Geostatistical model of the spatial distribution of arsenic in groundwaters in Gujarat State, India.

Geogenic arsenic contamination in groundwaters poses a severe health risk to hundreds of millions of people globally. Notwithstanding the particular risks to exposed populations in the Indian sub-continent, at the time of writing, there was a paucity of geostatistically based models of the spatial distribution of groundwater hazard in India. In this study, we used logistic regression models of secondary groundwater arsenic data with research-informed secondary soil, climate and topographic variables as principal predictors generate hazard and risk maps of groundwater arsenic at a resolution of 1 km across Gujarat State. By combining models based on different arsenic concentrations, we have generated a pseudo-contour map of groundwater arsenic concentrations, which indicates greater arsenic hazard (> 10 µg/L) in the northwest, northeast and south-east parts of Kachchh District as well as northwest and southwest Banas Kantha District. The total number of people living in areas in Gujarat with groundwater arsenic concentration exceeding 10 µg/L is estimated to be around 122,000, of which we estimate approximately 49,000 people consume groundwater exceeding 10 µg/L. Using simple previously published dose-response relationships, this is estimated to have given rise to 700 (prevalence) cases of skin cancer and around 10 cases of premature avoidable mortality/annum from internal (lung, liver, bladder) cancers-that latter value is on the order of just 0.001% of internal cancers in Gujarat, reflecting the relative low groundwater arsenic hazard in Gujarat State.

Prediction Modeling and Mapping of Groundwater Fluoride Contamination throughout India.

For about the past eight decades, high concentrations of naturally occurring fluoride have been detected in groundwater in different parts of India. The chronic consumption of fluoride in high concentrations is recognized to cause dental and skeletal fluorosis. We have used the random forest machine-learning algorithm to model a data set of 12 600 groundwater fluoride concentrations from throughout India along with spatially continuous predictor variables of predominantly geology, climate, and soil parameters. Despite only surface parameters being available to describe a subsurface phenomenon, this has produced a highly accurate prediction map of fluoride concentrations exceeding 1.5 mg/L at 1 km resolution throughout the country. The most affected areas are the northwestern states/territories of Delhi, Gujarat, Haryana, Punjab, and Rajasthan and the southern states of Andhra Pradesh, Karnataka, Tamil Nadu, and Telangana. The total number of people at risk of fluorosis due to fluoride in groundwater is predicted to be around 120 million, or 9% of the population. This number is based on rural populations and accounts for average rates of groundwater consumption from nonmanaged sources. The new fluoride hazard and risk maps can be used by authorities in conjunction with detailed groundwater utilization information to prioritize areas in need of mitigation measures.

Deep learning for water quality.

Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduous and expensive data collection that exacerbate the issue of data scarcity. Traditional process-based and statistical models often fall short in predicting water quality. In this Review, we posit that deep learning represents an underutilized yet promising approach that can unravel intricate structures and relationships in high-dimensional data. We demonstrate that deep learning methods can help address data scarcity by filling temporal and spatial gaps and aid in formulating and testing hypotheses via identifying influential drivers of water quality. This Review highlights the strengths and limitations of deep learning methods relative to traditional approaches, and underscores its potential as an emerging and indispensable approach in overcoming challenges and discovering new knowledge in water-quality sciences.

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.

Groundwater salinity in the Horn of Africa: Spatial prediction modeling and estimated people at risk.

