Characterization of groundwater nitrate exposure using Monte Carlo and Sobol sensitivity approaches in the diverse aquifer systems of an agricultural semiarid region of Lower Ganga Basin, India

Temporal evaluation of seawater intrusion vulnerability in Shabestar Plain using GALDIT and AI techniques

Groundwater contamination, with seawater intrusion (SWI) being the most widespread form particularly in coastal areas, has become a pressing global environmental challenge. Groundwater serves as a vital freshwater resource, particularly in arid and semi-arid regions, making its efficient management essential. In this study, the GALDIT method-an index-based approach that evaluates the vulnerability of aquifers by scoring six key parameters based on expert judgment (groundwater occurrence (G), aquifer hydraulic conductivity (A), groundwater elevation above sea level (L), distance from the shoreline (D), impact of existing seawater intrusion (I), and aquifer thickness (T))-was employed to assess the vulnerability of the Shabestar aquifer to SWI. The study employs the GALDIT method to map aquifer vulnerability for 2002, 2012, and 2022, enabling a temporal comparison of changes over time. The final GALDIT index map, categorized into low, moderate, and high vulnerability classes, revealed an increase in very high vulnerability areas from 10.9% in 2002 to 17.8% in 2022, alongside a decrease in moderate vulnerability areas from 56.4 to 37.3%, indicating a deteriorating condition of the aquifer. However, the reliance on expert judgment introduces potential subjectivity and bias in the vulnerability assessment. To mitigate these limitations, AI-based models, namely artificial neural networks (ANNs) and random forest (RF), were applied to enhance model performance. The GALDIT parameters served as input for the AI models, while observed electrical conductivity (EC), a key indicator of water salinity, and total dissolved solids (TDS), an indicator of drinking water quality, were used as output variables to estimate condition for the year 2022. Results demonstrated that the ANN model outperformed the RF model during verification, improving estimation accuracy by up to 10% and 9% in terms of the determination coefficient (DC), respectively. To further enhance model interpretability and identify the most influential parameters for EC and TDS estimation, a global, variance-based sensitivity analysis using the Sobol method was conducted. This analysis revealed that factors I and D were the most influential for EC, while factors I and T had the greatest impact on TDS in the study region.

Hydrochemical evolution and formation mechanism of groundwater affected by human activities in Zhangxuan basin, northwest of Yanshan mountains, China

The Zhangxuan Basin serves as an ecological barrier and water conservation area for Beijing, the capital of China. Clarifying the hydrochemical evolution of groundwater in this region is essential for the effective management of groundwater resources and the protection of ecological security. In this study, based on data regarding chemistry and level of shallow groundwater from 2016 to 2022, hydrogeochemical analysis methods and geostatistical techniques were applied to investigate the hydrochemical evolution and genesis mechanisms of groundwater under the influence of human activities. The results showed that: (1) From 2016 to 2022, the groundwater remained predominantly characterized by Ca2+ and HCO3-, with the primary hydrochemical type unchanged as HCO3-Ca·Mg. (2) From 2016 to 2022, an overall decreasing trend in hydrochemical components was observed, alongside an increase in HCO3-Na type groundwater. Spatially, along the direction of groundwater flow, a general trend of increasing hydrochemical components was noted, with a significant rise in HCO3-Na type groundwater. (3) The spatiotemporal distribution and evolution of hydrochemistry were influenced by water-rock interactions, lithological characteristics, groundwater flow patterns, and human activities. Along the groundwater flow direction, lithological particles became finer, enhancing forward cation exchange and leaching, with the dissolution of silicate and carbonate minerals intensifying. In localized areas, the hydrochemical components were influenced by the extraction of groundwater source areas and the discharge of industrial waste.

Comprehensive assessment of groundwater quality in the Prayagraj District, Ganga Basin

Urbanization has severely impacted the world water resources especially the shallow groundwater systems. There is a need of a robust method for quantifying the water quality degradation, which is still a challenge for most of the urban centers across the world. In this study, a highly urbanized region of Ganga basin is selected to critically evaluate commonly used WQIs and compare with fuzzy modeling. A total of 28 water samples were collected from diverse sources (surface and groundwaters) in the vicinity of urban region covering an area of 216 km2 during the premonsoon period. TDS, TH, NO3-, and F- values were found to be above the permissible limits in 57%, 89%, 4%, and 7% samples, respectively. The WQIs (entropy and integrated) outputs were found to be similar with 89% of the samples falling under moderate category. Fuzzy modeling was carried out allowing user-defined weighting factors for the most influential ions, and the output suggested 96% of the samples falling under moderate to excellent categories. Based on the chemical results and considering the lithology of the study area, the geochemical reactions controlling the water quality were deduced. This study outlines a systematic approach of evaluating the overall water quality of an urban region highlighting the merits and limitations of WQIs. It also justifies the immediate need to generate more robust data to achieve the sustainable development goals 6 (clean water and sanitation) and 11 (sustainability of cities and human settlement).

