Occurrence, predictors and hazards of elevated groundwater arsenic across India through field observations and regional-scale AI-based modeling

Advancing groundwater quality predictions: Machine learning challenges and solutions

Machine learning (ML) is revolutionizing groundwater quality research by enhancing predictive accuracy and management strategies for contamination. This comprehensive review explores the evolution of ML technologies and their integration into environmental science, assessing 230 papers to understand the advancements and challenges in groundwater quality research. It reveals that a substantial portion of the research neglects critical preprocessing steps, crucial for model accuracy, with 83 % of the studies overlooking this phase. Furthermore, while model optimization is more commonly addressed, being implemented in 65 % of the papers, there is a noticeable gap in model interpretability, with only 15 % of the research providing explanations for model outcomes. Comparative evaluation of ML algorithms and careful selection of evaluation metrics are deemed essential for determining model fitness and reliability. The review underscores the need for interdisciplinary collaboration, methodological rigor, and continuous innovation to advance ML in groundwater management. By addressing these challenges and implementing solutions, the full potential of ML can be harnessed to tackle complex environmental issues and ensure sustainable groundwater management. This comprehensive and critical review paper can serve as a guiding framework to establish minimum standards for developing ML in groundwater quality studies.

Prediction of arsenic and fluoride in groundwater of the North China Plain using enhanced stacking ensemble learning

Chronic exposure to elevated geogenic arsenic (As) and fluoride (F-) concentrations in groundwater poses a significant global health risk. In regions around the world where regular groundwater quality assessments are limited, the presence of harmful levels of As and F- in shallow groundwater extracted from specific wells remains uncertain. This study utilized an enhanced stacking ensemble learning model to predict the distributions of As and F- in shallow groundwater based on 4,393 available datasets of observed concentrations and forty relevant environmental factors. The enhanced model was obtained by fusing well-suited Extreme Gradient Boosting, Random Forest, and Support Vector Machine as the base learners and a structurally simple Linear Discriminant Analysis as the meta-learner. The model precisely captured the patchy distributions of groundwater As and F- with an AUC value of 0.836 and 0.853, respectively. The findings revealed that 9.0% of the study area was characterized by a high As risk in shallow groundwater, while 21.2% was at high F- risk identified as having a high risk of fluoride contamination. About 0.2% of the study area shows elevated levels of both of them. The affected populations are estimated at approximately 7.61 million, 34.1 million, and 0.2 million, respectively. Furthermore, sedimentary environment exerted the greatest influence on distribution of groundwater As, with human activities and climate following closely behind at 29.5%, 28.1%, and 21.9%, respectively. Likewise, sedimentary environment was the primary factor affecting groundwater F- distribution, followed by hydrogeology and soil physicochemical properties, contributing 27.8%, 24.0%, and 23.3%, respectively. This study contributed to the identification of health risks associated with shallow groundwater As and F-, and provided insights into evaluating health risks in regions with limited samples.

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.

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.

Gamma irradiation in modulating arsenic bioremediation potential of Pseudomonas sp. AK1 and AK9

PURPOSE

Present study deals with the role of gamma irradiation in modulating arsenic bioremediation of Pseudomonas sp. AK1 and AK9 strains.

MATERIALS AND METHODS

The bacterial strains AK1 and AK9 of Pseudomonas sp. were irradiated at different doses (5 Gy, 10 Gy, 15 Gy and 20 Gy) of gamma irradiation. The effect of γ-irradiation on the growth and arsenic modulating ability of AK1 and AK9 strains was determined in the presence and absence of arsenic along with non-irradiated strains. Further, a comparative study of non-irradiated and irradiated strains by protein profiling in absence and presence of arsenic was carried out to confirm of the increased expression ofarsenite oxidase.

RESULTS

Both strains were able to transform AsIII to AsV. Both strains AK1 and AK9 decrease the arsenic concentration by 626.68 ppb (13.36%) and 686.40 ppb (14.71%) after an incubation period of 96 h in presence of arsenic. Gamma irradiated AK9 strains showed doubled growth in presence of arsenic as compared to non-irradiated strains at 10 Gy treatment whereas no changes in growth was observed in irradiated AK1 strains. Gamma irradiated AK9 strain decrease 378.65 ppb (7.27%) more arsenic concentration from natural water sample supplemented with AsIII than non-irradiated AK9 strain. Further, in the protein profile, increased expression of arsenite oxidase (∼85 kDa) was observed in irradiated AK9 strains in presence of arsenic.

