Predicting Potential Climate Change Impacts on Groundwater Nitrate Pollution and Risk in an Intensely Cultivated Area of South Asia

Assessing water quality index and health risk using deterministic and probabilistic approaches in Darab County, Iran; A machine learning for fluoride prediction

The present study employed deterministic and probabilistic approaches to determine the Water Quality Index (WQI) and assess health risks associated with water consumption in Darab County, Iran. Additionally, pollution levels were predicted using a machine-learning algorithm. The study's findings indicate that certain physicochemical parameters of water in some locations exceeded permissible limits (WHO or EPA), with 79.00 % of total hardness (TH) and 21.74 % of Total dissolved solids (TDS) levels exceeding standard values. The WQI for drinking water was determined to be 94.56 % using the deterministic approach, and 98.4 % of samples included the excellent and good categories according to the WQI classification system using the probabilistic approach. Fluoride (F) exhibited the most substantial impact on WQI values. The Artificial Neural Network (ANN) analysis findings suggest that the pH, nitrate (NO3), and TDS are the most significant factors affecting the prediction of F concentration in water. Multivariate analysis demonstrated that anthropogenic, especially agriculture and geogenic factors, contributed to the water quality in this area. The health risk assessment (HRA) using deterministic methods revealed that water consumption posed a relatively high risk in certain areas. However, Monte Carlo simulation demonstrated that the 5th and 95th percentiles of Hazard Index (HI) for children, teenagers, and adults were within limits of (0.14-2.38), (0.09-1.29), and (0.10-1.00) respectively, with a certainty level of 70 %, 91 %, and 95 %. Interactive indices revealed that the intake of IR and NO3-IR in children, BW and F-BW in teenagers, and NO3 and NO3-IR in adults significantly impacted health risks. Based on these findings, augmenting water treatment processes, regulating fluoride concentrations, and advocating for sustainable agricultural practices complemented by continuous monitoring is imperative.

Groundwater Salinity Across India: Predicting Occurrences and Controls by Field-Observations and Machine Learning Modeling

Elevated groundwater salinity is unsuitable for drinking and harmful to crop production. Thus, it is crucial to determine groundwater salinity distribution, especially where drinking and agricultural water requirements are largely supported by groundwater. This study used field observation (n = 20,994)-based machine learning models to determine the probabilistic distribution of elevated groundwater salinity (electrical conductivity as a proxy, >2000 μS/cm) at 1 km2 across parts of India for near groundwater-table conditions. The final predictions were made by using the best-performing random forest model. The validation performance also demonstrated the robustness of the model (with 77% accuracy). About 29% of the study area (including 25% of entire cropland areas) was estimated to have elevated salinity, dominantly in northwestern and peninsular India. Also, parts of the northwestern and southeastern coasts, adjoining the Arabian Sea and the Bay of Bengal, were assessed with elevated salinity. The climate was delineated as the dominant factor influencing groundwater salinity occurrence, followed by distance from the coast, geology (lithology), and depth of groundwater. Consequently, ∼330 million people, including ∼109 million coastal populations, were estimated to be potentially exposed to elevated groundwater salinity through groundwater-sourced drinking water, thus substantially limiting clean water access.

The impacts of climate change on groundwater quality: A review

Groundwater has been known as the second largest freshwater storage in the world, following surface water. Over the years, groundwater has already been under overwhelming pressure to satisfy human needs for artificial activities around the world. Meanwhile, the most noticeable footprint of human activities is the impact of climate change. Climate change has the potential to change the physical and chemical properties of groundwater, thereby affecting its ecological functions. This study summarizes existing research affiliated with the possible effects of a changing climate on the quality of groundwater, including changes in water availability, increased salinity and pollution from extreme weather events, and the potentiality of seawater intrusion into coastal aquifers. Previous works dealing with groundwater-induced responses to the climate system and climate impacts on groundwater quality through natural and anthropogenic processes have been reviewed. The climate-induced changes in groundwater quality including pH, dissolved oxygen level, salinity, and concentrations of organic and inorganic compounds were assessed. Some future research directions are proposed, including exploring the potential changes in the occurrences and fate of micropollutants in groundwater, examining the relationship between the increase of microcystin in groundwater and climate change, studying the changes in the stability of metals and metal complexation, and completing studies across different regional climate regions.