Identifying spatial heterogeneity of groundwater and its response to anthropogenic activities

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.

The potential evaluation of groundwater by integrating rank sum ratio (RSR) and machine learning algorithms in the Qaidam Basin

Groundwater is a vital resource in arid areas that sustains local industrial development and environmental preservation. Mapping groundwater potential zones and determining high-potential regions are essential for the responsible use of the local groundwater resource. When utilizing machine learning or deep learning algorithms to forecast groundwater potential in arid areas, difficulties such as inaccurate and overfitting predictions might occur due to a shortage of borehole samples. In this study, a database of groundwater conditioning factors with a size of 275,157 × 9 was created in the Qaidam Basin, and 85 known borehole samples were collected. The groundwater potential was evaluated using a combination of rank sum ratio (RSR), projection pursuit regression (PPR) and random forest (RF) algorithms, resulting in four models: PPR, RSR-PPR, RSR-RF, and RF. Results indicated that the groundwater potential was higher in mountainous regions surrounding the Qaidam Basin and decreased progressively towards the central and northwestern regions where most industries and facilities are located. The two primary factors, according to the PPR and RF models, were evapotranspiration (0.246, 0.225) and landform (0.176, 0.294). In terms of their ability to accurately forecast the borehole samples, the four models ranked as follows: RF > RSR-RF > RSR-PPR > PPR. The accuracy of the four models in the low-potential area was 0.73 (PPR), 0.60 (RSR-PPR), 0.87 (RSR-RF), and 0.80 (RF), respectively. However, the RF model showed overfitting due to a lack of samples, especially in high-potential regions, which limits its applicability. The RSR-RF method was applied directly to evaluate the entire factor database, avoiding the risk of overfitting caused by a limited number of training samples. The results demonstrate that the RSR-RF model is effective for classifying groundwater potential types in samples and mapping groundwater potential of the study area. This research presents a novel approach for groundwater potential predictions in areas with insufficient sample sizes, providing a reference for policymakers and researchers.

Coupling of multi-hydrochemical and statistical methods for identifying apparent background levels of major components and anthropogenic anomalous activities in shallow groundwater of the Liujiang Basin, China

Natural background levels (NBLs) is a prerequisite for distinguishing anthropogenic groundwater pollution and judging the evolution of groundwater quality. However, due to regional differences of hydrogeochemitry and water-rock interaction, coupled with long-term anthropogenic activities, it is no longer accurate to assess NBLs with only statistical methods or without considering human impact. Herein, multi-hydrochemical and statistical methods were examined to identify apparent background levels and anthropogenic anomalous activities of shallow groundwater by selecting Liujiang Basin as a study area. The results showed that the differences in hydrochemical characteristics among each hydrogeological unit (HU) fully illustrated the necessity of rationally dividing HU for background value identification. The application of the concept of apparent background levels (ABLs), that is, incorporating normal human activities into the background levels, efficiently solved the problem of being unable to obtain pristine NBLs due to long-term human activities. The coupling of Hydrochemistry and Grubbs' test (Hydro-Grubbs) was confirmed as the optimal method in identifying and eliminating anthropogenic groundwater anomalies, performing sufficiently superiority when compared with purely statistical methods. It is mainly because the Hydro-Grubbs method not only considers the discreteness of the data itself, but also considers the internal connection and evolution process of the hydrochemical compositions. For the eliminated abnormal points, 91.0-93.6% of which have been effectively explained by pollution percentage index and the impact of coal mining, industrial activities, residents, agricultural activities, and septic tanks leakage, proving the rationality and reliability of Hydro-Grubbs method and ABLs evaluation result. This finding will assist in accurately identifying anthropogenic pollution on a regional scale and guiding future efforts to protect groundwater resources.