CONCLUSIONS

Overall, the results suggested that the gamma irradiated AK9 strain having potential for arsenic accumulation and increased arsenite tolerance may play a great role in the bioremediation of the arsenite at arsenic contaminated sites.

Inorganic Hydrogeochemistry in the 21st Century

Chemical and isotopic processes occur in every segment of the hydrological cycle. Hydrogeochemistry-the subdiscipline that studies these processes-has seen a transformation from "witch's brew" to credible science since 2000. Going forward, hydrogeochemical research and applications are critical to meeting urgent societal needs of climate change mitigation and clean energy, such as (1) removing CO2 from the atmosphere and storing gigatons of CO2 in soils and aquifers to achieve net-zero emissions, (2) securing critical minerals in support of the transition from fossil fuels to renewable energies, and (3) protecting water resources by adapting to a warming climate. In the last two decades, we have seen extensive activity and progress in four research areas of hydrogeochemistry related to water-rock interactions: arsenic contamination of groundwater; the use of isotopic and chemical tracers to quantify groundwater recharge and submarine groundwater discharge; the kinetics of chemical reactions and the mineral-water interface's control of contaminant fate and transport; and the transformation of geochemical modeling from an expert-only exercise to a widely accessible tool. In the future, embracing technological advances in machine learning, cyberinfrastructure, and isotope analytical tools will allow breakthrough research and expand the role of hydrogeochemistry in meeting society's needs for climate change mitigation and the transition from fossil fuels to renewable energies.

Groundwater Salinity Across India: Predicting Occurrences and Controls by Field-Observations and Machine Learning Modeling

Elevated groundwater salinity is unsuitable for drinking and harmful to crop production. Thus, it is crucial to determine groundwater salinity distribution, especially where drinking and agricultural water requirements are largely supported by groundwater. This study used field observation (n = 20,994)-based machine learning models to determine the probabilistic distribution of elevated groundwater salinity (electrical conductivity as a proxy, >2000 μS/cm) at 1 km2 across parts of India for near groundwater-table conditions. The final predictions were made by using the best-performing random forest model. The validation performance also demonstrated the robustness of the model (with 77% accuracy). About 29% of the study area (including 25% of entire cropland areas) was estimated to have elevated salinity, dominantly in northwestern and peninsular India. Also, parts of the northwestern and southeastern coasts, adjoining the Arabian Sea and the Bay of Bengal, were assessed with elevated salinity. The climate was delineated as the dominant factor influencing groundwater salinity occurrence, followed by distance from the coast, geology (lithology), and depth of groundwater. Consequently, ∼330 million people, including ∼109 million coastal populations, were estimated to be potentially exposed to elevated groundwater salinity through groundwater-sourced drinking water, thus substantially limiting clean water access.

Arsenic and fluoride exposure in drinking water caused human health risk in coastal groundwater aquifers

Groundwater (GW) is a precious resource for human beings as we depend on it as a source of fresh drinking water, agricultural practices, industrial and domestic uses, etc. Extreme exposure of arsenic (As) and fluoride (F-) concentrations along the coastal GW aquifers of "South 24 Parganas and East Medinipur" diluted the quality of GW and created serious health issues. Various chronic health disorders such as - black foot disease, fluorosis skin cancer, cardiac problems, and other water borne diseases have been noticed in these two coastal districts. The comprehensive entropy-weighted water quality index (EWQI) and health risk assessment (HRA) were applied to evaluate the quality of GW and probable health risks in the coastal districts. Monte Carlo simulation and sensitivity analysis methods were simultaneously adopted to identify the non-carcinogenic health risk assessment due to regular ingestion of contaminated GW. As the study region is densely populated and part of the Sundarbans Ramsar site, it has greater importance at the international level along with regional importance to address the GWQ of this region. The major findings of the present study highlight that almost 55% of the study area is confronting serious GW quality issues and associated probable health risk (HR) due to the intense accumulation of As and F- in the GW aquifers of the study area. Children's health is more vulnerable due to the consumption of As containing GW, and adults are highly affected due to the intake of F- bearing GW in the coastal districts. The findings of the current study will draw the attention of hydrologists, groundwater management authorities, government bodies, and NGOs to regulate and monitor the GW aquifers routinely, enhance GW quality, minimizing the health hazards and sustainable water management in a more scientific and sustainable way which must be advantageous for coastal people.

