Modeling regional-scale groundwater arsenic hazard in the transboundary Ganges River Delta, India and Bangladesh: Infusing physically-based model with machine learning

Predicting natural arsenic enrichment in peat-bearing, alluvial and coastal depositional systems: A generalized model based on sequence stratigraphy

Hazardously high concentrations of arsenic exceeding the threshold limits for soils and drinking waters have been widely reported from Quaternary sedimentary successions and shallow aquifers of alluvial and coastal lowlands worldwide, raising public health concerns due to potential human exposure to arsenic. A combined sedimentological and geochemical analysis of subsurface deposits, 2.5-50 m deep, from the SE Po Plain (Italy) documents a systematic tendency for naturally-occurring arsenic to accumulate in peat-rich layers, with concentrations invariably greater than maximum permissible levels. A total of 366 bulk sediment samples from 40 cores that penetrated peat-bearing deposits were analysed by X-ray fluorescence. Arsenic concentrations associated with 7 peat-free lithofacies associations (fluvial-channel, levee/crevasse, floodplain, swamp, lagoon/bay, beach-barrier, and offshore/prodelta) exhibit background values invariably below threshold levels (<20 mg/kg). In contrast, total arsenic contents from peaty clay and peat showed 2-6 times larger As accumulation. A total of 204 near-surface (0-2.5 m) samples from modern alluvial and coastal depositional environments exhibit the same trends as their deeper counterparts, total arsenic peaking at peat horizons above the threshold values for contaminated soils. The arsenic-bearing, peat-rich Quaternary successions of the Po Plain accumulated under persisting reducing conditions in wetlands of backstepping estuarine and prograding deltaic depositional environments during the Early-Middle Holocene sea-level rise and subsequent stillstand. Contamination of the Holocene and underlying Pleistocene aquifer systems likely occurred through the release of As by microbially-mediated reductive dissolution. Using high-resolution sequence-stratigraphic concepts, we document that the Late Pleistocene-Holocene lithofacies architecture dictates the subsurface distribution of As. The "wetland trajectory", i.e. the path taken by the landward/seaward shift of peat-rich depositional environments during the Holocene, may help predict spatial patterns of natural As distribution, delineating the highest As-hazard zones and providing a realistic view of aquifer contamination even in unknown areas.

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.

Site-scale groundwater pollution risk assessment using surrogate models and statistical analysis

Petroleum pollution in soil and groundwater has emerged as a significant environmental concern worldwide. As a sustainable and cost-effective in-situ remediation technique, Monitored Natural Attenuation (MNA) exhibits significant promise in addressing sites contaminated by petrochemicals. This study specifically targets a typical petrochemical-contaminated site in northern China and employs GMS software to establish a comprehensive physical model. The model relies on time-series monitoring data of phenol concentrations spanning from 2018 to 2020, effectively simulating both the leakage and natural attenuation of phenol. Within this study, the adsorption coefficient and maximum adsorption capacity emerge as the foremost influential factors shaping the outcomes of the model. Given the inherent heterogeneity of the site and the variability of hydrochemical conditions, parameters such as dispersion, porosity, and adsorption coefficient exhibit significant uncertainties. Consequently, relying on traditional deterministic models to predict the feasibility of MNA technology is not reliable. Therefore, this study employs machine learning (ML) methods to construct stochastic parameter models based on physical processes. The Random Forest Regression (RFR) algorithm, after trained, demonstrates strong alignment with numerical model output, exhibiting an average Nash-Sutcliffe Efficiency (NSE) >0.96. Using a stochastic approach, RFR iteratively computes phenol concentration across 6000 sets of parameters. Applying probability statistics, the model shows a notable reduction in the likelihood of phenol concentrations exceeding a threshold, dropping from 64.0% to 15.7% before and after natural attenuation. In parameter uncertainty, the stochastic model emphasizes natural attenuation's efficacy in mitigating phenol pollution risk (porosity being the most influential factor). This case study proposed a novel method to quickly assess the pollution risks at petrochemical sites under the influence of the uncertainty of pollutant transport and reaction parameters. The results can provide a reference for the pollution risk assessment at petrochemical sites, especially in sites with high stratigraphic heterogeneity or insufficient transport parameter data.

Hydro-chemical based assessment of groundwater vulnerability in the Holocene multi-aquifers of Ganges delta

Determining the degree of high groundwater arsenic (As) and fluoride (F-) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F- concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F- contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.

