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

Comprehensive assessment of heavy metal pollution of agricultural soils impacted by the Kalsaka abandoned gold mine and artisanal gold mining in northern Burkina Faso

In this study, the environmental quality of agricultural soils around the Kalsaka abandoned gold mine was evaluated. A total of 117 samples including industrial mine wastes, topsoil, and control soil were collected in and around the mine, and their heavy metal concentrations were determined using ICP-MS after aqua regia digestion. Except for Zn, the average concentrations of the metals were higher in mine wastes and the agricultural soils than their average upper continental crust (UCC) counterpart, whereas those of Ag, As, and Hg exceeded the UCC in the control soils. The control soils had the lowest contaminations and the lowest pollution levels for all metals except for Hg. Based on the average concentrations and coefficients of variation, it can be concluded that Cr, Ni, and Zn abundance in the agricultural soils was controlled by natural factors, whereas that of Ag, As, Hg, Co, Cu, and Mn was mainly associated with mining. The absence of Hg in industrial mine wastes and its high contents in agricultural and control soils reflected the artisanal gold mining source of this metal. Thus, single and integrated pollution indices showed that ecological risk and toxicity are much higher when Hg is included in the index calculation, suggesting cumulative effects of industrial and artisanal gold mining on the agricultural soil quality. The results also showed that Hg and As are the pollutants of major concern in the Kalsaka abandoned gold mine. Because of its proximity to human settlements, the Kalsaka abandoned gold mine necessitates an immediate rehabilitation.

Comprehensive review on toxic heavy metals in the aquatic system: sources, identification, treatment strategies, and health risk assessment

Heavy metal pollution in water sources has become a major worldwide environmental issue, posing a threat to aquatic ecosystems and human health. The pollution of the aquatic environment is increasing as a result of industrialization, climate change, and urban development. The sources of heavy metal pollution in water include mining waste, leachates from landfills, municipal and industrial wastewater, urban runoff, and natural events such as volcanism, weathering, and rock abrasion. Heavy metal ions are toxic and potentially carcinogenic. They can also buildup in biological systems and cause bioaccumulation even at low levels of exposure, heavy metals can cause harm to organs such as the nervous system, liver and lungs, kidneys and stomach, skin, and reproductive systems. There were various approaches tried to purify water and maintain water quality. The main purpose of this article was to investigate the occurrence and fate of the dangerous contaminants (Heavy metal and metalloids) found in domestic and industrial effluents. This effluent mixes with other water streams and is used for agricultural activities and other domestic activities further complicating the issue. It also discussed conventional and non-conventional treatment methods for heavy metals from aquatic environments. Conclusively, a pollution assessment of heavy metals and a human health risk assessment of heavy metals in water resources have been explained. In addition, there have been efforts to focus on heavy metal sequestration from industrial waste streams and to create a scientific framework for reducing heavy metal discharges into the aquatic environment.

Hotspot mapping and risk prediction of fluoride in natural waters across the Tibetan Plateau

Intake of high fluoride concentrations through water affects up to 1 billion people worldwide, and the Tibetan Plateau (TP) is one of the most severely affected areas. Knowledge regarding the high fluoride risk areas, the driving factors, and at-risk populations on the TP remains fragmented. We collected 1581 natural water samples from the TP to model surface water and groundwater fluoride hazard maps using machine learning. The geomean concentrations of surface water and groundwater were 0.26 mg/L and 0.92 mg/L, respectively. Surface water fluoride hazard hotspots were concentrated in the north-central region; high fluoride risk areas of groundwater were mainly concentrated in the southern TP. Hazard maps showed a maximum estimate of 15% of the total population in the TP (approximately 1.47 million people) at risk, and 500,000 people considered the most reasonable estimate. Critical environment driving factors were identified, in which climate condition was taken for the vital one. Under the moderate climate change scenario (SSP2.45) for 2089-2099, the high fluoride risk change rate differed inside the TP (surface water -24%-55% and groundwater -56%-50%), and the overall risk increased in natural waters throughout the TP, particularly in the southeastern TP.

ASC/Caspase-1-activated endothelial cells pyroptosis is involved in vascular injury induced by arsenic combined with high-fat diet

Environmental arsenic (As) or high-fat diet (HFD) exposure alone are risk factors for the development of cardiovascular disease (CVDs). However, the effects and mechanisms of co-exposure to As and HFD on the cardiovascular system remain unclear. The current study aimed to investigate the combined effects of As and HFD on vascular injury and shed some light on the underlying mechanisms. The results showed that co-exposure to As and HFD resulted in a significant increase in serum lipid levels and significant lipid accumulation in the aorta of rats compared with exposure to As or HFD alone. Meanwhile, the combined exposure altered blood pressure and disrupted the morphological structure of the abdominal aorta in rats. Furthermore, As combined with HFD exposure upregulated the expression of vascular endothelial cells pyroptosis-related proteins (ASC, Pro-caspase-1, Caspase-1, IL-18, IL-1β), as well as the expression of vascular endothelial adhesion factors (VCAM-1 and ICAM-1). More importantly, we found that with increasing exposure time, vascular injury-related indicators were significantly higher in the combined exposure group compared with exposure to As or HFD alone, and the vascular injury was more severe in female rats compared with male rats. Taken together, these results suggested that the combination of As and HFD induced vascular endothelial cells pyroptosis through activation of the ASC/Caspase-1 pathway. Therefore, vascular endothelial cells pyroptosis may be a potential molecular mechanism for vascular injury induced by As combined with HFD exposure.