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.

Shaping the concentration of petroleum hydrocarbon pollution in soil: A machine learning and resistivity-based prediction method

A new method relying on machine learning and resistivity to predict concentrations of petroleum hydrocarbon pollution in soil was proposed as a means of investigation and monitoring. Currently, determining pollutant concentrations in soil is primarily achieved through costly sampling and testing of numerous borehole samples, which carries the risk of further contamination by penetrating the aquifer. Additionally, conventional petroleum hydrocarbon geophysical surveys struggle to establish a correlation between survey results and pollutant concentration. To overcome these limitations, three machine learning models (KNN, RF, and XGBOOST) were combined with the geoelectrical method to predict petroleum hydrocarbon concentrations in the source area. The results demonstrate that the resistivity-based prediction method utilizing machine learning is effective, as validated by R-squared values of 0.91 and 0.94 for the test and validation sets, respectively, and a root mean squared error of 0.19. Furthermore, this study confirmed the feasibility of the approach using actual site data, along with a discussion of its advantages and limitations, establishing it as an inexpensive option to investigate and monitor changes in petroleum hydrocarbon concentration in soil.

The machine learning and geostatistical approach for assessment of arsenic contamination levels using physicochemical properties of water

Arsenic contamination in groundwater due to natural or anthropogenic sources is responsible for carcinogenic and non-carcinogenic risks to humans and the ecosystem. The physicochemical properties of groundwater in the study area were determined in the laboratory using the samples collected across the Varanasi region of Uttar Pradesh, India. This paper analyses the physicochemical properties of water using machine learning, descriptive statistics, geostatistical and spatial analysis. Pearson correlation was used for feature selection and highly correlated features were selected for model creation. Hydrochemical facies of the study area were analyzed and the hyperparameters of machine learning models, i.e., multilayer perceptron, random forest (RF), naïve Bayes, and decision tree were optimized before training and testing the groundwater samples as high (1) or low (0) arsenic contamination levels based on the WHO 10 μg/L guideline value. The overall performance of the models was compared based on accuracy, sensitivity, and specificity value. Among all models, the RF algorithm outclasses other classifiers, as it has a high accuracy of 92.30%, a sensitivity of 100%, and a specificity of 75%. The accuracy result was compared to prior research, and the machine learning model may be used to continually monitor the amount of arsenic pollution in groundwater.

An appraisal of the principal concerns and controlling factors for Arsenic contamination in Chile

Although geogenic Arsenic (As) contamination is well-recognized in northern Chile, it is not restricted to this part of the country, as the geological conditions favoring As release to the human environment exist across the country as well, although not at the same level, based on comparatively fewer studies in central and southern Chile. The present work provides a critical evaluation of As sources, pathways, and controls with reports and case studies from across the country based on an exhaustive bibliographic review of its reported geogenic sources and processes that affect its occurrence, systematization, and critical revision of this information. Arc magmatism and associated geothermal activities, identified as the primary As sources, are present across the Chilean Andes, except for the Pampean Flat Slab and Patagonian Volcanic Gap. Metal sulfide ore zones, extending from the country's far north to the south-central part, are the second most important geogenic As source. While natural leaching of As-rich mineral deposits contaminates the water in contact, associated mining, and metallurgical activities result in additional As release into the human environment through mining waste and tailings. Moreover, crustal thickness has been suggested as a principal controlling factor for As release, whose southward decrease has been correlated with lower As values.

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.