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.

Surface water quality, public health, and ecological risks in Bangladesh-a systematic review and meta-analysis over the last two decades

Water quality has recently emerged as one of the utmost severe ecological problems being faced by the developing countries all over the world, and Bangladesh is no exception. Both surface and groundwater sources contain different contaminants, which lead to numerous deaths due to water-borne diseases, particularly among children. This study presents one of the most comprehensive reviews on the current status of water quality in Bangladesh with a special emphasis on both conventional pollutants and emerging contaminants. Data show that urban rivers in Bangladesh are in a critical condition, especially Korotoa, Teesta, Rupsha, Pashur, and Padma. The Buriganga River and few locations in the Turag, Balu, Sitalakhya, and Karnaphuli rivers have dissolvable oxygen (DO) levels of almost zero. Many waterways contain traces of NO3, NO2, and PO4-3 pollutants. The majority of the rivers in Bangladesh also have Zn, Cu, Fe, Pb, Cd, Ni, Mn, As, and Cr concentrations that exceed the WHO permissible limits for safe drinking water, while their metal concentrations exceed the safety threshold for irrigation. Mercury poses the greatest hazard with 90.91% of the samples falling into the highest risk category. Mercury is followed by zinc 57.53% and copper 29.16% in terms of the dangers they pose to public health and the ecosystem. Results show that a considerable percentage of the population is at risk, being exposed to contaminated water. Despite hundreds of cryptosporidiosis cases reported, fecal contamination, i.e., Cryptosporidium, is totally ignored and need serious considerations to be regularly monitored in source water.

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.

Application of machine learning in groundwater quality modeling - A comprehensive review

Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The prediction of groundwater pollution due to various chemical components is vital for planning, policymaking, and management of groundwater resources. In the last two decades, the application of machine learning (ML) techniques for groundwater quality (GWQ) modeling has grown exponentially. This review assesses all supervised, semi-supervised, unsupervised, and ensemble ML models implemented to predict any groundwater quality parameter, making this the most extensive modern review on this topic. Neural networks are the most used ML model in GWQ modeling. Their usage has declined in recent years, giving rise to more accurate or advanced techniques such as deep learning or unsupervised algorithms. Iran and the United States lead the world in areas modeled, with a wealth of historical data available. Nitrate has been modeled most exhaustively, targeted by nearly half of all studies. Advancements in future work will be made with further implementation of deep learning and explainable artificial intelligence or other cutting-edge techniques, application of these techniques for sparsely studied variables, the modeling of new or unique study areas, and the implementation of ML techniques for groundwater quality management.

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.

Effect of hydrogeochemical behavior on groundwater resources in Holocene aquifers of moribund Ganges Delta, India: Infusing data-driven algorithms

One of the fundamental sustainable development goals has been recognized as having access to clean water for drinking purposes. In the Anthropocene era, rapid urbanization put further stress on water resources, and associated groundwater contamination expanded into a significant global environmental issue. Natural arsenic and related water pollution have already caused a burden issue on groundwater vulnerability and corresponding health hazard in and around the Ganges delta. A field based hydrogeochemical analysis has been carried out in the elevated arsenic prone areas of moribund Ganges delta, West Bengal, a part of western Ganga- Brahmaputra delta (GBD). New data driven heuristic algorithms are rarely used in groundwater vulnerability studies, specifically not yet used in the elevated arsenic prone areas of Ganges delta, India. Therefore, in the current study, emphasis has been given on integration of heuristic algorithms and random forest (RF) i.e., "RF-particle swarm optimization (PSO)", "RF-grey wolf optimizer (GWO)" and "RF-grasshopper optimization algorithm (GOA)", to identify groundwater vulnerable zones on the basis of field based hydrogeochemical parameters. In addition, correspondence health hazard of this area was assessed through human health hazard index. The spatial distribution of groundwater vulnerability revealed that middle-eastern and north-western part of the study area covered by very high and high, whereas central, western and south-western part are covered by very low and low vulnerability zones in outcomes of all the applied models. The evaluation result indicates that RF-GOA (AUC = 0.911) model performed the best considering testing dataset, and thereafter RF-GWO, RF-PSO and RF with AUC value is 0.901, 0.892 and 0.812 respectively. Findings also revealed the groundwater in this study region is quite unfavorable for drinking and irrigation purposes. The suggested models demonstrate their usefulness in foretelling sustainable groundwater resource management in various deltaic regions of the world through taking appropriate measures by policy-makers.

