Providing predictive models for quality parameters of groundwater resources in arid areas of central Iran: A case study of kashan plain.

Groundwater pollution can occur due to both anthropogenic and natural causes, leading to a decline in water quality and posing a threat to human health and the environment. The pollution of ground water resources with chemical pollutants is often considered. To manage water resources sustainably, ensuring their quality and quantity is crucial. Yet, testing groundwater can be expensive and time-consuming. So, using modeling to predict the chemical parameters of groundwater resources is considered to be an efficient and economical method. In this study, we examined three models to predict groundwater quality in dry regions by using R programming language. The random forest (RF) outperformed the other models in developing predictive models for water quality. Also, the multiple linear regression (MLR) model demonstrated strong performance, particularly in predicting total hardness (TH) in Aran Va Bidgol groundwater resources. The decision tree (DT) model did well but had lower performance than the RF model in predicting quality parameters. This approach can be efficacious in the field of effective management and protection of groundwater resources and enables the assessment of risks related to water resources.

Groundwater chemical evolution characteristics and human health risk assessment in Shicheng County, Jiangxi Province.

Groundwater plays a pivotal role in the water resources of Shicheng County; however, the issue of excessive fluoride content in groundwater and its associated health risks often goes unnoticed. Groundwater assumes a crucial role in the hydrological dynamics of Shicheng County; nevertheless, the matter concerning elevated levels of fluoride within groundwater and its accompanying health hazards frequently evades attention. The hydrogeochemical analysis, obscure comprehensive water quality assessment based on cloud model, and probabilistic human health risk assessment using Monte Carlo simulation were conducted on 34 collected water samples. The findings indicate that the predominant groundwater hydrochemical types are SO4·Cl-Na and HCO3-Na. The processes of rock weathering and cation exchange play crucial roles in influencing water chemistry. Groundwater samples generally exhibit elevated concentrations of F-, surpassing the drinking water standard, primarily attributed to mineral dissolution. The concentrations of F- in more than 52.94% and 23.53% of the groundwater samples exceeded the acceptable non-carcinogenic risk limits for children and adults, respectively. Considering the inherent uncertainty in model parameters, it is anticipated that both children and adults will have a probability exceeding 49.36% and 30.50%, respectively, of being exposed to elevated levels of F ions in groundwater. The utilization of stochastic simulations, in contrast to deterministic methods, enables a more precise depiction of health risks. The outcomes derived from this investigation possess the potential to assist policymakers in formulating strategies aimed at ensuring the provision of secure domestic water supplies.

Groundwater vulnerability to fluoride pollution and health risk assessment in the western part of Odisha, India.

The fluoride dynamics of the Dharmagarh Block of Kalahandi District, Odisha, India, and associated health risk assessment have been studied. Complex data matrices were evaluated using groundwater quality index, fluoride pollution index, and principal component analysis to understand the geological evolution and identify potential sources for fluoride pollution. The study region comprises granite, granitic gneiss, and khondalite of hard and compact rock of Precambrian Eon, which supplies mostly the fluoride-bearing minerals. Altogether thirty-four (34) groundwater samples across the entire study area were collected and subjected to various physico-chemical analyses. The majority of the groundwater in the proposed region is hard to very hard type with Mg-HCO3 and Na-HCO3 being the two dominant facies. Groundwater contains fluoride in concentrations ranging from 0.21 to 2.26 mg/L. The statistical analysis of the quality parameters reveals the moderate positive correlation of fluoride with sodium (0.392) and pH (0.313) and week positive correlation with EC, TDS, TH, TA, Mg2+, and HCO3-, which directly depicts the initiation of fluoride problem within the study area. Based on the water quality index, 23.53% samples are good, 73.53% are poor, and 2.94% are very poor in nature. With respect to fluoride pollution index, 5.88% samples show high pollution, 55.88% samples show medium pollution, and 38.24% of samples show low pollution index. Human health risk assessment has also been carried out using the hazard quotient of fluoride. Altogether 70.59% of samples show Total Hazard Index (THI) values < 1 suggesting low risk of cancer and within the permissible range, whereas 29.41% of samples show THI > 1 suggesting the non-carcinogenic risk of pollutants, which exceeds the allowable limit for all the classes of male, female and children.