Health risk assessment and Sobol' sensitivity analysis of power plant air pollution (SO2 and NOX): the effect of power plant fuel change

This study aimed to evaluate the concentrations of sulfur dioxide (SO2) and nitrogen oxides (NOX) around the Qom (a province in Iran) combined cycle power plant in relation to seasonal variations and fuel type from December 2014 to May 2015. Passive sampling was used in three monitoring sites around the power plant to assess noncarcinogenic health risks associated with exposure to SO2 and NOX. Results showed the higher concentrations of NOX and SO2 in winter than in spring. The highest concentrations of NOX in winter (when power plants used gasoil) and spring (when power plants used gas fuel) were 52.70 ± 21.70/52.70 ± 13.20 and 22.10 ± 7.00 µg/m3. Similarly, the highest concentrations of SO2 in winter and spring were 5.00 ± 0.20 and 3.90 ± 0.70 µg/m3. There was a moderately significant correlation between NOx and SO2 concentrations when the power plant consumed gasoil (Spearman rho coefficient, 0.68, p < .05). A significant difference was observed between gas concentration and fuel type (p < .05). The hazard quotient values for SO2 exposure were below 1 for all age groups, whereas those for NOx were above 1 for infants and children. The hazard index for NOX was above 1, indicating an unacceptable risk to human health. Sobol' sensitivity analysis identified the inhalation rate and NOX concentration as the most significant factors determining the noncarcinogenic risks across age groups. In conclusion, the use of gasoil by power plants increases the emission of pollutants, thereby raising the health risks for residents in these regions, particularly vulnerable demographics like children. Therefore, prioritizing air quality management such as using cleaner fuels and emission control technologies in power plants as well as continuous air quality monitoring are of utmost importance.

Spatiotemporal analysis of airborne pollutants and health risks in Mashhad metropolis: enhanced insights through sensitivity analysis and machine learning

The study delved into an extensive assessment of outdoor air pollutant levels, focusing specifically on PM2.5, SO2, NO2, and CO, across the Mashhad metropolis from 2017 to 2021. In tandem, it explored their intricate correlations with meteorological conditions and the consequent health risks posed. Employing EPA health risk assessment methods, the research delved into the implications of pollutant exposure on human health. Results unveiled average annual concentrations of PM2.5, SO2, NO2, and CO, standing at 27.22 µg/m3, 72.48 µg/m3, 26.8 µg/m3, and 2.06 mg/m3, respectively. Intriguingly, PM2.5 displayed positive correlations with temperature and wind speed, while exhibiting negative associations with relative humidity and precipitation. Conversely, both SO2 and NO2 concentrations showcased negative correlations with temperature, relative humidity, wind speed, and precipitation. Furthermore, CO demonstrated negative relationships with both wind speed and precipitation. The analysis of mean hazard quotients (HQ) for PM2.5 and NO2 indicated values exceeding 1 under 8- and 12-h exposure scenarios, pointing towards concerning health risks. Spatial distribution revealed elevated CO levels in the northwest, north, and east areas, while NO2 concentrations were predominant in the north and south regions. Through Sobol sensitivity analysis, PM2.5, EF, and NO2 emerged as pivotal influencers, offering valuable insights for refining environmental models and formulating effective pollution mitigation strategies. Air pollution index (AQI) forecasting was modeled using advanced machine learning comprising Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KKN), and Naive Bayesian (NB). Results showed that the RF model with the highest accuracy (R2 = 0.99) was the best prediction model.

Comparison of three spatial interpolation methods in predicting time-dependent toxicities of single substances and mixtures

This study aims to optimize the time-dependent toxicity assessments for both single substances, particularly those causing hormesis, and mixtures that exhibit toxicological interactions. To achieve this, three time-dependent toxicity prediction methods were developed using geologic interpolation techniques: Inverse distance weighted (IDW), Kriging, and linear interpolation based on Delaunay triangulation (LDT). The toxicity of 7 single substances and 80 mixtures on Vibrio qinghaiensis sp.-Q67, along with 6 single substances and 19 mixtures on Microcystis aeruginosa, were assessed to evaluate the predictive accuracy of these methods. The coefficient of determination (R2), mean absolute error (MAE), and root-mean-square error (RMSE) were employed as performance metrics during cross-validation. The results showed that IDW underperformed LDT and Kriging in terms of both RMSE and MAE, indicating that LDT and Kriging had superior accuracy compared to IDW. Although LDT and Kriging demonstrated comparable predictive capabilities, LDT was identified as the more practical option for time-dependent toxicity prediction due to its simplicity and no requirement for parameter tuning. Consequently, LDT was presented as a new, efficient, and user-friendly tool for assessing the time-dependent toxicity of both individual chemicals and chemical mixtures. LDT will help to better assess the ecological risks of chemicals.

Geospatial mapping and entropy-based analysis for groundwater evaluation with estimation of potential health risks due to nitrate and fluoride exposure

Groundwater is a vital source of freshwater, but its quality is often compromised by various physiochemical factors. In the Mid-Gangetic Plains, there is a concerning escalation in the degradation of groundwater quality due to anthropogenic interventions. However, there remains a paucity of comprehensive knowledge concerning groundwater quality and the associated health hazards it poses. In response to this gap, the current study focuses on Nalanda district (Bihar), where 78 groundwater samples were collected across district in the month of May 2022 and their various water quality parameters were quantified as per standard methods. The adequacy of groundwater for human use was assessed using an entropy-based water quality index (EWQI), which also evaluated the potential human health risk stemming from nitrate and fluoride contamination. Furthermore, an empirical Bayesian kriging (EBK) driven geostatistical approach was utilized to predict water quality parameters at ungauged sites. The analysis of results disclosed that the ionic dominance in groundwater followed the sequence as cations Ca2+  > Mg2+, and anions HCO3- > SO42- > Cl- > NO3- > F- > PO43-. The concentration of NO3- and F- exceeded the permissible BIS levels by 11.5% and 6.5% of the samples respectively. The analysis of EBK models suggested K-Bessel as the best-fit model for pH, Mg2+, TH, F-, NO3-, and SO42- spatial interpolation while exponential EBK model for EC, Cl-, and PO43- and whittle EBK model for TDS, Ca2+, and HCO3- spatial interpolation. Spearman's correlation analysis revealed that elevated TDS and EC levels, coupled with correlations between NO3-, SO42-, and Cl-, suggest anthropogenic influences. The EWQI of the groundwater samples ranged from 36.28 to 180.80. The analysis of EWQI values revealed predominantly fair to good groundwater quality across the study area, suitable for drinking purposes. The hazard quotients for NO3- and F- indicate that non-carcinogenic health risks are more significant with nitrate pollution. The combined health impact was assessed using total hazard index (HI), ranging from 0.20 to 3.29 for children, 0.19 to 3.05 for males, and 0.17 to 2.70 for females. The cumulative probability distribution revealed total hazard index (HI) > 1 in 41.56%, 34.62%, and 28.21% of samples for children, males, and females. The HI analysis indicated a substantially higher risk for children compared to adults within the study area. This study offers a novel combination of entropy-based water quality assessment and geostatistical EBK modeling to evaluate groundwater quality and health risks in ungauged areas. The findings provide valuable insights for improved groundwater management and health risk mitigation.

