This study investigates the groundwater quality in the Kadiri Basin, Ananthapuramu district of Andhra Pradesh, India. Groundwater samples from 77 locations were collected and tested for the ...concentration of various physicochemical parameters. The collected data were assimilated in the form of a groundwater quality index to estimate groundwater quality (drinking and irrigation) using an information entropy-based weight determination approach (EWQI). The water quality maps obtained from the study area suggest a definite trend in groundwater contamination of the study area. Furthermore, the influence of different physicochemical parameters on groundwater quality was determined using machine learning techniques. Learning and prediction accuracies of four different techniques, namely artificial neural network (ANN), deep learning (DL), random forest (RF), and gradient boosting machine (GBM), were investigated. The performance of the ANN model (MEA = 11.23, RSME = 21.22, MAPE = 7.48, and
R
2
= 0.91) was found to be highly effective for the present dataset. The ANN model was then used to understand the relative influence of physicochemical parameters on groundwater quality. It was observed that the deterioration in groundwater quality in the study area was primarily due to the excess concentration of turbidity and iron values. The relatively higher concentration of sulfate and nitrate had caused a significant impact on the groundwater quality. The study has wider implications for modeling in similar drought-prone agricultural areas elsewhere for assessing the groundwater quality.
This study reported a set pair analysis (SPA)-Markov chain model for groundwater quality assessment and prediction. This model combines the SPA concept and the Markov chain theory. Xi'an City was ...taken as a case study area, and groundwater quality monitoring data from 1996 to 2015 were used as an example to justify the feasibility of the SPA-Markov model. SPA was used to determine the groundwater quality grades, and entropy weighted Markov chain was used in groundwater quality prediction based on the weighted average connection number calculated by SPA. The assessment results indicate that groundwater quality in the study area is very poor in the year from 1996 to 1998, and it becomes improved after 1998. The prediction results show that the groundwater quality in 2014 and 2015 is both classified into the good quality grade, which is consistent with the actual groundwater quality grade. The proposed model is able to treat uncertainties in groundwater quality assessment and is effective in short term groundwater quality prediction.
•Evaluation indices & statistical approaches are applied to characterize water quality.•Spatial distribution of groundwater quality are determined by geostatistical modeling.•The study provides ...insights for decision makers for groundwater quality management.
This study investigates the groundwater quality in the Faridpur district of central Bangladesh based on preselected 60 sample points. Water evaluation indices and a number of statistical approaches such as multivariate statistics and geostatistics are applied to characterize water quality, which is a major factor for controlling the groundwater quality in term of drinking purposes. The study reveal that EC, TDS, Ca2+, total As and Fe values of groundwater samples exceeded Bangladesh and international standards. Ground water quality index (GWQI) exhibited that about 47% of the samples were belonging to good quality water for drinking purposes. The heavy metal pollution index (HPI), degree of contamination (Cd), heavy metal evaluation index (HEI) reveal that most of the samples belong to low level of pollution. However, Cd provide better alternative than other indices. Principle component analysis (PCA) suggests that groundwater quality is mainly related to geogenic (rock–water interaction) and anthropogenic source (agrogenic and domestic sewage) in the study area. Subsequently, the findings of cluster analysis (CA) and correlation matrix (CM) are also consistent with the PCA results. The spatial distributions of groundwater quality parameters are determined by geostatistical modeling. The exponential semivariagram model is validated as the best fitted models for most of the indices values. It is expected that outcomes of the study will provide insights for decision makers taking proper measures for groundwater quality management in central Bangladesh.
To ensure safe drinking water sources in the future, it is imperative to understand the quality and pollution level of existing groundwater. The prediction of water quality with high accuracy is the ...key to control water pollution and the improvement of water management. In this study, a deep learning (DL) based model is proposed for predicting groundwater quality and compared with three other machine learning (ML) models, namely, random forest (RF), eXtreme gradient boosting (XGBoost), and artificial neural network (ANN). A total of 226 groundwater samples are collected from an agriculturally intensive area Arang of Raipur district, Chhattisgarh, India, and various physicochemical parameters are measured to compute entropy weight-based groundwater quality index (EWQI). Prediction performances of models are determined by introducing five error metrics. Results showed that DL model is the best prediction model with the highest accuracy in terms of R2, i.e., R2 = 0996 against the RF (R2 = 0.886), XGBoost (R2 = 0.0.927), and ANN (R2 = 0.917). The uncertainty of the DL model output is cross-verified by running the proposed algorithm with newly randomized dataset for ten times, where minor deviations in the mean value of performance metrics are observed. Moreover, input variable importance computed by prediction models highlights that DL model is the most realistic and accurate approach in the prediction of groundwater quality.