BACKGROUND: Changes in climate and anthropogenic activities have made water salinization a significant threat worldwide, affecting biodiversity, crop productivity and contributing to water insecurity. The Horn of Africa, which includes eastern Ethiopia, northeast Kenya, Eritrea, Djibouti, and Somalia, has natural characteristics that favor high groundwater salinity. Excess salinity has been linked to infrastructure and health problems, including increased infant mortality. This region has suffered successive droughts that have limited the availability of safe drinking water resources, leading to a humanitarian crisis for which little spatially explicit information about groundwater salinity is available. METHODS: Machine learning (random forest) is used to make spatial predictions of salinity levels at three electrical conductivity (EC) thresholds using data from 8646 boreholes and wells along with environmental predictor variables. Attention is paid to understanding the input data, balancing classes, performing many iterations, specifying cut-off values, employing spatial cross-validation, and identifying spatial uncertainties. RESULTS: Estimates are made for this transboundary region of the population potentially exposed to hazardous salinity levels. The findings indicate that about 11.6 million people (∼7% of the total population), including 400,000 infants and half a million pregnant women, rely on groundwater for drinking and live in areas of high groundwater salinity (EC > 1500 µS/cm). Somalia is the most affected and has the largest number of people potentially exposed. Around 50% of the Somali population (5 million people) may be exposed to unsafe salinity levels in their drinking water. In only five of Somalia's 18 regions are less than 50% of infants potentially exposed to unsafe salinity levels. The main drivers of high salinity include precipitation, groundwater recharge, evaporation, ocean proximity, and fractured rocks. The combined overall accuracy and area under the curve of multiple runs is âˆ¼ 82%. CONCLUSIONS: The modelled groundwater salinity maps for three different salinity thresholds in the Horn of Africa highlight the uneven spatial distribution of salinity in the studied countries and the large area affected, which is mainly arid flat lowlands. The results of this study provide the first detailed mapping of groundwater salinity in the region, providing essential information for water and health scientists along with decision-makers to identify and prioritize areas and populations in need of assistance.

Gaseous and soil OCPs and PCBs along the Indus River, Pakistan: spatial patterns and air-soil gradients.

This study presents first-hand information on the occurrence of persistent organic pollutants (POPs) in the ambient air and surface soil along the Indus flood-plain, Pakistan. The sampling campaign was conducted at 15 site locations during 2014-15, along the Indus River (approximately 1300 km). Composite surface soil samples (N = 15) and passive air samples (N = 15) were collected for the estimation of gaseous POPs as well as air-soil exchange to evaluate the POP emission and distribution or dispersion patterns, source tracking, and contribution of the local and regional sources towards POP accumulation in the Indus River system. Among the studied POPs, levels of DDTs and PCBs were noticeably higher in ambient air (50-560 and 10-1100 pg m-3) and in soil (0.20-350 and 1.40-20 ng g-1), respectively. Regarding the spatial patterns, higher DDT concentrations (ng g-1) were detected in the air and soil samples collected from the wet mountain zone (WMZ) (p < 0.05), followed by the alluvial riverine zone (ARZ), low-lying mountain zone (LLZ), and frozen mountain zone (FMZ). The PCB data did not exhibit significant differences (p > 0.05) for the air samples, while PCB concentrations were significantly higher (p < 0.05) in soil from the LLZ, which may be associated with rapid urbanization and industrial activities in this area. The air-soil exchange of DDTs and PCBs showed net volatilization at most of the studied sites except for a few samples from the FMZ and WMZ. Results of this study about air-soil exchange gradients indicate the long range regional atmospheric transport (LRAT) of POPs to the colder areas (FMZ) of Pakistan, where these act as a secondary source of POPs in these areas.

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.

Geogenic manganese and iron in groundwater of Southeast Asia and Bangladesh - Machine learning spatial prediction modeling and comparison with arsenic.

Naturally occurring, geogenic manganese (Mn) and iron (Fe) are frequently found dissolved in groundwater at concentrations that make the water difficult to use (deposits, unpleasant taste) or, in the case of Mn, a potential health hazard. Over 6000 groundwater measurements of Mn and Fe in Southeast Asia and Bangladesh were assembled and statistically examined with other physicochemical parameters. The machine learning methods random forest and generalized boosted regression modeling were used with spatially continuous environmental parameters (climate, geology, soil, topography) to model and map the probability of groundwater Mn > 400 µg/L and Fe > 0.3 mg/L for Southeast Asia and Bangladesh. The modeling indicated that drier climatic conditions are associated with a tendency of elevated Mn concentrations, whereas high Fe concentrations tend to be found in a more humid climate with elevated levels of soil organic carbon. The spatial distribution of Mn > 400 µg/L and Fe > 0.3 mg/L was compared and contrasted with that of the critical geogenic contaminant arsenic (As), confirming that high Fe concentrations are often associated with high As concentrations, whereas areas of high concentrations of Mn and As are frequently found adjacent to each other. The probability maps draw attention to areas prone to elevated concentrations of geogenic Mn and Fe in groundwater and can help direct efforts to mitigate their negative effects. The greatest Mn hazard is found in densely populated northwest Bangladesh and the Mekong, Red and Ma River Deltas of Cambodia and Vietnam. Widespread elevated Fe concentrations and their associated negative effects on water infrastructure pose challenges to water supply. The Mn and Fe prediction maps demonstrate the value of machine learning for the geospatial prediction modeling and mapping of groundwater contaminants as well as the potential for further constituents to be targeted by this novel approach.