Hydrochemical characterization of groundwater quality using chemometric analysis and water quality indices in the foothills of Himalayas

Groundwater pollution of the watershed is mainly influenced by the multifaceted interactions of natural and anthropogenic process. To analyse the spatial-temporal variation and pollution source identification and apportionment, the dataset was subjected to a globally acknowledged coherent technique using water quality indices and chemometric techniques (principal component analysis (PCA) and cluster analysis. The bulk of the samples tested were below the BIS's permissible levels. Groundwater samples from the pre- and post-monsoon seasons mostly contained the anions HCO- 3 > Cl- > SO2- 4 > NO- 3, while the primary cations were Ca2+ > Mg2+ > Na+ > K+. Groundwater was alkaline and hard at most of the sites. According to hydro-geochemical facies and relationships, Piper diagrams, and principal component analysis, weathering, dissolution, leaching, ion exchange, and evaporation were the key mechanisms influencing groundwater quality. The hydrochemical facies classified the groundwater samples into the Ca-Mg-HCO3 type. For all the sampling locations, PIG was determined to be 0.43, 0.52, 0.47, 0.48, 1.00, and 0.70; respectively. The majority of the test locations fell into the low to medium contamination zone, as determined by the groundwater pollution index (PIG) and contamination index. Three principal components, which together account for 93.8% of the total variance, were identified via PCA. The study's findings confirm the value of these statistical techniques in interpreting and understanding large datasets and offering reliable information to reduce the time and expense of programmes for monitoring and evaluating water quality.

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.

Regional-scale hydrogeochemical evolution across the arsenic-enriched transboundary aquifers of the Ganges River Delta system, India and Bangladesh

Arsenic (As) dynamics within the extensively contaminated aquifers of the Ganges River delta have been widely studied over the past few decades, but the hydrogeochemical signatures across the delta aquifers remain to be characterized. Here, we characterize the varied geochemical and isotopic (δ¹⁸O, δ²H) signatures of groundwater across the delta and interpret the hydrogeochemical evolution pathways and the driving processes on a regional-scale as a function of the delta hydrostratigraphy. Our hydrostratigraphic model identifies three major aquifer sub-systems across the delta from north-west to south-east: a single continuous unconfined aquifer (Type I); a semiconfined vertically-segregated aquifer sub-system (Type II); and a nearly confined multilayered aquifer sub-system (Type III). The Type I aquifer is dominated by Ca-Mg-HCO₃-rich waters, while the aquifers to the south (Type II and Type III) exhibits increasing dominance of Na-Cl hydrogeochemical facies at shallow and intermediate depths and Na-HCO₃ hydrogeochemical facies in the deep aquifers. The spatial distribution of As is also found to be heavily dictated by hydrostratigraphy, wherein the Type I aquifer sub-system yields similar concentrations across depths, while the Type II and Type III aquifer sub-systems exhibit a sharp increase in As-safe aquifers with depth. Although dominant reducing conditions occur within the delta groundwater, co-occurrence of redox-sensitive solutes from varying redox stability fields indicates to the development of overlapping redox zones. Stable isotopic signatures of groundwater exhibit a progressive depletion away from the Bay of Bengal. The Type I aquifer exhibits relatively homogenous hydrogeochemical signatures, possibly suggesting deeper infiltration of recharge under higher vertical hydraulic gradients, while the Type II and Type III aquifers exhibit variability across depth, which is possibly a reflection of horizontally stratified groundwater flows, dictated by the spatial geometry of the intervening aquitard layers.

Assessment of groundwater arsenic contamination using machine learning in Varanasi, Uttar Pradesh, India

This paper presents a machine learning approach for classification of arsenic (As) levels as safe and unsafe in groundwater samples collected from the Indo-Gangetic region. As water is essential for sustaining life, heavy metals like arsenic pose a public health concern. In this study, various tree-based machine learning models namely Random Forest, Optimized Forest, CS Forest, SPAARC, and REP Tree algorithms have been applied to classify water samples. As per the guidelines of the World Health Organization (WHO), the arsenic concentration in water should not exceed 10 μg/L. The groundwater quality parameter was ranked using a classifier attribute evaluator for training and testing the models. Parameters obtained from the confusion matrix, such as accuracy, precision, recall, and FPR, were used to analyze the performance of models. Among all models, Optimized Forest outperforms other classifier as it has a high accuracy of 80.64%, a precision of 80.70%, recall of 97.87%, and a low FPR of 73.33%. The Optimized Forest model can be used to test new water samples for classification of arsenic in groundwater samples.