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.

Spatial distribution characteristics and prediction of fluorine concentration in groundwater based on driving factors analysis

Excess fluoride (F^-) in groundwater can be hazardous to human health. A total of 360 ground water samples was collected from northern Anhui, China, to study the levels, distribution, and source of F^-. And on this basis, predicting the spatial distribution of F^- in a wider scale space. The range of F^- was 0.1-5.8 mg/L, with a mean value of 1.2 mg/L, and 26.4 % of the samples exceeded the acceptable level of 1.5 mg/L. Moreover, the water-rock interaction (fluorite dissolution) and cation alternate adsorption were considered to be two main driving factors of high F^- in groundwater. To further illustrate the spatial effects, the BME-RF model was established by combining the main environmental factors. The spatial distribution of F^- was quantitatively predicted, and the response to environmental variables was analyzed. The R² of BME-RF model reached 0.93, the prediction results showed that the region with 1.0-1.5 mg/L of F^- accounts for 47.2 % of the total area. The predicted F^- content of nearly 70 % of groundwater in this area has exceeded 1.0 mg/L, which was dominated by Na⁺ and HCO₃^- type. The spatial variability of F^- in the study area was mainly affected by hydrogeological conditions, and the vertical distribution characteristics were related to the spatial variation of slope, distance from runoff, and hydrochemical types. The results of the study provide new insights into the F^- concentration prediction in underground environment, especially in the borehole gap area.

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.

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.

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

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

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.

Global diagnosis of nitrate pollution in groundwater and review of removal technologies

Clean water and sanitation for the world population is one of the most important challenges established by the Sustainable Development Goals of the United Nations since worldwide, one in three people do not have access to safe drinking water. Groundwater, one of the main sources of fresh water, has been considerably damaged by human activities. Nevertheless, while numerous plants are globally aimed at removing pollutants from surface waters, a much scarcer number of facilities have focused on groundwater remediation. Nowadays, there is increasing concern about the presence of nitrates (NO₃^-) in groundwaters as a consequence of the intensive use of fertilizers and other anthropogenic sources, such as sewage or industrial wastewater discharge. In this context, the selection and development of highly effective and low-cost solutions for the sustainable management of groundwater resources need to be addressed. Thus, this work collects data from the literature regarding the presence of nitrates in groundwater, and, simultaneously, it reviews the main alternatives available to remove NO₃^- from groundwater sources. A total of 292 sites have been analyzed categorized by continents, carefully discussing the possible origins of nitrate pollution. In addition, a discussion is carried out of the different technologies currently employed to treat groundwater, highlighting the progress made and the main challenges to be overcome. Finally, the review gathers the data available in the literature for nitrate treatment plants at full-scale.

Effects of Silicic Acid on Leaching Behavior of Arsenic from Spent Calcium-Based Adsorbents with Arsenite

The spent adsorbents that remain after being used to purify As-contaminated water constitute waste containing a large amount of As. These spent adsorbents, after being disposed, are likely to come into contact with silicic acid leached from the soil or cementitious solidification materials. Thus, it is crucial the evaluate the effects of silicic acid on spent adsorbents. In this study, the effects of silicic acid on spent Ca-based (CaO and Ca(OH)2) adsorbents with arsenite were investigated. The As leaching ratio for the spent adsorbents decreased with an increase in the initial concentration of silicic acid in the liquid. Under the tested conditions, the As leaching ratio decreased from 8–9% to less than 0.7% in the presence of silicic acid at an initial Si-normalized concentration of 100 mg/L. The primary mechanism behind the inhibition of As leaching by silicic acid was determined to be re-immobilization via the incorporation of arsenite during the formation of calcium silicates. In the presence of silicic acid, spent Ca-based adsorbents with arsenite had a lower As leaching ratio than those with arsenate. Therefore, spent Ca-based adsorbents with arsenite were found to have a higher environmental stability than those with arsenate.

Machine learning in natural and engineered water systems

Water resources of desired quality and quantity are the foundation for human survival and sustainable development. To better protect the water environment and conserve water resources, efficient water management, purification, and transportation are of critical importance. In recent years, machine learning (ML) has exhibited its practicability, reliability, and high efficiency in numerous applications; furthermore, it has solved conventional and emerging problems in both natural and engineered water systems. For example, ML can predict various water quality indicators in situ and real-time by considering the complex interactions among water-related variables. ML approaches can also solve emerging pollution problems with proven rules or universal mechanisms summarized from the related research. Moreover, by applying image recognition technology to analyze the relationships between image information and physicochemical properties of the research object, ML can effectively identify and characterize specific contaminants. In view of the bright prospects of ML, this review comprehensively summarizes the development of ML applications in natural and engineered water systems. First, the concept and modeling steps of ML are briefly introduced, including data preparation, algorithm selection and model evaluation. In addition, comprehensive applications of ML in recent studies, including predicting water quality, mapping groundwater contaminants, classifying water resources, tracing contaminant sources, and evaluating pollutant toxicity in natural water systems, as well as modeling treatment techniques, assisting characterization analysis, purifying and distributing drinking water, and collecting and treating sewage water in engineered water systems, are summarized. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of ML algorithms for different studies, as well as prospects on the application and development of ML in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.