Health risk modeling and risk factors of fluorosis in the fluoride endemic village of Maharashtra: A cross-sectional study

Fluorosis, a debilitating disease, causes dental mottling and bone deformities among other adverse health effects. The current epidemiological study focused on the prevalence and risk factors of fluorosis in the fluoride endemic village, Dongargaon, Maharashtra, India. The clinical diagnosis of subjects showed 96.49% prevalence of dental fluorosis, and 63.16% prevalence of skeletal fluorosis. Duration of exposure, age of subjects and physiological symptoms like chronic joint pain were found to be significantly correlated with skeletal fluorosis (p < 0.05). Fluoride in drinking water showed seasonal variation with highest level in winter (6.54 mg/L) and lowest level in monsoon (0.54 mg/L). Consequently, the mean urinary fluoride concentration was also highest in winter (9.37 mg/L) and lowest in monsoon (3.88 mg/L). Multilogit regression analysis demonstrated that subjects excreting urinary fluoride above 4 mg/L had 7.25 times higher odds of developing skeletal fluorosis (p < 0.05). Health risk assessment showed a high value of hazard quotient, indicating a high probability of adverse health effects. The estimated daily ingestion of 8.80 mg F and a predicted bone uptake of 0.19 mg/kg/day raised significant health concerns for the studied population. Exposure and bone F uptake modeling suggested that the predicted mean fluoride concentrations of 4018.77 ± 1762.45 mg/kg bone ash in asymptomatic, and 5535.12 ± 1526.30 mg/kg bone ash in symptomatic subjects were significantly different (p < 0.05). The comprehensive risk evaluation in the study clearly brings out the high risk of fluoride exposure and adverse effects in Dongargaon village for the first time. Further, urinary fluoride was shown to have significant association with the risk of skeletal fluorosis. The study warrants implementation of immediate control measures at the study site.

Natural background level, source apportionment and health risk assessment of potentially toxic elements in multi-layer aquifers of arid area in Northwest China

Groundwater contaminated by potentially toxic elements has become an increasing global concern for human health. Therefore, it is crucial to identify the sources and health risks of potentially toxic elements, especially in arid areas. Despite the necessity, there is a notable research gap concerning the sources and risks of these elements within multi-layer aquifers in such regions. To address this gap, 54 phreatic and 24 confined groundwater samples were collected from an arid area in Northwest China. This study aimed to trace the sources and evaluate the human health risks of potentially toxic elements by natural background level (NBL), positive matrix factorization (PMF) model, and health risk model. Findings revealed exceeding levels of potentially toxic elements existed in phreatic and confined aquifers. Source apportionment and NBL results indicated that mineral dissolution, evaporation, redox reactions, and human activities were the main factors for elevated concentrations of potentially toxic elements. High Fe and Mn concentrations were attributed to reduction environments, while F accumulation resulted from slow runoff, and irrigation from the Yellow River. Due to high F levels, more than one-third of groundwater samples (phreatic: 33.14 %, confined: 56.22 %) posed non-carcinogenic health risks to population groups. Adults displayed higher carcinogenic risks (phreatic: 19.47 %, confined: 34.16 %) than infants (phreatic: 0 %, confined: 0 %) and children (phreatic: 1.26 %, confined: 7.97 %) owing to the toxic elements of Cr. The confined aquifer presented greater health risks than the phreatic aquifer. Consequently, controlling the levels of F and Cr in multi-layered aquifers is key to reducing health risks. These findings provide valuable insights into protecting groundwater from contamination by potentially toxic elements in multi-layered aquifers worldwide.

Deciphering geochemical fingerprints and health implications of groundwater fluoride contamination in mica mining regions using machine learning tactics

The contribution of mica mining activities to fluoride (F-) contamination in groundwater has been chased in this study. For the purpose, groundwater samples (n = 40, replicated thrice) were collected during the post-monsoons (September-October) from a mica mining area in the Tisri block of Giridih district, Jharkhand. The study has employed a synergy of classical aquifer chemistry, statistical approaches, different indices, Self-Organising Maps (SOM), and Sobol sensitivity index (SSI) to unveil the underlying aquifer chemistry, identify the impacts of mining activities on groundwater quality and its associated health hazard. Fluoride levels varied from 0.34 to 2.8 ppm, with 40% of samples exceeding the World Health Organization's permissible limit (1.5 ppm). Physicochemical analysis revealed significant differences in electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH) and major ion concentrations (Na+, HCO3-, Ca2+) between fluoride-contaminated (FC) and fluoride-uncontaminated (FU) groups. Higher Na+ and HCO3- associated with F- contaminated samples, were indicative of silicate weathering and carbonate dissolution as primary geogenic sources for this ion. Health risk assessment (HRA) revealed hazard quotient (HQ) values exceeding unity, indicating non-carcinogenic risks, particularly for children in most samples from group FC. The mean Water Quality Index (WQI) of FC group (156.76 ± 7.30) was significantly higher (p < 0.05) than group FU indicating of its unsuitability. SOM could accurately (80%) predict presence of fluoride in water samples based on other major ions. Sobol sensitivity analysis successfully identified fluoride concentration and body weight as most impactful parameters affecting human health. The integration of advanced modelling techniques and geospatial analysis as Inverse Distance Weightage (IDW) maps has provided a robust framework for ongoing groundwater quality monitoring in mining-affected regions and can help proactive intervention in risk-prone areas. Overall, this comprehensive study takes us a step ahead towards ensuring safe drinking water access for the global community.