Evaluation and health risk assessment of arsenic and potentially toxic elements pollution in groundwater of Majha Belt, Punjab, India.

Concentrations of potentially toxic elements (PTEs) like arsenic, uranium, iron, and nitrate in the groundwater of the Majha Belt (including Tarn Taran, Amritsar, Gurdaspur, and Pathankot districts) in Punjab, India were measured to evaluate the health risks associated with its consumption and daily use. The average concentrations of these elements in some locations exceeded the WHO-recommended values. Arsenic and iron toxicity levels were found to be higher in the Amritsar district, while uranium toxicity was more prevalent in Tarn Taran. The Trace Element Evaluation Index suggests that Amritsar is one of the districts most affected by toxic elements. According to the US Environmental Protection Agency's (USEPA) guidelines, the HQ values of U, Fe, and nitrate were less than one, indicating that there is no non-carcinogenic health risk for adults and children. However, the hazard quotient (HQ) value for arsenic was greater than one, indicating a higher possibility of health risk due to arsenic in the study area. The total hazard index values of 44.10% of samples were greater than four for arsenic, indicating that people in the Majha Belt are at a very high health risk due to the usage of water for drinking and domestic purposes. The cancer risk assessment values for arsenic in children (5.69E + 0) and adults (4.07E + 0) were higher than the accepted limit of USEPA (10-4 to 10-6) in the Majha Belt. The average radiological cancer risk values of U for children and adults were 8.68E-07 and 9.45E-06, respectively, which are well below the permissible limit of 1.67 × 10-4 suggested by the Atomic Energy Regulatory Board of DAE, India. The results of this study confirm that the residents of the Majha Belt who use contaminated groundwater are at a serious risk of exposure to arsenic in the Amritsar district and uranium in Tarn Taran district.

Groundwater suitability assessment for irrigation and drinking purposes by integrating spatial analysis, machine learning, water quality index, and health risk model.

An in-depth understanding of nitrate-contaminated surface water and groundwater quality and associated risks is important for groundwater management. Hydrochemical characteristics and driving forces of groundwater quality and non-carcinogenic risks of nitrate were revealed by the integrated approaches of self-organizing map analysis, spatial visualization by geography information system, entropy and irrigation water quality indices, and human health risk model. Groundwater samples were categorized into two clusters by SOM analysis. Cluster I including three samples were Ca-SO4 type and cluster II of remaining 136 samples were Ca-HCO3 type. Hydrochemical compositions of two cluster samples were dominated by water-rock interaction: (1) calcite and gypsum dissolution for cluster I samples and (2) calcite dissolution, silicate weathering, and positive cation exchange for cluster II samples. Nitrate contamination occurred in both cluster I and II samples, primarily induced by agricultural nitrogen fertilizer. The EWQI results showed that 90.97% in total groundwater samples were suitable for drinking purpose, while the IWQI results demonstrated that 65.03% in total groundwater samples were appropriate for irrigation purpose. The HHR model and Monte Carlo simulation indicated that the non-carcinogenic nitrated risk was highest in children. Exposure frequency was the most sensitive factor (86.33% in total) influencing the total non-carcinogenic risk, indicated by sensitivity analysis. Compared with the two clusters of groundwater, surface water has a shorter circulation cycle and lower ion concentrations resulting in better water quality. This study can provide scientific basis for groundwater quality evaluation in other parts of the world.

Arsenic and fluoride in groundwater triggering a high risk: Probabilistic results using Monte Carlo simulation and species sensitivity distribution.