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.

Assessing the potential value and mechanism of Ginkgo biloba L. On coal-fired arsenic-induced skin damage: In vitro and human evidence

Exposure through arsenic-contaminated air and food caused by the burning of coal is a major environmental public health concern in Guizhou Province of China. Previous studies have shown that immunological dysfunction is involved in the pathogenesis and carcinogenesis of arsenic; however, knowledge regarding effective prevention measures have not been fully examined. The effect of Ginkgo biloba extract (EGb761) on arsenic-induced skin damage of human immortalized keratinocyte cells (HaCaT) was first evaluated in this study. The results showed that 200 μg/mL EGb761 can reduce the expression of miR-155-5p, and the indicators reflecting arsenic-induced skin damage (Krt1, Krt6c and Krt10) in arsenic-exposed cells (P < 0.05), the expression levels of NF-AT1; the indicators reflecting arsenic-induced immunological dysfunction (IL-2, IFN-γ) in cells; and the levels of secreted IL-2 and IFN-γ in cell supernatants were significantly increased (P < 0.05). Further randomized controlled double-blind experiments showed that compared to the placebo control group, the expression level of miR-155-5p in the plasma of the Ginkgo biloba intervention group, the indicators in the serum reflecting arsenic-induced skin damage (Krt1, Krt6c, and Krt10) and the epithelial-mesenchymal transformation (EMT) vimentin were significantly reduced (P < 0.05), but the levels of NF-AT1 and the indicators reflecting arsenic-induced immunological dysfunction (IL-2, IFN-γ) and EMT (E-cadherin) in serum were significantly increased (P < 0.05). Our study provides some limited evidence that Ginkgo biloba L. can increase the expression of NF-AT1 by downregulating the level of miR-155-5p, alleviating immunological dysfunction, and decreasing the expression of EMT biomarkers, thus indirectly improving arsenic-induced skin damage.

Mapping Ground Water Access in Two Rural Communes of Burkina Faso

Granting safe water access worldwide is a major objective of the Sustainable Development Goals. Water access is a manifold concept that encompasses collection time, distance from the household, water quality, affordability, and reliability of water sources, among other factors. GIS-based methods can be particularly useful in improving water access estimates, particularly in rural areas of developing countries. Based on an extensive water point database (n = 770), this paper explores the main challenges involved in mapping water access in two rural communes of Burkina Faso. Water access is estimated in terms of coverage per surface area. Coverage is filtered into four distinct categories of improved water sources, namely existing infrastructures, operational infrastructures, permanent infrastructures, and permanent infrastructures that provide safe water. The outcomes suggest that the study area is better endowed with water access than rural Burkina Faso and the remainder of the African continent, although there are important questions regarding groundwater quality. The outcomes highlight the conceptual differences between coverage and access, as well as some of the practical difficulties involved in estimating water access beyond standard ratios. The shortcomings include the absence of continuous monitoring of infrastructure functionality and water quality, as well as water affordability, among others. Enhancing national borehole databases with items aligned with the United Nations’ definition of water access is recommended.

Machine Learning Models of Arsenic in Private Wells Throughout the Conterminous United States As a Tool for Exposure Assessment in Human Health Studies

Arsenic from geologic sources is widespread in groundwater within the United States (U.S.). In several areas, groundwater arsenic concentrations exceed the U.S. Environmental Protection Agency maximum contaminant level of 10 μg per liter (μg/L). However, this standard applies only to public-supply drinking water and not to private-supply, which is not federally regulated and is rarely monitored. As a result, arsenic exposure from private wells is a potentially substantial, but largely hidden, public health concern. Machine learning models using boosted regression trees (BRT) and random forest classification (RFC) techniques were developed to estimate probabilities and concentration ranges of arsenic in private wells throughout the conterminous U.S. Three BRT models were fit separately to estimate the probability of private well arsenic concentrations exceeding 1, 5, or 10 μg/L whereas the RFC model estimates the most probable category (≤5, >5 to ≤10, or >10 μg/L). Overall, the models perform best at identifying areas with low concentrations of arsenic in private wells. The BRT 10 μg/L model estimates for testing data have an overall accuracy of 91.2%, sensitivity of 33.9%, and specificity of 98.2%. Influential variables identified across all models included average annual precipitation and soil geochemistry. Models were developed in collaboration with public health experts to support U.S.-based studies focused on health effects from arsenic exposure.

Arsenic exposure from groundwater: environmental contamination, human health effects, and sustainable solutions

Arsenic (As) occurs naturally in geologic conditions, but groundwater contamination might also be found due to the consequences of mining, agricultural and industrial processes. Human exposure to As after drinking contaminated water is commonly associated with acute toxicity outcomes and chronic effects ranging from skin lesions to cancer. Integrated actions from environmental and health authorities are needed to reduce exposure, monitoring outcomes, and promotion of actions to offer sustainable As-safe water alternatives. Considering recent research trends, the present review summarizes and discusses current issues associated with the process and effects of contamination and decontamination in an environmental health perspective. Recent findings reinforce the harmful effects of the consumption of As-contaminated water and broaden the scope of related diseases including intestinal maladies, type 2 diabetes, cancers of bladder, kidneys, lung, and liver. Among the main strategies to diminish or remove As from water, the following are highlighted (1) ion exchange system and membrane filtration (micro, ultra, and nanofiltration) as physicochemical treatment systems; (2) use of cyanobacteria and algae in bioremediation programs and (3) application of nanotechnology for water treatment.