Fluoride contamination of groundwater resources in Ghana: Country-wide hazard modeling and estimated population at risk.

Most people in Ghana have no or only basic access to safely managed water. Especially in rural areas, much of the population relies on groundwater for drinking, which can be contaminated with fluoride and lead to dental fluorosis. Children under the age of two are particularly susceptible to the adverse effects of fluoride and can retain 80-90% of a fluoride dose, compared to 60% in adults. Despite numerous local studies, no spatially continuous picture exists of the fluoride contamination across Ghana, nor is there any estimate of what proportion of the population is potentially exposed to unsafe fluoride levels. Here, we spatially model the probability of fluoride concentrations exceeding 1.0 mg/L in groundwater across Ghana to identify risk areas and estimate the number of children and adults exposed to unsafe fluoride levels in drinking water. We use a set of geospatial predictor variables with random forest modeling and evaluate the model performance through spatial cross-validation. We found that approximately 15% of the area of Ghana, mainly in the northeast, has a high probability of fluoride contamination. The total at-risk population is about 920,000 persons, or 3% of the population, with an estimated 240,000 children (0-9 years) in at-risk areas. In some districts, such as Karaga, Gushiegu, Tamale and Mion, 4 out of 10 children are potentially exposed to fluoride poisoning. Geology and high evapotranspiration are the main drivers of fluoride enrichment in groundwater. Consequently, climate change might put even greater pressure on the area's water resources. Our hazard maps should raise awareness and understanding of geogenic fluoride contamination in Ghana and can advise decision making at local levels to avoid or mitigate fluoride-related risks.

A critical review on the occurrence and distribution of the uranium- and thorium-decay nuclides and their effect on the quality of groundwater.

This critical review presents the key factors that control the occurrence of natural elements from the uranium- and thorium-decay series, also known as naturally occurring radioactive materials (NORM), including uranium, radium, radon, lead, polonium, and their isotopes in groundwater resources. Given their toxicity and radiation, elevated levels of these nuclides in drinking water pose human health risks, and therefore understanding the occurrence, sources, and factors that control the mobilization of these nuclides from aquifer rocks is critical for better groundwater management and human health protection. The concentrations of these nuclides in groundwater are a function of the groundwater residence time relative to the decay rates of the nuclides, as well as the net balance between nuclides mobilization (dissolution, desorption, recoil) and retention (adsorption, precipitation). This paper explores the factors that control this balance, including the relationships between the elemental chemistry (e.g., solubility and speciation), lithological and hydrogeological factors, groundwater geochemistry (e.g., redox state, pH, ionic strength, ion-pairs availability), and their combined effects and interactions. The various chemical properties of each of the nuclides results in different likelihoods for co-occurrence. For example, the primordial 238U, 222Rn, and, in cases of high colloid concentrations also 210Po, are all more likely to be found in oxic groundwater. In contrast, in reducing aquifers, Ra nuclides, 210Pb, and in absence of high colloid concentrations, 210Po, are more mobile and frequently occur in groundwater. In highly permeable sandstone aquifers that lack sufficient adsorption sites, Ra is often enriched, even in low salinity and oxic groundwater. This paper also highlights the isotope distributions, including those of relatively long-lived nuclides (238U/235U) with abundances that depend on geochemical conditions (e.g., fractionation induced from redox processes), as well as shorter-lived nuclides (234U/238U, 228Ra/226Ra, 224Ra/228Ra, 210Pb/222Rn, 210Po/210Pb) that are strongly influenced by physical (recoil), lithological, and geochemical factors. Special attention is paid in evaluating the ability to use these isotope variations to elucidate the sources of these nuclides in groundwater, mechanisms of their mobilization from the rock matrix (e.g., recoil, ion-exchange), and retention into secondary mineral phases and ion-exchange sites.

Monitoring and prediction of high fluoride concentrations in groundwater in Pakistan.

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

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

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

Freely dissolved organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) along the Indus River Pakistan: spatial pattern and risk assessment.