Distribution, geochemical behavior, and risk assessment of arsenic in different floodplain aquifers of middle Gangetic basin, India

The present study interprets the distribution and geochemical behavior of As in groundwaters of different regions along the floodplains of Ganga river (Varanasi, Ghazipur, Ballia), Ghaghara river (Lakhimpur Kheri, Gonda, Basti), and Rapti river (Balrampur, Shrawasti) in the middle Gangetic basin, India for risk assessment (non-carcinogenic and carcinogenic). The concentration of As in groundwaters of these floodplains ranged from 0.12 to 348 μg/L (mean 24 μg/L), with around ~ 37% of groundwater samples exceeding the WHO limit of 10 μg/L in drinking water. Highest As concentration (348 μg/L) was recorded in groundwater samples from Ballia (Ganga Floodplains), where 50% of the samples had As > 10 μg/L in groundwater. In the study area, a relatively higher mean concentration was recorded in deep wells (28.5 μg/L) compared to shallow wells (20 μg/L). Most of the high As-groundwaters were associated with the high Fe, bicarbonate and low nitrate and sulfate concentrations indicating the release of As via reductive dissolution of Fe oxyhydroxides. The saturation index values of the Fe minerals such as goethite, hematite, ferrihydrite, and siderite showed the oversaturation to near equilibrium in groundwater, suggesting that these mineral phases may act as source/sink of As in the aquifers of the study area. The health risk assessment results revealed that a large number of people in the study area were prone to carcinogenic and non-carcinogenic health risks due to daily consumption of As-polluted groundwater. The highest risks were estimated for the aquifers of Ganga floodplains, as indicated by their mean HQ (41.47) and CR (0.0142) values.

Prediction of elevated groundwater fluoride across India using multi-model approach: insights on the influence of geologic and environmental factors

Elevated fluoride in groundwater is a severe problem in India due to its extensive occurrence and detrimental health impacts on the large population that thrives on groundwater. Although fluoride is primarily a geogenic pollutant, existing model-based studies lack the amalgamation of the influence of geologic factors, specifically tectonics, for identifying groundwater fluoride distribution. This drawback encourages the present study to investigate the association of the tectonic framework with fluoride in a multi-model approach. We have applied three machine learning models (random forest, boosted regression tree, and logistic regression) to predict elevated groundwater fluoride based on fluoride measurements across India. The random forest model outperformed other models with an accuracy of 93%. Tectonics was found to be one of the most important predictors alongside "depth to water table." Two major areas of high risk identified were the northwest parts and the south-southeast cratonic peninsular region. The random forest model also performed significantly well over the validation dataset. We estimate that nearly 257 million people are exposed to elevated fluoride risk in India. We endeavor that the findings of our study would be an effective tool for identifying the areas at risk of elevated fluoride and also assist in undertaking effective groundwater management strategies.

Role of tectonics and climate on elevated arsenic in fluvial systems: Insights from surface water and sediments along regional transects of Chile

Globally, arsenic (As) contamination is widespread in hydrological systems and the link between As enrichment and regional tectonic and climatic factors is still not well understood in orogenic environments. This work provides new insights on the relationship between As, tectonics, and climate by assessing the hydrochemistry of Chile, an active subduction zone with highly diverse natural settings. Selected study sites include fluvial courses along four regional transects connecting the Chilean coast to the Andes Cordillera in the northern, central, and southern areas of the country. The results indicate that As concentrations in surface water and fluvial sediments show a general positive correlation to crustal thickness and they tend to decrease progressively from northern to southern Chile. In contrast, As concentrations are negatively correlated to average annual precipitation which shows a significant increase toward southern Chile. From a regional tectonic perspective, northern Chile presents greater Andes shortening and higher crustal thicknesses, which induces increased crustal contamination and As content at the surface. Extremely low precipitation rates are also tied to local As enrichment and a sediment-starved trench that might favor higher plate coupling and shortening. On the contrary, decreased shortening of the Andes in southern Chile and related lower crustal thickness induces lower crustal contamination, thus acting as an As-poor provenance for surficial sediments and surface water. High precipitation rates further induce dilution of surface water, potential mobilization from the solid phase, and a significant amount of trench sediments that could induce lower plate coupling and lower shortening. At the local scale, a low potential for As mobilization was found in northern Chile where a greater distribution of As-bearing minerals was observed in sediments, mostly as finer particles (<63 μm). The abundance of Fe-oxides potentially acts as a secondary surficial sink of As under the encountered physicochemical conditions.