Polycentric Solutions for Groundwater Governance in Sub-Saharan Africa: Encouraging Institutional Artisanship in an Extended Ladder of Participation

The growth of groundwater irrigation poses opportunities and challenges, particularly in Africa where substantial potential exists for increased groundwater irrigation but has been constrained by limited access to energy, technology for pumps and drilling, markets, and other factors. Conventional groundwater governance concepts for state-led regulation or co-management are problematic for conditions where state capacity or political support for regulation to reconcile conflicting interests is limited. Experience in Africa and elsewhere does offer examples that may help recognize feasible patterns for collective action that can influence the equity, efficiency, and sustainability of groundwater development. An extended ladder of participation helps look beyond state-led water governance and co-management to a more diverse range of opportunities for supporting local autonomy and initiative to expand opportunities and solve problems in groundwater development. Collective action in groundwater governance can include well spacing; sharing of wells, pumps, and pipes; protecting domestic water sources; crop coordination; groundwater recharge; water imports; and aquifer management. Even where non-state organizations and collective action play primary roles in water governance, they may still be empowered by, receive advice from, or share information with government agencies and other actors. Polycentric groundwater governance can be supported by improving information, facilitating cooperation, endorsing standards, providing a legal framework for resolving conflicts and constituting governance agreements, and through polycentric social learning. Polycentric institutional artisanship by water users and their organizations can help find feasible solutions for improving groundwater governance.

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.

Arsenic removal with zero-valent iron filters in Burkina Faso: Field and laboratory insights

Groundwater contaminated with geogenic arsenic (As) is frequently used as drinking water in Burkina Faso, despite adverse health effects. This study focused on testing low-cost filter systems based on zero-valent iron (ZVI), which have not yet been explored in West Africa for As removal. The active ZVI bed was constructed using small-sized iron nails, embedded between sand layers. Household filters were tested for nine months in a remote village relying on tube well water with As concentrations of 400-1350 μg/L. Daily filtered volumes were 40-60 L, with flow rates of ~10 L/h. In parallel, downscaled laboratory filter columns were run to find the best set-up for optimal As removal, with special attention given to the influence of input pH, flow rate and water/nail contact time. Arsenic removal efficiencies in the field were 60-80% in the first six months of operation. The laboratory experiments revealed that trapped air in the nail layer greatly lowered As removal due to preferential flow and decreased water/nail contact time. Measures taken to avoid trapped air led to a partial improvement in the field filters, but effluent As remained >50 μg/L. Similar structural modifications were however very successful in the laboratory columns, where As removal efficiencies were consistently >95% and effluent concentrations frequently <10 μg/L, despite inflow As >1000 μg/L. A constantly saturated nail bed and careful flow control is necessary for optimal As removal. Slow flow and longer pauses between filtrations are important for sufficient contact times and for transformation of brown amorphous Fe-hydroxides to dense magnetite with incorporated As(V). This preliminary study has shown that nail-based filters have the potential to achieve As removal >90% in a field context if conditions (filter bed saturation, flow rate, pauses between filtrations) are well controlled.

Co-exposure to Arsenic-Fluoride Results in Endoplasmic Reticulum Stress-Induced Apoptosis Through the PERK Signaling Pathway in the Liver of Offspring Rats

Arsenic and fluoride are two of the major groundwater pollutants. To better understand the liver damage induced during development, 24 male rats exposed to fluoride (F), arsenic (As), and their combination (As + F) from the prenatal stage to 90 days after birth were selected for analysis. Histopathological results showed vacuolar degeneration in the As and As + F groups. Compared to those in the control group, aspartate aminotransferase and alanine aminotransferase levels were significantly increased in the combined group. Catalase activity significantly decreased in the treatment groups compared to that in the controls, and the malondialdehyde content in the As and As + F groups was significantly higher than those in the control group. We further evaluated whether this damage is linked to endoplasmic reticulum stress and its related pathways. The mRNA expression levels of PERK, GRP78, EIF2α, ATF4, and CHOP as well as the protein levels of CHOP was significantly increased in the As + F group compared with the control group. These results demonstrate that As, F, and their combination could lead to liver function damage and reduce the antioxidant capacity of the liver to cause oxidative damage to tissues. Moreover, the combination of As and F triggers endoplasmic reticulum stress-induced apoptosis in liver cells by activating the PERK pathway in the unfolded protein response. As and F seem to have different independent effects, whereas their combination resulted in more severe effects overall.

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.

