Extensive arsenic contamination in high-pH unconfined aquifers in the Indus Valley

Reducing arsenic mobilization in sediments: A synergistic effect of oxidation and adsorption with zirconium-manganese binary metal oxides

Remediation of arsenic (As)-contaminated sediments is challenging, due to surface sediment often being subjected to hypoxic/anoxic conditions where As(Ⅲ) is the dominant species. In this study, a novel capping material comprising zirconium-manganese binary oxides (ZMBO) was synthesized and its feasibility in controlling sedimentary As release investigated using high-resolution sampling, X-ray absorption near edge structure (XANES) spectroscopy, and scanning electron microscopy (SEM) techniques. Results showed ZMBO exhibited both high oxidation efficiency (94 %) and strong adsorption capacity (151.8 mg As/g) for As(Ⅲ). Capping As-contaminated sediments with ZMBO resulted in a negative diffusive flux of -0.08 ng/cm2/s, effectively maintaining low As levels in the overlying water over 150 days. XANES spectra showed As in surface sediments existed predominantly As(V), consistent with high-resolution data indicating ∼90 % of labile As(Ⅲ) was oxidized and adsorbed by ZMBO. Furthermore, ZMBO also promoted Fe(Ⅱ) oxidation to stable hematite in sediments, providing additional adsorption sites for As. By comparing with current capping materials, ZMBO exhibited a balanced performance in terms of its cost-effectiveness, adsorption capacity, remediation effects, and environmental adaptability. This study highlights the potential of ZMBO as a promising capping material for remediating As-contaminated sediments through combined chemical oxidation and adsorption mechanisms, offering sustainable solutions for improving water quality management worldwide.

A comprehensive analysis of the impact of arsenic, fluoride, and nitrate-nitrite dynamics on groundwater quality and its health implications

Groundwater contamination is a growing global concern. The objective of the present study is to assess the groundwater quality of Khairpur district, Sindh, Pakistan-a region which is emblematic of broad environmental and public health challenges prevalent in South Asian countries. The study also aims to comprehend the impact of arsenic (As), fluoride (F-), and nitrate (NO3-) dynamics and its health implications. Additionally, this work is vital in shaping the policies of higher authorities to take actions regarding public health. The American Society for Testing and Materials (ASTM) methods and modern analytical techniques, such as Inductively Coupled Plasma - Optical Emission Spectroscopy (ICP-OES), Ion Chromatography (IC), and UV-Vis Spectrophotometry were employed to analyze a series of groundwater samples (n = 50), taken from various locations of Khairpur district. Present study reveals a high occurrence of arsenic (As) with mean concentrations of 26.05 µg/L, and low occurrence of fluoride (0.12-0.59 mg/L) in majority of samples (68 %), hence not according to the safe drinking limits of World Health Organization (WHO). The occurrence of low nitrate (range: 0.53-22.63; mean: 3.36 mg/L) and high nitrite (range: 10.23-30.3; mean: 20.48 mg/L) in 90 and 98 % of total samples respectively suggests that an active reduction process is taking place in the study area. A link was identified between numerous anthropogenic and natural geochemical processes, including As, F-, and NO3- contamination, which are contributing towards this groundwater pollution and significantly affecting the health of pediatric population in terms of skin manifestations like skin irritation, melanosis and keratosis which are early symptoms of skin cancer. Findings of present study emphasizes. Findings of the present study emphasizes an urgent need for remedial measures to mitigate the identified risk and call for a comprehensive plan to monitor and improve the quality of groundwater, in order to ensure the safety and well-being of the vulnerable communities. Our work also highlights the urgent need for environmental health awareness and policy reforms to prevent the long-term health repercussions from compromised groundwater quality.

Mechanism of arsenic-induced liver injury in rats revealed by metabolomics and ionomics based approach

Arsenic (As) is an environmental toxicant and human carcinogen, long-term exposure to As can lead to varying degrees of liver injury. In this study, the liver injury model of As poisoned Sprague-Dawley (SD) rats was established, and the potential mechanism was investigated by metabonomics and ionomics. A total of 164 differential expressed metabolites (DEMs) were identified between the As poisoned group and the control group, which mainly involved in nicotinate and nicotinamide metabolism, steroid hormone biosynthesis, taurine and hypotaurine metabolism, and porphyrin metabolism. The levels of 10 ions were significantly increased in As poisoned group, including As, bismuth (Bi), cadmium (Cd), mercury (Hg), manganese (Mn), rubidium (Rb), antimony (Sb), strontium (Sr), uranium(U), and zinc (Zn), in contrast, the levels of lead (Pb) and thallium (TI) were significantly decreased. Spearman correlation analysis showed that As, Cd, Hg and Pb were negatively correlated with androstenedione, protoporphyrinogen IX and estriol, whereas As and Mn was positively correlated with progesterone (PROG), Cd was positively correlated with NAD+ and 3-Sulfino-L-alanine. There are sex differences in changes in metabolites and ions levels. Male and female rats shared 60 DEMs and 2 pathways (steroid hormone biosynthesis and porphyrin metabolism pathway). The levels of As, Cd, Hg, and Sr were significantly changed in both male and female rats. In both female and male rats, As was positively correlated with PROG, and Cd was positively correlated with coproporphyrin III. The results of this study provide new insights to elucidate the mechanism of As-induced liver injury in rats.

Geothermal Arsenic Threats to Intensive Groundwater Utilization in an Arid Basin

Groundwater quality is critical for safe drinking water and irrigation supplies but can be threatened by geogenic toxins that are difficult to predict. In the arid, high desert San Luis Valley (SLV), Colorado, a groundwater basin serves as the primary water supply with observed arsenic concentrations exceeding the maximum contaminant level (MCL) of 10 μg/L set by the U.S. EPA. However, the sources and processes responsible for As occurrence are unclear. Through a community-engaged sampling effort, we collected 244 groundwater samples and measured major/trace element concentrations. Long-term land subsidence and depth-resolved sediment texture were computed at the same locations. We tested three plausible geochemical processes responsible for As release: (1) overpumping-induced dewatering of As-bearing clays (proxied by land subsidence), (2) pH-promoted desorption as well as reductive dissolution of As(V)/Fe(III) (hydr)oxides, and (3) incursion of higher-As geothermal fluids (proxied by lithium, boron, tungsten, and molybdenum) into groundwater. We find that statistics, statistical/machine learning, and aqueous thermodynamics all agree that geothermal fluid mixing within the aquifer is the main source of dissolved As. Our findings suggest that overpumping draws higher-As thermal fluid from the bottom of the aquifer to pumping depth, leading to increased concentrations of As in drinking/irrigation water supplies at wells.

Impact of aeration on plant growth-promoting rhizobacteria assisted phytoremediation capability of arsenic in artificial wetland system

The increasing demand for sustainable, robust, and cost-efficient arsenic (As) treatment techniques strengthens the implementation of new constructed wetland (CW) designs like aerated CWs in the agricultural sector. The aim was to assess and contrast the influence of various aeration rates on As elimination in subsurface flow CW utilizing Pennisetum purpureum plants for treating As-polluted sand. This study consisted of an experiment with 16 subsurface flow CW, operating at different As concentrations of 0, 5, 22, and 39 mg kg-1 and aeration rates of 0, 0.18, 1, and 2 L min-1. The highest elimination of As from treatment sand in the subsurface flow CWs was 96.19 ± 3.09%, 93.95 ± 2.17%, and 91.91 ± 1.92% for 5, 22, and 39 mg kg-1 As, respectively, at 0.18 L min-1 aeration. A negative influence of As pollution on growth was detected in the 0, 1, and 2 L min-1 aeration but Pennisetum purpureum grows well in polluted sand with 0.18 L min-1 aeration. Bacterial population and different enzyme activity showed statistically significant differences with 0, 0.18, 1, and 2 L min-1 aerations at all As levels. These results suggest that this treatment can be used for As phytoremediation in anthropogenically polluted environments due to its high capability to uptake As.

Sodium arsenite induces islets β-cells apoptosis and dysfunction via SET-Rac1-mediated cytoskeleton disturbance

Sodium arsenite (NaAsO2), the most common form of inorganic arsenic prevalent in the environment, has been closely linked to islet β-cell dysfunction, a critical pathological hallmark of type 2 diabetes (T2D). Even though apoptosis plays a pivotal role in arsenic-induced islet β-cell dysfunction, the explicit underlying mechanisms remain elusive. Here, we have identified that the SET-Rac1 signaling pathway is instrumental in the apoptosis and dysfunction of islet β-cells induced by NaAsO2. During NaAsO2-induced islet β-cell apoptosis and dysfunction, our observations indicated downregulation of SET (almost 0.5-fold) and upregulation of Rac1 (0.5-fold). Notably, overexpression of SET or inhibition of Rac1 substantially mitigated the apoptosis of islet β-cells and ameliorated the impaired insulin secretion (increased from 0.1 ng/ml to 0.2 ng/ml) caused by NaAsO2 exposure. In addition, we detected cytoskeletal disorganization following NaAsO2 treatment, characterized by elevated Cofilin-1 protein expression (approximately 2.5-fold) and disrupted cytoskeleton arrangement. Significantly, overexpression of SET or deletion of Rac1 rectified the NaAsO2-induced cytoskeletal abnormalities, as evidenced by the reduced Cofilin-1 expression and enhanced F-actin fluorescence. Our research delineates that NaAsO2 triggers apoptosis and functional impairment of islet β-cells through cytoskeletal rearrangement mediated by the SET-Rac1 pathway. This discovery could provide novel insights into therapeutic strategies for T2D provoked by environmental toxicants.