The widespread presence of arsenic (As) and fluoride (F-) in groundwater poses substantial risks to human health on a global scale. These elements have been identified as the most prevalent geogenic contaminants in groundwater in northern Mexico. Consequently, this study aimed to evaluate the human health and ecological risks associated with the content of As and F- in the Meoqui-Delicias aquifer, which is in one of Mexico's most emblematic irrigation districts. Concentrations of As and F- were measured in 38 groundwater samples using ICP-MS and ion chromatography, respectively. Overall, these elements showed a similar trend across the aquifer, revealing a positive correlation between them and pH. The concentration of As and F- in the groundwater ranged from 5.3 µg/L to 303 µg/L and from 0.5 mg/L to 8.8 mg/L, respectively. Additionally, the levels of As and F- surpassed the established national standards for safe drinking water in 92% and 97% of samples, respectively. Given that groundwater is used for both agricultural purposes and human activities, this study also assessed the associated human health and ecological risks posed by these elements using Monte Carlo simulation and Species Sensitivity Distribution. The findings disclosed a significant noncarcinogenic health risk associated with exposure to As and F-, as well as an unacceptable carcinogenic health risk to As through water consumption for both adults and children. Furthermore, a high ecological risk to aquatic species was identified for F- and high to medium risks for As in the sampling sites. Therefore, the findings in this study provide valuable information for Mexican authorities and international organizations (e.g., WHO) about the adverse effects that any exposure without treatment to groundwater from this region represents for human health.

Source-oriented health risk assessment of groundwater nitrate by using EMMTE coupled with HHRA model.

Conventional concentration-oriented approaches for nitrate risk diagnosis only provide overall risk levels without identifying risk values of individual sources or sources accountable for potential health risks. Therefore, a hybrid model combining the end-member mixing model tool on Excel™ (EMMTE) with human health risk assessment (HHRA) was developed to assess the source-oriented health risks for groundwater nitrate, particularly in the Poyang Lake Plain (PLP) region. The results indicated that the EMMTE and the Bayesian stable isotope mixing model (MixSIAR) exhibited remarkable consistency in source apportionment of groundwater nitrate. The source contribution of groundwater nitrate in PLP was related to land use types, hydrogeological conditions, and soil properties. Notably, manure and sewage sources, contributing up to 53.4 %, represented the largest nitrate pollution sources, with a significant contribution of soil nitrogen and nitrogen fertilizers. The non-carcinogenic risk for four potential sources was below the acceptable threshold of 1. Given the factors including rainfall dilution and economic development, attention should be directed towards mitigating the health risks posed by manure and sewage. This study can verify the efficacy of EMMTE in source apportionment and offer valuable insights for decision-makers to regulate the largest sources of nitrate contamination and enhance groundwater management efficiency.

Integrated Approach to Hydrogeochemical Assessment of Groundwater Quality in Major Industrial Zone of Punjab, Pakistan.

Groundwater contamination with arsenic (As) is a significant concern in Pakistan's Punjab Province. This study analyzed 69 groundwater samples from Faisalabad, Gujranwala, Lahore, and Multan to understand hydrogeochemistry, health impacts, contamination sources, and drinking suitability. Results revealed varying as concentrations across districts, with distinctive cation and anion orders. Faisalabad exhibited Na+ > Mg2+ > Ca2+ > K+ > Fe2+ for cations and SO42- > Cl- > HCO3- > NO3- > F- for anions. Gujranwala showed Na+ > Ca2+ > Mg2+ > K+ for cations and HCO3- > SO42- > Cl- > NO3- > F- for anions. In Lahore, demonstrated: Na+ > Ca2+ > Mg2+ > Fe > K+ for cations and HCO3- > SO42- > Cl- > NO3- > F- for anions. Multan indicated K+ > Ca2+ > Mg2+ > Na+ > Fe for cations and HCO3- > SO42- > Cl- > F- > NO3- ) for anions. Hydrochemical facies were identified as CaHCO3 and CaMgCl types. Principal Component Analysis (PCA), highlighted the influence of natural processes and human activities on groundwater pollution. Water Quality Index (WQI) result reveal that most samples met water quality standards. The carcinogenic risk values for children exceeded permissible limits in all districts, emphasizing a significant cancer risk. The study highlights the need for rigorous monitoring to mitigate (As) contamination and protect public health from associated hazards.