Human-induced arsenic pollution modeling in surface waters - An integrated approach using machine learning algorithms and environmental factors

In recent years, assessment of sediment contamination by heavy metals, i.e., arsenic, has attracted the interest of scientists worldwide. The present study provides a new methodology to better understand the factors influencing surface water vulnerability to arsenic pollution by two advanced machine learning algorithms including boosted regression trees (BRT) and random forest (RF). Based on the sediment quality guidelines (Effects range low) polluted and non-polluted arsenic sediment samples were defined with concentrations >8 ppm and <8 ppm, respectively. Different conditioning factors such as topographical, lithology, erosion, hydrological, and anthropogenic factors were acquired to model surface waters' vulnerability to arsenic. We trained and validated the models using 70 and 30% of both polluted and non-polluted samples, respectively, and generated surface vulnerability maps. To verify the maps to arsenic pollution, the receiver operating characteristics (ROC) curve was implemented. The results approved the acceptable performance of the RF and BRT algorithms with an area under ROC values of 85% and 75.6%, respectively. Further, the findings showed higher importance of precipitation, slope aspect, distance from residential areas, and slope length in arsenic pollution in the modeling process. Erosion, lithology, and land use maps were introduced as the least important factors. The introduced methodology can be used to define the most vulnerable areas to arsenic pollution in advance and implement proper remediation actions to reduce the damages.

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.

Influence of hydrogeochemical reactions along flow paths on contrasting groundwater arsenic and manganese distribution and dynamics across the Ganges River

The groundwater within the aquifers of the Ganges River delta exhibits significant spatial variability in concentrations of redox-sensitive solutes [e.g., arsenic (As), iron (Fe), manganese (Mn)]. The groundwater As and Mn concentrations show conspicuous contrasting distribution on the opposite banks of the Bhagirathi-Hooghly (B-H) River, the Indian distributary of the Ganges River. Here, we investigate the differences in hydrostratigraphic framework and groundwater evolutionary pathways across the B-H River that might have resulted in such variations. We developed a hydrostratigraphic model for the region and also used inverse reaction-path modeling along three hypothesized end-member flow paths to understand the dominant processes that might control As and Mn cycling within the aquifers. Our results indicate that the variability of As and Mn across the B-H River is a function of a complex interplay between the aquifer architecture, groundwater chemistry, and redox conditions.

Arsenic contamination, impact and mitigation strategies in rice agro-environment: An inclusive insight

Arsenic (As) contamination and its adverse consequences on rice agroecosystem are well known. Rice has the credit to feed more than 50% of the world population but concurrently, rice accumulates a substantial amount of As, thereby compromising food security. The gravity of the situation lays in the fact that the population in theAs uncontaminated areas may be accidentally exposed to toxic levels of As from rice consumption. In this review, we are trying to summarize the documents on the impact of As contamination and phytotoxicity in past two decades. The unique feature of this attempt is wide spectrum coverages of topics, and that makes it truly an interdisciplinary review. Aprat from the behaviour of As in rice field soil, we have documented the cellular and molecular response of rice plant upon exposure to As. The potential of various mitigation strategies with particular emphasis on using biochar, seed priming technology, irrigation management, transgenic variety development and other agronomic methods have been critically explored. The review attempts to give a comprehensive and multidiciplinary insight into the behaviour of As in Paddy -Water - Soil - Plate prospective from molecular to post-harvest phase. From the comprehensive literature review, we may conclude that considerable emphasis on rice grain, nutritional and anti-nutritional components, and grain quality traits under arsenic stress condition is yet to be given. Besides these, some emerging mitigation options like seed priming technology, adoption of nanotechnological strategies, applications of biochar should be fortified in large scale without interfering with the proper use of biodiversity.

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.

