of Andhra Pradesh, India

The present study focused on understanding the factors responsible for groundwater salinity in the coastal region, Prakasam district, Andhra Pradesh, India. Groundwater samples were collected and analysed for pH, EC, TDS, TA, TH, CH, NCH, EA, Ca2+, Mg2+, Na+, K+, HCO3−, Cl−, SO42−, NO3−, and F−. Groundwater quality was assessed using entropy weighted water quality index (EWQI), Chadha and Gibbs diagrams, ionic ratios, chloro-alkaline indices (CA), saturation indices (SI), principal component analysis (PCA), and hierarchical cluster analysis (HCA). TDS vs TH indicated that 86% of groundwater samples fall under brackish-cum-very hard water-quality type, while TA and TH relationship showed that 70% and 30% of groundwater samples fall under non‑carbonate hardness (NCH) and excess alkalinity (EA), respectively. EWQI classified groundwater samples into medium (22%), poor (40%), and exremely poor (38%) water quality types, indicating that most samples are not suitable for drinking purposes. Hydrogeochemical types in Chadha diagram showed saline water (Na+-Cl−) type in 92% of groundwater samples. Ionic ratios indicated that anthropogenic activities resulting from the leaching of surface water pollutants are the main source of groundwater pollution. Base ion exchange was indicated as the main process in CA indices. SI revealed precipitation of the calcite phase and dissolution of the gypsum and halite phases in groundwater. Evaporation appeared in Gibb's diagram as a primary process rather than a geogenic origin. PC1 (Na+, Cl−, SO42−, Mg2+, K+, and NO3−) and PC2 (HCO3− and F−) were considered salinity-process and alkalinity-process, respectively. The main sources of salinity in groundwater are brackish-water aquaculture and salt-making activities with household waste, septic tank spills, irrigation-return-flows, and chemical fertilizers being secondary sources. HCA classified groundwater samples into Group-I (46%), which represents domestic water, agricultural activities, etc., Group-II (30.24%), which shows the influence of both Group-I and Group-II, and Group-III (23.76%), which specifies brackish-water aquaculture and salt-making activities. Consequently, the present study obviously indicated that the groundwater quality of anthropogenic origin has largely overcome the influence of geogenic sources. The EWQI classification spatially delineated the study region into medium, high, and very high vulnerable zones, covering 28.69%, 32.75%, and 38.56%, respectively. Therefore, it is suggested to control the dumping of domestic waste and septic tank leaks, limit irrigation-return-flows and chemical fertilizers, ban brackish water aquaculture and salt production activities, and strictly implement an aquifer recharge management strategy to ensure human health. This study will assist decision-makers in addressing groundwater salinity issues in coastal regions.

A framework based on multivariate distribution-based virtual sample generation and DNN for predicting water quality with small data

Deep Neural Network (DNN) is a powerful tool for predicting and monitoring water quality. However, its application is only limited to well-monitored zones where the availability of data for training and validation phases. In this study, we attempt to develop a novel framework based on Multivariate distributions (MVD) (elliptical copulas)-based Virtual Sample Generation (VSG) method to broaden the application of DNN to predict water quality even with a small dataset. This framework is evaluated to predict the Entropy Weighted Water Quality Index (EWQI) using DNN and Electrical Conductivity, Temperature, and pH as input variables, in Berrechid and Chaouia aquifer systems, Morocco. Validation results showed that the virtual samples generated from 400, 50, 30, and 20 original samples improved the NSE from 0.88 to 0.92, from 0.53 to 0.91, from 0.42 to 0.91, and from 0.24 to 0.87, respectively. Besides, sensitivity analysis of the methodology to the virtual data sizes and the original samples showed that the RMSE and NSE of the DNN models have limits in function to virtual data sizes according to the first order Exponential Decay and logistic trends, respectively. These limits highly depend on original sample sizes. Such empirical trends are crucial for reproducing the proposed methodology in other sites to determine optimal virtual datasets. Overall, the proposed methodology provided new insights to improve the DNN model performances in predicting water quality with small datasets. Hence, it is useful to manage water quality in order to supply clean water for the population in poorly monitored zones.

Geochemical Characteristics and Quality Appraisal of Groundwater From Huatugou of the Qaidam Basin on the Tibetan Plateau

Groundwater is the foremost water resource for various purposes in arid regions. The extremely arid climate makes groundwater geochemistry there evolve faster in a short distance and water supply face higher pressure of poor geochemical quality. A hyper-arid watershed on the Tibetan Plateau was investigated to get insights into the geochemical signature, formation, and quality suitability of groundwater there. A total of 13 surface water samples and 32 phreatic groundwater samples were collected for hydrogeochemical analysis. The results showed groundwater had better hydrogeochemical quality than surface water and was more favorable for human society utilization. Groundwater was dominated by relatively fresh hydrochemical facies of HCO3–Ca, mixed HCO3–Na·Ca, and mixed Cl–Mg·Ca type with more than 93% of samples having the TDS below 1,000 mg/L. Most of the groundwaters were soft fresh water (84.38%) and had excellent to good quality (93%) for domestic purposes based on entropy-weighted water quality index evaluation. Groundwater was suitable for irrigation in terms of sodium and permeability hazard, but the potential salinity hazard should be concerned. The poor geochemical quality of groundwater was ascribed to the salinity caused by strong evaporation. Natural rock–water interactions including silicate weathering, carbonate dissolution, and cation exchange were still the predominated processes governing groundwater chemical composition. The influence of human activities was very limited. Groundwater resource exploitation and management should mainly consider the salinity and strong evaporation due to shallow water depth.