Hydrogeochemical characterization and assessment of factors controlling groundwater salinity in the Chamwino granitic complex, central Tanzania.

Chamwino district, central Tanzania is a semi-arid granitic complex province, where groundwater is the major source of water for domestic and other uses. However, groundwater in the area is affected by salinity, thus, lowering the availability of potable water for various uses, decrease in crop production, taste less, wastage of soap, and abnormal pain. Due to this, this study sought to characterize groundwater using hydrogeochemical facies and signatures in order to identify the factors influencing the distribution of salt water in the Chamwino Granitic Complex. A total of 141 groundwater samples were collected from wells spatially distributed within the study area from January 2023 to April 2023, (a season of relatively low rainfall). All samples were subjected to in situ analyses of physicochemical parameters pH, temperature (T), total dissolved solids (TDS), electrical conductivity (EC), and salinity using a multi-parameter water analyzer and analyses of major ions (Ca2+, Mg2+, K+, Na+, Cl-, SO42-, HCO3-, and NO3-). The study revealed that the dominant cations in the groundwater are Na+ > Ca2+ > Mg2+, and the anions are Cl- > HCO3- > SO42. Five geological formations (granodiorite, tonalitic orthogenesis, migmatite, tonalite, and alluvium) were identified, and each is characterized by its unique groundwater facie. In the areas that are dominated with granodiorite, the major hydrogeochemical facies were Ca-HCO3, Na-Cl, Ca-Na-HCO3, Ca-Mg-Cl, and Ca-Cl water types; tonalitic orthogenesis was dominated by Ca-HCO3, Na-Cl, Ca-Mg-Cl, and Ca-Cl water types; migmatite was dominated by Ca-HCO3, Na-Cl, Ca-Mg-Cl, and Ca-Cl water types; tonalite was dominated by Na-Cl, Ca-Mg-Cl, and Ca-Cl water types; and alluvium was dominated by Na-Cl and Ca-Mg-Cl and Ca-Cl water types. The common hydrogeochemical facies in all five geological units are Na-Cl, Ca-Mg-Cl, and Ca-Cl water types. It is revealed that the groundwater in the study area is alkaline in nature and slightly saline with salinity level between 0.2 mg/L (fresh water) and 2.8 mg/L (brackish water) with mean 1.07 mg/L (of 141 samples). The factors controlling groundwater salinity distribution are mainly rock-water interaction and ion exchange reactions. Groundwater salinity in the study area is largely attributed to the abundance of Na+, Ca2+, Cl- and SO42-. Abundance of Na+ and Ca2+ is the results of both, weathering of feldspar minerals particularly plagioclase (Na-Ca feldspars) which are the major mineral in granites, and evaporation crystallization cycles of evaporates in semi-arid areas such as Chamwino. Also, such evaporation crystallization cycles account for the abundance of Cl- and SO42- especially in areas dominated by alluvium. However, anthropogenic activities as evidenced by elevated nitrate up to 212.6 mg/L in congested areas are also likely to contribute in area) to the elevated Cl- and SO42-. In other geological units such as tonalitic orthogneiss, migmatite and granodiorite, there was an ostensible mixing of saline water with fresh water from local recharge as indicated by the abundance of HCO3- ions. Nonetheless, the hydrogeochemical characterization of groundwater in the Chamwino granitic complex suggests that there is little possibility for groundwater to evolve to a carbonate water type (fresh water) because the groundwater salinity is mainly geogenic, unless artificial recharge through rainwater harvesting is applied.

Detecting changes in seafloor elevation in sandy coastal environments using low-cost opensource tooling.

