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From the Forward: \"Contemporary groundwater management has moved well beyond a concern with how much water is stored underground or can be extracted from aquifers. Today we recognise that integrated, effective and efficient groundwater management relies on pulling together work in a variety of disciplines such as climate science, ecology, socioeconomics, public policy and law, as well as hydrogeology. However, whilst we realise the importance of multiple perspectives and a diversity of contexts and data, the challenge of integrating and organising all of this information into a decision making framework remains. It is also abundantly clear that sharing and access to water is a fundamentally political issue and that solutions depend on full engagement of stakeholders as well as mobilisation of knowledge and technologies.\"

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.

In the North China Plain (NCP), groundwater is an important source of water supply and plays a pivotal role in social and economic development. This study investigated the hydrochemical characteristics and genetic mechanism of groundwater in the southeastern part of the NCP using hydrogeochemical and GIS methods, and evaluated groundwater quality using the entropy weighted water quality index (EWQI). To this end, groundwater quality data were collected from 47 locations in 2016 (dry season) and 2017 (wet season). The results showed that the main anion and cation in groundwater in the study area are Na+ and HCO3−. The pH value of all the water samples exceeded 7, and most of the samples were classified as hard-brackish water. In terms of the spatial distribution, areas with relatively high values of the main components (TH, TDS, NO3−, Na+, Cl− and SO42−) of groundwater were mainly distributed in the northeast of the study area and east of Liaocheng. According to the Piper diagram, the groundwater can be mainly classified as HCO3-Ca, Cl-Na, and mixed types. The hydrochemical characteristics of groundwater is mainly controlled by rock weathering (silicates, carbonates and sulfates), and affected significantly by evaporation and cation exchange processes, and to a certain extent by anthropogenic inputs. According to the EWQI, groundwater quality in the study area can be mainly divided into good water and poor water. In addition, the range of areas with very poor water was significantly larger in 2017 than in 2016.

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–HCO3 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.

● The application of ML in groundwater quality assessment and prediction is reviewed. ● Bibliometric analysis is performed and summarized to promote application. ● The details of the application of ML in GQAP are comprehensively summarized. ● Challenges and opportunities of using ML models in GQAP are discussed. Groundwater quality assessment and prediction (GQAP) is vital for protecting groundwater resources. Traditional GQAP methods can not adequately capture the complex relationships among attributes and have the disadvantage of being computationally demanding. Recently, the application of machine learning (ML) in GAQP (GQAPxML) has been widely studied due to ML’s reliability and efficiency. While many GQAPxML publications exist, a thorough review is missing. This review provides a comprehensive summary of the development of ML applications in the field of GQAP. First, the workflow of ML modeling is briefly introduced, as are data preparation, model development, model evaluation, and model application. Second, 299 publications related to the topic are filtered, mainly through ML modeling. Subsequently, many aspects of GQAPxML, such as publication trends, the spatial distribution of study areas, the size of data sets, and ML algorithms, are discussed from a bibliometric perspective. In addition, we review in detail the well-established applications and recent findings for several subtopics, including groundwater quality assessment, groundwater quality modeling using groundwater quality parameters, groundwater quality spatial mapping, probability estimation of exceeding the groundwater quality threshold, groundwater quality temporal prediction, and the hybrid use of ML and physics-based models. Finally, the development of GQAPxML is explored from three perspectives: data collection and preprocessing, model building and evaluation, and the broadening of model applications. This review provides a reference for environmental scientists to better understand GQAPxML and promotes the development of innovative methods and improvements in modeling quality.

Awka and Nnewi metropolises are known for intensive socioeconomic activities that could predispose the available groundwater to pollution. In this paper, an integrated investigation of the drinking water quality and associated human health risks of contaminated groundwater was carried out using geochemical models, numerical water quality models, and the HHRISK code. Physicochemical analysis revealed that the groundwater pH is acidic. Predicted results from PHREEQC model showed that most of the major chemical and trace elements occurred as free mobile ions while a few were bounded to their various hydrated, oxides and carbonate phases. This may have limited their concentration in the groundwater; implying that apart from anthropogenic influx, the metals and their species also occur in the groundwater as a result of geogenic processes. The PHREEQC-based insights were also supported by joint multivariate statistical analyses. Groundwater quality index, pollution index of groundwater, heavy metal toxicity load, and heavy metal evaluation index revealed that 60–70% of the groundwater samples within the two metropolises are unsuitable for drinking as a result of anthropogenic influx, with Pb and Cd identified as the priority elements influencing the water quality. The HHRISK code evaluated the ingestion and dermal exposure pathway of the consumption of contaminated water for children and adult. Results revealed that groundwater from both areas poses a very high chronic and carcinogenic risk from ingestion than dermal contact with the children population showing greater vulnerability. Aggregated and cumulative HHRISK coefficients identified Cd, Pb, and Cu, to have the highest health impact on the groundwater quality of both areas; with residents around Awka appearing to be at greater risks. There is, therefore, an urgent need for the adoption of a state-of-the-art waste management and water treatment strategies to ensure safe drinking water for the public.

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.

An assessment of global groundwater quality is needed in response to the threats posed by anthropogenic and geogenic contaminants. This essay summarises the challenges involved, including a large number of potentially relevant water quality parameters, the poor availability of data in many regions and the complex nature of groundwater systems. Direct monitoring data can sometimes be augmented by indirect methods such as earth observations, and by involving citizen science. A new web portal is being developed to complement existing databases.