Groundwater characterization and non-carcinogenic and carcinogenic health risk assessment of nitrate exposure in the Mahanadi River Basin of India.

Agriculture is the mainstay of India's economy and chemical fertilizers have been extensively used to meet increasing demands. Anthropogenic interventions at the soil surface, especially the application of nitrogenous fertilizers in agricultural fields, provide essential nutrients but become major pollutant sources in terrestrial ecosystems and aquatic environments. Groundwater samples from phreatic aquifers of the Mahanadi River Basin, Chhattisgarh, India, showed that the Ca-Mg-HCO freshwater type dominates, followed by the Ca-Mg-Cl- and Na-HCO types.

Prediction of groundwater quality using efficient machine learning technique.

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).

Effectiveness of groundwater heavy metal pollution indices studies by deep-learning.

Globally, groundwater heavy metal (HM) pollution is a serious concern, threatening drinking water safety as well as human and animal health. Therefore, evaluation of groundwater HM pollution is essential to prevent accompanying hazardous ecological impacts. In this aspect, the effectiveness of various groundwater HM pollution evaluation approaches should be examined for their level of trustworthiness.