An assessment of arsenic hazard in groundwater-soil-rice system in two villages of Nadia district, West Bengal, India

The present study measured arsenic (As) concentrations in soil, groundwater and rice grain samples in two villages, Sarapur and Chinili, under Chakdaha block, Nadia district, West Bengal, India. This study also included a survey of the two villages to understand the knowledge among villagers about the As problem. Soil and groundwater samples were collected from fields in two villages while rice grain samples were collected from villagers' houses. The results revealed the presence of As in higher concentrations than the maximum permissible limit of As in drinking water (10 µg L^-1 and 50 µg L^-1 by WHO and Indian standard, respectively) in groundwater [124.50 ± 1.11 µg L^-1 (Sarapur) and 138.20 ± 1.34 µg L^-1 (Chinili)]. The level of As in soil was found to range from 47.7 ± 0.14 to 49.3 ± 0.19 mg Kg^-1 in Sarapur and from 57.5 ± 0.25 to 62.5 ± 0.44 mg Kg^-1 in Chinili which are also higher than European Union maximum acceptable limit in agricultural soil (i.e. 20 mg Kg^-1). The analysis of As in rice grains of five varieties, collected from residents of two villages, showed the presence of higher than recommended safe level of As in rice by FAO/WHO (0.2 mg Kg^-1). The As concentration order was Gosai (0.95 ± 0.044 mg kg^-1), Satabdi (0.79 ± 0.038 mg kg^-1), Banskathi (0.60 ± 0.026 mg kg^-1), Kunti (0.47 ± 0.018 mg kg^-1) and Ranjit (0.29 ± 0.021 mg kg^-1). Importantly, Gosai and Satabdi were the most popular varieties being consumed by local people. The data of consumption of rice per day in the survey was used for the measurement of average daily dose and hazard quotient. It was seen that the As hazard was negatively correlated to the age of residents. Therefore, children and toddlers were at higher risk of As exposure than elderly people. In addition, people with skin related As toxicity symptoms were also cited in the two villages. The study emphasized the severity of As problem in remote areas of West Bengal, India where people consume As tainted rice due to lack of awareness about the As problem and associated health issues.

Performance evaluation of surfactant modified kaolin clay in As(III) and As(V) adsorption from groundwater: adsorption kinetics, isotherms and thermodynamics

In this paper surfactant modified kaolin clay for As(III) and As(V) was prepared by intercalating hexadecyltrimethylamonium bromide (HDTMA-Br) cationic surfactant onto the clay interlayers. Batch experiments were used to evaluate the effectiveness of surfactant modified kaolin clay towards As(III) and As(V) removal. The results revealed that adsorption of As(III) and As(V) is optimum at pH range 4–8. The maximum As(III) and As(V) adsorption capacities were found 2.33 and 2.88 mg/g, respectively after 60 min contact time. The data for adsorption of As(III) showed a better fit too pseudo first order model of reaction kinetics while the data for As(V) fitted better to pseudo second order model. The adsorption isotherm data for As(III) and As(V) fitted well to Langmuir model indicating that adsorption of both species occurred on a mono-layered surface. Adsorption thermodynamics model revealed that adsorption of As(III) and As(V) was spontaneous and exothermic. The presence of Mg²⁺ and Ca²⁺ increased As(III) and As(V) adsorption efficiency. The regeneration study showed that synthesized adsorbent can be used for up to 5 times with maximum As(III) and As(V) percentage removal of 54.2% and 62.33%, respectively achieved after 5^th cycle. Surfactant modified kaolin clay mineral showed higher adsorption capacity towards As(III) and As(V) as compared to unmodified kaolin clay mineral and competitive with other adsorbent in the literature. The results obtained from this study revealed that surfactant modified kaolin mineral is a candidate material for arsenic remediation from groundwater.

miR-191 is involved in renal dysfunction in arsenic-exposed populations by regulating inflammatory response caused by arsenic from burning arsenic-contaminated coal

Chronic exposure to arsenic may result in the manifestation of damage in multiple organs or systems of the body. Arsenic-induced renal dysfunction has been determined, but their pathogenesis has not been fully examined. In this study, we measured the expression levels of miR-191 in plasma, the contents of pro-inflammatory (interleukin (IL)-6 and tumor necrosis factor alpha) and anti-inflammatory (IL-2 and transforming growth factor beta) cytokines, and renal dysfunction indicators (blood urea nitrogen, blood creatinine, uric acid, and cystatin C) in serum from control and arsenic poisoning populations and analyzed the relationship between the miR-191, cytokines, and renal dysfunction indicators. The results clearly show the alteration of miR-191 expression was significantly associated with arsenic-induced renal dysfunction. Overall, the association of miR-191, inflammatory response and renal dysfunction, is clearly supported by the current findings. In other words, miR-191 is involved in renal dysfunction in exposed populations by regulating inflammatory response caused by coal-burning arsenic. The study provides a scientific basis for further studies of the causes of the arsenic-induced renal dysfunction, the biological role of miR-191, and targeted prevention strategies.