Natural background levels, source apportionment and health risks of potentially toxic elements in groundwater of highly urbanized area

Identifying the natural background levels (NBLs), threshold values (TVs), sources and health risks of potentially toxic elements in groundwater is crucial for ensuring the water security of residents in highly urbanized areas. In this study, 96 groundwater samples were collected in urban area of Sichuan Basin, SW China. The concentrations of potentially toxic elements (Li, Fe, Cu, Zn, Al, Pb, B, Ba and Ni) were analyzed for investigating the NBLs, TVs, sources and health risks. The potentially toxic elements followed the concentration order of Fe > Ba > B > Al > Zn > Li > Cu > Ni > Pb. The NBLs and TVs indicated the contamination of potentially toxic elements mainly occurred in the northern and central parts of the study area. The Positive Matrix Factorization (PMF) model identified elevated concentrations of Fe, Al, Li, and B were found to determine groundwater quality. The primary sources of Fe, Al, Pb, and Ni were attributed to the dissolution of oxidation products, with Fe additionally affected by anthropogenic reduction environments. Li and B were determined to be originated from the weathering of tourmaline. High levels of Ni and Cu concentrations were derived from electronic waste leakage, while excessive Ba and Zn were linked to factory emissions and tire wear. The reasonable maximum exposure (RME) of hazard index (HI) was higher than safety standard and reveal the potential health risks in the southwestern study area. Sensitivity analysis demonstrated the Li concentrations possessed the highest weight contributing to health risk. This study provides a valuable information for source-specific risk assessments of potentially toxic elements in groundwater associated with urban areas.

A machine learning framework for spatio-temporal vulnerability mapping of groundwaters to nitrate in a data scarce region in Lenjanat Plain, Iran

The temporal aspect of groundwater vulnerability to contaminants such as nitrate is often overlooked, assuming vulnerability has a static nature. This study bridges this gap by employing machine learning with Detecting Breakpoints and Estimating Segments in Trend (DBEST) algorithm to reveal the underlying relationship between nitrate, water table, vegetation cover, and precipitation time series, that are related to agricultural activities and groundwater demand in a semi-arid region. The contamination probability of Lenjanat Plain has been mapped by comparing random forest (RF), support vector machine (SVM), and K-nearest-neighbors (KNN) models, fed with 32 input variables (dem-derived factors, physiography, distance and density maps, time series data). Also, imbalanced learning and feature selection techniques were investigated as supplementary methods, adding up to four scenarios. Results showed that the RF model, integrated with forward sequential feature selection (SFS) and SMOTE-Tomek resampling method, outperformed the other models (F1-score: 0.94, MCC: 0.83). The SFS techniques outperformed other feature selection methods in enhancing the accuracy of the models with the cost of computational expenses, and the cost-sensitive function proved more efficient in tackling imbalanced data issues than the other investigated methods. The DBEST method identified significant breakpoints within each time series dataset, revealing a clear association between agricultural practices along the Zayandehrood River and substantial nitrate contamination within the Lenjanat region. Additionally, the groundwater vulnerability maps created using the candid RF model and an ensemble of the best RF, SVM, and KNN models predicted mid to high levels of vulnerability in the central parts and the downhills in the southwest.

Evaluate the groundwater quality and human health risks for sustainable drinking and irrigation purposes in mountainous region of Chongqing, Southwest China

Groundwater is crucial for agriculture and domestic consumption. This research investigated the hydrogeochemical properties and contaminant sources of groundwater within the mountainous terrain of northern Chongqing, with the objective of evaluating its appropriateness for irrigation and potable use. The hydrochemical type of the groundwater was HCO3 - Ca, dominated by silicate and calcite dissolutions. High NO3- (29.03% exceeds 10 mg/L) were attributed to the overuse of agricultural fertilizers. A comprehensive evaluation was conducted to determine the groundwater suitability for agricultural and potable uses. The results showed that groundwater in the southwestern region, particularly within the Yangtze River mainstem watershed, exhibited less suitability for irrigation owing to its lower mineralization, in contrast to the northeastern region near the Daning River watershed. But this trend is reversed for drinking purposes. Overall, the groundwater was appropriate for both drinking (93.55% were classified as excellent) and irrigation (70.98% were classified as low restriction) purposes in the study area. Deterministic and probabilistic noncarcinogenic health risk analyses centered on nitrate exposure revealed that infants (with 13.79% of samples >1) were at greater risk than children (8.58%), adult males (6.98%), and adult females (5.24%). This underscores the urgency to reduce nitrogen fertilizer usage and improve water management in the region. This research will provide guidance for the sustainable groundwater management in mountainous regions.