Characteristics of boron isotopes and their indicative significance in groundwater arsenic mobilization from an alluvial basin

Groundwater with high arsenic (As) concentration is widely distributed all over the world and seriously threatens human health. Due to the similar chemical properties, boron (B) would be used to understand the formation mechanism of high As groundwater. Thirty groundwater samples were collected from alluvial fan, transition area, and flat plain generally along the flow path in the northwestern Hetao Basin, China. Groundwater As concentration generally showed an increasing trend along the path. The δ11B values ranged from 3.36 ‰ to 26.19 ‰, and exhibited a decreasing trend (from alluvial fan to transition area; Stage I) followed by an increasing trend (from transition area to flat plain; Stage II). Boron release from incongruent dissolution of silicate minerals and B adsorption onto secondary clay minerals resulted in high δ11B values in groundwater with low As concentrations from the alluvial fan, where As was adsorbed and immobilized on Fe(III) oxides or clay minerals. During Stage I, B concentrations increased slightly and the δ11B values decreased, as the result of B desorption. A negative correlation between As concentrations and δ11B values illustrated that desorption was an important process of As enrichment. During Stage II, degradation of organic matter and reductive dissolution of Fe(III) oxides increased concentrations of B and As. However, the decreasing trend of B/Cl and the increasing trend of δ11B showed that co-precipitation of B and carbonates removed B from groundwater. The positive correlation between As and SIcalcite+dolomite supported that secondary Ca precipitation decreased As adsorption by directly isolating As from Fe(III) oxides, which promoted As enrichment. This study provides insights into hydrogeochemical processes associated with As and B enrichment in groundwater.

Comparative study on genesis mechanism of high arsenic groundwater in typical alluvial plain of the Upper and lower Yellow River, China

Groundwater with naturally high‑arsenic (As) concentrations is a pervasive issue across several major plains (basins) traversed by the Yellow River in China. The genesis of this high-As groundwater and its interrelationships among different plains (basins) have consistently been focal and challenging topics for domestic and international experts. The study focuses on the Hetao Basin in the upper Yellow River and the North Henan Plain in its lower reaches. Selecting typical profiles within the study area, a comparative analysis is performed based on the hydrochemical and isotopic signatures of these profiles. This analysis elucidates the distribution patterns, formation environments, and hydrogeochemical evolution models of high-As groundwater under the influence of different sedimentary environments. The results demonstrate that the shallow groundwater in the upper Yellow River is progressively salinizing, and conversely, the lower reaches are exhibiting a gradual alkalization. Redox conditions, the degree of evaporative concentration, and the intensity of groundwater recharge can influence the distribution disparities of high-As groundwater in the upper and lower reaches of the Yellow River. High-As groundwater in the Hetao Basin of the upper Yellow River is predominantly affected by the strength of groundwater reduction. Conversely, in the North Henan Plain of the lower Yellow River, the chemical conditions of the groundwater are more intricate, with As enrichment being influenced by a confluence of factors such as redox conditions, evaporative concentration, and the intensity of groundwater replenishment.

Mitigating toxic metals contamination in foods: Bridging knowledge gaps for addressing food safety

Background

Reducing exposure to harmful substances in food is highly desired, especially for infants, young children, and pregnant women. A workshop focused on understanding and reducing toxic metal contamination in food was conducted involving leading scientists, educators, practitioners, and key stakeholders in conjunction with the USDA National Institute of Food and Agriculture.

Scope and approach

The goal of this review and the workshop was to advance the current knowledge of major toxic metals concerning food safety, viz. arsenic (As), lead (Pb), cadmium (Cd), mercury (Hg), and chromium (Cr), preventive measures, identify critical knowledge gaps, and the need for research, extension, and education. Being a part of the "Closer to Zero (C2Z)" initiative of the USDA, FDA, and other federal agencies, the workshop adopted a "One Health" approach to mitigate dietary exposure and environmental pollution of hazardous elements.

Key findings and conclusions

The experts discussed the accumulation of toxic metals in food crops and drinking water in relation to soil biogeochemistry, plant uptake, and multidisciplinary factors such as food processing, detection, regulatory standards, etc. To forward food safety, this workshop critically examined toxic metals contamination, exposure and toxicity along the farm-to-fork-to-human continuum, research gaps, prevailing regulations, and sustainable remediation approaches, and offered significant recommendations. This review paper provides perspective on key findings of the workshop relative to addressing this important aspect of food safety, emphasizing interdisciplinary research that can effectively investigate and understand the complex and dynamic relationships between soil biogeochemistry, the microbiome, plant tolerance and accumulation strategies, uniform standards for acceptable and safe toxic element levels in food and water, and raising public awareness. This article also provides a foundation for decision-making regarding toxic metal fate and effects, including risk management strategies, in the face of modern industrialization and a changing climate.

Overexpression of bacterial γ-glutamylcysteine synthetase increases toxic metal(loid)s tolerance and accumulation in Crambe abyssinica

KEY MESSAGE

Transgenic Crambe abyssinica lines overexpressing γ-ECS significantly enhance tolerance to and accumulation of toxic metal(loid)s, improving phytoremediation potential and offering an effective solution for contaminated soil management. Phytoremediation is an attractive environmental-friendly technology to remove metal(loid)s from contaminated soils and water. However, tolerance to toxic metals in plants is a critical limiting factor. Transgenic Crambe abyssinica lines were developed that overexpress the bacterial γ-glutamylcysteine synthetase (γ-ECS) gene to increase the levels of non-protein thiol peptides such as γ-glutamylcysteine (γ-EC), glutathione (GSH), and phytochelatins (PCs) that mediate metal(loid)s detoxification. The present study investigated the effect of γ-ECS overexpression on the tolerance to and accumulation of toxic As, Cd, Pb, Hg, and Cr supplied individually or as a mixture of metals. Compared to wild-type plants, γ-ECS transgenics (γ-ECS1-8 and γ-ECS16-5) exhibited a significantly higher capacity to tolerate and accumulate these elements in aboveground tissues, i.e., 76-154% As, 200-254% Cd, 37-48% Hg, 26-69% Pb, and 39-46% Cr, when supplied individually. This is attributable to enhanced production of GSH (82-159% and 75-87%) and PC2 (27-33% and 37-65%) as compared to WT plants under AsV and Cd exposure, respectively. The levels of Cys and γ-EC were also increased by 56-67% and 450-794% in the overexpression lines compared to WT plants under non-stress conditions, respectively. This likely enhanced the metabolic pathway associated with GSH biosynthesis, leading to the ultimate synthesis of PCs, which detoxify toxic metal(loid)s through chelation. These findings demonstrate that γ-ECS overexpressing Crambe lines can be used for the enhanced phytoremediation of toxic metals and metalloids from contaminated soils.

Mobilization mechanisms and spatial distribution of arsenic in groundwater of western Bangladesh: Evaluating water quality and health risk using EWQI and Monte Carlo simulation

Arsenic (As) contamination in groundwater is emerging as a significant global concern, posing serious risks to the safety of drinking water and public health. To understand the release mechanisms, mobilization processes, spatial distribution, and probabilistic health risks of As in western Bangladesh, forty-seven samples were collected and analyzed using an atomic absorption spectrometer (AAS). The As concentrations in groundwater ranged from 1.97 to 697.4 μg L⁻1 (mean: 229.9), significantly exceeding recommended levels. The dominant hydrochemistry of As-enriched groundwater was Ca-Mg-HCO₃, with the primary sources of arsenic in groundwater being the dissolution of arsenic-bearing minerals in sediment and the recharge of aquifers from the Ganges River Basin. The assessment using the Entropy Water Quality Index revealed that the groundwater is unsuitable for drinking, with 89.36% (n = 42) of the samples surpassing the WHO's limit for arsenic. Rock-water interactions, including calcite dissolution and silicate weathering within the confined aquifer, predominantly influenced hydrochemical properties. The significant relationships among Fe, Mn, and As indicate that the reductive dissolution of FeOOH and/or MnOOH considerably contributes to the release of As from sediment into groundwater. Geochemical modeling analysis revealed that siderite and rhodochrosite precipitate into aquifer solids, suggesting a weak to moderate relationship among As, Fe, and Mn. The long residence time of groundwater, combined with the presence of a clayey aquitard, likely controls the mobilization of arsenic in the aquifer. For the first time, Monte Carlo simulations have been used in arsenic-prone areas to assess the severity of arsenic contamination in western Bangladesh. The analysis indicates that out of 100,000 people, 10 may develop cancer as a result of drinking arsenic-contaminated water, with children being more susceptible than adults.

Predicting leachate impact on groundwater using electrical conductivity and oxidation-reduction potential measurements: An empirical and theoretical approach

This study developed innovative predictive models of groundwater pollution using in situ electrical conductivity (EC) and oxidation-reduction potential (ORP) measurements at livestock carcass burial sites. Combined electrode analysis (EC and ORP) and machine learning techniques efficiently and accurately distinguished between leachate and background groundwater. Two models-empirical and theoretical-were constructed based on a supervised classification framework. The empirical model constructs a classifier with high accuracy, sensitivity, and specificity, utilizing the comprehensive in situ EC and ORP measurements. The theoretical model with only two end members achieves comparable performance by simulating the leachate-groundwater interactions using a geochemical mixing model. Besides enhancing the early detection capabilities, our approach considerably reduces the reliance on extensive hydrochemical analyses, thus streamlining the monitoring process. Moreover, the use of field parameters was found to proactively identify potential pollution incidents, enhancing the efficiency of groundwater monitoring strategies. Our approach is applicable to various waste disposal sites, indicating its extensive potential for environmental monitoring and management.