Association and risk of five miRNAs with arsenic-induced multiorgan damage

Chronic exposure to arsenic remains a major environmental public health concern worldwide, affecting hundreds of millions of people. Arsenic-induced multiorgan damage and miRNA expression changes after arsenic exposure have been determined, but their associations and risks have not been fully examined. In this study, we measured the expression levels of five miRNAs in plasma from control and arsenic poisoned populations, and we analyzed the relationship between miRNAs and multiorgan damage. The results clearly show that the upregulation of miR-155 expression can increase the risk of arsenic induced skin damage (OR = 10.55; 95% CI: 6.02, 18.47); further, there is a link between the expression of miR-21 (OR = 11.84; 95% CI: 5.34, 26.28) and miR-145 (OR = 2.39; 95% CI: 1.61, 3.55) and liver damage, and miR-191 and kidney damage (OR = 3.65; 95% CI: 1.49, 8.93). In addition, we analyzed the diagnostic value of miRNAs associated with specific organ damage in arsenic-induced multiorgan damage. It was found that the miR-155 has a certain diagnostic value in arsenic-induced skin damage (AUC = 0.83), miR-21 and miR-145 have diagnostic value for liver damage (AUC = 0.80, 0.81) and miR-191 has diagnostic value for kidney damage (AUC = 0.83). This study provides the first comprehensive assessment of the association and risk of five miRNAs with arsenic-induced multiorgan damage. The study can provide a scientific basis for further understanding the causes of arsenic-induced multiorgan damage, identification of possible biological markers, and improvement of targeted prevention and control strategies.

Design of Scorodite@Fe3O4 Core–Shell Materials and the Fe3O4 Shell Prevents Leaching of Arsenic from Scorodite in Neutral and Alkaline Environments

In recent years, arsenic pollution has seriously harmed human health. Arsenic-containing waste should be treated to render it harmless and immobilized to form a stable, solid material. Scorodite (iron arsenate) is recognized as the best solid arsenic material in the world. It has the advantages of high arsenic content, good stability, and a low iron/arsenic molar ratio. However, scorodite can decompose and release arsenic in a neutral and alkaline environment. Ferroferric oxide (Fe3O4) is a common iron oxide that is insoluble in acid and alkali solutions. Coating a Fe3O4 shell that is acid- and alkali-resistant on the surface of scorodite crystals will improve the stability of the material. In this study, a scorodite@Fe3O4 core–shell structure material was synthesized. The synthesized core–shell material was detected by X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), Raman, and energy-dispersive X-ray spectroscopy (EDS) techniques, and the composition and structure were confirmed. The synthesis condition and forming process were analyzed. Long-term leaching tests were conducted to evaluate the stability of the synthesized scorodite@Fe3O4. The results indicate that the scorodite@Fe3O4 had excellent stability after 20 days of exposure to neutral and weakly alkaline solutions. The inert Fe3O4 shell could prevent the scorodite core from corrosion by the external solution. The scorodite@Fe3O4 core–shell structure material was suitable for the immobilization of arsenic and has potential application prospects for the treatment of arsenic-containing waste.

Predicting groundwater arsenic contamination: Regions at risk in highest populated state of India

Arsenic (As) contamination of groundwater is a public health concern, impacting the lives of approximately 100 million people in India. Chronic exposure to As significantly increases mortality due to the occurrence of several types of cancer, respiratory and cardiac diseases. Uttar Pradesh is a part of the middle Indo-Gangetic plains and has been found to be severely affected by As contamination of groundwater, as established by several small-scale studies. The current study incorporates a hybrid method based on a random forest ensemble algorithm and univariate feature selection using 1473 data points for predicting As in the region. Twenty direct/proxy predictor variables were considered to describe the geochemical environment, aquifer conditions and topography that are responsible for As enrichment in groundwater. The map of As predicted through the hybrid random forest ensemble model shows an overall accuracy of 84.67%. The hybrid random forest model performs better than the univariate, logistic, fuzzy, adaptive fuzzy and adaptive neuro fuzzy inference systems, which have been widely used for As prediction. The projected number of rural populations at risk due to high As exposure is 12% of the total population of the region, which accounts for 23.48 million people who are at risk. The predictive map provides insight for the regions where future testing campaigns and interventions for mitigation should be prioritized by policymakers.

Uptake of As(V) from Groundwater Using Fe-Mn Oxides Modified Kaolin Clay: Physicochemical Characterization and Adsorption Data Modeling

The present study evaluated the effectiveness of Fe-Mn modified kaolin clay in the uptake of As(V) from the solution. The elemental composition was characterized using X-ray fluorescence (XRF), while the surface morphology was characterized using Scanning Electron Microscopy (SEM). The surface area, pore diameter, and pore volume were determined using Brunauer, Emmett, and Teller (BET) and Barrett-Joyner-Halenda (BJH) techniques. The efficiency of the Arsenic removal of the adsorbent was evaluated using batch experiments. The results showed that the percentage of As(V) removal is optimum at acidic pH and decreased as the solution pH becomes more alkaline. The adsorption kinetics data fitted better to pseudo-second-order compared to the pseudo-first-order of reaction kinetics. The adsorption isotherm data fitted to the Langmuir isotherm model showing a maximum adsorption capacity of 2.44 mg/g, adsorbate concentration range of 1–30 mg/L, and adsorbent dosage of 0.4 g/100 mL. The value of ∆G° of the thermodynamic parameter was found to be negative, while ∆H° and ∆S° were found to be positive. For the regeneration-reuse study, the percentage of removal was found to be >85% after the sixth cycle of reuse. The results obtained from this study showed that Fe-Mn modified kaolin (FMK) is suitable for use in the uptake of As(V) from groundwater.