Human health risk of nitrate in groundwater of Tehran-Karaj plain, Iran

Groundwater pollution by nitrate has is a major concern in the Tehran-Karaj aquifer, Iran, where the wells provide up to 80% of the water supply for a population of more than 18 million-yet detailed human health risks associated with nitrate are unknown due to the lack of accessible data to adequately cover the aquifer in both place and time. Here, using a rich dataset measured annually in more than 75 wells, we mapped the non-carcinogenic risk of nitrate in the aquifer between 2007 and 2018, a window with the most extensive anthropogenic activities in this region. Nitrate concentration varied from ~ 6 to ~ 150 mg/L, around three times greater than the standard level for drinking use, i.e. 50 mg/L. Samples with a non-carcinogenic risk of nitrate, which mainly located in the eastern parts of the study region, threatened children's health, the most vulnerable age group, in almost all of the years during the study period. Our findings revealed that the number of samples with a positive risk of nitrate for adults decreased in the aquifer from 2007 (17 wells) to 2018 (6 wells). Although we hypothesized that unsustainable agricultural practices, the growing population, and increased industrial activities could have increased the nitrate level in the Tehran-Karaj aquifer, improved sanitation infrastructures helped to prevent the intensification of nitrate pollution in the aquifer during the study period. Our compilation of annually mapped non-carcinogenic risks of nitrate is beneficial for local authorities to understand the high-risk zones in the aquifer and for the formulation of policy actions to protect the human health of people who use groundwater for drinking and other purposes in this densely populated region.

Research on predicting the diffusion of toxic heavy gas sulfur dioxide by applying a hybrid deep learning model to real case data

Toxic heavy gas sulfur dioxide (SO2) is a specific life and environmental hazard. Predicting the diffusion of SO2 has become a research focus in fields such as environmental and safety studies. However, traditional methods, such as kinetic models, cannot balance precision and time. Thus, they do not meet the needs of emergency decision-making. Deep learning (DL) models are emerging as a highly regarded solution, providing faster and more accurate predictions of gas concentrations. To this end, this study proposes an innovative hybrid DL model, the parallel-connected convolutional neural network-gated recurrent unit (PC CNN-GRU). This model utilizes two CNNs connected in parallel to process gas release and meteorological datasets, enabling the automatic extraction of high-dimensional data features and handling of long-term temporal dependencies through the GRU. The proposed model demonstrates good performance (RMSE, MAE, and R2 of 20.1658, 10.9158, and 0.9288, respectively) with real data from the Project Prairie Grass (PPG) case. Meanwhile, to address the issue of limited availability of raw data, in this study, time series generative adversarial network (TimeGAN) are introduced for SO2 diffusion studies for the first time, and their effectiveness is verified. To enhance the practicality of the research, the contribution of drivers to SO2 diffusion is quantified through the utilization of the permutation importance (PIMP) and Sobol' method. Additionally, the maximum safe distance downwind under various conditions is visualized based on the SO2 toxicity endpoint concentration. The results of the analyses can provide a scientific basis for relevant decisions and measures.

Probabilistic human health risk assessment and Sobol sensitivity reveal the major health risk parameters of aluminum in drinking water in Shiraz, Iran

Overuse of aluminum salts (a.k.a., alum) in coagulation and flocculation processes in water treatment raises concerns about increased levels of aluminum (Al) in drinking water. In this study, we present a probabilistic human health risk assessment (HRA) for non-cancerogenic risks, with Sobol sensitivity analysis, to vet the concern of increased health risk from Al in drinking water in Shiraz, Iran, for children, adolescents, and adults. The results show that the concentration of Al in the drinking water in Shiraz varies significantly between winter and summer seasons and varies considerably spatially across the city irrespective of the season. However, all concentrations are below the guideline concentration. The HRA results show that the highest health risk is for children in summer, and the lowest is for adolescents and adults during winter, with generally higher health risks for younger age groups. However, Monte Carlo results for all age groups suggest no adverse health effects due to Al exposure. The sensitivity analysis shows that the sensitive parameters vary across age groups. For example, the Al concentration and ingestion rate pose the most risk for adolescent and adult groups, and children group, respectively. More importantly, the interaction of Al concentration with ingestion rate and body weight is the controlling parameters for evaluating HRA rather than Al concentration alone. We conclude that while the HRA of Al in Shiraz drinking water did not indicate significant health risk, regular monitoring and optimal operation of the coagulation and flocculation processes are essential.

Analytical model for organic contaminant transport in a cut-off wall and aquifer dual-domain system considering barrier arrangements

This paper presents an analytical study of organic contaminants transport in a cut-off wall and aquifer dual-domain system, considering the effects of the inlet boundary conditions and cut-off structural arrangements. The comprehensive sensitivity analysis of parameters focusing on the breakthrough time, attenuation time and cumulative concentration are presented using the Monte Carlo simulation and Sobol global method. The simplified constant inlet boundary condition can lead to an excessively conservative prediction of the contaminant breakthrough compared to the 'finite mass' and 'decaying source' boundary conditions. The cut-off wall hydraulic performance can be enhanced by reducing the contaminant's head loss, shape factor, half-life and cut-off wall hydraulic conductivity while increasing the retardation factor. The contaminant's half-life can largely influence the maximum contaminant concentration, attenuation time and breakthrough time. For example, the maximum contaminant concentrations for T1/2 = 1.4 years and T1/2 = 100 years are 13 and 123 times greater than that for T1/2 = 0.1 year, respectively. The influence of the variation of shape factor on the breakthrough curve should be taken into consideration. Altering the structural arrangement of the cut-off wall to accommodate a smaller shape factor increases the contaminant breakthrough time. The proposed solution allows the analysis of a cut-off wall and aquifer system with different inlet boundary conditions and structural arrangements of the cut-off wall.

