Hydrochemical processes and groundwater quality assessment in North eastern region of Jordan valley, Jordan

Groundwater quality assessment using machine learning models: a comprehensive study on the industrial corridor of a semi-arid region

Water plays a significant role in sustaining the lives of humans and other living organisms. Groundwater quality analysis has become inevitable, because of increased contamination of water resources and global warming. This study used machine learning (ML) models to predict the water quality index (WQI) and water quality classification (WQC). Forty groundwater samples were collected near the Ranipet industrial corridor, and the hydrogeochemistry and heavy metal contamination were analyzed. WQC prediction employed random forest (RF), gradient boosting (GB), decision tree (DT), and K-nearest neighbor (KNN) models, and WQI prediction used extreme gradient boosting (XGBoost), support vector regressor (SVR), RF, and multi-layer perceptron (MLP) models. The grid search method is used to evaluate the ML model by F1 score, accuracy, recall, precision, and Matthews correlation coefficient (MCC) for WQC and the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), and median absolute percentage error (MAPE) for WQI. The WQI results indicate that the groundwater quality of the study area is very poor and unsuitable for drinking or irrigation purposes. The performance metrics of the RF model excelled in predicting both WQC (accuracy = 97%) and WQI (R2 = 91.0%), outperforming other models and emphasizing ML's superiority in groundwater quality assessment. The findings suggest that ML models perform well and yield better accuracy than conventional techniques used in groundwater quality assessment studies.

Geochemical processes, salinity sources and utility characterization of groundwater in a semi-arid region of Iraq through geostatistical and isotopic techniques

Identifying factors contributing to water salinity is paramount in efficiently managing limited water resources in arid environments. The primary objective of this study is to enhance understanding regarding the hydrochemistry, source, and mechanism of water salinity, as well as to assess the suitability of water for various uses in southern Iraq. The groundwater samples were collected from water wells and springs and analyzed for major cations and anions along with stable isotopes (δ18O and δ2H) to accomplish the objective. The analysis of major ion chemistry, hydrochemical techniques, principal component analysis (PCA), and isotope signatures were adopted to determine the primary factors contributing to water mineralization. The study inferred that evaporation and geological processes encompassing water-rock interactions, such as dissolution precipitation and ion exchange, were key processes. The stable isotope analysis revealed that the water originated from meteoric sources and underwent significant evaporation during or before infiltration. The utility assessment of water samples indicates that most samples are not appropriate for consumption and are significantly below the established standards for potable water. In contrast, a significant portion of the groundwater samples were found to meet the criteria for irrigation suitability by adopting Wilcox and the US Salinity Laboratory criteria. The groundwater could be considered for irrigation with proper salinity control management. Overall, this study has significantly improved the understanding of the hydrogeochemical regimes and acts as a first step toward the sustainable utilization of water resources.

Hydrogeochemical and multivariate statistical approaches to investigate the characteristics of groundwater quality in fluoride-enriched hard rock region in Tirupathur district of Tamil Nadu, India

Fluoride pollution is a major issue worldwide, posing health risks such as dental and skeletal fluorosis. This study was conducted in fluoride enrichment hard rock regions of Vaniyambadi and Ambur talks in Tirupathur district, Tamil Nadu. Four hundred eighty groundwater samples were collected from May 2021 to April 2022 and grouped as summer, southwest monsoon (SWM), northeast monsoon (NEM), and winter. Maximum concentration of fluoride was found to be 4 mg/L in the summer season in Vellakuttai region of Vaniyambadi taluk. The study aims to investigate the hydrogeochemical process and mechanism influencing groundwater chemistry and it also provides the confirmation of exploratory data analysis in groundwater quality using structural equation modeling. The Piper and Gibbs diagrams illustrate the rock-water interaction and anthropogenic sources that contribute to the NaHCO3 and NaCl-type waters, respectively. Multivariate statistical analysis such as hierarchical cluster analysis (HCA), principal component analysis (PCA), multiple linear regression (MLR), and structural equation modeling (SEM) has been carried out to determine the groundwater quality. HCA manifests the nature and sources of groundwater, whereas PCA divides all the physicochemical parameters into two PC loadings, accounting for 97.46%, 99.46%, 99.18%, and 98.93% of cumulative % of variance during the summer, SWM, NEM, and winter seasons, respectively. PC1 has a higher loading factor to Cl, Ca, and Mg, whereas PC2 has a higher loading factor to Na, HCO3, SO4, and NO3. The results of the MLR model provide higher accuracy in detecting the contamination factors associated with the environment and natural rocks. SEM revealed the goodness-of-fit indices 0.993, 0.999, 1.000, and 0.999 in summer, SWM, NEM, and winter, respectively. Hence, this study provides insight view of variation of fluoride concentration in groundwater in different seasons and also mentions the factors that influence fluoride concentration in Vaniyambadi and Ambur taluk.