Toward an Integrated Disaster Management Approach: How Artificial Intelligence Can Boost Disaster Management

Technical and methodological enhancement of hazards and disaster research is identified as a critical question in disaster management. Artificial intelligence (AI) applications, such as tracking and mapping, geospatial analysis, remote sensing techniques, robotics, drone technology, machine learning, telecom and network services, accident and hot spot analysis, smart city urban planning, transportation planning, and environmental impact analysis, are the technological components of societal change, having significant implications for research on the societal response to hazards and disasters. Social science researchers have used various technologies and methods to examine hazards and disasters through disciplinary, multidisciplinary, and interdisciplinary lenses. They have employed both quantitative and qualitative data collection and data analysis strategies. This study provides an overview of the current applications of AI in disaster management during its four phases and how AI is vital to all disaster management phases, leading to a faster, more concise, equipped response. Integrating a geographic information system (GIS) and remote sensing (RS) into disaster management enables higher planning, analysis, situational awareness, and recovery operations. GIS and RS are commonly recognized as key support tools for disaster management. Visualization capabilities, satellite images, and artificial intelligence analysis can assist governments in making quick decisions after natural disasters.

Modeling and Management Option Analysis for Saline Groundwater Drainage in a Deltaic Island

Understanding the interactions between shallow saline groundwater and surface water is crucial for managing water logging in deltaic islands. Water logging conditions result in the accumulation of salt in the root zone of crops and detrimentally affect agriculture in the economically and socially backward deltaic region of West Bengal and Bangladesh. In this paper, we undertook a modeling study of surface water–groundwater interactions in the Gosaba Island of Sundarbans region of the Ganges delta using MODFLOW followed by comprehensive parameter sensitivity analysis. Further, scenario analyses (i.e., no-drain, single drain, three drains) were undertaken to evaluate the effectiveness of drainage infrastructure to reduce saline water logging conditions. The evaluation indicated that installation of three drains can remove water at a rate of up to −123.3 m3day−1 and lower the water table up to 0.4 m. The single drain management scenario could divert water at the rate of −77.9 m3day−1 during post monsoon season, lowering the shallow saline groundwater table up to 0.1 m. This preliminary modeling study shows encouraging results to consider drainage management as to solve the increasing challenge of water logging and salinity management in the deltaic region. The insights will be useful for farmers and policymakers in the region for planning various sustainable saline groundwater management. Building drainage infrastructure could potentially be part of initiatives like the national employment guarantee scheme in India. In the future, this model can be coupled with solute transport models for understanding the current status and future expansion of salinity in the study area. Further modeling and optimization analysis can help identify the optimal depth and spacing of drains.

Novel micro-structured carbon-based adsorbents for notorious arsenic removal from wastewater

The contamination of groundwater by arsenic (As) in Bangladesh is the biggest impairing of a population, with a large number of peoples affected. Specifically, groundwater of Gangetic Delta is alarmingly contaminated with arsenic. Similar, perilous circumstances exist in many other countries and consequently, there is a dire need to develop cost-effective decentralized filtration unit utilizing low-cost adsorbents for eliminating arsenic from water. Morphological synthesis of carbon with unique spherical, nanorod, and massive nanostructures were achieved by solvothermal method. Owing to their intrinsic adsorption properties and different nanostructures, these nanostructures were employed as adsorption of arsenic in aqueous solution, with the purpose to better understanding the morphological effect in adsorption. It clearly demonstrated that carbon with nanorods morphology exhibited an excellent adsorption activity of arsenite (about 82%) at pH 3, remarkably superior to the two with solid sphere and massive microstructures, because of its larger specific surface area, enhanced acid strength and improved adsorption capacity. Furthermore, we discovered that iron hydroxide radicals and energy-induced contact point formation in nanorods are the responsible for the high adsorption of As in aqueous solution. Thus, our work provides insides into the microstructure-dependent capability of different carbon for As adsorption applications.

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.