Modeling, quality assessment, and Sobol sensitivity of water resources and distribution system in Shiraz: A probabilistic human health risk assessment

Given water's vital role in supporting life and ecosystems, global climate change and human activities have significantly diminished its availability and quality. This study explores the health risks of drinking water consumption in the shiraz county water resources and distribution system. The result showed that the water was slightly alkaline. However, the average pH values during the study were within the permissible range. The area's abundance of total hardness and calcium was due to the high concentration of minerals in rocks and soils. The nitrate and fluoride concentrations in drinking groundwater varied from 0.02 to 116.70 mg/L and 0.10-1.85 mg/L, respectively. Although the water quality index indicated that 52.63, 45.03, and 20.3 percent of samples were of excellent, good, and poor quality in 2020, those percentages obtained 46.05, 52.09, and 14.0 percent in 2021. The regression values of training, testing, validation, and the proposed artificial neural network model were 0.93, 0.92, 0.85, and 0.92. The maximum levels of hazard quotient of nitrate and fluoride (except for adults) were higher than 1 in all age groups, indicating a high non-carcinogenic risk by exposure to nitrate. Furthermore, according to the Monte Carlo simulation, the 95th percentile hazard index in all groups was more than 1. Children and infants were more inclined towards risk than teens and adults based on the intake of nitrate and fluoride from drinking water. The Sobol sensitivity reflected that the nitrate concentration and ingestion rate are vital parameters that influence the outcome of the oral exposure model for all age groups. The interaction of ingestion rate with a concentration of nitrate and fluoride is an important parameter affecting the health risk assessment. In conclusion, these findings suggest that precise measures can reduce health risks and guarantee safe drinking water for residents of Shiraz County.

Human health risk assessment of nitrate in private well waters of shallow quaternary alluvial aquifer

Excessive nitrate intake via ingestion pathway and dermal absorption exposures has adverse health impacts on human health. This study evaluated groundwater (GW) nitrate concentrations and health risks which focused on ingestion and dermal exposures to residents in Bachok District, Kelantan, Malaysia. Three hundred (300) samples of private wells were collected and it is found that the nitrate concentrations ranging between 0.11 and 64.01 mg/L NO3-N with a mean value of 10.45 ± 12.67 mg/L NO3-N. The possible health hazards of nitrate by ingestion and dermal contact were assessed using USEPA human health risk assessment model for adult males and females. It is observed that the mean Hazard Quotient (HQ) values of adult males and females were 0.305 ± 0.364 and 0.261 ± 0.330, respectively. About 7.3% (n = 10) and 4.9% (n = 8) of adult males and females had HQ values more than 1, respectively. It was also observed that the mean of HQderm was lesser than HQoral for males and females. The spatial distribution of HQ by interpolation method showed high nitrate concentrations (> 10 mg/L NO3-N) were distributed from the centre to the southern part of the study location, which identified as an agricultural area, indicating the used of nitrogenous fertilizers as the main source of GW nitrate contamination in this area. The findings of this study are valuable for establishing private well water protection measures to stop further deterioration of GW quality caused by nitrate.

Multi-layer core-shell metal oxide/nitride/carbon and its high-rate electroreduction of nitrate to ammonia

The electroreduction of nitrate to ammonia is both an alternative strategy to industrial Haber-Bosch ammonia synthesis and a prospective idea for changing waste (nitrate pollution of groundwater around the world) into valuable chemicals, but still hindered by its in-process strongly competitive hydrogen evolution reaction (HER), low ammonia conversion efficiency, and the absence of stability and sustainability. Considering the unique electronic structure of anti-perovskite structured Fe4N, a tandem disproportionation reaction and nitridation-carbonation route for building a multi-layer core-shell oxide/nitride/C catalyst, such as MoO2/Fe4N/C, is designed and executed, in which abundant Fe-N active sites and rich phase interfaces are in situ formed for both suppressing HER and fast transport of electrons and reaction intermediates. As a result, the sample's NO3RR conversion displays a very high NH3 yield rate of up to 11.10 molNH3 gcat.-1 h-1 (1.67 mmol cm-2 h-1) with a superior 99.3% faradaic efficiency and the highest half-cell energy efficiency of 30%, surpassing that of most previous reports. In addition, it is proved that the NO3RR assisted by the MoO2/Fe4N/C electrocatalyst can be carried out in 0.50-1.00 M KNO3 electrolyte at a pH value of 6-14 for a long time. These results guide the rational design of highly active, selective, and durable electrocatalysts based on anti-perovskite Fe4N for the NO3RR.

Evaluation of water quality of Chahnimeh as natural reservoirs from Sistan region in southwestern Iran: a Monte Carlo simulation and Sobol sensitivity assessment

Maintaining the water quality is essential because of the limitation of drinking water bodies and their significant effects on life. Recently, much scientific interest has been attracted to the ecological condition assessment of water resources. Because of numerous health issues connected to water quality, the present work aimed to define the water quality status of Chahnimeh reservoirs, Sistan and Baluchistan province, Iran via the Iran Water Quality Index (IRWQISC), the National Sanitation Foundation Water Quality Index (NSFWQI), and human risk assessment. This cross-sectional descriptive work was accomplished in 4 seasons in 2020. The samples were gathered from 5 various points of Chahnimeh reservoirs. This study led to the results that the NSFWQI index was between 29.4 to 49.32, which showed "bad" quality, and the IRWQI index was between 19.27 and 39.23, which indicated "bad" and "relatively bad" quality. The best water quality based on both indexes was observed in the spring, and the worst was in the fall and summer. The highest value of HQ related to nitrate in drinking water was 1.60 in the group of children. However, according to the Monte Carlo simulation, HQ_95% was estimated as 1.29. The Sobol sensitivity analysis of the first-order effect showed that daily water's daily ingestion rate (IR) was the most sensitive input. In addition, the value of the second-order effect indicated that the interaction effect of concentration-ingestion rate was the most sensitive input parameter for HQ. Therefore, regular monitoring is necessary to ensure water safety for human consumption.