•Groundwater quality is assessed using EWQI method.•Machine learning (ML) algorithms are used for predicting groundwater quality.•Prediction performance of RF, XGBoost, ANN and DL models are compared.•DL based quality prediction model performs much better than other ML models.
Groundwater of alluvial fan plains is the foremost water source, especially in arid/semiarid regions. Its contaminants are big issues for water supply and public health concern. To reveal the ...groundwater chemistry, contaminants sources and health threats in alluvial aquifers, 81 groundwaters were collected from a typical alluvial fan plain of northern China for nitrogen, fluoride and major ions analysis. Statistical analysis and hydrochemical diagrams as well as human health risk assessment were performed. Nitrate is widely distributed and 53% of groundwaters exceed the permissible limit with the maximum concentration up to 326 mg/L. The distributions of nitrite, ammonia and fluoride contaminants are sporadic in spatial, and the concentrations of fluoride in groundwaters are slightly beyond the permissible limit of 1 mg/L. The hydrochemical facies shift from HCO3-Ca or Mixed HCO3-Na·Ca type to Mixed Cl-Mg·Ca and ClCa type with the increase of nitrate content. Two factors (Factor-1 and Factor-2) are extracted by factor analysis and account 63% of the total variances. The positive loading of F− and negative loading of NO3− on Factor-2 reveal geogenic and anthropogenic origins, respectively. The significant positive loadings of TDS, TH, SO42−, Cl−, Ca2+, Mg2+ on Factor-1 reveal the governing mechanisms on groundwater chemistry by intermixed sources of geogenic origins and anthropogenic inputs. Hydrogeochemical evolution in the study area is driven by both water-rock interaction and anthropogenic forces. Anthropogenic inputs/influences are the dominated forces increasing groundwater nitrate content and salinity in the piedmont zone and the residential and industrial zone of the southeastern lower parts, and would pose potential non-carcinogenic risks to various populations via oral intake pathway. Rational measures should be taken to protect groundwater quality out of the threats of anthropogenic pollution. The geogenic fluoride in groundwater would threat the health of children through oral pathway and should be also concerned.
The driving forces of groundwater chemistry in alluvial fan plains were revealed using integrated approach of factor analysis and geostatistical modelling.
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•Groundwater quality in alluvial fan plains is potentially threatened by both geogenic and anthropogenic contaminants.•Anthropogenic contamination inputs can lead to groundwater chemical facies evolution towards salty types.•Factor analysis coupled with geostatistical modelling was used to reveal the hydrogeochemical driving forces in spatial.•Anthropogenic nitrate can pose higher health risk than nitrite, ammonia and geogenic fluoride in present alluvial aquifer.
Sustainable groundwater quality is a key global concern and has become a major issue of disquiets in most parts of the world including Bangladesh. Hence, the assessment of groundwater quality is an ...important study to ensure its sustainability for various uses. In this study, a combination of multivariate statistics, geographical information system (GIS) and geochemical approaches was employed to evaluate the groundwater quality and its sustainability in Joypurhat district of Bangladesh. The results showed that the groundwater samples are mainly Ca–Mg–HCO
3
type. Principal component analysis (PCA) results revealed that geogenic sources (rock weathering and cation exchange) followed by anthropogenic activities (domestic sewage and agro-chemicals) were the major factors governing the groundwater quality of the study area. Furthermore, the results of PCA are validated using the cluster analysis and correlation matrix analysis. Based on the groundwater quality index (GWQI), it is found that all the groundwater samples belong to excellent to good water quality domains for human consumption, although iron, fluoride and iodide contaminated to the groundwater, which do not pose any significant health hazard according to World Health Organization’s and Bangladesh’s guideline values. The results of irrigation water quality index including sodium adsorption ratio (SAR), permeability index and sodium percentage (Na %) suggested that most of the groundwater samples are good quality water for agricultural uses. The spatial distribution of the measured values of GWQI, SAR, Fe (iron), EC (electrical conductivity) and TH (total hardness) were spatially mapped using the GIS tool in the study area.
Assessments for groundwater quality and potential health risk are significant for better utilization and exploitation. In the present study, seventy groundwater samples were collected from domestic ...tube wells and public water-supply wells in the Nanchong area, southwestern China. The integration of statistical analysis, ion correlation, geomodelling analysis, entropy water quality index and health risks assessment were compiled in this study. Statistical analysis indicated the cations followed the concentration order as Ca2+ > Na+ > Mg2+ > K+, while anions' concentrations were HCO3− > SO42− > Cl− > NO3− > F− based on Box and Whisker plot. Piper triangle diagram proposed the hydrochemical type was characterized as Ca-HCO3. Correlations of ions and geomodelling revealed the concentrations of major ions were mainly determined by calcite dissolution and ion exchange process and NO3− concentrations were controlled by agriculture activities. Entropy water quality index computation demonstrated that 96% of groundwater samples possessed the EWQI values of 29–95, and thus were suitable for drinking purpose. The HITotal values for 66% groundwater samples exceeded the acceptable limit for non-carcinogenic risk (HI =1) for infants, followed by 41% for children, 37% for adult males, and 30% for adult females. The non-carcinogenic human health risk of different population groups followed the order of infants > children > adult males > adult females. In future, targeted measures for human health risks of NO3− will focus on the improvements for agricultural activities, including reducing the use of nitrogenous fertilizer, changing irrigation pattern, etc. Our study provides the vital knowledge for groundwater management in the Nanchong and development of the Cheng -Yu Economic Circle.