Assessment of groundwater geochemistry using multivariate water quality index and potential health risk in industrial belt of central Odisha, India

Groundwater in India has been shown to have a variety of water quality issues, including fluoride, nitrate, and uranium pollution, all of which pose a health risk to humans. In the present study, a total of 106 groundwater samples from the Angul district of Odisha, an industrialized region in India, were analyzed for 14 different hydrochemical parameters. In almost 30%, 34.9%, and 4.7% of the groundwater samples, the concentrations of F-, NO3− and uranium, respectively, exceeded the permissible limit set by WHO. In addition to the fixed-weight groundwater quality index (GWQI), the entropy-weighted water quality index (EWQI), the principal component analysis (PCA) factor (or rotated factor) loading based water quality index (PCWQI) and human health risk assessment were used. Depending on the models, about 19.1 ± 0.9%, 70.5 ± 1.9% and 10.38 ± 1.9% of water samples were classified as “Excellent”, “Good” and “Medium” quality, respectively, across four water quality indexes with a nominal rating disagreement of 11.3%. More than 90% of samples are unanimously classified as excellent or good across the WQI rating. For children and adults, approximately 54.7% and 24.5% of samples exceeded the permitted limit for F−, (hazard quotient HQ > 1), posing non-carcinogenic health hazards, respectively. In contrast, 71.7% and 34.9% of NO3− samples respectively, surpassed the allowed limit and caused non-carcinogenic health concerns for children and adults. In terms of carcinogenic HQ values, about 13.2% and 7.5% of samples exhibit an uranium related carcinogenic health risk in children and adults, respectively. The existence of significant amounts of Cl -, NO3-, and especially HCO3− ions in groundwater in some samples, as well as their positive interdependence, may increase uranium pollution in the future through uranium dissolution.

Assessment of PTEs in water resources by integrating HHRISK code, water quality indices, multivariate statistics, and ANNs

The use of contaminated water for drinking and sanitary purposes can be detrimental to human health. In this article, the Human Health Risk (HHRISK) code was applied, alongside the modified heavy metal index (MHMI), synthetic pollution index (SPI), and entropy-weighted water quality index (EWQI), to investigate the pollution status, ingestion, and dermal health risks of potentially toxic elements (PTEs) (Fe, Zn, Mn, Pb, Cr, and Ni) in water resources from the Umunya area, Nigeria. Physicochemical measurements followed standard methods. Results of the MHMI, SPI, and EWQI revealed that about 60% of the water samples had low pollution and were considered suitable for human consumption, while 40% were unsuitable. Further, cumulative non-carcinogenic health risk scores indicated that 60% of the water samples pose low-medium risks while 40% pose high risks to child and adult populations. Contrarily, results of cumulative carcinogenic health risk showed that 6.67% of the samples expose water users to low risks, whereas 93.33% expose them to high risks. Although there are agreements between the results for both adult and child populations (regarding non-carcinogenic and carcinogenic risks), it is worth highlighting that the risk scores for children were higher. Therefore, children in the study area are more vulnerable to both carcinogenic and non-carcinogenic health risks. Also, it was revealed that the risk due to ingestion was higher than that due to dermal contact. Linear regression analysis showed strong agreement between the indexical models and the cumulative health risks. While artificial neural networks and multiple linear regression models accurately predicted the water quality indices, hierarchical dendrograms efficiently classed the water samples into various spatiotemporal water quality groups.

Hydrogeochemical insights into the signatures, genesis and sustainable perspective of nitrate enriched groundwater in the piedmont of Hutuo watershed, China

Groundwater nitrate contamination has been a global issue threatening water supply, especially in arid and semiarid piedmonts. An extensive hydrogeochemical investigation was performed in an intensively human-impacted semiarid piedmont in North China. Groundwater is nearly neutral to alkaline in nature with large variations of salinity. The concentration of nitrate in groundwater ranges from 4.89 mg/L to 402.40 mg/L with 64.29% of values beyond the permissible limit of 50 mg/L recommended by World Health Organization. Hydrogeochemistry changes gradually from fresh HCO3-Ca type waters to moderately saline Cl-Ca type with the increase of nitrate content in groundwater. Anthropogenic inputs from residential land usage are responsible for the nitrate enrichment and salinity increase. The Entropy-weighted water quality index value ranges from 29 to 327 with a mean of 82, and 21.43% of sampled groundwaters under the categories from medium to extremely poor quality for drinking purpose. Non-carcinogenic risks posed by nitrate, nitrite and ammonia via dermal and oral pathways were assessed for infants, children, adult females and males. The overall non-carcinogenic risk of investigated groundwaters ranges from 2.91 × 10−1 to 2.38 × 101 (mean of 4.00 × 100) for infants, 1.29 × 10−1 to 1.06 × 101 (mean of 1.78 × 100) for children, 5.74 × 10−2 to 4.70 × 100 (mean of 7.90 × 10−1) for adult females, and 4.24 × 10−2 to 3.47 × 100 (mean of 5.83 × 100) for adult males. Health risks vary but all population groups are at risk. These health threats are mainly posed by nitrate through the oral ingestion pathway, with minors being more susceptible. Therefore, measures including awareness improvement of local residents, environmental protection, differential water supplies based on water quality, and targeted water treatment are recommended to achieve sustainable groundwater resource development in arid and semiarid piedmonts.

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 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.