Nitrogen reduction by aerobic denitrifying fungi isolated from reservoirs using biodegradation materials for electron donor: Capability and adaptability in the lower C/N raw water treatment

Biological denitrification was considered an efficient and environmentally friendly way to remove the nitrogen in the water body. However, biological denitrification showed poor nitrogen removal performance due to the lack of electron donors in the low C/N water. In this study, three novel aerobic denitrifying fungi (Trichoderma sp., Penicillium sp., and Fusarium sp.) were isolated and enhanced the performance of aerobic denitrification of fungi in low C/N water bodies combined with polylactic acid/polybutylene adipate-co-terephthalate (PLA/PBAT). In this work, the aerobic denitrifying fungi seed were added to denitrifying liquid medium and mixed with PLA/PBAT. The result showed that Trichoderma sp., Penicillium sp., and Fusarium sp. could reduce 89.93 %, 89.20 %, and 87.76 % nitrate. Meanwhile, the nitrate removal efficiency adding PLA/PBAT exceeded 1.40, 1.68, and 1.46 times that of none. The results of material characterization suggested that aerobic denitrifying fungi have different abilities to secrete proteases or lipases to catalyze ester bonds in PLA/PBAT and utilize it as nutrients in denitrification, especially in Penicillium brasiliensis D6. Besides, the electron transport system activity and the intracellular ATP concentration were increased significantly after adding PLA/PBAT, especially in Penicillium brasiliensis D6. Finally, the highest removal efficiency of total nitrogen in landscape water by fungi combined with PLA/PBAT was >80 %. The findings of this work provide new insight into the possibility of nitrogen removal by fungi in low C/N and the recycling of degradable resources.

Accuracy assessment of inverse distance weighting interpolation of groundwater nitrate concentrations in Bavaria (Germany)

For the designation of nitrate vulnerable zones under the EU Nitrate Directive, some German federal states use inverse distance weighting (IDW) as interpolation method. Our study quantifies the accuracy of IDW with respect to the designation of areas with a groundwater nitrate concentration above the threshold of 50 mg NO₃/l using a dataset of 5790 groundwater monitoring sites in Bavaria. The results show that the absolute differences of nitrate concentrations between the monitoring sites are only weakly correlated within a range of no more than 0.4 km. The IDW cross-validated nitrate concentration of measurement sites shows a mean absolute error of 7.0 mg NO₃/l and the number of measurement sites above 50 mg NO₃/l is 44% too low by interpolation for all sites as a whole. The corresponding values for interpolation separately for the 18 hydrogeological regions in Bavaria are 7.1 mg NO₃/l and 38%. The sensitivity and the accuracy of nitrate concentration maps due to the variation of IDW parameters and the position of sampling points are analysed by Monte Carlo IDW interpolations using a Random Forest modelled map as reference spatial distribution. Compared to this reference map, the area with a concentration above 50 mg NO₃/l in groundwater is estimated by IDW to be 46% too low for the best IDW parametrization. Overall, IDW interpolation systematically underrates the occurrence of higher range nitrate concentrations. In view of these underestimations, IDW does not appear to be a suitable regionalization method for the designation of nitrate vulnerable zones, neither when applied for a federal state as a whole nor when interpolated separately for hydrogeological regions.

Health Risk Assessment during In Situ Remediation of Cr(VI)-Contaminated Groundwater by Permeable Reactive Barriers: A Field-Scale Study

The presence of residual Cr(VI) in soils causes groundwater contamination in aquifers, affecting the health of exposed populations. Initially, permeable reactive barriers(PRB) effectively removed Cr(VI) from groundwater. However, as PRB clogging increased and Cr(VI) was released from upstream soils, the contamination plume continued to spread downstream. By 2020, the level of contamination in the downstream was nearly identical to that in the upstream. The study results show that during normal operation, the PRB can successfully remove Cr(VI) from contaminated groundwater and reduce the carcinogenic and non-carcinogenic risks to humans from the downstream side of groundwater. However, the remediated groundwater still poses an unacceptable risk to human health. The sensitivity analysis revealed that the concentration of the pollutant was the most sensitive parameter and interacted significantly with other factors. Ultimately, it was determined that the residual Cr(VI) in the soil of the study region continues to contaminate the groundwater and constitutes a serious health danger to residents in the vicinity. As remediated groundwater still poses a severe threat to human health, PRB may not be as effective as people believe.

Evaluation of groundwater quality for irrigation water supply using multi-criteria decision-making techniques and GIS in an agroeconomic tract of Lower Ganga basin, India

Groundwater irrigation has evolved the monocropping cultivation pattern to multi-cropping, especially in many arid/semi-arid tracts globally. Irrigation practices with the groundwater of poor quality can limit the selection of the crop, reduce crop yields and degrade the soil quality. The present study has been undertaken to identify the hydrogeochemical phenomena of groundwater systems in the south-western Birbhum district, India and to analyze groundwater suitability for irrigation during the pre-and post-monsoon cycles by adopting the Irrigation Water Quality Index (IWQI) using Multivariate Factor Analysis along with some traditional methods viz. sodium adsorption ratio, sodium percentage, magnesium hazards, residual sodium bicarbonate (RSBC) and carbonate (RSC), Wilcox's and USSL diagrams, permeability index and Kelly's index. The hydrogeochemical analysis revealed that chemical weathering and evaporation are predominant in the aquifer systems. Groundwater quality reflected soil salinity, sodicity and magnesium hazards risks and water toxicity to the sensitive plants at 0-46.4% of the post-monsoon samples and 0-38.4% of the pre-monsoon samples based on the individual traditional methods whereas about 97.73-98.88% of the total area was classified as moderate to severely unsuitable for irrigation during both seasons when integrated multiple parameters using the IWQI method. Prolonged use of such groundwater for irrigation is susceptible to causing moderate to severe infiltration problems at a greater extent of the study area. The study recommends adaptation of salinity, sodicity and RSC/RSBC reduction procedures (e.g., the use of acid and gypsum amendments in the irrigation lands and through water blending) and advanced irrigation practices (viz. drips, sprinklers and micro irrigations) to prevent soil degradation and increase crops productivity. Adopting Managed Aquifer Recharge procedures as well as rainwater harvesting in the areas bearing unsuitable water quality can dilute the ionic concentrations of the groundwater facies which in turn will improve the groundwater quality for irrigation.