New Insights in factors affecting ground water quality with focus on health risk assessment and remediation techniques

Groundwater is considered as the primary source of water for the majority of the world's population. The preponderance of the nation's drinking water, as well as agricultural and industrial water, comes from groundwater. Groundwater level is becoming increasingly challenging to replenish due to climate change. Fertilizer application and improper processing of industrial waste are the two major anthropogenic drivers of groundwater pollution. Arsenic and cadmium are two of the principal heavy metal pollutants that have affected groundwater quality by human activity. When people are exposed to both non-carcinogenic and carcinogenic contaminants for an extended period, toxic effects might occur. It can have detrimental health effects from long-term exposure to contaminants, even in low amounts. As a result, metal contamination concentrations and fractions can be used to determine potential health concerns. At the same time, contaminants also need to be removed or converted to harmless products by groundwater remediation. Remediation of groundwater quality can be accomplished in several ways, including natural and artificial means. The purpose of this review is to explore a wide range of factors that affect groundwater quality, including their possible health effects. This communication provides state-of-the-art information about remediation approaches for groundwater contamination including hindrances and perspectives in this area of research. The in-depth information provided in different sections of this communication would expand the scope of interdisciplinary research.

Predicting Potential Climate Change Impacts on Groundwater Nitrate Pollution and Risk in an Intensely Cultivated Area of South Asia

One of the potential impacts of climate change is enhanced groundwater contamination by geogenic and anthropogenic contaminants. Such impacts should be most evident in areas with high land-use change footprint. Here, we provide a novel documentation of the impact on groundwater nitrate (GW_NO3 ) pollution with and without climate change in one of the most intensely groundwater-irrigated areas of South Asia (northwest India) as a consequence of changes in land use and agricultural practices at present and predicted future times. We assessed the probabilistic risk of GW_NO3 pollution considering climate changes under two representative concentration pathways (RCPs), i.e., RCP 4.5 and 8.5 for 2030 and 2040, using a machine learning (Random Forest) framework. We also evaluated variations in GW_NO3 distribution against a no climate change (NCC) scenario considering 2020 status quo climate conditions. The climate change projections showed that the annual temperatures would rise under both RCPs. The precipitation is predicted to rise by 5% under RCP 8.5 by 2040, while it would decline under RCP 4.5. The predicted scenarios indicate that the areas at high risk of GW_NO3 pollution will increase to 49 and 50% in 2030 and 66 and 65% in 2040 under RCP 4.5 and 8.5, respectively. These predictions are higher compared to the NCC condition (43% in 2030 and 60% in 2040). However, the areas at high risk can decrease significantly by 2040 with restricted fertilizer usage, especially under the RCP 8.5 scenario. The risk maps identified the central, south, and southeastern parts of the study area to be at persistent high risk of GW_NO3 pollution. The outcomes show that the climate factors may impose a significant influence on the GW_NO3 pollution, and if fertilizer inputs and land uses are not managed properly, future climate change scenarios can critically impact the groundwater quality in highly agrarian areas.

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.

Arsenic Exposure via Contaminated Water and Food Sources

Arsenic poisoning constitutes a major threat to humans, causing various health problems. Almost everywhere across the world certain “hotspots” have been detected, putting in danger the local populations, due to the potential consumption of water or food contaminated with elevated concentrations of arsenic. According to the relevant studies, Asia shows the highest percentage of significantly contaminated sites, followed by North America, Europe, Africa, South America and Oceania. The presence of arsenic in ecosystems can originate from several natural or anthropogenic activities. Arsenic can be then gradually accumulated in different food sources, such as vegetables, rice and other crops, but also in seafood, etc., and in water sources (mainly in groundwater, but also to a lesser extent in surface water), potentially used as drinking-water supplies, provoking their contamination and therefore potential health problems to the consumers. This review reports the major areas worldwide that present elevated arsenic concentrations in food and water sources. Furthermore, it also discusses the sources of arsenic contamination at these sites, as well as selected treatment technologies, aiming to remove this pollutant mainly from the contaminated waters and thus the reduction and prevention of population towards arsenic exposure.