Knowledge of sediment erosion and deposition can be useful for a variety of engineering, marine science, and environmental applications, but collecting detailed time-series measurements of the sediment-water interface can be challenging, particularly in coastal marine environments. We developed economical and open-source sediment level loggers to record sediment-water interface time-series data with accuracy up to 1 cm. The logger is composed of a programmable Circuit-Python (or Arduino) microcontroller and "breakout boards" that attach to a specially designed printed circuit board (PCB) and an array of evenly spaced photoresistors enclosed in a robust waterproof housing. These instruments were paired with temperature sensors in a study off the coast of Charleston, SC in the South Atlantic Bight where heat was used as a tracer to detect the flow of porewater in the permeable coastal sediments. This approach requires accurate knowledge of the depth of temperature sensors relative to the sediment-water interface. In this application, improved knowledge of the elevation of the sediment-water interface elevation data from the sediment level loggers reduced average root mean squared errors in modeling submarine groundwater discharge by as much as 25 %. The sediment level loggers can be easily installed, withstand long deployment times, and provide long-term recording abilities suitable for a range of environments.

Geostatistical assessment of groundwater arsenic contamination in the Padana Plain.

Arsenic (As) in groundwater from natural and anthropogenic sources is one of the most common pollutants worldwide affecting people and ecosystems. A large dataset from >3600 wells is employed to spatially simulate the depth-averaged As concentration in phreatic and confined aquifers of the Padana Plain (Northern Italy). Results of in-depth geostatistical analysis via PCA and simulations within a Monte Carlo framework allow the understanding of the variability of As concentrations within the aquifers. The most probable As contaminated zones are located along the piedmont areas in the confined aquifers and in the lowland territories in the phreatic aquifers. The distribution of the As contaminated zones has been coupled with hydrogeological, geological, and geochemical information to unravel the sources and mechanisms of As release in groundwater. The reductive dissolution of Fe oxyhydroxides and organic matter mineralization under anoxic conditions resulted to be the major drivers of As release in groundwater. This phenomenon is less evident in phreatic aquifers, due to mixed oxic and reducing conditions. This large-scale study provides a probabilistic perspective on As contamination, e.g. quantifying the spatial probability of exceeding national regulatory limits, and to outline As major sources and drivers.

Hydrochemical characterization and comprehensive water quality assessment of groundwater within the main stream area of Yishu River.

To gain a comprehensive understanding of the hydrochemical characteristics, controlling factors, and water quality of groundwater in the main stream area of Yishu River (MSYR), a study was conducted using water quality data collected during both the dry and wet seasons. Through statistical analysis, hydrochemical methods, fuzzy comprehensive evaluation, and health risk evaluation modeling, the water chemical characteristics of the main stream area of Yishu River were studied, and the water quality of the area was comprehensively evaluated. The findings indicate that HCO3- and Ca2+ are the predominant anions and cations in the MSYR during the dry and wet seasons, respectively. Moreover, anion concentration in groundwater follows HCO3- > SO42- > NO3- > Cl-, while cations are ranked as Ca2+ > Na+ > Mg2+ > K+. Overall, the groundwater manifests as weakly alkaline and is predominantly classified as hard-fresh water. During the wet season, there is greater groundwater leaching and filtration, with rock and soil materials more readily transferred to groundwater, and the concentrations of main chemical components in groundwater are higher than those during the dry season, and the hydrochemical types are primarily characterized as HCO3-Ca·Mg and SO4·Cl-Ca·Mg types. These results also suggest that the chemical composition of the groundwater in the MSYR is influenced mainly by water-rock interaction. The primary ions originate from the dissolution of silicate rock and carbonate rock minerals, while cation exchange plays a critical role in the hydrogeochemical process. Groundwater in the MSYR is classified mostly as class II water, indicating that it is generally of good quality. However, areas with high levels of class IV and V water are present locally, and NO3- concentration is a crucial factor affecting groundwater quality. In the wet season, more groundwater and stronger mobility lead to greater mobility of NO3- and wider diffusion. Therefore, the risk evaluation model shows that nitrate health risk index is higher in the wet season than it is in the dry season, with children being more vulnerable to health risks than adults. To study groundwater in this area, its hydrochemical characteristics, water quality, and health risk assessment are of great practical significance for ensuring water safety for residents and stable development of social economy.