Hydrogeochemical characterizations and quality evaluation of groundwater in the major river basins of a geologically and anthropogenically driven semi-arid tract of India

Access to clean drinking water has been acknowledged as a human right and assessing the hydrogeochemistry and groundwater quality status plays an important role in proving cleaner and safer water for human consumption. This study evaluated the sources and driving factors of the groundwater facies in the five major river basins (viz. Ajay, Mayurakshi, Kopai, Brahmani and Dwarka) of an agroeconomic semi-arid Indian tract through hydrogeochemical and principal component analyses based on 2200 groundwater samples (N_s = 2200) obtained during the pre- and post-monsoon cycles from 1100 wells (N_w = 1100). The results revealed that minerals weathering, ion/reverse ion exchange, mixing and evaporation processes along with anthropogenic inputs are responsible for the deteriorated groundwater quality of the river basins. The study has considered the cokriging approach that uses geostatistical and multivariate statistical techniques to interpolate a dataset. To determine the spatio-seasonal variabilities of the groundwater facies more accurately, the estimation accuracies of different interpolation techniques viz. inverse distance weighting, kriging/cokriging and splines techniques were compared and kriging/cokriging was found to represent the variability more accurately. Shannon's entropy theory was employed to assess the groundwater quality of the river basins as it eliminates the subjective bias and inherent uncertainties of the groundwater systems. Groundwater in ~37.45-38.42% of the total area was moderate to extremely poor for human consumption where 10.40-12.14%, 9.09-12.40%, 21.18-22.35%, 15.20-19.93% and 6.48-8.80% samples from the Ajay (N_w = 175), Brahmani (N_w = 175), Dwarka (N_w = 180), Kopai (N_w = 350) and Mayurakshi (N_w = 220) river basins exhibited unfit to drink water quality. The sensitivity of the water quality model was analyzed to identify the influences of the individual parameters which revealed that the outcome does not depend solely on one parameter. The study recommends adaptation of the treatment techniques to ensure clean drinking water for the residents. Managed aquifer recharge techniques might also improve the groundwater quality in certain areas.

Exploring a variance decomposition approach integrated with the Monte Carlo method to evaluate groundwater fluoride exposure on the residents of a typical fluorosis endemic semi-arid tract of India

This study appraised the groundwater fluoride (F^-) endemicity and the exposure levels under the Central Tendency Exposure (CTE) condition and the Reasonable Maximum Exposure (RME) condition on the residents of the semi-arid parts of the Birbhum district of Peninsular India using a Variance Decomposition (Sobol Sensitivity Indices) approach combined with Monte Carlo Simulations. The study finds the national scale drinking water standard limit for F^- (1.5 mg L^-1) is inappropriate for the present survey area where F^- concentration in groundwater varied between 0.26 and 11.82 mg L^-1 and ~54.5% of the samples (N = 400) exceeded this limit. Therefore, estimated the optimum F^- concentration of 0.733 mg L^-1 for the region using the method recommended by the World Health Organization (WHO) to calculate the optimum F^- limit at a regional scale. The average value of F^- concentrations for this region (1.71 mg L^-1) is considerably higher than the estimated optimum concentration or even the maximum permissible limits recommended for the subtropical regions (0.5-0.7 mg L^-1). The exposure analysis revealed the infants and children as potentially vulnerable populations compared to adolescents and adults of the study area for CTE and RME scenarios. The multi-exposure pathways indicated oral intake as the main exposure pathway whereas exposure through dermal contact was insignificant for the residents of all age groups of this region. Based on the first, second and total order Sobol Sensitivity Indices, F^- concentration (C) in groundwater, the groundwater ingestion rate and their combined interaction are the greatest significant parameters for the oral exposure model whereas C and its interaction effects with the proportion of the skin surface area in contact with groundwater as the utmost sensitive variables for the dermal health risks assessment model. The present study insists the inhabitants to intake defluoridated groundwater.

Water quality and health risk assessment of the water bodies in the Yamdrok-tso basin, southern Tibetan Plateau

Water resources in good quality guarantee the primary condition for the maintenance and development of the natural ecosystem and human society. Water quality status and health risk of the lake water bodies in the national nature reserve, the Yamdrok-tso basin, in the southern Tibetan Plateau are assessed by 25 water parameters including 12 heavy metal(loid)s. Results reveal that the lake water bodies possess relatively high pH (9.68), high concentrations of F (1.66 mg/L), Cu (13.92 μg/L), As (41.60 μg/L), Pb (26.69 μg/L), and U (19.53 μg/L), and a low value of dissolved oxygen (19.30%). The pollution indices (heavy metal pollution index of 0.88-22.88, heavy metal evaluation index of 0.18-3.75, and the degree of contamination of -8.82 to -5.25) demonstrate that the lake water bodies are in a low pollution level with respect to heavy metal(loid)s. The evaluation of water quality based on the fuzzy comprehensive assessment method suggests that 75.56% of the water samples meet the regulation of the China National Standard for water resources in national nature reserves. Health risk assessment shows that potential hazards exist on this region when the residents under long-term exposure to the lake water through oral and dermal pathways, of which children and adults are mostly exposed to As and F for non-carcinogenic and As for carcinogenic risks, especially for children. Results of this study contribute to targeted water resources management in the national nature reserves.