Health Risk Implication and Spatial Distribution of Radon in Groundwater Along the Lithological Contact in South India.

The presence of radioactive elements in groundwater results in high health risks on surrounding populations. Hence, a study was conducted in central Tamil Nadu, South India, to measure the radon levels in groundwater and determine the associated health risk. The study was conducted along the lithological contact of hard rock and sedimentary formation. The concentrations of uranium (U) varied from 0.28 to 84.65 µg/L, and the radioactivity of radon (Rn) varied from 258 to 7072 Bq/m3 in the collected groundwater samples. The spatial distribution of Rn in the study area showed that higher values were identified along the central and northern regions of the study area. The data also indicate that granitic and gneissic rocks are the major contributors to Rn in groundwater through U-enriched lithological zones. The radon levels in all samples were below the maximum concentration level, prescribed by Environmental Protection Agency. The effective dose levels for ingestion and inhalation were calculated according to parameters introduced by UNSCEAR and were found to be lesser (0.235-6.453 µSvy-1) than the recommended limit. Hence, the regional groundwater in the study area does not pose any health risks to consumers. The spatial distribution of Rn's effective dose level indicates the higher values were mainly in the central and northern portion of the study area consist of gneissic, quarzitic, and granitic rocks. The present study showed that Rn concentrations in groundwater depend on the lithology, structural attributes, the existence of uranium minerals in rocks, and the redox conditions. The results of this study provide information on the spatial distribution of Rn in the groundwater and its potential health risk in central Tamil Nadu, India. It is anticipated that these data will help policymakers to develop plans for management of drinking water resources in the region.

Novel machine learning algorithms to predict the groundwater vulnerability index to nitrate pollution at two levels of modeling.

The accurate mapping and assessment of groundwater vulnerability index are crucial for the preservation of groundwater resources from the possible contamination. In this research, novel intelligent predictive Machine Learning (ML) regression models of k-Neighborhood (KNN), ensemble Extremely Randomized Trees (ERT), and ensemble Bagging regression (BA) at two levels of modeling were utilized to improve DRASTIC-LU model in the Miryang aquifer located in South Korea. The predicted outputs from level 1 (KNN and ERT models) were used as inputs for ensemble bagging (BA) in level 2. The predictive groundwater pollution vulnerability index (GPVI), derived from DRASTIC-LU model was adjusted by NO3-N data and was utilized as the target data of the ML models. Hyperparameters for all models were tuned using a Grid Searching approach to determine the best effective model structures. Various statistical metrics and graphical representations were used to evaluate the superior predictive performance among ML models. Ensemble BA model in level 2 was more precise than standalone KNN and ensemble ERT models in level 1 for predicting GPVI values. Furthermore, the ensemble BA model offered suitable outcomes for the unseen data that could subsequently prevent the overfitting issue in the testing phase. Therefore, ML modeling at two levels could be an excellent approach for the proactive management of groundwater resources against contamination.

Distribution and accumulation of metals in the surface sediments of Coleroon river estuary, East coast of India.

This study deals with the geochemical nature of distribution of metals (iron, manganese, lead and zinc) in bulk sediments and its association with sand, silt, clay and organic carbon. Ten numbers of surface sediment samples were collected during summer season of 2009 from Coleroon estuary. The sediments are mostly sandy silt in nature. The organic carbon distribution indicates that they are brought in the surroundings of coastal areas. Correlation analysis clearly indicates that fine particles and organic carbon control the distribution of metals. The most evident the significant correlations where zinc vs manganese (r = 0.641), manganese versus iron (r = 0.618), lead versus manganese (r = 0.574). The correlation between organic carbon versus manganese (r = 0.768), organic carbon versus sand (r = 0.872), organic carbon versus silt (r = 0.902), organic carbon versus clay (r = 0.793). The degree of correlation between metals and other major constituents is often used to indicate the origin of the metals. Strong positive correlation coefficient of all the above said metals and organic carbon are mainly associated with the fine grained sediments.

Formulating Convolutional Neural Network for mapping total aquifer vulnerability to pollution.

Aquifer vulnerability mapping to pollution is topical research activity, and common frameworks such as the basic DRASTIC framework (BDF) suffer from the inherent subjectivity. This paper formulates an artificial intelligence modeling strategy based on Convolutional Neural Network (CNN) to decrease subjectivity. This formulation considers three definitions of intrinsic, specific, and total vulnerabilities. Accordingly, three CNN models are trained and tested to calculate IVI, SVI, and TVI, respectively referring to the intrinsic, specific, and total vulnerability indices. The formulation is applied in an unconfined aquifer northwest of Iran and delineates hotspots within the aquifer. The area under curve (AUC) values derived by the receiver operating curves evaluate the vulnerability indices versus nitrate concentrations. The AUC values for BDF, IVI, SVI, and TVI are 0.81, 0.91, 0.95, and 0.95, respectively. Therefore, CNNs significantly improve the results compared to BDF, but IVI, SVI, and TVI have approximately identical performances. However, the visual comparison between their results provides evidence that significant differences exist between the spatial patterns despite identical AUC values.

Factors controlling groundwater quality in the Yeonjegu District of Busan City, Korea, using the hydrogeochemical processes and fuzzy GIS.

The hydrogeochemical processes and fuzzy GIS techniques were used to evaluate the groundwater quality in the Yeonjegu district of Busan Metropolitan City, Korea. The highest concentrations of major ions were mainly related to the local geology. The seawater intrusion into the river water and municipal contaminants were secondary contamination sources of groundwater in the study area. Factor analysis represented the contamination sources of the mineral dissolution of the host rocks and domestic influences. The Gibbs plot exhibited that the major ions were derived from the rock weathering condition. Piper's trilinear diagram showed that the groundwater quality was classified into five types of CaHCO3, NaHCO3, NaCl, CaCl2, and CaSO4 types in that order. The ionic relationship and the saturation mineral index of the ions indicated that the evaporation, dissolution, and precipitation processes controlled the groundwater chemistry. The fuzzy GIS map showed that highly contaminated groundwater occurred in the northeastern and the central parts and that the groundwater of medium quality appeared in most parts of the study area. It suggested that the groundwater quality of the study area was influenced by local geology, seawater intrusion, and municipal contaminants. This research clearly demonstrated that the geochemical analyses and fuzzy GIS method were very useful to identify the contaminant sources and the location of good groundwater quality.