Assessment of hydraulic and thermal properties of the Antarctic active layer: Insights from laboratory column experiments and inverse modeling.

To better understand the changes in the hydrologic cycle caused by global warming in Antarctica, it is crucial to improve our understanding of the groundwater flow system, which has received less attention despite its significance. Both hydraulic and thermal properties of the active layer, through which groundwater can flow during thawing seasons, are essential to quantify the groundwater flow system. However, there has been insufficient information on the Antarctic active layer. The goal of this study was to estimate the hydraulic and thermal properties of Antarctic soils through laboratory column experiments and inverse modeling. The column experiments were conducted with sediments collected from two lakes in the Barton Peninsula, Antarctica. A sand column was also operated for comparison. Inverse modeling using HydroGeoSphere (HGS) combined with Parameter ESTimation (PEST) was performed with data collected from the column experiments, including permeameter tests, saturation-drain tests, and freeze-thaw tests. Hydraulic parameters (i.e., Ks, θs, Swr, α, ß, and Ss) and thermal diffusivity (D) of the soils were derived from water retention curves and temperature curves with depth, respectively. The hydraulic properties of the Antarctic soil samples, estimated through inverse modeling, were 1.6 × 10-5-3.4 × 10-4 cm s-1 for Ks, 0.37-0.42 for θs, 6.62 × 10-3-1.05 × 10-2 for Swr, 0.53-0.58 cm-1 for α, 5.75-7.96 for ß, and 5.11 × 10-5-9.02 × 10-5 cm-1 for Ss. The thermal diffusivities for the soils were estimated to be 0.65-4.64 cm2 min-1. The soil hydraulic and thermal properties reflected the physical and ecological characteristics of their lake environments. The results of this study can provide a basis for groundwater-surface water interaction in polar regions, which is governed by variably-saturated flow and freeze-thaw processes.

A web-based pesticide risk assessment tool for drinking water protection zones in Sweden.

To protect human health, wildlife and the aquatic environment, "safe uses" of pesticides are determined at the EU level while product authorization and terms of use are established at the national level. In Sweden, extra precaution is taken to protect drinking water, and permits are therefore required for pesticide use within abstraction zones. This paper presents MACRO-DB, a tool for assessing pesticide contamination risks of groundwater and surface water, used by authorities to support their decision-making for issuing such permits. MACRO-DB is a meta-model based on 583,200 simulations of the physically-based MACRO model used for assessing pesticide leaching risks at EU and national level. MACRO-DB is simple to use and runs on widely available input data. In a qualitative comparative assessment for two counties in Sweden, MACRO-DB outputs were in general agreement with groundwater monitoring data and matched or were more protective than the national risk assessment procedure for groundwater.

Evolution origin analysis and health risk assessment of groundwater environment in a typical mining area: Insights from water-rock interaction and anthropogenic activities.

Coal mining changes groundwater environment, results in deterioration of water quality and endangering human health in the mining area. However, the comprehensive study of groundwater evolution and its potential impact in mining area is still insufficient. In this study, 95 groundwater samples were collected from 2019 to 2020 in a typical mining area of China. Ion ratio coefficients, isotopic tracing technology, Entropy-weighted water quality index (EWQI) and human health risk assessment model (HHRA) were applicated to investigate the hydrochemical variation reasons, groundwater quality and its potential health risk in the study area. Results showed that the groundwater hydrochemical types changed from HCO3∙SO4-Ca∙Mg type to SO4-Ca∙Mg and SO4∙Cl-Ca∙Mg type. Water-rock interaction, agricultural activities, manure and sewage input, precipitation and evaporation controlled the groundwater hydrochemical composition. Groundwater quality showed a trend of fluctuation with an average EWQI of 59.23, 68.92, 63.75, 58.02 and 64.92, respectively. 91.6% of the water samples was fair and acceptable for drinking. The groundwater health risk of nitrate in the study area ranged from 0.03 to 17.80. Infants had the highest health risk and nitrate concentration was the most sensitive parameter. The results will present a comprehensive research of groundwater evolution and potential impacts through a typical mining area example. Thereby offering valuable insights into the influencing factors identification, hydrochemical processes evolution, protection and utilization of groundwater in global mining areas.