Geochemical insights of arsenic mobilization into the aquifers of Punjab, Pakistan

It is well known that groundwater arsenic (As) contamination affects million(s) of people throughout the Indus flood plain, Pakistan. In this study, groundwater (n = 96) and drilled borehole samples (n = 87 sediments of 12 boreholes) were collected to investigate geochemical proxy-indicators for As release into groundwater across floodplains of the Indus Basin. The mean dissolved (μg/L) and sedimentary As concentrations (mg/kg) showed significant association in all studied areas viz.; lower reaches of Indus flood plain area (71 and 12.7), upper flood plain areas (33.7 and 7.2), and Thal desert areas (5.3 and 4.7) and are indicative of Basin-scale geogenic As contamination. As contamination in aquifer sediments is dependent on various geochemical factors including particle size (3-4-fold higher As levels in fine clay particles than in fine-coarse sand), sediment types (3-fold higher As in Holocene sediments of floodplain areas vs Pleistocene/Quaternary sediments in the Thal desert) with varying proportion of Al-Fe-Mn oxides/hydroxides. The total organic carbon (TOC) of cored aquifer sediments yielded low TOC content (mean = 0.13 %), which indicates that organic carbon is not a major driver (with a few exceptions) of As mobilization in the Indus Basin. Alkaline pH, high dissolved sulfate and other water quality parameters indicate pH-induced As leaching and the dominance of oxidizing conditions in the aquifers of upper flood plain areas of Punjab, Pakistan while at the lower reaches of the Indus flood plain and alluvial pockets along the rivers with elevated flood-driven dissolved organic carbon (exhibiting high dissolved Mn and Fe and a wide range of redox conditions). Furthermore, we also identified that paired dissolved AsMn values (instead of AsFe) may serve as a geochemical marker of a range of redox conditions throughout Indus flood plains.

Arsenite-Induced Drug-Drug Interactions in Rats

Arsenite is an important heavy metal. Some Chinese traditional medicines contain significant amounts of arsenite. The aim of this study was to investigate subacute exposure of arsenite on activities of cytochrome P450 enzymes and pharmacokinetic behaviors of drugs in rats. Midazolam, tolbutamide, metoprolol, omeprazole, caffeine, and chlorzoxazone, the probe substrates for cytochrome P450 (CYP) s3A, 2C6, 2D, 2C11, 1A, and 2E, were selected as probe drugs for the pharmacokinetic study. Significant decreases in areas under the curves of probe substrates were observed in rats after consecutive 30-day exposure to As at 12 mg/kg. Microsomal incubation study showed that the subacute exposure to arsenite resulted in little change in effects on the activities of P450 enzymes examined. However, everted gut sac study demonstrated that such exposure induced significant decreases in intestinal absorption of these drugs by both passive diffusion and carrier-mediated transport. In addition, in vivo study showed that the arsenite exposure decreased the rate of peristaltic propulsion. The decreases in intestinal permeability of the probe drugs and peristaltic propulsion rate most likely resulted in the observed decreases in the internal exposure of the probe drugs. Exposure to arsenite may lead to the reduction of the efficiencies of pharmaceutical agents coadministered resulting from the observed drug-drug interactions. SIGNIFICANCE STATEMENT: Exposure to arsenite may lead to the reduction of the efficiencies of pharmaceutical agents coadministered resulting from the observed drug-drug interactions. The present study, we found that P450 enzyme probe drug exposure was reduced in arsenic-exposed animals (areas under the curve) and the intestinal absorption of the drug was reduced in the animals. Subacute arsenic exposure tends to cause damage to intestinal function, which leads to reduced drug absorption.

Prediction of arsenic concentration in groundwater of Chapainawabganj, Bangladesh: machine learning-based approach to spatial modeling

Groundwater in northwestern parts of Bangladesh, mainly in the Chapainawabganj District, has been contaminated by arsenic. This research documents the geographical distribution of arsenic concentrations utilizing machine learning techniques. The study aims to enhance the accuracy of model predictions by precisely identifying occurrences of groundwater arsenic, enabling effective mitigation actions and yielding more beneficial results. The reductive dissolution of arsenic-rich iron oxides/hydroxides is identified as the primary mechanism responsible for the release of arsenic from sediment into groundwater. The study reveals that in the research region, alongside elevated arsenic concentrations, significant levels of sodium (Na), iron (Fe), manganese (Mn), and calcium (Ca) were present. Statistical analysis was employed for feature selection, identifying pH, electrical conductivity (EC), sulfate (SO4), nitrate (NO3), Fe, Mn, Na, K, Ca, Mg, bicarbonate (HCO3), phosphate (PO4), and As as features closely associated with arsenic mobilization. Subsequently, various machine learning models, including Naïve Bayes, Random Forest, Support Vector Machine, Decision Tree, and logistic regression, were employed. The models utilized normalized arsenic concentrations categorized as high concentration (HC) or low concentration (LC), along with physiochemical properties as features, to predict arsenic occurrences. Among all machine learning models, the logistic regression and support vector machine models demonstrated high performance based on accuracy and confusion matrix analysis. In this study, a spatial distribution prediction map was generated to identify arsenic-prone areas. The prediction map also displays that Baroghoria Union and Rajarampur region under Chapainawabganj municipality are high-risk areas and Maharajpur Union and Baliadanga Union are comparatively low-risk areas of the research area. This map will facilitate researchers and legislators in implementing mitigation strategies. Logistic regression (LR) and support vector machine (SVM) models will be utilized to monitor arsenic concentration values continuously.

Sodium arsenite induces hepatic stellate cells activation by m6A modification of TGF-β1 during liver fibrosis

The compound known as Sodium arsenite (NaAsO2), which is a prevalent type of inorganic arsenic found in the environment, has been strongly associated with liver fibrosis (LF), a key characteristic of nonalcoholic fatty liver disease (NAFLD), which has been demonstrated in our previous study. Our previous research has shown that exposure to NaAsO2 triggers the activation of hepatic stellate cells (HSCs), a crucial event in the development of LF. However, the molecular mechanism is still unknown. N6-methyladenosine (m6A) modification is the most crucial post-transcriptional modification in liver disease. Nevertheless, the precise function of m6A alteration in triggering HSCs and initiating LF caused by NaAsO2 remains unknown. Here, we found that NaAsO2 induced LF and HSCs activation through TGF-β/Smad signaling, which could be reversed by TGF-β1 knockdown. Furthermore, NaAsO2 treatment enhanced the m6A modification level both in vivo and in vitro. Significantly, NaAsO2 promoted the specific interaction of METTL14 and IGF2BP2 with TGF-β1 and enhanced the TGF-β1 mRNA stability. Notably, NaAsO2-induced TGF-β/Smad pathway and HSC-t6 cells activation might be avoided by limiting METTL14/IGF2BP2-mediated m6A modification. Our findings showed that the NaAsO2-induced activation of HSCs and LF is made possible by the METTL14/IGF2BP2-mediated m6A methylation of TGF-β1, which may open up new therapeutic options for LF brought on by environmental hazards.

Hazards and influence factors of arsenic in the upper pleistocene aquifer, Hetao region, using machine learning modeling

The Hetao region is one of the regions with the most serious problem of the greatest measured arsenic concentrations in China. The enrichment of arsenic in groundwater may poses a great risk to the health of local residents. A comprehensive understanding of the groundwater quality, spatial distribution characteristics and hazard of the high arsenic in groundwater is indispensable for the sustainable utilization of groundwater resources and resident health. This study selected six environmental factors, climate, human activity, sedimentary environment, hydrogeology, soil, and others, as the independent input variables to the model, compared three machine learning algorithms (support vector machine, extreme gradient boosting, and random forest), and mapped unsafe arsenic to estimate the population that may be exposed to unhealthy conditions in the Hetao region. The results show that nearly half the number of the 605 sampling wells for arsenic exceeded the WHO provisional guide value for drinking water, the water chemistry of groundwater are mainly Na-HCO3-Cl or Na-Mg-HCO3-Cl type water, and the groundwater with excessive arsenic concentration is mainly concentrated in the ancient stream channel influence zone and the Yellow River crevasse splay. The results of factor importance explanation revealed that the sedimentary environment was the key factor affecting the primary high arsenic groundwater concentration, followed by climate and human activities. The random forest algorithm produced the probability distribution of high arsenic groundwater that is consistent with the observed results. The estimated area of groundwater with excessive arsenic reached 38.81 %. An estimated 940,000 people could be exposed to high arsenic in groundwater.

Effects of selenium on biogeochemical cycles of cadmium in rice from flooded paddy soil systems in the alluvial Indus Valley of Pakistan

This study delves into the pollution status, assesses the effects of Se on Cd biogeochemical pathways, and explores their interactions in nutrient-rich paddy soil-rice ecosystems through 500 soil-rice samples in Pakistan. The results showed that 99.6 % and 12.8 % of soil samples exceeded the World Health Organization (WHO) allowable Se and Cd levels (7 and 0.35 mg/kg). In comparison, 23 % and 6 % of the grain samples exceeded WHO's allowable Se and Cd levels (0.3 and 0.2 mg/kg), respectively. Geographically Weighted Regression (GWR) model results further revealed spatial nonstationarity, confirming diverse associations between dependent variables (Se and Cd in rice grain) and independent variables from paddy soil and plant tissues (root and shoot), such as Soil Organic Matter (SOM), pH, Se, and Cd concentrations. High Se:Cd molar ratios (>1) and a negative correlation (r = -0.16, p < 0.01) between the Cd translocation factor (Cd in rice grain/Cd in root) and Se in roots suggest that increased root Se levels inhibit the transfer of Cd from roots to grains. The inverse correlation between Se and Cd in paddy grains was further characterized as Se deficiency, no risk, high Cd risk, Se risk, Cd risk, and Se-Cd co-exposure risk. There was no apparent risk for human co-consumption in 42.6 % of grain samples with moderate Se and low Cd. The remaining categories indicate differing degrees of risk. In the study area, 31 % and 20 % of grain samples with low Se and Cd indicate Se deficiency and risk, respectively. High Se and low Cd levels in rice samples suggest a potential hazard for severe Se exposure due to frequent rice consumption. This study not only systematically evaluates the pollution status of paddy-soil systems in Pakistan but also provides a reference to thoroughly contemplate the development of a scientific approach for evaluating human risks and the potential dangers associated with paddy soils and rice, specifically in regions characterized by low Se and low Cd concentrations, as well as those with moderate Se and high Cd concentrations. SYNOPSIS: This study is significant for understanding the effects of Se on Cd geochemical cycles and their interactions in paddy soil systems in Pakistan.