Hydrogeochemical controls on arsenic mobility in an arid inland basin, Southeast of Iran: The role of alkaline conditions and salt water intrusion

Elevated inorganic arsenic concentrations in groundwater has become a major public and environmental health concern in different parts of the world. Currently, As-contaminated groundwater issue in many countries and regions is a major topic for publications at global level. However, there are many regions worldwide where the problem has still not been resolved or fully understood due to inadequate hydrogeochemical investigations. Hence, this study evaluates for the first time the hydrogeochemical behavior of the arid and previously unexplored inland basin of Sirjan Plain, south east (SE) Iran, in order to assess the controlling factors which influence arsenic (As) mobility and its distribution through groundwater resources. Total inorganic arsenic concentration was measured using inductive-coupled plasma optical emission spectrometry (ICP-OES). Arsenic content in groundwater of this region ranged between 2.4 and 545.8 μg/L (mean value: 86.6 μg/L) and 50% of the samples exceeded the World Health Organization (WHO) guideline value of 10 μg/L in drinking water. Groundwater was mainly of Na-Cl type and alkaline due to silicate weathering, ion exchange and evaporation in arid conditions. Elevated As concentrations were generally observed under weakly alkaline to alkaline conditions (pH > 7.4). Multivariate statistical analysis including cluster analysis and bi-plot grouped As with pH and HCO₃ and demonstrated that the secondary minerals including oxyhydroxides of Fe are the main source of As in groundwater in this region. The desorption of As from these mineral phases occurs under alkaline conditions in oxidizing arid environments thereby leading to high levels of As in groundwater. Moreover, evaporation, ion exchange and saltwater intrusion were the secondary processes accelerating As release and its mobility in groundwater. Based on the results of this study, desorption of As from metal oxy-hydroxides surfaces under alkaline conditions, evaporation and intrusion of As-rich saline water are considered to be the major factors causing As enrichment in arid inland basins such as those in southeast Iran. This study proposes the regular monitoring and proper groundwater management practices to mitigate high levels of arsenic in groundwater and related drinking water wells of Sirjan Plain.

Effectiveness of Ferric, Ferrous, and Aluminum (Hydr)Oxide Coprecipitation to Treat Water Contaminated with Arsenate

Coprecipitation of Fe and Al (hydr)oxides has been considered a low-cost process to remove As from wastewater. Arsenate is the most stable form of As in aerobic environments such as surface water, soils, and sediments and can be removed from water through methods based on this process. Iron/aluminum molar ratios of 100:0, 80:20, and 60:40 were used to treat water contaminated with As at concentrations of 50 and 500 mg L. Aluminum, ferrous, and ferric sulfates were used to coprecipitate Al and Fe (hydr)oxides at high pH. Maghemite, magnetite, lepidocrocite, and goethite were detected in precipitates from Fe(II), whereas hematite and ferrihydrite were identified in Fe(III) treatments. Segregation of Al (hydr)oxides as gibbsite and bayerite as well as the Al isomorphic substitution in Fe (hydr)oxides were detected in the presence of Al. The precipitates were classified as nonhazardous according to the leaching test based on Brazilian Technical Standard NBR 10005. The presence of Al increased the stability of the sludge from Fe(II) treatments but did not affect the stability of precipitates from Fe(III) treatments. High efficiencies for As removal from water were obtained for all treatments, but concentrations of soluble As were, in general, lower for Fe(III) treatments especially, in the absence of Al. Treatments were efficient in reaching the threshold to effluent discharge (0.5 mg L), but only treatments with initially 50 mg L of As reached the threshold for drinking water (10 μg L).

Evaluating Geologic Sources of Arsenic in Well Water in Virginia (USA)

We investigated if geologic factors are linked to elevated arsenic (As) concentrations above 5 μg/L in well water in the state of Virginia, USA. Using geologic unit data mapped within GIS and two datasets of measured As concentrations in well water (one from public wells, the other from private wells), we evaluated occurrences of elevated As (above 5 μg/L) based on geologic unit. We also constructed a logistic regression model to examine statistical relationships between elevated As and geologic units. Two geologic units, including Triassic-aged sedimentary rocks and Triassic-Jurassic intrusives of the Culpeper Basin in north-central Virginia, had higher occurrences of elevated As in well water than other geologic units in Virginia. Model results support these patterns, showing a higher probability for As occurrence above 5 μg/L in well water in these two units. Due to the lack of observations (<5%) having elevated As concentrations in our data set, our model cannot be used to predict As concentrations in other parts of the state. However, our results are useful for identifying areas of Virginia, defined by underlying geology, that are more likely to have elevated As concentrations in well water. Due to the ease of obtaining publicly available data and the accessibility of GIS, this study approach can be applied to other areas with existing datasets of As concentrations in well water and accessible data on geology.