Groundwater hydrochemistry, source identification and health assessment based on self-organizing map in an intensive mining area in Shanxi, China.

The Changzhi Basin in Shanxi is renowned for its extensive mining activities. It's crucial to comprehend the spatial distribution and geochemical factors influencing its water quality to uphold water security and safeguard the ecosystem. However, the complexity inherent in hydrogeochemical data presents challenges for linear data analysis methods. This study utilizes a combined approach of self-organizing maps (SOM) and K-means clustering to investigate the hydrogeochemical sources of shallow groundwater in the Changzhi Basin and the associated human health risks. The results showed that the groundwater chemical characteristics were categorized into 48 neurons grouped into six clusters (C1-C6) representing different groundwater types with different contamination characteristics. C1, C3, and C5 represent uncontaminated or minimally contaminated groundwater (Ca-HCO3 type), while C2 signifies mixed-contaminated groundwater (HCO3-Ca type, Mixed Cl-Mg-Ca type, and CaSO4 type). C4 samples exhibit impacts from agricultural activities (Mixed Cl-Mg-Ca), and C6 reflects high Ca and NO3- groundwater. Anthropogenic activities, especially agriculture, have resulted in elevated NO3- levels in shallow groundwater. Notably, heightened non-carcinogenic risks linked to NO3-, Pb, F-, and Mn exposure through drinking water, particularly impacting children, warrant significant attention. This research contributes valuable insights into sustainable groundwater resource development, pollution mitigation strategies, and effective ecosystem protection within intensive mining regions like the Changzhi Basin. It serves as a vital reference for similar areas worldwide, offering guidance for groundwater management, pollution prevention, and control.

Groundwater salinization risk assessment using combined artificial intelligence models.

Assessing the risk of groundwater contamination is of crucial importance for the management of water resources, particularly in arid regions such as Menzel Habib (south-eastern Tunisia). The aim of this research is to create and validate artificial intelligence models based on the original DRASTIC vulnerability methodology to explain groundwater salinization risk (GSR). To this end, several algorithms, such as artificial neural networks (ANN), support vector regression (SVR), and multiple linear regression (MLR), were applied to the Menzel Habib aquifer system. The results obtained indicate that the DRASTIC Vulnerability Index (VI) ranges from 91 to 141 and is classified into two categories: low and moderate vulnerability. However, the correlation between groundwater total dissolved solids (TDS) and the Vulnerability Index is relatively weak (r < 0.5). Indeed, the original DRASTIC index needs some improvements. To improve it, some adjustments are required, notably by incorporating the TDS-groundwater salinization risk (GSR) indicator. The seven parameters of the original DRASTIC model were used as inputs for the artificial intelligence models, while the GSR values were used as outputs. Performance indicators, such as the correlation coefficient (r) and the Willmott Agreement Index (d), showed that the ANN model outperformed the SVR and MLR models. Indeed, during the training phase, the ANN model obtained r values equal to 0.89 and d values of 0.4, demonstrating the superiority, robustness, and accuracy of ANN-based methodologies over the original DRASTIC model. The findings could provide valuable information to guide management of groundwater contamination risks, especially in arid regions.

Assessing the suitability of leachability-based screening levels for per- and polyfluoroalkyl substances (PFAS) risk assessment.