Potential health risk assessment, spatio-temporal hydrochemistry and groundwater quality of Yamuna river basin, Northern India

Groundwater which is an essential source of freshwater for various domestic, agricultural, industrial applications is facing a severe deterioration in quality due to demographic pressure and intense industrial activities. Present study appraises the influence of human induced activities on groundwater quality of Agra-Firozabad industrial belts of Western Uttar Pradesh, Yamuna basin, India. The maximum concentrations of metals and anions found during pre and post monsoon are as follows: Lead 0.302; 0.086, calcium 672; 1260, magnesium 215; 16.8, cadmium 0.0; 0.066, chromium 0.016; 0.005, manganese 0.340; 0.076, nickel 0.044; 0.028, sulfate 514; 286, nitrate 66.7; 3.56 and fluoride 1.17; 2.02 mg/L respectively. Based on results of Water Quality Index, groundwater samples were classified under 'Poor water' category in 34.2% and 52.63% during pre and post-monsoon period, respectively. Accordingly, higher concentrations of bicarbonate and sulfate might have attributed to excess hardness, instrumental in making it unsuitable for industrial usage. However, values of Percent Sodium, Sodium Adsorption Ratio, Magnesium Hazard and Permeability Index signified that groundwater from majority of locations was fit for agricultural use. Health risk assessment studies revealed that children consuming polluted water were affected more as compared to adults. Timely action and strict compliance of regulation is recommended towards groundwater management for defined usage to avert severe health effects and to meet sustainable development goals.

Characterisation of Hydro-Geochemical Processes Influencing Groundwater Quality in Rural Areas: A Case Study of Soutpansberg Region, Limpopo Province, South Africa

Groundwater is often the main or only source of fresh water supply in arid to semi-arid rural areas owing to decreasing rainfall patterns, reduced availability of surface water and socioeconomic activities. It is important to understand the hydro-geochemical processes influencing groundwater quality for improved management and sustainability of resources and to improve rural livelihoods. To understand the hydro-geochemical process influencing the hydro-geochemistry of the Soutpansberg region, this study assessed groundwater quality data from 12 boreholes and 2 geothermal springs collected between 1995 and 2017. This study indicated that the majority of the samples were classified as fresh groundwater dominated by Ca-HCO3 and mixed Ca-Mg-Cl types. Gibbs diagrams, Pearson correlations, bivariate plots and saturation indexes suggested that rock dominance processes, such as weathering of silicates, dissolution of carbonates and halite minerals and ion exchange processes, were the main hydro-geochemical processes influencing the groundwater quality in the Soutpansberg region. The high concentration of F− in the geothermal spring was attributed to the dissolution of fluorite mineral. Point source anthropogenic inputs from fertilisers were attributed to the high concentration of NO3− in the groundwater. This study recommends that research outputs should be used to influence and support policy change and groundwater allocation in arid to semi-arid rural environments for improved management of resources and livelihoods. This study further recommends that suitable deflouridation and denitrification techniques be applied to improve the quality of groundwater for drinking purposes.