Elevated arsenic concentrations in groundwater of the Upper Indus Plain of Pakistan across a range of redox conditions

Groundwater of the Ravi River floodplain is particularly elevated in arsenic (As) on both sides of the Pakistan-India border. To understand this pattern, 14 sites were drilled to 12-30 m depth across floodplains and doabs of Pakistan after testing over 20,000 wells. Drill cuttings were collected at 1.5 m intervals, 132 of which were sand overlain by 77 intervals of clay and/or silt. Radiocarbon dating of clay indicates deposition of the aquifer sands tapped by wells 20-30 kyr ago. Most (85 %) of the sand samples were gray in color, indicating partial reduction to Fe(II) oxides, whereas most (92 %) of the clay and/or silt samples were orange. Associations between groundwater electrical conductivity, dissolved Fe, sulfate, and nitrate suggest that wells can be elevated (>10 μg/L) in As in the region due to either reductive dissolution of Fe oxides, evaporative concentration, or alkali desorption. In the Ravi floodplain, 47 % of 6445 wells tested contain >10 μg/L As compared to only 9 % of 14,165 tested wells in other floodplains and doabs. The As content of aquifer sands in the Ravi floodplain of Pakistan averages 4 ± 4 mg/kg (n = 66) and is higher than the average of 2 ± 2 mg/kg (n = 51) for aquifer sands outside the Ravi. Synchrotron spectroscopy and column-based speciation indicate predominance of As(V) over As(III) in both aquifer sands and groundwater. Whereas multiple processes may be responsible for elevated levels of As in groundwater across the region, spatial heterogeneity in groundwater As concentrations in the Ravi floodplain seems linked to variations in As concentrations in aquifer sands. Regulation by the solid phase may limit variations in groundwater As over time in response to natural and human-induced changes in hydrology. This means spatial heterogeneity could be taken advantage of to lower the exposure across the region with more testing and targeted drilling.

Immunotherapy and targeted therapy for cholangiocarcinoma: Artificial intelligence research in imaging

Cholangiocarcinoma (CCA) is a highly aggressive hepatobiliary malignancy, second only to hepatocellular carcinoma in prevalence. Despite surgical treatment being the recommended method to achieve a cure, it is not viable for patients with advanced CCA. Gene sequencing and artificial intelligence (AI) have recently opened up new possibilities in CCA diagnosis, treatment, and prognosis assessment. Basic research has furthered our understanding of the tumor-immunity microenvironment and revealed targeted molecular mechanisms, resulting in immunotherapy and targeted therapy being increasingly employed in the clinic. Yet, the application of these remedies in CCA is a challenging endeavor due to the varying pathological mechanisms of different CCA types and the lack of expressed immune proteins and molecular targets in some patients. AI in medical imaging has emerged as a powerful tool in this situation, as machine learning and deep learning are able to extract intricate data from CCA lesion images while assisting clinical decision making, and ultimately improving patient prognosis. This review summarized and discussed the current immunotherapy and targeted therapy related to CCA, and the research progress of AI in this field.

A comprehensive review of human health risks of arsenic and fluoride contamination of groundwater in the South Asia region

The present study found that ∼80 million people in India, ∼60 million people in Pakistan, ∼70 million people in Bangladesh, and ∼3 million people in Nepal are exposed to arsenic groundwater contamination above 10 μg/L, while Sri Lanka remains moderately affected. In the case of fluoride contamination, ∼120 million in India, >2 million in Pakistan, and ∼0.5 million in Sri Lanka are exposed to the risk of fluoride above 1.5 mg/L, while Bangladesh and Nepal are mildly affected. The hazard quotient (HQ) for arsenic varied from 0 to 822 in India, 0 to 33 in Pakistan, 0 to 1,051 in Bangladesh, 0 to 582 in Nepal, and 0 to 89 in Sri Lanka. The cancer risk of arsenic varied from 0 to 1.64 × 1-1 in India, 0 to 1.07 × 10-1 in Pakistan, 0 to 2.10 × 10-1 in Bangladesh, 0 to 1.16 × 10-1 in Nepal, and 0 to 1.78 × 10-2 in Sri Lanka. In the case of fluoride, the HQ ranged from 0 to 21 in India, 0 to 33 in Pakistan, 0 to 18 in Bangladesh, 0 to 10 in Nepal, and 0 to 10 in Sri Lanka. Arsenic and fluoride have adverse effects on animals, resulting in chemical poisoning and skeletal fluorosis. Adsorption and membrane filtration have demonstrated outstanding treatment outcomes.

Assessment and Exposure Analysis of Trace Metals in Different Age Groups of the Male Population in Southern Punjab, Pakistan

In developing countries, like Pakistan, the pursuit of urbanization and economic development disrupts the delicate ecosystem, resulting in additional biogeochemical emissions of heavy metals into the human habitat and posing significant health risks. The levels of these trace elements in humans remain unknown in areas at higher risk of pollution in Pakistan. In this investigation, selected trace metals including Copper (Cu), Chromium (Cr), Lead (Pb) Cadmium (Cd), Cobalt (Co), Nickel (Ni), and Arsenic (As) were examined in human hair, urine, and nail samples of different age groups from three major cities (Muzaffargarh, Multan, and Vehari) in Punjab province, Pakistan. The results revealed that the mean concentrations (ppm) of Cr (1.1) and Cu (9.1) in hair was highest in Muzaffargarh. In urine samples, the mean concentrations (μg/L) of Co (93), As (79), Cu (69), Cr (56), Ni (49), Cd (45), and Pb (35) were highest in the Multan region, while As (34) and Cr (26) were highest in Vehari. The mean concentrations (ppm) of Ni (9.2), Cr (5.6), and Pb (2.8), in nail samples were highest in Vehari; however, Multan had the highest Cu (28) concentration (ppm). In urine samples, the concentrations of all the studied metals were within permissible limits except for As (34 µg/L) and Cr (26 µg/L) in Vehari. However, in nail samples, the concentrations of Ni in Multan (8.1 ppm), Muzaffargarh (9 ppm), Vehari (9.2 ppm), and Cd (3.69 ppm) in Muzaffargarh exceeded permissible limits. Overall, the concentrations of metals in urine, nail, and hair samples were higher in adults (39-45 age group). Cr, Cu, and Ni revealed significantly higher concentrations of metals in hair and water in Multan, whereas As in water was significantly (p < 0.001) correlated with urinary As in Multan, indicating that the exposure source was region-specific.

Hydrogeochemical and sediment parameters improve predication accuracy of arsenic-prone groundwater in random forest machine-learning models

The relative importance of groundwater geochemicals and sediment characteristics in predicting groundwater arsenic distributions was rarely documented. To figure this out, we established a random forest machine-learning model to predict groundwater arsenic distributions in the Hetao Basin, China, by using 22 variables of climate, topographic features, soil properties, sediment characteristics, groundwater geochemicals, and hydraulic gradients of 492 groundwater samples. The established model precisely captured the patchy distributions of groundwater arsenic concentrations in the basin with an AUC value of 0.84. Results suggest that Fe(II) was the most prominent variable in predicting groundwater arsenic concentrations, which supported that the enrichment of arsenic in groundwater was caused by the reductive dissolution of Fe(III) oxides. The high relative importance of SO42- indicated that sulfate reduction was also conducive to groundwater arsenic enrichment in inland basins. Nevertheless, parameters of climate variables, sediment characteristics, and soil properties showed secondly important roles in predicting groundwater arsenic concentrations. The other two models, which excluded parameters of groundwater geochemicals and/or sediment characteristics, showed much worse predictions than the model considering all variables. This highlights the importance of variables of groundwater geochemicals and sediment characteristics in improving the precision and accuracy of predicting results. Future studies should probe a method constructing the random forest predicting model with high precision based on the limited number of groundwater samples and sediment samples.

Use of agricultural bio-wastes to remove arsenic from contaminated water

Arsenic (As) is a highly toxic metalloid. High As levels have been recorded in groundwater aquifers at a global scale. This study investigated As level in groundwater of District Vehari and assessed the potential of different agricultural by-products (sugarcane bagasse, cottonseed hulls, soybean hulls, corncobs and rice husk) to remove As from water. The study was carried out in two steps. In the first step, a total of 38 groundwater samples were obtained from Vehari. Groundwater samples were analyzed for total As contents and physicochemical parameters. Results indicated that As content ranged from below detection limit to 49 µg/L in the groundwater samples. The values of hazard quotient and cancer risk were up to 1.5 and 0.0004, respectively, which delineated severe risk of As poisoning. During the second step, six As-contaminated groundwater samples (total As contents: 49, 40, 29, 24, 18, 16 µg/L) were selected to remove As using agricultural by-products. Furthermore, four As solutions (200, 100, 50 and 25 µg/L) were prepared in the laboratory. Results revealed that corncobs and soybean hulls removed, respectively, 98% and 71% As from aqueous mediums after 120 min. Moreover, agricultural by-products were less effective in removing As from groundwater samples than synthetic solutions. The adsorption/removal capacity of by-products was lower at low initial As concentration compared to high initial levels, which needs further studies to explore the underlying mechanisms. Overall, the As removal efficiency of agriculture by-products differed significantly with respect to initial As level, contamination category, type of agricultural by-products and interaction duration. Therefore, these aspects need to be optimized before the possible use of an agricultural by-product as a potential biosorbent.