Nanofibers of resorcinol-formaldehyde for effective adsorption of As (III) ions from mimicked effluents

In the present study, the core-shell structured RF/PVA nanofibers have been developed and used for the adsorption of As³⁺ ions from the mimicked liquid effluents. Efficient-facile fabrication of the structured nanofibers (300-417 nm diameter) was accomplished using facile electrospinning technique. Chi parameter (χ = 25.56) and free energy of mixing (E_mix = 17.19 kcal/mol) calculated via molecular dynamics simulations depicted compatibility of the polymeric system resulting supermolecular core-shell nanofibers, whose adsorption results were also supported by the FE-SEM, FT-IR, and UV-VIS spectroscopy analysis. The adsorption analysis was performed using both linear and non-linear regression methods, for kinetic models and adsorption isotherms. The developed nanofibers demonstrated an adsorption capacity of 11.09 mg/g at a pH of 7, and an adsorption efficiency of 97.46% on protracted exposure, which is even adaptable at high temperatures with 93.1% reclamation. FE-SEM analysis and FT-IR spectra confirm the adsorption of As (III) ions on RF/PVA nanofibers and the presence of embedded hydrophilic oxygen sites for metal ion adsorption. The developed RF/PVA nanofibers demonstrate scalability in fabrication, low-cost, recycling, and less solid waste generation, depicting the large-scale applicability in removing arsenic ions from effluent waste. Graphical Abstract ᅟ.

Synthesis of mesoporous bismuth-impregnated aluminum oxide for arsenic removal: Adsorption mechanism study and application to a lab-scale column

High mobility and toxicity of arsenic [As (III)] limit its removal from an aquatic environment and pose a threat to human health. In this work, batch adsorption experiments were conducted to investigate the adsorption capacity of bismuth-impregnated aluminum oxide (BiAl). Continuous application of As (III) removal was achieved via a lab-scale column reactor. Bismuth impregnation decreased the specific surface area of aluminum oxide and affected its pore size distribution. However, because of its abundant and well-proportioned mesoporous character, it also enhanced its adsorption capacity through the surface complexation of As (III). Batch adsorption experiments demonstrated a suitable Freundlich model and a fitted pseudo-second-kinetic model for As (III) adsorption. The main mechanism was chemisorption with both bismuth and aluminum atoms; however, physisorption also contributed to arsenic adsorption at the initial stage of the reaction. The Adams-Bohart model better described the breakthrough curves than the Thomas model. BiAl exhibited efficient As (III) adsorption over a wide pH range and could be applied to As (III) removal from wastewater. A high As (III) removal efficiency (91.6%) was obtained at an initial As (III) concentration of 5 mg L^-1 at a flow rate of 1 mL min^-1. This study indicates the potential for the practical application of BiAl in As (III) removal.

Challenges to Sustainable Safe Drinking Water: A Case Study of Water Quality and Use across Seasons in Rural Communities in Limpopo Province, South Africa

Consumption of microbial-contaminated water can result in diarrheal illnesses and enteropathy with the heaviest impact upon children below the age of five. We aimed to provide a comprehensive analysis of water quality in a low-resource setting in Limpopo province, South Africa. Surveys were conducted in 405 households in rural communities of Limpopo province to determine their water-use practices, perceptions of water quality, and household water-treatment methods. Drinking water samples were tested from households for microbiological contamination. Water from potential natural sources were tested for physicochemical and microbiological quality in the dry and wet seasons. Most households had their primary water source piped into their yard or used an intermittent public tap. Approximately one third of caregivers perceived that they could get sick from drinking water. All natural water sources tested positive for fecal contamination at some point during each season. The treated municipal supply never tested positive for fecal contamination; however, the treated system does not reach all residents in the valley; furthermore, frequent shutdowns of the treatment systems and intermittent distribution make the treated water unreliable. The increased water quantity in the wet season correlates with increased treated water from municipal taps and a decrease in the average contaminant levels in household water. This research suggests that wet season increases in water quantity result in more treated water in the region and that is reflected in residents' water-use practices.

The problem of identifying arsenic anomalies in the basin of Sahand dam through risk-based 'soft modelling'

An investigation is undertaken to identify arsenic anomalies at the complex of Sahand dam, East Azerbaijan, northwest Iran. The complex acts as a system, in which the impounding reservoir catalyses system components related to Origin-Source-Pathways-Receptor-Consequence (OSPRC) viewed as a risk system. This 'conceptual framework' overlays a 'perceptual model' of the physical system, in which arsenic with geogenic origins diffused into the formations through extensive fractures swept through the region during the Miocene era. Impacts of arsenic anomalies were local until the provision of the impounding reservoir in the last 10years, which transformed it into active system-wide risk exposures. The paper uses existing technique of: statistical, graphical, multivariate analysis, geological survey and isotopic study, but these often seem ad hoc and without common knowledgebase. Risk analysis approaches are sought to treat existing fragmentation in practices of identifying and mitigating arsenic anomalies. The paper contributes towards next generation best practice through: (i) transferring and extending knowledge on the OSPRC framework; (ii) introducing 'OSPRC cells' to capture unique idiosyncrasies at each cell; and (iii) suggesting a 'soft modelling' procedure based on assembling knowledgebase of existing techniques with partially converging and partially diverging information levels, where knowledgebase invokes model equations with increasing resolutions. The data samples from the study area for the period of 2002-12 supports the study and indicates the following 'risk cells' for the study area: (i) local arsenic risk exposures at south of the reservoir, (ii) system-wide arsenic risks at its north; and (iii) system-wide arsenic risk exposures within the reservoir even after dilution.