Spatiotemporal characteristics of groundwater quality and health risk assessment in Jinghe River Basin, Chinese Loess Plateau

Multi-model exploration of groundwater quality and potential health risk assessment in Jajpur district, Eastern India

The presence of fluoride and nitrate is a serious groundwater quality issue in India impacting human health. In the present study, 14 different hydrochemical parameters for 76 groundwater samples collected from the Jajpur district of Odisha, India, were evaluated. Entropy-weighted water quality index (EWQI), fixed-weight groundwater quality index (GWQI), principal component analysis (PCA), and rotated factor loading-based water quality index (PCWQI) were employed to assess groundwater quality. About 65.79 ± 4.68%, 33.55 ± 3.95%, and 0.66 ± 0.76% of the samples were rated as "excellent," "good," or "medium" quality, respectively, across the four different water quality indices, with a nominal rating discrepancy of 13.15%. Though 86% of samples consistently received excellent or good ratings across all WQI frameworks, concentrations of F- and NO3- in 36.8% and 11.84% of the samples exceeded the WHO permissible limit. In health risk assessment, about 38.15% of samples surpassed the F- hazard quotient (HQ > 1) posing non-carcinogenic health risks for children. The non-carcinogenic health risks due to NO3- were evident in 55.26% and 11.84% of samples for children and adults, respectively. The higher concentration of NO3- in some of the water samples, together with its positive correlation with HCO3-, may worsen groundwater pollution. The moderate correlation between Ca2+ and HCO3- (r = 0.410) and the insignificant correlation between Mg2+ and HCO3- (r = 0.234) suggests calcite dissolution is far more common than dolomite.

A hydrochemical and isotopic approach for source identification and health risk assessment of groundwater arsenic pollution in the central Yinchuan basin

Arsenic contamination of groundwater is becoming a major global issue as it can severely affect the safety of drinking water and human health. In this paper, 448 water samples were investigated to study the spatiotemporal distribution, source identification and human health risk of groundwater arsenic pollution in the central Yinchuan basin by applying a hydrochemical and isotopic approach. The results showed that arsenic concentrations in groundwater ranged from 0.7 μg/L to 26 μg/L with a mean of 2.19 μg/L, and 5.9% of samples were above 5 μg/L, indicating the arsenic pollution of groundwater in the study area. High arsenic groundwater was mainly distributed in the northern and eastern areas along the Yellow river. The main hydrochemistry type of high arsenic groundwater was HCO3·SO4-Na·Mg, and the dissolution of arsenic-bearing minerals in sediment, irrigation water infiltration and aquifer recharge from the Yellow river were the main sources of arsenic in groundwater. The arsenic enrichment was dominantly controlled by the TMn redox reaction and the competitive adsorption of HCO3-, and the influence of anthropogenic activities was limited. The health risk assessment suggested that the carcinogenic risk of As for children and adults greatly exceeded the acceptable risk threshold of 1E-6, displaying a high carcer risk, while the non-carcinogenic risks of As, F-, TFe, TMn and NO3- in 2019 were largely higher than the acceptable risk threshold (HQ > 1). The present study provides insight into the occurrence, hydrochemical processes and potential health risk of arsenic pollution in groundwater.

Simultaneous identification of groundwater contaminant source and hydraulic parameters based on multilayer perceptron and flying foxes optimization

Groundwater contaminant source identification (GCSI) has practical significance for groundwater remediation and liability. However, when applying the simulation-optimization method to precisely solve GCSI, the optimization model inevitably encounters the problems of high-dimensional unknown variables to identify, which might increase the nonlinearity. In particular, to solve such optimization models, the well-known heuristic optimization algorithms might fall into a local optimum, resulting in low accuracy of inverse results. For this reason, this paper proposes a novel optimization algorithm, namely, the flying foxes optimization (FFO) to solve the optimization model. We perform simultaneous identification of the release history of groundwater pollution sources and hydraulic conductivity and compare the results with those of the traditional genetic algorithm. In addition, to alleviate the massive computational load caused by the frequent invocation of the simulation model when solving the optimization model, we utilized the multilayer perception (MLP) to establish a surrogate model of the simulation model and compared it with the method of backpropagation algorithm (BP). The results show that the average relative error of the results of FFO is 2.12%, significantly outperforming the genetic algorithm (GA); the surrogate model of MLP can replace the simulation model for calculation with fitting accuracy of more than 0.999, which is better than the commonly used surrogate model of BP.

Potential risk assessment due to groundwater quality deterioration and quantifying the major influencing factors using geographical detectors in the Gunabay watershed of Ethiopia

Groundwater quality has become deteriorated due to natural and anthropogenic activities. Poor water quality has a potential risk to human health and the environment. Therefore, the study aimed to assess the potential risk of groundwater quality contamination levels and public health risks in the Gunabay watershed. For this task, seventy-eight groundwater samples were collected from thirty-nine locations in the dry and wet seasons during 2022. The groundwater contamination index was applied to assess the overall quality of groundwater. Six major driving forces (temperature, population density, soil, land cover, recharge, and geology) and their quantitative impact of each factor on groundwater quality deterioration were demonstrated using Geodetector. The results showed that low groundwater quality was detected in urban and agricultural land. Especially nitrate contamination was highly linked to groundwater quality deterioration and public health risks, and a medium contamination level was observed in the area. This indicates that the inappropriate application of fertilizer on agricultural land and wastewater from urban areas has a great impact on shallow aquifers in the study area. Furthermore, the major influencing factors are ranked as soil type (0.33-0.31) > recharge (0.17-0.15) > temperature (0.13-0.08) > population density (0.1-0.08) > land cover types (0.07-0.04) > lithology (0.05-0.04). The interaction detector revealed that the interaction between soil ∩ recharge, soil ∩ temperature, and soil ∩ land cover, temperature ∩ recharge is more influential to deteriorate groundwater quality in both seasons. Identification and quantification of the major influencing factors may provide new insight into groundwater resource management.

An integrated approach for quantifying source apportionment and source-oriented health risk of heavy metals in soils near an old industrial area

Soil contamination of heavy metals (HMs) caused by the long-term industrial activities has become a major environmental issue due to its adverse effects on human health and ecosystem. In this paper, 50 soil samples were analyzed to evaluate the contamination characteristics, source apportionment and source-oriented health risk of HMs in soils near an old industrial area in NE China by applying an integrated approach of Pearson correlation analysis, Positive matrix factorization (PMF) model and Monte Carlo simulation. The results showed that the mean concentrations of all HMs greatly exceeded the soil background values (SBV), and the surface soils in the study area were heavily polluted with HMs, displaying a very high ecological risk. The toxic HMs emitted from the bullet production were identified as the primary source of HMs contamination in soils, with a contribution rate of 33.3%. The human health risk assessment (HHRA) suggested that the Hazard quotient (HQ) values of all HMs for children and adults are within the acceptable risk level (HQ < 1). The carcinogenic risk (CR) values of HMs for children and adults significantly exceeded the acceptable threshold of 1E-6 with a basic trend: As > Pb > Cr > Co > Ni, indicating a high cancer risk. For source-oriented health risk, the CR of four pollution sources for children and adults shows a same trend: Factor 4 > Factor 3 > Factor 2 > Factor 1. Among those, the source of HMs pollution from bullet production is the largest contributor to cancer risk, and As and Pb are the most important HMs pollutants that cause cancer risk to humans. The present study sheds some light on the contamination characteristics, source apportionment and source-health risk assessment of HMs in industrially contaminated soils, which helps improve the management of environmental risk control, prevention and remediation.