Freely dissolved OCPs and PCBs were measured by using polyethylene passive samplers at 15 sites during 2014 throughout the stretch of the Indus River to investigate the spatial pattern and risk assess. Levels (pg/L) of dissolved ∑OCPs and ∑PCBs ranged from 34 to 1600 and from 3 to 230. Among the detected OCPs, dissolved DDTs (p,p'-DDE, followed by p,p'-DDT) predominated with levels of 0.48 to 220 pg/L. The order of occurrence for other studied OCPs was as follows: HCB, endosulfans, chlordanes, and HCHs. Spatially, dissolved (pg/L) ∑OCPs varied (p < 0.05) as the following: surface water of the alluvial riverine zone (ARZ) showed the highest levels (114) followed by the frozen mountain zone (FMZ) (52.9), low-lying zone (LLZ) (28.73), and wet mountain zone (WMZ) (14.43), respectively. However, our zone-wise PCB data did not exhibit significant differences (p > 0.05). Principal component analysis/multilinear regression results showed pesticide usage in the crop/orchard fields and health sector, electric and electronic materials, and widespread industrial activities as the main source of OCPs and PCBs along the Indus River. Our results showed that OCPs and PCBs contaminated water intake, playing an important role towards the considerable cancer/non-cancer risk (HI and CR values) along the Indus River Flood-Plain.

Global threat of arsenic in groundwater.

Naturally occurring arsenic in groundwater affects millions of people worldwide. We created a global prediction map of groundwater arsenic exceeding 10 micrograms per liter using a random forest machine-learning model based on 11 geospatial environmental parameters and more than 50,000 aggregated data points of measured groundwater arsenic concentration. Our global prediction map includes known arsenic-affected areas and previously undocumented areas of concern. By combining the global arsenic prediction model with household groundwater-usage statistics, we estimate that 94 million to 220 million people are potentially exposed to high arsenic concentrations in groundwater, the vast majority (94%) being in Asia. Because groundwater is increasingly used to support growing populations and buffer against water scarcity due to changing climate, this work is important to raise awareness, identify areas for safe wells, and help prioritize testing.

Groundwater Arsenic Distribution in India by Machine Learning Geospatial Modeling.

Groundwater is a critical resource in India for the supply of drinking water and for irrigation. Its usage is limited not only by its quantity but also by its quality. Among the most important contaminants of groundwater in India is arsenic, which naturally accumulates in some aquifers. In this study we create a random forest model with over 145,000 arsenic concentration measurements and over two dozen predictor variables of surface environmental parameters to produce hazard and exposure maps of the areas and populations potentially exposed to high arsenic concentrations (>10 µg/L) in groundwater. Statistical relationships found between the predictor variables and arsenic measurements are broadly consistent with major geochemical processes known to mobilize arsenic in aquifers. In addition to known high arsenic areas, such as along the Ganges and Brahmaputra rivers, we have identified several other areas around the country that have hitherto not been identified as potential arsenic hotspots. Based on recent reported rates of household groundwater use for rural and urban areas, we estimate that between about 18-30 million people in India are currently at risk of high exposure to arsenic through their drinking water supply. The hazard models here can be used to inform prioritization of groundwater quality testing and environmental public health tracking programs.

A global-scale dataset of direct natural groundwater recharge rates: A review of variables, processes and relationships.

Groundwater recharge indicates the existence of renewable groundwater resources and is therefore an important component in sustainability studies. However, recharge is also one of the least understood, largely because it varies in space and time and is difficult to measure directly. For most studies, only a relatively small number of measurements is available, which hampers a comprehensive understanding of processes driving recharge and the validation of hydrogeological model formulations for small- and large-scale applications. We present a new global recharge dataset encompassing >5000 locations. In order to gain insights into recharge processes, we provide a systematic analysis between the dataset and other global-scale datasets, such as climatic or soil-related parameters. Precipitation rates and seasonality in temperature and precipitation were identified as the most important variables in predicting recharge. The high dependency of recharge on climate indicates its sensitivity to climate change. We also show that vegetation and soil structure have an explanatory power for recharge. Since these conditions can be highly variable, recharge estimates based only on climatic parameters may be misleading. The freely available dataset offers diverse possibilities to study recharge processes from a variety of perspectives. By noting the existing gaps in understanding, we hope to encourage the community to initiate new research into recharge processes and subsequently make recharge data available to improve recharge predictions.