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•Over 21% of 70 groundwater wells exceeded nitrate (NO3−) above WHO limit (45 mg/L).•Factors controlling NO3− contamination were domestic sewage and agriculture fertilizer.•Groundwater quality was poor in the eastern area, visualized by GIS software.•Non-carcinogenic health risks of NO3− were assessed (infants > children > adults).
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•More studies of MPs in soils and groundwater are needed in South America, Africa, and Oceania.•Soil migration and wastewater effluents may be the potential sources and pathways of MP ...contamination in groundwater.•Most MPs negatively affect soil properties, animals, and plants.•Humans may expose to antibiotic resistance genes through exposure to MP-polluted soils and groundwater.
Microplastic (MP) pollution has raised public concerns in recent decades globally due to its wide global spreading and potential toxicity. Most of existing studies have focused on MP pollution in marine, freshwater, and sediment systems. However, much less research attention has been paid to soil, which is a major sink of MPs. Furthermore, research interest in MPs in groundwater is even lower than that in soils. There is a critical need to digest and summarize the existing knowledge and the latest advancements to promote research on MP pollution in soils and groundwater. As the first of its kind, this work provides a systematical review of the newest knowledge on occurrences, sources, analytical methods, and impacts of MPs in both soils and groundwater. It first outlines the characteristics (global occurrences, sources, and pathways) of MP pollution in soils and groundwater. Commonly used analytical methods including sample collection (sites, tools, depth, points, and quantity), sample preparation (drying and sieving), extraction (separation, digestion, etc.), identification (visual sorting, chromatography, and vibration spectroscopy), and quality assurance/quality control are then systematically reviewed. Furthermore, the risks and impacts of MPs on soil properties, plants, animals, and antibiotic resistance genes (ARGs) between microorganisms and humans are discussed. At the end, this review also identifies the knowledge gaps and points out potential directions for future research.
Evaluation of groundwater quality and related health hazards is a prerequisite for taking preventive measures. The rural region of Telangana, India, has been selected for the present study to assess ...the sources and origins of inferior groundwater quality and to understand the human health risk zones for adults and children due to the consumption of nitrate (
NO
3
-
)- and fluoride (F
−
)-contaminated groundwater for drinking purposes. Groundwater samples collected from the study region were determined for various chemical parameters. Groundwater quality was dominated by Na
+
and
HCO
3
-
ions. Piper’s diagram and bivariate plots indicated the carbonate water type and silicate weathering as a main factor and man-made contamination as a secondary factor controlling groundwater chemistry; hence, the groundwater quality in the study region is low. According to the Groundwater Quality Index (GQI) classification, 48.3% and 51.7% of the total study region are excellent (GQI: < 50) and good (GQI: 50 to 100) water quality types, respectively, for drinking purposes. However,
NO
3
-
ranged from 0.04 to 585 mg/L, exceeding the drinking water quality limit of 45 mg/L in 34% of the groundwater samples due to the effects of nitrogen fertilizers. This was supported by the relationship of
NO
3
-
with TDS, Na
+
, and Cl
−
. The F
−
content was from 0.22 to 5.41 mg/L, which exceeds the standard drinking water quality limit of 1.5 mg/L in 25% of the groundwater samples. The relationship of F
−
with pH, Ca
2+
, Na
+
, and
HCO
3
-
supports the weathering and dissolution of fluoride-rich minerals for high F
−
content in groundwater. They were further supported by a principal component analysis. The Health Risk Index (HRI) values ranged from 0.20 to 20.10 and 0.36 to 30.90 with a mean of 2.82 and 4.34 for adults and children, respectively. The mean intensity of HRI (> 1.0) was 1.37 times higher in children (5.70) than in adults (4.16) due to the differences in weight size and exposure time. With an acceptable limit of more than 1.0, the study divided the region into Northern Safe Health Zone (33.3% for adults and 28.1% for children) and Southern Unsafe Health Zone (66.7% for adults and 71.9% for children) based on the intensity of agricultural activity. Therefore, effective strategic measures such as safe drinking water, denitrification, defluoridation, rainwater harvesting techniques, sanitary facilities, and chemical fertilizer restrictions are recommended to improve human health and protect groundwater resources.
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