Assessment of groundwater geochemistry using multivariate water quality index and potential health risk in industrial belt of central Odisha, India

Groundwater in India has been shown to have a variety of water quality issues, including fluoride, nitrate, and uranium pollution, all of which pose a health risk to humans. In the present study, a total of 106 groundwater samples from the Angul district of Odisha, an industrialized region in India, were analyzed for 14 different hydrochemical parameters. In almost 30%, 34.9%, and 4.7% of the groundwater samples, the concentrations of F-, NO^3- and uranium, respectively, exceeded the permissible limit set by WHO. In addition to the fixed-weight groundwater quality index (GWQI), the entropy-weighted water quality index (EWQI), the principal component analysis (PCA) factor (or rotated factor) loading based water quality index (PCWQI) and human health risk assessment were used. Depending on the models, about 19.1 ± 0.9%, 70.5 ± 1.9% and 10.38 ± 1.9% of water samples were classified as "Excellent", "Good" and "Medium" quality, respectively, across four water quality indexes with a nominal rating disagreement of 11.3%. More than 90% of samples are unanimously classified as excellent or good across the WQI rating. For children and adults, approximately 54.7% and 24.5% of samples exceeded the permitted limit for F^-, (hazard quotient HQ > 1), posing non-carcinogenic health hazards, respectively. In contrast, 71.7% and 34.9% of NO₃^- samples respectively, surpassed the allowed limit and caused non-carcinogenic health concerns for children and adults. In terms of carcinogenic HQ values, about 13.2% and 7.5% of samples exhibit an uranium related carcinogenic health risk in children and adults, respectively. The existence of significant amounts of Cl ^-, NO₃^-, and especially HCO₃^- ions in groundwater in some samples, as well as their positive interdependence, may increase uranium pollution in the future through uranium dissolution.

Hydro-geochemical analysis based on entropy and geostatistics model for delineation of anthropogenic ground water pollution for health risks assessment of Dhenkanal district, India

Consumption of poor quality water causes serious human health hazards. Therefore, it is very crucial to investigate factors influencing the quality of groundwater and its suitability for drinking purpose. In the present study, groundwater quality of the Dhenkanal district of Odisha, India was characterized and the spatial distribution of different water quality parameters were analyzed using the multivariate statistics, entropy theory, and geostatistics techniques. In the present study 112 number of groundwater tube well samples were collected from the study area. The entropy theory revealed that SO₄^2-, Mg⁺² and Cl^- were the most influencing parameters. A similar observation was also observed based on the correlation coefficient analysis. Groundwater quality index (GWQI) and entropy-weighted water quality index (EWQI) classifications indicated that 78.57 and 43.75% of the collected groundwater samples were categorized under excellent water quality, whereas, the rest of the samples were varying from good to medium drinking water quality. In addition, the result of EWQI classification offers more realistic assessment than that of GWQIs owing to its high precision, simplicity and without application of artificial weight. The correlation coefficient between Ca⁺² and HCO₃^-, Mg⁺² and PO₄^- were significantly high which might be due the presence of CaHCO₃ and MgPO₄ in the groundwater samples. The GWQI revealed a weak spatial dependence of groundwater quality.

Influence of a low-head dam on water quality of an urban river system

Dam removal in the United States is becoming a common practice for stream restoration as these structures age, climate driven precipitation patterns change, and ecological uplift becomes desirable. Yet in highly urbanized watersheds, these dams may operate as retention basins removing pollutants and mitigating hydrological change. While elimination may be ecologically and economically advantageous, sediment and pollutant removal processes may be better protective of water quality and damaging flooding. In Central Virginia, we compared a watershed split between an urbanized subwatershed (>20% impervious surface encompassing 37.8% of the total watershed land surface) flowing through a 18 Ha reservoir with a rural subwatershed (<5% impervious encompassing 63.2% of the total watershed land surface) located in the James River and Chesapeake Bay watersheds. This reservoir is scheduled for removal in the near future. Comparisons of data suggest that while portions of the urbanized watershed are degraded, this condition is not reflected at the confluence where water quality more closely resembles the rural and minimally impervious subwatershed. This conclusion was further strengthened from data collected following an unexpected dam overtopping in August 2018 where the reservoir was temporarily drained because of safety concerns. After the draining, water quality reversed with the confluence resembling the urbanized rather than the rural subwatershed. Most significantly, water quality flowing into the James River quickly and significantly shifted from a good to a degraded condition. This case study suggests reservoirs in highly urbanized watersheds may serve as critical water quality improvement structures and removal as part of a stream restoration strategy must be carefully considered.