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

Existence of wide spread elevated concentrations of groundwater arsenic (As) across South Asia, including India, has endangered a huge groundwater-based drinking water dependent population. Here, using high-spatial resolution As field-observations (~3 million groundwater sources) across India, we have delineated the regional-scale occurrence of elevated groundwater As (≥10 μg/L), along with the possible geologic-geomorphologic-hydrologic and human-sourced predictors that influence the spatial distribution of the contaminant. Using statistical and machine learning method, we also modeled the groundwater As concentrations probability at 1 Km resolution, along with probabilistic delineation of high As-hazard zones across India. The observed occurrence of groundwater As was found to be most strongly influenced by geology-tectonics, groundwater-fed irrigated area (%) and elevation. Pervasive As contamination is observed in major parts of the Himalayan mega-river Indus-Ganges-Brahmaputra basins, however it also occurs in several more-localized pockets, mostly related to ancient tectonic zones, igneous provinces, aquifers in modern delta and chalcophile mineralized regions. The model results suggest As-hazard potential in yet-undetected areas. Our model performed well in predicting groundwater arsenic, with accuracy: 82% and 84%; area under the curve (AUC): 0.89 and 0.88 for test data and validation datasets. An estimated ~90 million people across India are found to be exposed to high groundwater As from field-observed data, with the five states with highest hazard are West Bengal (28 million), Bihar (21 million), Uttar Pradesh (15 million), Assam (8.6 million) and Punjab (6 million). However it can be much more if the modeled hazard is considered (>250 million). Thus, our study provides a detailed, quantitative assessment of high groundwater As across India, with delineation of possible intrinsic influences and exogenous forcings. The predictive model is helpful in predicting As-hazard zones in the areas with limited measurements.

Distribution of Groundwater Arsenic in Uruguay Using Hybrid Machine Learning and Expert System Approaches

Groundwater arsenic in Uruguay is an important environmental hazard, hence, predicting its distribution is important to inform stakeholders. Furthermore, occurrences in Uruguay are known to variably show dependence on depth and geology, arguably reflecting different processes controlling groundwater arsenic concentrations. Here, we present the distribution of groundwater arsenic in Uruguay modelled by a variety of machine learning, basic expert systems, and hybrid approaches. A pure random forest approach, using 26 potential predictor variables, gave rise to a groundwater arsenic distribution model with a very high degree of accuracy (AUC = 0.92), which is consistent with known high groundwater arsenic hazard areas. These areas are mainly in southwest Uruguay, including the Paysandú, Río Negro, Soriano, Colonia, Flores, San José, Florida, Montevideo, and Canelones departments, where the Mercedes, Cuaternario Oeste, Raigón, and Cretácico main aquifers occur. A hybrid approach separating the country into sedimentary and crystalline aquifer domains resulted in slight material improvement in a high arsenic hazard distribution. However, a further hybrid approach separately modelling shallow (<50 m) and deep aquifers (>50 m) resulted in the identification of more high hazard areas in Flores, Durazno, and the northwest corner of Florida departments in shallow aquifers than the pure model. Both hybrid models considering depth (AUC = 0.95) and geology (AUC = 0.97) produced improved accuracy. Hybrid machine learning models with expert selection of important environmental parameters may sometimes be a better choice than pure machine learning models, particularly where there are incomplete datasets, but perhaps, counterintuitively, this is not always the case.

Urban river pollution in Bangladesh during last 40 years: potential public health and ecological risk, present policy, and future prospects toward smart water management

River water is very much important for domestic, agriculture and industrial use in Bangladesh which is in critical condition from long time based on research data. During last 40 years, extreme pollution events occurred in peripheral rivers surrounding Dhaka city and Karnaphuli River in Chittagong city. Present data showed that other urban rivers are also in critical condition especially Korotoa, Teesta, Rupsha, Pashur and Padma. The pollutants flowing with water made a severe pollution in downstream areas of rivers. Metals concentrations in river water was found to be higher in dry season. Dissolve oxygen (DO) was nearly zero in Buriganga River and several points in Turag, Balu, Sitalakhya and Karnaphuli River. NO₃^-, NO₂^- and PO₄^3- pollution occurred in different rivers. Zn, Cu, Fe, Pb, Cd, Ni, Mn, As and Cr concentration was above drinking water standard in most of the river and some metals was even above irrigation standard in water from several rivers. Sediment data showed very much higher metal concentrations in most of the rivers especially peripheral rivers in Dhaka and Karnaphuli, Korotoa, Teesta, Rupsha and Meghna River. Metal concentrations in sediment was above US EPA threshold value in most of the rivers. Metal concentrations in fish and agricultural crops showed that bioaccumulations of metals had occurred. The concentration of metals showed the trend like: water<fish<sediment. Agricultural crops were found to contain toxic metals through polluted water irrigation. The calculated data of daily intake for the non-carcinogenic and carcinogenic showed that consumption of the contaminated foodstuff can cause serious health injuries.