Predicting the Distribution of Arsenic in Groundwater by a Geospatial Machine Learning Technique in the Two Most Affected Districts of Assam, India: The Public Health Implications

Arsenic (As) is a well-known carcinogen and chemical contaminant in groundwater. The spatial heterogeneity in As distribution in groundwater makes it difficult to predict the location of safe areas for tube well installations, consumption, and agriculture. Geospatial machine learning techniques have been used to predict the location of safe and unsafe areas of groundwater As. We used a similar machine learning technique and developed a habitation-level (spatial resolution 250 m) predictive model to determine the risk and extent of As >10 μg/L in groundwater in the two most affected districts of Assam, India, with an aim to advise policymakers on targeted interventions. A random forest model was employed in Python environments to predict the probabilities of As at concentrations >10 μg/L using intrinsic and extrinsic predictor variables, which were selected for their inherent relationship with As occurrence in groundwater. The relationships between predictor variables and proportions of As occurrences >10 μg/L follow the well-documented processes leading to As release in groundwater. We identified potential As hotspots based on a probability of ≥0.7 for As >10 μg/L, including regions not previously surveyed and extending beyond previously known As hotspots. Of the total land area (6,500 km2), 25% was identified as a high-risk zone, with an estimated 155,000 people potentially consuming As through drinking water or cooking food. The ternary hazard probability map (showing high, moderate, and low risk for As >10 μg/L) could inform policymakers on establishing newer drinking water treatment plants and providing safe drinking water connections to rural households.

Remote sensing of wetland evolution in predicting shallow groundwater arsenic distribution in two typical inland basins

A large number of studies have shown that the existence of wetlands may influence arsenic concentrations in adjacent shallow groundwater. However, little is known about the linkage between wetland evolution and arsenic enrichment in shallow groundwater. This study investigated wetland evolutions from 1973 to 2015 in two arid-semiarid inland basins along the Yellow River catchment (i.e., the Yinchuan Basin and the Hetao Basin) based on remote sensing data, and their association with arsenic distributions based on arsenic concentrations of 244 and 570 shallow groundwater samples, respectively. The long-term Landsat images reveal that the covering area of wetlands exhibited increasing trends in both the Yinchuan Basin and the Hetao Basin. Wetlands in the Yinchuan Basin and the Yellow River water-irrigation area in the Hetao Basin varied with precipitation before 2000, but exhibited increasing trends because of wetland restoration policies since 2000. Wetlands in groundwater-irrigation area in the Hetao Basin decreased due to increasing exploitation of shallow groundwater. Wetlands with long existence time were mainly distributed along the Yellow River and drainage channels and in large lakes in the northern Yinchuan Basin and the Hetao Basin, where high‑arsenic (>10 μg/L) groundwater occurred. The probability of high‑arsenic groundwater distribution increased with the proportion of wetland existence time to the entire studied period (42 years), which can be best explained by a BiDoseResp growth curve. Longer existence of wetlands may cause greater probability of high‑arsenic groundwater. This was likely related to long-term introduction of biodegradable organic matter into shallow aquifers and thereafter enhancement of arsenic mobility and/or arsenic being released beneath wetlands and transported into shallow aquifers under continuing wetland water recharge. We therefore suggest that mapping wetland evolutions could probably serve as a good indicator for predicting high arsenic groundwater distributions in shallow aquifers.

Surface Flooding as a Key Driver of Groundwater Arsenic Contamination in Southeast Asia

Chronic exposure to groundwater contaminated with geogenic arsenic (As) poses a significant threat to human health worldwide, especially for those living on floodplains in South and Southeast (S-SE) Asia. In the alluvial and deltaic aquifers of S-SE Asia, aqueous As concentrations vary sharply over small spatial scales (10-100 m), making it challenging to identify where As contamination is present and mitigate exposure. Improved mechanistic understanding of the factors that control groundwater As levels is essential to develop models that accurately predict spatially variable groundwater As concentrations. Here we demonstrate that surface flooding duration and interannual frequency are master variables that integrate key hydrologic and biogeochemical processes that affect groundwater As levels in S-SE Asia. A machine-learning model based on high-resolution, satellite-derived, long-term measures of surface flooding duration and frequency effectively predicts heterogeneous groundwater As concentrations at fine spatial scales in Cambodia, Vietnam, and Bangladesh. Our approach can be reliably applied to identify locations of safe and unsafe groundwater sources with sufficient accuracy for making management decisions by solely using remotely sensed information. This work is important to evaluate levels of As exposure, impacts to public health, and to shed light on the underlying hydrogeochemical processes that drive As mobilization into groundwater.