Persistent organic pollutant emission via dust deposition throughout Pakistan: Spatial patterns, regional cycling and their implication for human health risks.

In the current study, Persistent Organic Pollutants (POPs) in outdoor dustfall was monitored for the first time along the Indus river system of Pakistan. Among the studied OCPs (ng/g, dry weight), DDTs (0.16-62) were the predominant contaminants identified in deposited dust followed by HCHs (0.1-10.2), HCB (0.09-7.4) and chlordanes (0.1-2.8). The indicative diagnostic ratio for DDTs and HCHs suggested recent emission of DDTs as well as historical emission of both chemicals in regions where they were used for crop protection and malarial control. The levels of ∑31PCBs (ng/g, dry weight) in dust ranged from 0.95-125, and compositional profiles suggested arochlor-1248, -1254 commercial mixtures as source. A few exceptions were samples from urban areas that reflected the use of aroclor-1260, and-1262 and/or unintentional leakage from several industrial processes. The WHO05-TEQ values for dioxin-like PCBs (with major contributions of PCB-126) were found to be 0.07-34.5 (median; 1.87) pg TEQg-1dw for all the studied samples. Correlation analysis identified that DDTs, HCHs, HCB and PCBs were significantly associated (r=90; p<0.01) with dusts collected in proximity to urban centers with widespread anthropogenic activities in these areas. A few cases where high levels of POPs from remote mountain highlands were detected, point to the potential for long range transport of these chemicals. Human risk assessment analysis of contaminated dust showed that DDTs and PCBs are major constituent chemicals of concern with regard to the development of cancer in children, with ingestion being the main route of exposure of dust-borne DDTs (0.12-1.03×10-6) and PCBs (0.86-12.43×10-6).

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

Arsenic-contaminated aquifers are currently estimated to affect ~150 million people around the world. However, the full extent of the problem remains elusive. This is also the case in Pakistan, where previous studies focused on isolated areas. Using a new data set of nearly 1200 groundwater quality samples throughout Pakistan, we have created state-of-the-art hazard and risk maps of arsenic-contaminated groundwater for thresholds of 10 and 50 µg/liter. Logistic regression analysis was used with 1000 iterations, where surface slope, geology, and soil parameters were major predictor variables. The hazard model indicates that much of the Indus Plain is likely to have elevated arsenic concentrations, although the rest of the country is mostly safe. Unlike other arsenic-contaminated areas of Asia, the arsenic release process in the arid Indus Plain appears to be dominated by elevated-pH dissolution, resulting from alkaline topsoil and extensive irrigation of unconfined aquifers, although pockets of reductive dissolution are also present. We estimate that approximately 50 million to 60 million people use groundwater within the area at risk, with hot spots around Lahore and Hyderabad. This number is alarmingly high and demonstrates the urgent need for verification and testing of all drinking water wells in the Indus Plain, followed by appropriate mitigation measures.

Groundwater arsenic contamination in Burkina Faso, West Africa: Predicting and verifying regions at risk.

Arsenic contamination in groundwater from crystalline basement rocks in West Africa has only been documented in isolated areas and presents a serious health threat in a region already facing multiple challenges related to water quality and scarcity. We present a comprehensive dataset of arsenic concentrations from drinking water wells in rural Burkina Faso (n=1498), of which 14.6% are above 10µg/L. Included in this dataset are 269 new samples from regions where no published water quality data existed. We used multivariate logistic regression with arsenic measurements as calibration data and maps of geology and mineral deposits as independent predictor variables to create arsenic prediction models at concentration thresholds of 5, 10 and 50µg/L. These hazard maps delineate areas vulnerable to groundwater arsenic contamination in Burkina Faso. Bedrock composed of schists and volcanic rocks of the Birimian formation, potentially harbouring arsenic-containing sulphide minerals, has the highest probability of yielding groundwater arsenic concentrations >10µg/L. Combined with population density estimates, the arsenic prediction models indicate that ~560,000 people are potentially exposed to arsenic-contaminated groundwater in Burkina Faso. The same arsenic-bearing geological formations that are positive predictors for elevated arsenic concentrations in Burkina Faso also exist in neighbouring countries such as Mali, Ghana and Ivory Coast. This study's results are thus of transboundary relevance and can act as a trigger for targeted water quality surveys and mitigation efforts.