GIS based hotspot analysis and health risk assessment of groundwater arsenic from an unconfined deep aquifer of Lahore, Pakistan

Use of groundwater for drinking purpose poses serious hazards of arsenic contamination particularly in plains of western Himalayan region. Therefore, current study was designed to investigate the level of Arsenic (As) in the water obtained from tubewells in a metropolitan city of Lahore, Pakistan and assess the human health risk. So, a total of 73 tubewells were sampled randomly in the manner that the whole study region was covered without any clustering. The water samples were analyzed for As using atomic absorption spectrophotometer. These samples were also tested for total dissolved solids, chlorides, pH, alkalinity, turbidity, hardness and calcium. GIS based hotspots analysis technique was used to investigate the spatial distribution patterns. Our results revealed that only one sample out of total 73 had arsenic level below the WHO guideline of 10 μg/L. The spatial distribution map of arsenic revealed that the higher concentrations of arsenic are present in the north-western region of Lahore. The cluster and outlier analysis map using Anselin Local Moran's I statistic indicated the presence of an arsenic cluster in the west of River Ravi. Furthermore, the optimized hotspot analysis based on Getis-Ord Gi* statistics confirmed the statistical significance (P < 0.05) and (P < 0.01) of these samples from the vicinity of River Ravi. Regression analysis showed that variables such as turbidity, alkalinity, hardness, chlorides, calcium and total dissolved solids were significantly (all P < 0.05) associated with level of Arsenic in tubewells. Whereas, PH and electrical conductivity and other variables like town, year of installation, depth and diameter of the wells were not significantly associated with Arsenic concentrations in tubewells. Principal component analysis (PCA) exhibited that the random distribution of tubewell samples showed no distinct clustering with towns studied. Health risk assessment based on hazard and Cancer risk index revealed serious risk of developing carcinogenic and non-carcinogenic diseases particularly in children. The health risk due to prevalence of high As concentration in tubewells' water need to be mitigated immediately to avoid worst consequences in future.

Coupling hydrogeochemistry and stable isotopes (δ2H, δ18O and δ13C) to identify factors affecting arsenic enrichment of surface water and groundwater in Precambrian sedimentary rocks, eastern salt range, Punjab, Pakistan

The study area is a part of the Salt Range, where water quality is being deteriorated by natural and anthropogenic sources. This research integrates water quality assessment, arsenic enrichment, hydrogeochemical processes, groundwater recharge and carbon sources in aquifer. Total dissolved solid (TDS) contents in springs water, lake water and groundwater are in range of 681-847 mg/L, 2460-5051 mg/L and 513-7491 mg/L, respectively. The higher concentrations of magnesium and calcium in water bodies next to sodium are because of carbonates, sulfates, halite and silicates dissolution. The average concentrations of ions in groundwater are in order of HCO3- > SO42- > Cl- > Na+  > Mg2+ > Ca2+ > K+ > NO3-, virtually analogous to springs water, but different from lake water, categorized as poor quality and unfit for drinking purposes. Based on major ions hydrochemistry, NaCl and mixed Ca-Mg-Cl type hydrochemical facies are associated with concentration of arsenic (4.2-39.5 µg/L) in groundwater. Groundwater samples (70%) having arsenic concentration (11 ≤ As ≤ 39.5 µg/L) exceeded from World Health Organization (WHO) guideline (As ≤ 10 µg/L) in near neutral to slightly alkaline (6.7 ≤ pH ≤ 8.3), positive Eh(6 ≤ Eh ≤ 204 mV), signifying its oxic condition. Eh-pH diagrams for arsenic and iron indicate that 80% of groundwater for arsenic and iron were in compartments of HAsO42- and Fe(OH)3, unveil oxic environment. Arsenic is moderately positive correlated with TDS, sodium, chloride, bicarbonate, nitrate, sulfate and weak negative with δ13CDIC in surface and groundwater, forecasting multiple sources of arsenic to aquifer. Stable isotopes of waters show recharge of groundwater from local rain and lake water. The lower δ13CDIC values of groundwater are modified by influx of CO2 produced during biological oxidation of soil natural organic matter.

Spatial prediction and influencing factors identification of potential toxic element contamination in soil of different karst landform regions using integration model

The prediction of contamination distribution of potentially toxic elements (PTEs) in soils of Guangxi province, China and the identification of their controlling factors pose great challenges due to diverse bedrock types, intense leaching and weathering, and discontinuous terrain distributions. Herein, we integrated the random forest (RF) and empirical Bayesian kriging (EBK) to interpret and predict complex PTEs contamination distribution from three different karst landform regions (fenglin, fengcong, isolated peak plain) in Guangxi province. The modeling results are compared with the commonly used ordinary kriging and regression-kriging. In this study, our developed RF-EBK model combines the advantages of the RF and EBK model to promote the prediction accurately and efficiently. In this study, it was shown that the integration RF-EBK model exhibited desirable for Cd and As concentrations, with R² of 0.89 and 0.83, respectively. The average RMSE and MAE of integration RF-EBK model decreased by 39% and 44%, respectively, relative to the regression-kriging with the second highest accuracy. Furthermore, the modeling results showed that approximately 41.96% and 18.96% of total area was classified as Cd and As polluted and above regions (I_geo >0) in Guangxi province, respectively. Higher Cd concentration was observed in the soil of fenglin and fengcong regions than that in isolated peak plain region due to the secondary enrichment and parent rock inheritance, while the As concentration exhibited no significant difference among the three regions. The modeling results indicated that the elevated Cd concentration might be associated with soil CaO concentration and alkaline soil environment, whereas As concentration tended to be increased with the elevating Fe₂O₃ concentrations in weakly acidic soil environment. This result confirmed the applicability and effectiveness of integration model in predicting complex spatial patterns of soil PTEs and identifying their controlling factors.

Machine learning approach for assessment of arsenic levels using physicochemical properties of water, soil, elevation, and land cover

Groundwater is an essential resource; around 2.5 billion people depend on it for drinking and irrigation. Groundwater arsenic contamination is due to natural and anthropogenic sources. The World Health Organization (WHO) has proposed a guideline value for arsenic concentration in groundwater samples of 10[Formula: see text]g/L. Continuous consumption of arsenic-contaminated water causes various carcinogenic and non-carcinogenic health risks. In this paper, we introduce a geospatial-based machine learning method for classifying arsenic concentration levels as high (1) or low (0) using physicochemical properties of water, soil type, land use land cover, digital elevation, subsoil sand, silt, clay, and organic content of the region. The groundwater samples were collected from multiple sites along the river Ganga's banks of Varanasi district in Uttar Pradesh, India. The dataset was subjected to descriptive statistics and spatial analysis for all parameters. This study assesses the various contributing parameters responsible for the occurrence of arsenic in the study area based on the Pearson correlation feature selection method. The performance of machine learning models, i.e., Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Decision Tree, Random Forest, Naïve Bayes, and Deep Neural Network (DNN), were compared to validate the parameters responsible for the dissolution of arsenic in groundwater aquifers. Among all the models, the DNN algorithm outclasses other classifiers as it has a high accuracy of 92.30%, a sensitivity of 100%, and a specificity of 75%. Policymakers can utilize the accuracy of the DNN model to approximate individuals prone to arsenic poisoning and formulate mitigation strategies based on spatial maps.

Arsenic pollution and associated human health hazards in Rupnagar district, Punjab, India

The hydrosphere although covering almost 70% of the Earth contributes only 3% of fresh water out of which groundwater covers almost 98%. The presence of some unwanted substance in this limited natural resource causes pollution when the substance causes serious harm to human beings and to the total ecosystem in a way. Arsenic is such a pollutant that is most naturally released in groundwater and long-term exposure to As-rich groundwater causes skin lesions and often leads to different types of cancers in humans. Rupnagar district in the Malwa region of Punjab is situated alongside the river Satluj which is one of the five important tributaries of Indus. The lowest reported concentration of As in this district is 10 µg/L and the highest is 91 µg/L. The higher values of As (> 50 µg/L) that are above the permissible limit of IS 10500, 2004 in drinking water, are dominantly found in the western and south-western parts of the district. The average hazard quotient (HQ) indicates high risk for the consumers of the As-polluted groundwater in the district. The present study deals with the major cause of high arsenic (As) concentration in groundwater and its correlation with intensive agriculture in the Rupnagar district. Owing to the large size of the district, GIS techniques like ArcGIS 10.4.1 and QGIS 3.22.8 software were used for analysis in this study. The study reveals that high As concentration (> 50 µg/L) is mostly found in agricultural lands and moderate concentration of As (10-50 µg/L) in groundwater is distributed all over the district and are mostly reported from the urbanised areas. Overall, the water table shows a declining trend but no such decline is observed in the western and south-western parts of the district. As pollution in groundwater can also be caused due to water level decline owing to intensive agriculture and rapid water abstraction though As is naturally sourced in groundwater. A detailed study using the geochemical analysis of groundwater in the district can be effective in clearing out the scenario in the study area.

Estimation of heavy metal soil contamination distribution, hazard probability, and population at risk by machine learning prediction modeling in Guangxi, China

Due to superposition of diverse pollution sources, soil heavy metal concentrations have been detected to exceed the recommended maximum permissible levels in many areas of Guangxi province, China. However, the heavy metal contamination distribution, hazard probability, and population at risk of heavy metals in the entire Guangxi province remain largely unclear. In this study, machine learning prediction models with different standard risk values determined according to land use types were used to identify high-risk areas and estimate populations at risk of Cr and Ni based on 658 topsoil samples from Guangxi province, China. Our results showed that soil Cr and Ni contamination derived from carbonate rocks was relatively serious in Guangxi province, and that their co-enrichment during soil formation was associated with Fe and Mn oxides and alkaline soil environment. Our established model exhibited excellent performance in predicting contamination distribution (R² > 0.85) and hazard probability (AUC>0.85). Pollution of Cr and Ni exhibited a pattern of decreasing gradually from the central-west areas to the surrounding areas with the polluted area (I_geo>0) of Cr and Ni accounting for approximately 24.46% and 29.24% of total area in Guangxi province, respectively, but only 10.4% and 8.51% of total area was classified as Cr and Ni high-risk regions. We estimated approximately 1.44 and 1.47 million people were potentially exposed to the risk of Cr and Ni contamination, which were mainly concentrated in the Nanning, Laibin, and Guigang. These regions are main heavily-populated agricultural regions in Guangxi, and thus heavy metal contamination localization and risk control in these regions are urgent and essential from the perspective of food safety.