In recent years, soil screening levels have been adopted by regulatory agencies for certain per- and polyfluoroalkyl substances (PFAS) to assess the risk of groundwater contamination through leaching. These soil screening levels, determined using an established equilibrium-based partitioning equation, have high variability among regulatory groups largely attributed to the diverse reported partitioning coefficients in the literature. This variability between reported partitioning coefficients, and subsequently soil screening levels, is due to the complex leaching behavior of PFAS not being predicted well by the standard equilibrium-based model. This has led one regulatory group to require batch leaching to assess risk rather than setting default soil screening levels based on partitioning equations. In this work, we conducted leaching experiments on five field-sampled soils impacted by aqueous film-forming foams (AFFF), following Leaching Environmental Assessment Framework (LEAF) Method 1316 and compared the results to expected leaching utilizing an equilibrium-based partitioning equation commonly employed by regulatory agencies to establish soil screening levels. Our analysis found among the six PFAS detected in the soils, which have regulatory leaching thresholds established, the partitioning values assumed by the U.S. EPA exhibited the highest accuracy in predicting leachate concentrations. These partitioning values predicted actual leaching within a ± 20 % margin of error for approximately 50 % of sample points, highlighting limitations in relying solely on equilibrium-based partitioning values as predictors of leaching behavior. This discrepancy between predicted and actual leaching has implications for site managers and regulatory entities overseeing PFAS-contaminated sites, suggesting that soil screening level determinations for PFAS might need to be revised to account for the unique transport characteristics of PFAS.

Determination of high-risk factors and related spatially influencing variables of heavy metals in groundwater.

Identifying high-risk factors (heavy metals (HMs) and pollution sources) by coupling receptor models and health risk assessment model (HRA) is a novel approach within the field of risk assessment. However, this coupled model ignores the contribution of spatial differentiation to high-risk factors, resulting in the assessment being subjective. Taking Dongting Plain (DTP) as an example, a coupling framework by jointly using the positive matrix factorization model (PMF), HRA, Monte Carlo simulation, and geo-detector was developed, aiming to identify high-risk factors in groundwater, and further explore key environmental variables influencing the spatial heterogeneity of high-risk factors. The results showed that at least 82.86 % of non-carcinogenic risks and 97.41 % of carcinogenic risks were unacceptable for people of all ages, especially infants and children. According to the relationships among HMs, pollution sources, and health risks, As and natural sources were defined as high-risk HMs and sources, respectively. The interactions among Holocene thickness, oxidation-reduction potential, and dissolved organic carbon emerged as the primary drivers of spatial variability in high-risk factors, with their combined explanatory power reaching up to 74%. This proposed framework provides a scientific reference for future studies and a practical reference for environmental authorities in developing effective pollution management measures.

Tracing the land use specific impacts on groundwater quality: a chemometric, information entropy WQI and health risk assessment study.

Understanding the nexus of land use and water quality can potentially underline the influences within the groundwater management. The study envisages land use-specific qualitative assessment of the groundwater resources in Ghaziabad district, in western Uttar Pradesh, India. For encountering the relative impacts of land use on the groundwater quality, chemometric analysis has been employed to apportion the pollution sources. The integration of quality parameters, in the information entropy index modeling, has segregated the quality classes and visualized the seasonal suitability trends as per potability standards along with non-carcinogenic health hazard risk assessment (HHRA). The qualitative assessment of the groundwater resources, along with spatial distribution, has deciphered a polluting impact, specifically in western and south western parts of district, and observed the linkages with direct and indirect discharges/seepages from densely populated residential and industrial land use types localized in urbanized areas. Statistically significant annual and seasonal variations have been found exclusively for EC, Mg2+, F-, Cd, Cr(total), Ni, and Pb which inferred variable concentrations, whereas land use types showed a non-significant variation within groundwater quality. Chemometric-based source apportioning and hierarchical cluster analysis (HCA) have derived salinization and enrichment of dissolved salts, arising from mixed sources and contributes to metal pollution, i.e., mainly from anthropogenic sources. Information EWQI derived poor to extremely poor category represented degraded potability specifically for fewer sites located within western and southern parts on the Yamuna-Hindon flood plains for limited sites of residential, industrial, and agricultural in an urbanized region. However, majority of the samples fall under excellent to good groundwater quality, recommendable in the north and north-eastern (peri-urban) regions. Non-carcinogenic HHRA has shown that majority of the samples categorized under unsafe value for hazard index (HI > 1), for females and children and thus, presumed probable health hazard risk from metal groundwater pollution in south-western part, eastern, and northern regions.