Hydrogeochemistry and prediction of arsenic contamination in groundwater of Vehari, Pakistan: comparison of artificial neural network, random forest and logistic regression models.

Arsenic contamination in the groundwater occurs in various parts of the world due to anthropogenic and natural sources, adversely affecting human health and ecosystems. The current study intends to examine the groundwater hydrogeochemistry containing elevated arsenic (As), predict As levels in groundwater, and determine the aptness of groundwater for drinking in the Vehari district, Pakistan. Four hundred groundwater samples from the study region were collected for physiochemical analysis. As levels in groundwater samples ranged from 0.1 to 52 µg/L, with an average of 11.64 µg/L, (43.5%), groundwater samples exceeded the WHO 2022 recommended limit of 10 µg/L for drinking purposes. Ion-exchange processes and the adsorption of ions significantly impacted the concentration of As. The HCO3- and Na+ are the dominant ions in the study area, and the water types of samples were CaHCO3, mixed CaMgCl, and CaCl, demonstrating that rock-water contact significantly impacts hydrochemical behavior. The geochemical modeling indicated negative saturation indices with calcium carbonate and other salt minerals, encompassing aragonite, calcite, dolomite, and halite. The dissolution mechanism suggested that these minerals might have implications for the mobilization of As in groundwater. A combination of human-induced and natural sources of contamination was unveiled through principal component analysis (PCA). Artificial neural networks (ANN), random forest (RF), and logistic regression (LR) were used to predict As in the groundwater. The data have been divided into two parts for statistical analysis: 20% for testing and 80% for training. The most significant input variables for As prediction was determined using Chi-squared analysis. The receiver operating characteristic area under the curve and confusion matrix were used to evaluate the models; the RF, ANN, and LR accuracies were 0.89, 0.85, and 0.76. The permutation feature and mean decrease in impurity determine ten parameters that influence groundwater arsenic in the study region, including F-, Fe2+, K+, Mg2+, Ca2+, Cl-, SO42-, NO3-, HCO3-, and Na+. The present study shows RF is the best model for predicting groundwater As contamination in the research area. The water quality index showed that 161 samples represent poor water, and 121 samples are unsuitable for drinking. Establishing effective strategies and regulatory measures is imperative in Vehari to ensure the sustainability of groundwater resources.

Assessment of landcover impacts on the groundwater quality using hydrogeochemical and geospatial techniques.

Groundwater quality is influenced by urbanization and land use land cover (LULC) changes. This study investigated their impact on groundwater quality in Quetta City, Pakistan, from 2015 to 2021. About 58 groundwater samples from monitoring wells were analyzed using hydrogeochemical and statistical methods. The water quality index (WQI), Wilcox, USSL, and various agricultural indices were employed to assess water quality trends. LULC analysis and NDVI using Sentinel-2 imagery revealed increased urban and agricultural areas and decreased barren land. Rapid urbanization was evident, with the buildup class expanding by 7.50% during this period. NDVI findings emphasized monitoring vegetation health and water quality for environmental assessments. The groundwater in Quetta was primarily classified as Cl-Ca·Mg, Cl-Ca, and Cl-Na according to the Piper diagram, with water-rock interactions and rock weathering evident. Most groundwater samples were suitable for irrigation according to the Wilcox and USSL diagrams. The WQI demonstrated overall safety for human consumption, but declining WQI values in northern parts due to urbanization are concerning. The results also revealed a moderate positive relationship between landcover classes and WQI values. It can be concluded that urbanization and excessive use of pesticides contributed to declining agricultural land quality. The spatial overlay of agricultural indices with landcover class suggested that barren land was most suitable, followed by build-up and agriculture were suitable for drinking and agriculture purposes. Moreover, agricultural indices moderately declined due to excessive fertilizers and pesticides in the agriculture landcover class. Thus, effective water resource management is crucial to address challenges. This comprehensive study serves as a baseline for future research and recommends larger-scale studies to implement efficient management strategies, urbanization planning, and safe irrigation and drinking water practices to prevent groundwater pollution.

Groundwater fluoride and nitrate contamination and associated human health risk assessment in South Punjab, Pakistan.

Consumption of high fluoride (F-) and nitrate (NO3-) containing water may pose serious health hazards. One hundred sixty-one groundwater samples were collected from drinking wells in Khushab district, Punjab Province, Pakistan, to determine the causes of elevated F- and NO3- concentrations, and to estimate the human health risks posed by groundwater contamination. The results showed pH of the groundwater samples ranged from slightly neutral to alkaline, and Na+ and HCO3- ions dominated the groundwater. Piper diagram and bivariate plots indicated that the key factors regulating groundwater hydrochemistry were weathering of silicates, dissolution of evaporates, evaporation, cation exchange, and anthropogenic activities. The F- content of groundwater ranged from 0.06 to 7.9 mg/L, and 25.46% of groundwater samples contained high-level fluoride concentration (F- > 1.5 mg/L), which exceeds the (WHO Guidelines for drinking-water quality: incorporating the first and second addenda, WHO, Geneva, 2022) guidelines of drinking-water quality. Inverse geochemical modeling indicates that weathering and dissolution of fluoride-rich minerals were the primary causes of F- in groundwater. High F- can be attributed to low concentration of calcium-containing minerals along the flow path. The concentrations of NO3- in groundwater varied from 0.1 to 70 mg/L; some samples are slightly exceeding the (WHO Guidelines for drinking-water quality: incorporating the first and second addenda, WHO, Geneva, 2022) guidelines for drinking-water quality. Elevated NO3- content was attributed to the anthropogenic activities revealed by PCA analysis. The high levels of nitrates found in the study region are a result of various human-caused factors, including leaks from septic systems, the use of nitrogen-rich fertilizers, and waste from households, farming operations, and livestock. The hazard quotient (HQ) and total hazard index (THI) of F- and NO3- showed high non-carcinogenic risk (> 1) via groundwater consumption, demonstrating a high potential risk to the local population. This study is significant because it is the most comprehensive examination of water quality, groundwater hydrogeochemistry, and health risk assessment in the Khushab district to date, and it will serve as a baseline for future studies. Some sustainable measures are urgent to reduce the F- and NO3- content in the groundwater.

Physiological characteristics, geochemical properties and hydrological variables influencing pathogen migration in subsurface system: What we know or not?

The global outbreak of coronavirus infectious disease-2019 (COVID-19) draws attentions in the transport and spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in aerosols, wastewater, surface water and solid wastes. As pathogens eventually enter the subsurface system, e.g., soils in the vadose zone and groundwater in the aquifers, they might survive for a prolonged period of time owing to the uniqueness of subsurface environment. In addition, pathogens can transport in groundwater and contaminate surrounding drinking water sources, possessing long-term and concealed risks to human society. This work critically reviews the influential factors of pathogen migration, unravelling the impacts of pathogenic characteristics, vadose zone physiochemical properties and hydrological variables on the migration of typical pathogens in subsurface system. An assessment algorithm and two rating/weighting schemes are proposed to evaluate the migration abilities and risks of pathogens in subsurface environment. As there is still no evidence about the presence and distribution of SARS-CoV-2 in the vadose zones and aquifers, this study also discusses the migration potential and behavior of SARS-CoV-2 viruses in subsurface environment, offering prospective clues and suggestions for its potential risks in drinking water and effective prevention and control from hydrogeological points of view.