Neural Network and Random Forest-Based Analyses of the Performance of Community Drinking Water Arsenic Treatment Plants

A plethora of technologies has been developed over decades of extensive research on arsenic remediation, although the technical and financial perspective of arsenic removal plants in the field requires critical evaluation. In the present study, focusing on some of the pronounced arsenic-affected areas in West Bengal, India, we assessed the implementation and operation of different arsenic removal technologies using a dataset of 4000 spatio-temporal data collected from an in-depth field survey of 136 arsenic removal plants engaged in the public water supply. Our statistical analysis of this dataset indicates a 120% rise in the average cumulative capacity of the plants during 2014–2021. The majorities of the plants are based on the activated alumina with FeCl3 technology and serve about 49% of the population in the study area. The average cost of water production for the activated alumina with FeCl3 technology was found to be ₹7.56/m3 (USD $1 ≈ INR ₹70), while the lowest was ₹0.39/m3 for granular ferric hydroxide technology. A machine learning-based framework was employed to analyze the impact of water quality and treatment plant parameters on the removal efficiency, capital, and operational cost of the plants. The artificial neural network model exhibited adequate statistical significance, with a high F-value and R2 of 5830.94 and 0.72 for the capital cost model, 136,954, and 0.98 for the operational cost model, respectively. The relative importance of the process variables was identified through random forest models. The models indicated that flow rate, media, and chemicals are the predominant costs, while contaminant loading in influent water and a coagulating agent was important for removal efficiency. The established framework may be instrumental as a decision-making tool for water providers to assess the expected performance and financial involvement for proposed or ongoing arsenic removal plants concerning various design and quality parameters.

Arsenic exposure induces a bimodal toxicity response in zebrafish

In toxicology, standard sigmoidal concentration-response curves are used to predict effects concentrations and set chemical regulations. However, current literature also establishes the existence of complex, bimodal concentration-response curves, as is the case for arsenic toxicity. This bimodal response has been observed at the molecular level, but not characterized at the whole organism level. This study investigated the effect of arsenic (sodium arsenite) on post-gastrulated zebrafish embryos and elucidated effects of bimodal concentration-responses on different phenotypic perturbations. Six hour post fertilized (hpf) zebrafish embryos were exposed to arsenic to 96 hpf. Hatching success, mortality, and morphometric endpoints were evaluated both in embryos with chorions and dechorionated embryos. Zebrafish embryos exhibited a bimodal response to arsenic exposure. Concentration-response curves for exposed embryos with intact chorions had an initial peak in mortality (88%) at 1.33 mM arsenic, followed by a decrease in toxicity (~20% mortality) at 1.75 mM, and subsequently peaked to 100% mortality at higher concentrations. To account for the bimodal response, two distinct concentration-response curves were generated with estimated LC10 values (and 95% CI) of 0.462 (0.415, 0.508) mM and 1.69 (1.58, 1.78) mM for the 'low concentration' and 'high concentration' peaks, respectively. Other phenotypic analyses, including embryo length, yolk and pericardial edema all produced similar concentration-response patterns. Tests with dechorionated embryos also resulted in a bimodal toxicity response but with lower LC10 values of 0.170 (0.120, 0.220) mM and 0.800 (0.60, 0842) mM, respectively. Similarities in bimodal concentration-responses between with-chorion and dechorionated embryos indicate that the observed effect was not caused by the chorion limiting arsenic availability, thus lending support to other studies such as those that hypothesized a conserved bimodal mechanism of arsenic interference with nuclear receptor activation.

Floodplains landforms, clay deposition and irrigation return flow govern arsenic occurrence, prevalence and mobilization: A geochemical and isotopic study of the mid-Gangetic floodplains