Large scale occurrence of aluminium-rich shallow groundwater in the Pearl River Delta after the rapid urbanization: Co-effects of anthropogenic and geogenic factors

Aluminium(Al)-rich (> 0.2 mg/L) groundwater has received more concerns because of its harmful to human beings. Origins of large-scale occurrence on Al-rich groundwater in urbanized areas such as the Pearl River Delta (PRD) are still little known. The current work was conducted to investigate spatial distribution of Al-rich groundwater in the PRD, and to discuss its origins in various aquifers. For that, 265 groundwater samples and 15 river water samples were collected, and 21 hydrochemical parameters including Al were analyzed by using conventional analytical procedures. The results showed that groundwater Al concentrations were up to 22.64 mg/L, and Al-rich groundwater occurred in 15% of the area occupied by the PRD. Al-rich groundwater in the coastal-alluvial aquifer was about 2 times those in alluvial-proluvial and fissured aquifers, whereas the karst aquifer was absent. In the coastal-alluvial aquifer, Al-rich groundwater in the peri-urban area was 2 or more times those in urbanized and agricultural areas, whereas the remaining area was absent. By contrast, in the alluvial-proluvial aquifer, Al-rich groundwater in the remaining area was 1.5-3.5 times that in other areas; in the fissured aquifer, the distribution of Al-rich groundwater was independent of land-use types. The infiltration of wastewater from township enterprises was main anthropogenic source for Al-rich groundwater in urbanized and peri-urban areas, whereas irrigation of Al-rich river water was the main one in the agricultural area. Naturally dissolution of Al-rich minerals in soils/rocks, triggered by both of pH decrease resulted from nitrification of contaminated ammonium (e.g., sewage leakage, the use of nitrogen fertilizer) and acid deposition, was the main geogenic source for Al-rich groundwater in the PRD. The contribution of anthropogenic sources to Al-rich groundwater in the coastal-alluvial aquifer was more than that in alluvial-proluvial and fissured aquifers, whereas the contribution of geogenic sources was opposite. In conclusion, the discharge of township enterprises wastewater and ammonium-rich sewage, the emission of nitrogen-containing gas, and the use of nitrogen fertilizer should be preferentially limited to decrease the occurrence of Al-rich groundwater in urbanized areas such as the PRD.

Iron Vacancy Accelerates Fe(II)-Induced Anoxic As(III) Oxidation Coupled to Iron Reduction

Chemical oxidation of As(III) by iron (Fe) oxyhydroxides has been proposed to occur under anoxic conditions and may play an important role in stabilization and detoxification of As in subsurface environments. However, this reaction remains controversial due to lack of direct evidence and poorly understood mechanisms. In this study, we show that As(III) oxidation can be facilitated by Fe oxyhydroxides (i.e., goethite) under anoxic conditions coupled with the reduction of structural Fe(III). An excellent electron balance between As(V) production and Fe(III) reduction is obtained. The formation of an active metastable Fe(III) phase at the defective surface of goethite due to atom exchange is responsible for the oxidation of As(III). Furthermore, the presence of defects (i.e., Fe vacancies) in goethite can noticeably enhance the electron transfer (ET) and atom exchange between the surface-bound Fe(II) and the structural Fe(III) resulting in a two time increase in As(III) oxidation. Atom exchange-induced regeneration of active goethite sites is likely to facilitate As(III) coordination and ET with structural Fe(III) based on electrochemical analysis and theoretical calculations showing that this reaction pathway is thermodynamically and kinetically favorable. Our findings highlight the synergetic effects of defects in the Fe crystal structure and Fe(II)-induced catalytic processes on anoxic As(III) oxidation, shedding a new light on As risk management in soils and subsurface environments.

Appraisal of groundwater arsenic on opposite banks of River Ganges, West Bengal, India, and quantification of cancer risk using Monte Carlo simulations

This study was conducted to inspect the spatial distribution, source identification, and risk assessment of groundwater arsenic (As) in different blocks that lie on the opposite banks of river Bhagirathi (a distributary of river Ganges), Murshidabad, West Bengal, India. It has been observed that the blocks that lie towards the eastern bank of river Bhagirathi have elevated arsenic and comparatively more reducing groundwater (lower oxidation-reduction potential and high iron). About 66% of groundwater samples across the district have arsenic concentration higher than the World Health Organization (WHO) permissible limit. Speciation of groundwater arsenic reveals that about 90% of arsenic species were present as arsenic (III). Further, principal component analysis (PCA) was employed to identify the controlling factors that favor the release of arsenic. PC1 comprises EC, TDS, As, Fe, TOC, and HCO₃^- with moderate loadings, which suggests microbially mediated degradation of organic matter (OM), helps in reductive dissolution of arsenic-bearing Fe-Mn oxyhydroxides. Results pointed out severe groundwater arsenic poisoning; hence, a health risk assessment was performed for the exposure of arsenic in groundwater, using incremental lifetime cancer risk (ILCR) models coupled with Monte Carlo simulations. On the eastern bank of river Bhagirathi, incremental lifetime cancer risk (ILCR) due to oral exposure (5th to 95th percentile values) ranged from 1.30538E - 04 to 9.31398E - 03 with a mean of 2.84194E - 03 for adults, which is 2841 times higher than the USEPA high safety risk guidelines of one in 1 million. The outcomes of the results will be useful for the policymakers and regulatory boards in defining the actual impact and deciding the pre-remediation goals.

Characterization and health risk assessment of arsenic in natural waters of the Indus River Basin, Pakistan

The elevated concentrations of arsenic in natural water are one of the major environmental threats to human health. However, the existing characteristics, controlling mechanisms, and associated risks of arsenic in natural waters in the Indus River Basin (IRB), Pakistan, are yet to be unequivocally understood. In this study, a total of 203 samples of surface water (SW), shallow groundwater (SGW), and deep groundwater (DGW) were collected from the IRB to assess the geochemical characteristics of arsenic and its associated health risks, as all three kinds of waters are the main sources of drinking and domestic usage. The results revealed that the arsenic concentrations in the SW, SGW, and DGW were in the ranges of 1.1-26.45, 1.05-44.44, and 0.67-41.09 μg L^-1, respectively. Furthermore, the predominance of As (V) (97 %) over As(III) (3 %) confirmed that the desorption of As in oxidizing environments with elevated pH and Eh is the controlling mechanism. The hazard quotient of 11-45 % and 20-60 % samples and cancer risk of 26-64 % and 26-68 % samples indicated high health risks for the adults and children, respectively, suggesting an immense need for appropriate measures of reducing natural water arsenic concentrations in IRB from the human health perspectives.

Arsenic exposure induces urinary metabolome disruption in Pakistani male population

Millions of people are at risk of consuming arsenic (As) contaminated drinking water in Pakistan. The current study aimed to investigate urinary arsenic species [iAs^III, iAs^V, dimethylarsinic acid (DMA), methylarsonic acid (MMA)] and their potential toxicity biomarkers (based on urinary metabolome) in order to characterize the health effects in general adult male participants (n = 588) exposed to various levels of arsenic in different floodplain areas of Pakistan. The total urinary arsenic concentration (mean; 161 μg/L) of studied participants was lower and/or comparable than those values reported from other highly contaminated regions, but exceeded the Agency for Toxic Substances and Disease Registry (ATSDR) limits. For all the participants, the most excreted species was DMA accounting for 65% of the total arsenic, followed by MMA (20%) and iAs (16%). The percentage of MMA detected in this study was higher than those of previously reported data from other countries. These results suggested that studied population might have high risk of developing arsenic exposure related adverse health outcomes. Furthermore, random forest machine learning algorithm, partial correlation and binary logistic regression analysis were performed to screen the arsenic species-related urinary metabolites. A total of thirty-eight metabolites were extracted from 2776 metabolic features and identified as the potential arsenic toxicity biomarkers. The metabolites were mainly classified into xanthines, purines, and amino acids, which provided the clues linking the arsenic exposure with oxidative stress, one-carbon metabolism, purine metabolism, caffeine metabolism and hormone metabolism. These results would be helpful to develop early health warning system in context of arsenic exposure among the general populations of Pakistan.

Controls of Geochemical and Hydrogeochemical Factors on Arsenic Mobility in the Hetao Basin, China

High arsenic (As) groundwater is frequently found in inland basins, but little is known about As pools in sediments and their influences on aqueous As distributions. The Hetao Basin is a typical inland basin, where groundwater As concentrations generally increase from alluvial fans to flat plain. Two sites are only 1700 m apart, but groundwater As concentrations at the depth range of 15 to 80 m are quite different, ranging from 7.0 to 31.7 μg/L at site B1 and from 5.2 to 99.8 μg/L at site B2. Sediment geochemistry and groundwater hydrochemistry at two sites were characterized. No distinct differences were observed in the bulk geochemistry of sediments. Sequential extractions of 39 sediments were conducted to determine why As was easily released to groundwater at one site and not the other. Results showed that at site B1 most of solid As was associated with amorphous Fe-(oxyhydr)oxides, whereas at site B2 the strong adsorption pool dominated. Furthermore, higher dissolved Fe²⁺ and lower ORP in groundwater at site B2 suggested more strongly reducing conditions compared to site B1. High concentrations of NH₄ ⁺ and HCO₃ ^- at site B2 were consistent with As release coupled to microbially induced reductive dissolution of Fe-(oxyhydr)oxides. Other processes, such as the competitive adsorption of HCO₃ ^- , As desorption under weakly alkaline pH conditions, may also influence the partitioning of As between groundwater and sediments. This study highlights the differences in how As is associated with sediments between high and low As aquifers and the contribution of chemical characteristics to As release.