A GIS-Based Spatiotemporal Impact Assessment of Droughts in the Hyper-Saline Urmia Lake Basin on the Hydro-Geochemical Quality of Nearby Aquifers

Urmia Lake is a hyper-saline lake in northwestern Iran that has been drying up since 2005. The main objective of this study was to evaluate the water quality in aquifers that are the main source of fresh water for the eastern plains Urmia Lake, which has been drying up due to intensive land use/cover changes and climate change. We evaluated hydro-geochemical data and factors contributing to aquifer pollution and quality variation for nine aquifers in the vicinity of Urmia Lake during the dry and wet seasons from 2000–2020. Our methodology was based on the analysis of 10 years of data from 356 deep and semi-deep wells using GIS spatial analysis, multivariate statistical analysis, and agglomerative hierarchical clustering. We developed a Water Quality Index (WQI) for spatiotemporal assessment of the status of the aquifers. In doing so, we highlighted the value of combining Principal Component Analysis (PCA), WQI, and GIS to determine the hydro-geochemical attributes of the aquifers. We found that the groundwater in central parts of the study area was unsuitable for potable supplies. Anthropogenic sources of contamination, such as chemical fertilizers, industrial waste, and untreated sewage water, might be the key factors causing excessive concentrations of contaminants affecting the water quality. The PCA results showed that over 80% of the total variance could be attributed to two principal factors for most aquifers and three principal factors for two of the aquifers. We employed GIS-based spatial analysis to map groundwater quality in the study area. Based on the WQI values, approximately 48% of groundwater samples were identified as poor to unsuitable for drinking purposes. Results of this study provide a better hydro-geochemical understanding of the multiple aquifers that require preventive action against groundwater damage. We conclude that the combined approach of using a multivariate statistical technique and spatial analysis is effective for determining the factors controlling groundwater quality.

Socio-Environmental Determinants and Human Health Exposures in Arid and Semi-Arid Zones of Iran-Narrative Review

Lifestyle is different in arid and semi-arid zones. However, where people are born and live have a lasting influence on their social and environmental exposure. This review focuses on the, various dimensions of environmental health imbalance inequality especially in significant environmental sources such as (ie, air, water, soil) among provinces that creates a big health gap in the center, East and the Southeast of Iran. Thus, the population of the arid and semi-arid zones of Iran is facing respiratory, cardiovascular, cancer and infection diseases linked to environmental problems such as chemical and microbial pollution due to air pollution and unsafe water sources, respectively. The prevalence of certain types of cancer such as skin, stomach, bladder, prostate and colorectal cancer together with some respiratory and cardiovascular diseases in arid and semiarid zones such as Kerman, Yazd, etc., has been reported in comparison with other provinces frequently. These impacts have effects on multiple levels of health security in those zones. Based on these concerns, we propose key questions that should guide research in the context of the socio environmental science to support science-based management actions in Iran and other similar semi-arid areas worldwide.

Hydrochemical Characteristics and Water Quality Evaluation of Rivers in Different Regions of Cities: A Case Study of Suzhou City in Northern Anhui Province, China

To study the disparity of river hydrochemical characteristics and water quality in different regions of the city, this paper took the Tuo River in the center of Suzhou, Northern Anhui, China and the Bian River on the edge of the urban area as the research objects, used Piper trigram, Gibbs diagram, and hydrogen and oxygen isotope content characteristics to analyze the geochemical characteristics of surface water in the study area, and then the improved fuzzy comprehensive evaluation method was used to evaluate the water quality. The results showed that the hydrochemical types of the two rivers were SO4-Cl-Na type, and the contents of Na+, K+, SO42−, Cl−, Ca2+, total phosphorus (TP) in the Bian River at the edge of the city were much higher than those in the Tuo River at the center of the city (ANOVA, p < 0.001). Gibbs diagram showed that the ion composition of the two rivers was mainly affected by rock weathering. The results of correlation analysis and water quality evaluation showed that Bian River was greatly affected by agricultural non-point source pollution, and its water quality was poor, class IV and class V water account for 95%, while, for Tuo River, due to the strong artificial protection, class II and class III accounted for 40.74% and 59.26%, respectively, and the overall water quality was better than that of Bian River. The evaluation results of irrigation water quality showed that the samples from Tuo River were high in salt and low in alkali, which could be used for irrigation when the soil leaching conditions were good, while Bian River water samples were high in salt and medium in alkali, which was suitable for irrigation of plants with strong salt tolerance.