This article attempts to understand the evolution of groundwater chemistry in the mid Gangetic floodplain through the identification of hydrogeochemical processes including the impact of surface recharge and geological features. Isotopic investigations identified that irrigation return flow is partly responsible for arsenic (As) enrichment through preferential vertical recharge. Further, the floodplain geomorphological attributes and associated As hydrogeochemical behaviour traced through isotopes tracers highlighted that meandering and ox-bow like geomorphological features owing to clay deposition leads to the anoxic condition induced reductive microbial dissolution of As-bearing minerals causing the arsenic contamination in the investigated aquifer of the mid-Gangetic plain (MGP). To achieve the objectives, 146 water samples for water chemistry and 62 samples for the isotopic study were collected from Bhojpur district, Bihar (district bounded by the river Ganges in the north and Son in the east) located in MGP during the pre-monsoon season of 2018. The chemical results revealed high arsenic concentration (BDL to 206 μg.L^-1, 32% samples are exceeding the 10 μg.L^-1 limit) in the Holocene recent alluviums which are characterized by various geomorphological features such as meander scars and oxbow lake (northern part of the district). Arsenic is more concentrated in the depth range of 15-40 m below ground surface. All other trace metals viz. Ni, Pb, Zn, Cd and Al were found in low concentration except Fe and Mn. The geochemical analyses suggest that rock-water interaction is controlling the hydro-geochemistry while the chemical constituent of the groundwater is mainly controlled by carbonate weathering with limited contribution from silicate weathering. The isotopic signatures revealed that the Son river is recharging groundwater while the groundwater is contributing to the Ganges river. A clear pattern of fast vertical recharge in the arsenic contaminated area is observed in the proximity to the river Ganges with an elevated nitrate concentration resulted from the reduced As dissolution. The origin of groundwater is local precipitation with low to high evaporation enrichment effect which is further indicating the vertical mixing of groundwater from the irrigation return flow and/or recharge from domestic discharge causing enhanced As mobilization through microbial assisted reductive dissolution of As-bearing minerals.

Groundwater Arsenic-Attributable Cardiovascular Disease (CVD) Mortality Risks in India

Cardiovascular diseases (CVDs) have been recognized as the most serious non-carcinogenic detrimental health outcome arising from chronic exposure to arsenic. Drinking arsenic contaminated groundwaters is a critical and common exposure pathway for arsenic, notably in India and other countries in the circum-Himalayan region. Notwithstanding this, there has hitherto been a dearth of data on the likely impacts of this exposure on CVD in India. In this study, CVD mortality risks arising from drinking groundwater with high arsenic (>10 μg/L) in India and its constituent states, territories, and districts were quantified using the population-attributable fraction (PAF) approach. Using a novel pseudo-contouring approach, we estimate that between 58 and 64 million people are exposed to arsenic exceeding 10 μg/L in groundwater-derived drinking water in India. On an all-India basis, we estimate that 0.3–0.6% of CVD mortality is attributable to exposure to high arsenic groundwaters, corresponding to annual avoidable premature CVD-related deaths attributable to chronic exposure to groundwater arsenic in India of between around 6500 and 13,000. Based on the reported reduction in life of 12 to 28 years per death due to heart disease, we calculate value of statistical life (VSL) based annual costs to India of arsenic-attributable CVD mortality of between USD 750 million and USD 3400 million.

Impact of Covid-19 Lockdown on Availability of Drinking Water in the Arsenic-Affected Ganges River Basin

The 2020 COVID-19 pandemic has not only resulted in immense loss of human life, but it also rampaged across the global economy and socio-cultural structure. Worldwide, countries imposed stringent mass quarantine and lockdowns to curb the transmission of the pathogen. While the efficacy of such lockdown is debatable, several reports suggest that the reduced human activities provided an inadvertent benefit by briefly improving air and water quality. India observed a 68-days long, nation-wide, stringent lockdown between 24 March and 31 May 2020. Here, we delineate the impact of the lockdown on groundwater and river sourced drinking water sustainability in the arsenic polluted Ganges river basin of India, which is regarded as one of the largest and most polluted river basins in the world. Using groundwater arsenic measurements from drinking water wells and water quality data from river monitoring stations, we have studied ~700 km stretches of the middle and lower reaches of the As (arsenic)-polluted parts of the river for pre-lockdown (January-March 2020), syn-lockdown (April-May), and post-lockdown periods (June-July). We provide the extent of As pollution-free groundwater vis-à-vis river water and examine alleviation from lockdown as an opportunity for sustainable drinking water sources. The overall decrease of biochemical oxygen demand (BOD) and chemical oxygen demand (COD) concentrations and increase of pH suggests a general improvement in Ganges water quality during the lockdown in contrast to pre-and-post lockdown periods, potentially caused by reduced effluent. We also demonstrate that land use (agricultural/industrial) and land cover (urban-periurban/rural) in the vicinity of the river reaches seems to have a strong influence on river pollutants. The observations provide a cautious optimistic scenario for potentially developing sustainable drinking water sources in the arsenic-affected Ganges river basin in the future by using these observations as the basis of proper scientifically prudent, spatially adaptive strategies, and technological interventions.