Natural Clay Minerals as Potential Arsenic Sorbents from Contaminated Groundwater: Equilibrium and Kinetic Studies

Arsenic (As) contaminated groundwater is a worldwide concern due to its chronic effects on human health. The objectives of the study were to evaluate natural inexpensive raw laterite (RL) and kaolinite (RK) for their potential use as As sorbents and to understand the As sorption on laterite and kaolinite by employing sorption and kinetic models. Raw laterite and RK were tested for EC, pH, XRF and CEC as basic parameters. Batch sorption and kinetic experiments data were fitted in the sorption (Langmuir and Freundlich) model and kinetic (pseudo-first and pseudo-second order) reaction equations, respectively. Morphological and structural changes were observed in RL and RK samples before and after As saturation by employing FTIR and SEM. The major constituent in RL was Fe and Al oxides while in RK major oxides were silica and Al. The Freundlich sorption model well explained the experimental data, indicating a greater sorption capacity of RL on a hetero-layered surface compared to RK. The kinetic reaction equations showed that equilibrium was achieved after a contact time of 240 min and the adsorption was chemisorption in nature. The RL and RK were found to be effective sorbents for As removal, however, RL showed maximum As adsorption and thus superior in comparison with RK. Structural and morphological characterization reveals the role of Fe and Al oxides in the case of RL, and Al oxides in the case of RK, in the adsorption of As. Hence this study concludes that these naturally occurring inexpensive resources can be used as sorbent agents for As-contaminated drinking water treatment.

Arsenic release from arsenopyrite weathering in acid mine drainage: Kinetics, transformation, and effect of biochar

Arsenopyrite (FeAsS) oxidative dissolution provides an important source for the occurrence of high arsenic in acid mine drainage (AMD). Biochar is a potent material that can dramatically sequestrate an array of heavy metals in water. However, little is known about the role of biochar on the fate of As from arsenopyrite in AMD. This study investigates the effects of biochar concentrations, AMD acidities, and temperatures on the release of As from arsenopyrite in a simulated AMD over a range of environmentally relevant conditions. Results show that biochar inhibits As release and further acidification without changing the arsenopyrite weathering mechanism. Arsenopyrite is first oxidized to Fe(II), As(III) and S⁰ and ultimately oxidized to Fe(III), As(V) and SO₄^2-, respectively. Higher concentration, temperature or higher acidity promotes the arsenic release rate. Electrochemical studies showed that biochar inhibited As release and acidification for reduced the charge transfer resistance at the double layer and film resistance at the passivation layer, which was mainly attributed to Fe(III) ions in AMD being adsorbed, oxidized, and As complexed to biochar-Fe-As(V). This study reveals the release mechanism of As from arsenopyrite weathering in AMD and suggests the applicability of biochar in mitigating arsenic pollution and further acidification in sulfide mineral mine drainage.

Evolution Mechanism of Arsenic Enrichment in Groundwater and Associated Health Risks in Southern Punjab, Pakistan

Arsenic (As) contamination in groundwater is a worldwide concern for drinking water safety. Environmental changes and anthropogenic activities are making groundwater vulnerable in Pakistan, especially in Southern Punjab. This study explores the distribution, hydrogeochemical behavior, and pathways of As enrichment in groundwater and discusses the corresponding evolution mechanism, mobilization capability, and health risks. In total, 510 groundwater samples were collected from three tehsils in the Punjab province of Pakistan to analyze As and other physiochemical parameters. Arsenic concentration averaged 14.0 μg/L in Vehari, 11.0 μg/L in Burewala, and 13.0 μg/L in Mailsi. Piper-plots indicated the dominance of Na+, SO42-, Ca2+, and Mg2+ ions in the groundwater and the geochemical modeling showed negative saturation indices with calcium carbonate and salt minerals, including aragonite (CaCO3), calcite (CaCO3), dolomite (CaMg(CO3)2), and halite (NaCl). The dissolution process hinted at their potential roles in As mobilization in groundwater. These results were further validated with an inverse model of the dissolution of calcium-bearing mineral, and the exchange of cations between Ca2+ and Na+ in the studied area. Risk assessment suggested potential carcinogenic risks (CR > 10-4) for both children and adults, whereas children had a significant non-carcinogenic risk hazard quotient (HQ > 1). Accordingly, children had higher overall health risks than adults. Groundwater in Vehari and Mailsi was at higher risk than in Burewala. Our findings provide important and baseline information for groundwater As assessment at a provincial level, which is essential for initiating As health risk reduction. The current study also recommends efficient management strategies for As-contaminated groundwater.

Flavonoids and stilbenoids as a promising arsenal for the management of chronic arsenic toxicity

Rapid industrial and technological development has impacted ecosystem homeostasis strongly. Arsenic is one of the most detrimental environmental toxins and its management with chelating agents remains a matter of concern due to associated adverse effects. Thus, safer and more effective alternative therapy is required to manage arsenic toxicity. Based on existing evidence, native and indigenous plant-based active biomolecules appear as a promising strategy to mitigate arsenic-induced toxicity with an acceptable safety profile. In this regard, various phytochemicals (flavonoids and stilbenoids) are considered important classes of polyphenolic compounds with antioxidant and chelation effects, which may facilitate the removal of arsenic from the body more effectively and safely with regard to conventional approaches. This review presents an overview of conventional chelating agents and the potential role of flavonoids and stilbenoids in ameliorating arsenic toxicity. This report may provide a roadmap for identifying novel prophylactic/therapeutic strategies for managing arsenic toxicity.

Monitoring and prediction of high fluoride concentrations in groundwater in Pakistan

Concentrations of naturally occurring fluoride in groundwater exceeding the WHO guideline of 1.5 mg/L have been detected in many parts of Pakistan. This may lead to dental or skeletal fluorosis and thereby poses a potential threat to public health. Utilizing a total of 5483 fluoride concentrations, comprising 2160 new measurements as well as those from other sources, we have applied machine learning techniques to predict the probability of fluoride in groundwater in Pakistan exceeding 1.5 mg/L at a 250 m spatial resolution. Climate, soil, lithology, topography, and land cover parameters were identified as effective predictors of high fluoride concentrations in groundwater. Excellent model performance was observed in a random forest model that achieved an Area Under the Curve (AUC) of 0.92 on test data that were not used in modeling. The highest probabilities of high fluoride concentrations in groundwater are predicted in the Thar Desert, Sargodha Division, and scattered along the Sulaiman Mountains. Applying the model predictions to the population density and accounting for groundwater usage in both rural and urban areas, we estimate that about 13 million people may be at risk of fluorosis due to consuming groundwater with fluoride concentrations >1.5 mg/L in Pakistan, which corresponds to ~6% of the total population. Both the fluoride prediction map and the health risk map can be used as important decision-making tools for authorities and water resource managers in the identification and mitigation of groundwater fluoride contamination.

Siamese Network-Based Transfer Learning Model to Predict Geogenic Contaminated Groundwaters

Exposure to geogenic contaminated groundwaters (GCGs) is a significant public health concern. Machine learning models are powerful tools for the discovery of potential GCGs. However, the insufficient groundwater quality data and the fact that GCGs are typically a minority class in data hinder models to produce meaningful GCG predictions. To address this issue, a deep learning method, Siamese network-based transfer learning (SNTL), is used to estimate the probability that hazardous substances are present in groundwater above a threshold based on limited and class-imbalanced data. SNTL greatly reduces the amount of required training data and eliminates negative effects of class-imbalanced data on prediction model performance. The predictions of three typical GCGs (high arsenic/fluoride/iodine groundwater) show that the SNTL models provide higher (about 80%) and more balanced sensitivity and specificity than benchmark Random Forest models, indicating that SNTL models can predict both GCGs and non-GCGs. Therefore, protecting populations from GCG exposure in areas where other prediction methods fail to contribute risk information due to poor groundwater quality data can be enabled by SNTL.

Global analysis and prediction of fluoride in groundwater

The health of millions of people worldwide is negatively impacted by chronic exposure to elevated concentrations of geogenic fluoride in groundwater. Due to health effects including dental mottling and skeletal fluorosis, the World Health Organization maintains a maximum guideline of 1.5 mg/L in drinking water. As groundwater quality is not regularly tested in many areas, it is often unknown if the water in a given well or spring contains harmful levels of fluoride. Here we present a state-of-the-art global fluoride hazard map based on machine learning and over 400,000 fluoride measurements (10% of which >1.5 mg/L), which is then used to estimate the human population at risk. Hotspots indicated by the groundwater fluoride hazard map include parts of central Australia, western North America, eastern Brazil and many areas of Africa and Asia. Of the approximately 180 million people potentially affected worldwide, most reside in Asia (51-59% of total) and Africa (37-46% of total), with the latter representing 6.5% of the continent's population. Africa also contains 14 of the top 20 affected countries in terms of population at risk. We also illuminate and discuss the key globally relevant hydrochemical and environmental factors related to fluoride accumulation.

A Systems Approach to Remediating Human Exposure to Arsenic and Fluoride From Overexploited Aquifers

In semiarid agricultural regions, aquifers have watered widespread economic development. Falling water tables, however, drive up energy costs and can make the water toxic for human consumption. The study area is located in central Mexico, where arsenic and fluoride are widely present at toxic concentrations in well water. We simulated the holistic outcomes from three pumping scenarios over 100 years (2020-2120); (S1) pumping rates increase at a similar rate to the past 40 years, (S2) remain constant, or (S3) decrease. Under scenario S1, by 2120, the depth to water table increased to 426 m and energy consumption for irrigation increased to 4 × 109 kWh/yr. Arsenic and fluoride concentrations increased from 14 to 46 μg/L and 1.0 to 3.6 mg/L, respectively. The combined estimated IQ point decrements from drinking untreated well water lowered expected incomes in 2120 by 27% compared to what they would be with negligible exposure levels. We calculated the 100-year Net Present Value (NPV) of each scenario assuming the 2020 average crop value to water footprint ratio of 0.12 USD/m3. Without drinking water mitigation, S1 and S3 yielded relative NPVs of -5.96 × 109 and 1.51 × 109 USD, respectively, compared to the base case (S2). The relative NPV of providing blanket reverse osmosis treatment, while keeping pumping constant (S2), was 11.55 × 109 USD and this gain increased when combined with decreased pumping (S3). If a high value, low water footprint crop was substituted (broccoli, 1.51 USD/m3), the net gains from increasing pumping were similar in size to those of implementing blanket drinking water treatment.

