A holistic approach for understanding the status of water quality and causes of its deterioration in a drought-prone agricultural area of Southeastern India.

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 R2 = 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.

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 Ca2+-Mg2+-HCO3- freshwater type dominates, followed by the Ca2+-Mg2+-Cl- and Na+-HCO3- types. Increasing trends in the ionic ratios of (NO3-+Cl-)/HCO3- over TDS and of NO3-/Cl- over Cl- indicated the significant impact of anthropogenic pollution on groundwater contamination. Deterministic and probabilistic approaches were used to assess the non-carcinogenic and carcinogenic health risks of nitrate to children and adults. Both approaches produced the same results and indicated children were more prone to non-carcinogenic health risk than adults. An excess gastric cancer risk (ER) exposure model showed that approximately 42% of the groundwater samples had a non-negligible ER (1.00 × 10-4 to 1.00 × 10-5). Sensitivity analysis indicated groundwater nitrate concentration, ingestion rate, and the percentage of nitrite from nitrate were the most significant variables in determining HI and ER. It is suggested to adopt proper management of control policies for reducing the elevated groundwater nitrate concentration in the present study area.

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). A total of 226 groundwater samples are collected from an agriculturally intensive area Arang of Raipur district, Chhattisgarh, India, and various physicochemical parameters are measured to compute entropy weight-based groundwater quality index (EWQI). Prediction performances of models are determined by introducing five error metrics. Results showed that DL model is the best prediction model with the highest accuracy in terms of R2, i.e., R2 = 0996 against the RF (R2 = 0.886), XGBoost (R2 = 0.0.927), and ANN (R2 = 0.917). The uncertainty of the DL model output is cross-verified by running the proposed algorithm with newly randomized dataset for ten times, where minor deviations in the mean value of performance metrics are observed. Moreover, input variable importance computed by prediction models highlights that DL model is the most realistic and accurate approach in the prediction of groundwater quality.

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. In this study, 226 groundwater samples from Arang of Chhattisgarh state, India, were collected and analyzed. Measured concentration for various HMs were further used to calculate six groundwater pollution indices, such as the HM pollution index (HPI), HM evaluation index (HEI), contamination index (CI), entropy-weight based HM contamination index (EHCI), Heavy metal index (HMI), and principal component analysis-based metal index (PMI). Groundwater in the study area was mainly contaminated by elevated Cd, Fe, and Pb concentrations due to natural and anthropogenic pollution. Moreover, this study explored the performance of deep learning (DL)-based predictive models via comparative study. Two hidden layers with 26 and 19 neurons in the first and second hidden layers, respectively, were optimised along with rectified linear unit activation function. A mini-batch gradient descent was also applied to ensure smooth convergence of the training dataset into the model. Results demonstrated that the DL-PMI scored lowest errors, 0.022 for mean square error (MSE), 0.140 for mean absolute error (MAE), and 0.148 for root mean square error (RMSE), in the model validation than the other DL-based groundwater HM pollution model. Prediction performances of all pollution indices were also verified using artificial neural network (ANN)-based models, which also highlighted the lowest validation error for ANN-PMI (MSE = 3.93, MAE = 1.38, and RMSE = 1.98). Furthermore, the prediction accuracies of PMI using both ANN and DL models scored the highest R2 value of 0.95 and 0.99, respectively. Therefore it is suggested that groundwater HM pollution using PMI as the best indexing approach in the present study area. Moreover, compared to benchmark, ANN, the DL performed better; hence, it could be concluded that the proposed DL model may be suitable approach in the field of computational chemistry by handling overfitting problems.

Modification, characterization and investigations of key factors controlling the transport of modified nano zero-valent iron (nZVI) in porous media.

Enhancement of nano zero-valent iron (nZVI) stability and transport in the subsurface environment is important for in situ degradation of contaminants. Various biodegradable dispersants (poly (acrylic acid) (PAA), Tween 20 and Reetha Extracts) have been tested to evaluate their effectiveness in this regard. Application of dispersants during the synthesis of nZVI have positively affected the reduction in particle size. The transport capacity in terms of fraction elution at different pore water velocities and collector grain size (filter media) was analyzed using correlation equation for the filtration model by Rajagopalan and Tien (RT model). At a surfactant concentration of 5% for PAA, Tween 20 and Reetha (Sapindus trifoliata) extracts, the lowest particle size and the highest zeta potential achieved are 8.67 nm and -55.29 mV, 75.24 nm and -62.68 mV, 61.6 nm and -37.82 mV, respectively. The trend of colloidal stability by The Derjaguin-Landau-Verwey-Overbeek (DLVO) Theory model for PAA and Reetha applied concentration was 3% > 4% > 5% > 2% > 1% > 0%. For Tween 20, modified nZVI particle shows a higher repulsive force with increasing Tween 20 concentration. Results indicated that some mechanisms such as aggregation, ripening and surface modification with different carrier pore water velocities had a considerable impact on nZVI retention in porous media. The results indicate that natural surfactant like Reetha extracts exhibits an alternative potential capacity for nZVI modification in comparison with synthetic surfactants (PAA and Tween 20).