Chromium contamination in paddy soil-rice systems and associated human health risks in Pakistan

Chromium (Cr) contamination in paddy soil-rice systems threatens human health through the food chain. This study used a new dataset of 500 paddy soil and plant tissue samples collected in the rice-growing regions of Sindh and Punjab Provinces of Pakistan. Overall, 97.4% of grain samples exceeded the Cr threshold values of 1.0 mg kg^-1, determined by the China National Food Standard (CNFS). The Cr in paddy soil, 62.6% samples exceeding the China natural background threshold value (90 mg kg^-1) for Cr concentration in paddy soil, and lower than the (pH-dependant > 7.5 threshold value for Cr 350 mg kg^-1) as determined by China Environmental Quality Standards (EQSs) for paddy soil (GB15618-2018). Geographically weighted regression (GWR) modelling showed spatially nonstationary correlations, confirming the heterogeneous relationship between dependent (rice grain Cr) and independent paddy soil (pH, SOM, and paddy soil Cr) and plant tissue variables (shoot Cr and root Cr) throughout the study area. The GWR model was then used to determine the critical threshold (CT) for the measured Cr concentrations in the paddy soil system. Overall, 38.4% of paddy soil samples exceeding CT values confirm that the paddy soil Cr risk prevails in the study area. Furthermore, the GWR model was applied to assess the loading capacity (LC), the difference between the CT, and the actual concentration of Cr in paddy soil. Loading capacity identified potential paddy soil Cr pollution risk to rice grain and assessed the risk areas. Overall LC% of samples paddy soil Cr risk areas grade: low-risk grade I (34.6%); moderate-risk grade II (15.8%); high-risk grade III (11.2%); and very high-risk grade IV (38.4%) have been assessed in the study area. The human health index, total hazard quotient (THQ ≪ 1), indicates no potential health risk originating from Cr exposure to the population. However, the excess Cr level in paddy soil and rice grain is still a concern. The current study's results are also valuable for the national decision-making process regarding Cr contamination in the paddy soil-rice system.

Assessing the Association of Element Imbalances With Arsenism and the Potential Application Value of Rosa roxburghii Tratt Juice

Endemic arsenism caused by coal burning is a unique type of biogeochemical disease that only exists in China, and it is also a disease of element imbalances. Previous studies have shown that element imbalances are involved in the pathogenesis of arsenic; however, the interaction between the various elements and effective preventive measures have not been fully studied. This study first conducted a cross-sectional study of a total of 365 participants. The results showed that arsenic exposure can increase the content of elements (Al, As, Fe, Hg, K, and Na) in the hair (p < 0.05), but the content of other elements (Ca, Co, Cu, Mn, Mo, P, Se, Sr, V, and Zn) was significantly decreased (p < 0.05). Also, the high level of As, Fe, and Pb and the low level of Se can increase the risk of arsenism (p < 0.05). Further study found that the combined exposure of Fe–As and Pb–As can increase the risk of arsenism, but the combined exposure of Se–As can reduce the risk of arsenism (p < 0.05). In particular, a randomized, controlled, double-blind intervention study reveals that Rosa roxburghii Tratt juice (RRT) can reverse the abovementioned element imbalances (the high level of Al, As, and Fe and the low level of Cu, Mn, Se, Sr, and Zn) caused by arsenic (p < 0.05). Our study provides some limited evidence that the element imbalances (the high level of As, Fe, and Pb and the low level of Se) are the risk factors for the occurrences of arsenism. The second major finding was that RRT can regulate the element imbalances, which is expected to improve arsenism. This study provides a scientific basis for further understanding a possible traditional Chinese health food, RRT, as a more effective detoxication of arsenism.

Changes of groundwater arsenic risk in different seasons in Hetao Basin based on machine learning model

Arsenic pollution of shallow groundwater is serious in Hetao Basin. At present, there are few studies on the seasonal variation and mechanism of high As groundwater. In order to master the risk difference and influence mechanism of high As groundwater in different seasons, we collected 506 shallow groundwater samples in the Hetao Basin, and used climatic factors, topographic factors, and others (influence of irrigation channels, vegetation index) that are closely distributed with As in groundwater to establish a high-precision random forest model of high As groundwater in the Hetao Basin in summer. We used climate factors as dynamic predictors to predict the distribution of high As risks in winter and established human health risk zones in the Hetao Basin. The results show that from winter to summer, the probability of high As in high risk areas further increases with the influence of factors such as temperature increase, rainfall increase, and enhanced evapotranspiration, while the probability of high As in low risk areas is the opposite and shows a downward trend. The areas with increased probability of high human health risks and stable areas are mainly distributed along the drainage canals and concentrated in the middle of the basin. From winter to summer, as the local residents' demand for groundwater increases, the probability of high As has increased and stabilized in high risk areas. The number of threatened populations reached 246,000 and 108,000, respectively. Therefore, we need to focus on them. The results of this research explored the changing trend and mechanism of high As groundwater risks under the influence of climate, further enriching the regional high As groundwater research system, and can also be provided as a reference for similar research in other regions.

A modified approach to quantify aquifer vulnerability to pollution towards sustainable groundwater management in Irrigated Indus Basin

The quality of groundwater in the study watershed has worsened because of industrial effluents and residential wastes from the urbanized cities; therefore, there is an important need to explore the aquifer vulnerability to pollution for sustainable groundwater management in the Irrigated Indus Basin (IIB). This study proposed a novel methodology to quantify groundwater vulnerability using two fully independent methodologies: the first by reintroducing an improved recharge factor (R) map and the second by incorporating three different weight and rating schemes into a traditional DRASTIC framework to improve the performance of the DRASTIC approach. In the current study, we composed a recharge map from Soil and Water Assessment Tool (SWAT) output (namely SWAT recharge map) with a drainage density map to retrieve an improved composite recharge map (SWAT-CRM). SWAT-CRM along with other thematic layers was combined using weightage overlay analysis to prepare the maps of groundwater vulnerability index (VI). The weight scale (w) and rating scale (r) were assigned based on a survey of available literature, and we then amended them using the analytical hierarchy process (AHP) and a probability frequency ratio (PFR) technique. Results depicted that the region under high groundwater vulnerability was found to be 5-22% using traditional recharge maps, while those are 9-23% using improved SWAT-CRM. The area under the curve (AUC) revealed that groundwater vulnerability zones predicted with SWAT-CRM outperformed the DRASTIC model applied with the traditional recharge map. Groundwater electrical conductivity (EC) was>2500 mS/cm in the high groundwater vulnerability zones, while it was <1000 mS/cm in the low groundwater vulnerability zones. The outcomes of this study can be used to improve the sustainability of the groundwater resources in IIB through proper land-use management practices.

Rice Industry By-Products as Adsorbent Materials for Removing Fluoride and Arsenic from Drinking Water—A Review

In drinking water, high concentrations of fluoride and arsenic can have adverse effects on human health. Waste deriving from the rice industry (rice husk, rice straw, rice bran) can be promising adsorbent materials, because they are (i) produced in large quantities in many parts of the world, (ii) recoverable in a circular economy perspective, (iii) at low cost if compared to expensive conventional activated carbon, and (iv) easily manageable even in developing countries. For the removal of fluoride, rice husk and rice straw allowed to obtain adsorption capacities in the range of 7.9–15.2 mg/g. Using rice husk for arsenic adsorption, excellent results were achieved with adsorption capacities above 19 mg/g. The best results both for fluorides and arsenic (>50 mg/g) were found with metal- or chemical-modified rice straw and rice husk. Identifying the next steps of future research to ensure the upscaling of biochar from recovered by-products, it is fundamental to perform: (i) tests on real waters for multicomponent adsorption; (ii) experiments with pilot plants in continuous operation; (iii) cost analysis/real applicability of modification treatments such as metal coupling or chemical synthesis; (iv) more studies on the biochar stability and on its regeneration or recovery after use.

Spatial variation in dissolved phosphorus and interactions with arsenic in response to changing redox conditions in floodplain aquifers of the Hetao Basin, Inner Mongolia

Increasing numbers of studies have reported groundwater with naturally high phosphorous (P) and arsenic (As) concentrations, which can potentially threaten the environment and human health. However, the cycling of P and its interactions with As in groundwater under changing redox conditions remain largely unknown. In this study, 83 groundwater samples and 14 sediment samples were collected from the Hetao Basin, Inner Mongolia, for systematic hydrogeochemical investigation and complementary geochemical evaluation. The results showed that P cycling in floodplain aquifers was tightly constrained by redox conditions. Under oxic/suboxic conditions, mineralization of organic matter and weathering of P-bearing minerals were the two dominant processes that mobilized considerable amounts of P in groundwater. When redox conditions became reducing, Fe(III)-oxide reduction dominated, resulting in enrichment of both P and As in groundwater. In Fe(III)-reducing conditions, secondary Ca/Fe(II)-minerals might serve as an important sink for P. When redox conditions became SO42--reducing, preferential adsorption and incorporation of P over As on Fe(II)-sulfides might constrain the As immobilization pathway, resulting in immediate retardation of P and hysteretic immobilization of As. This P-immobilization pathway in natural aquifers has not been described before. This study provides novel insights into P cycling and As enrichment in groundwater systems. Understanding the roles of Fe(II)- and S(-II)-minerals in the immobilization of and interaction between P and As in response to SO42- reduction may help to inspire effective in-situ remediation of contaminated groundwater, in which P and As coexist and remain mobile for decades or longer.