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Can arsenic occurrence rates in bedrock aquifers be predicted?

ABSTRACT: A high percentage (31%) of groundwater samples from bedrock aquifers in the greater Augusta area, Maine was found to contain greater than 10 ?g L(-1) of arsenic. Elevated arsenic concentrations are associated with bedrock geology, and more frequently observed in samples with high pH, low dissolved oxygen, and low nitrate. These associations were quantitatively compared by statistical analysis. Stepwise logistic regression models using bedrock geology and/or water chemistry parameters are developed and tested with external data sets to explore the feasibility of predicting groundwater arsenic occurrence rates (the percentages of arsenic concentrations higher than 10 ?g L(-1)) in bedrock aquifers. Despite the under-prediction of high arsenic occurrence rates, models including groundwater geochemistry parameters predict arsenic occurrence rates better than those with bedrock geology only. Such simple models with very few parameters can be applied to obtain a preliminary arsenic risk assessment in bedrock aquifers at local to intermediate scales at other localities with similar geology.

SUBMITTER: Yang Q

PROVIDER: S-EPMC3307801 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Can arsenic occurrence rates in bedrock aquifers be predicted?

Yang Qiang Q Jung Hun Bok HB Marvinney Robert G RG Culbertson Charles W CW Zheng Yan Y

Environmental science & technology 20120209 4

A high percentage (31%) of groundwater samples from bedrock aquifers in the greater Augusta area, Maine was found to contain greater than 10 μg L(-1) of arsenic. Elevated arsenic concentrations are associated with bedrock geology, and more frequently observed in samples with high pH, low dissolved oxygen, and low nitrate. These associations were quantitatively compared by statistical analysis. Stepwise logistic regression models using bedrock geology and/or water chemistry parameters are develop ...[more]

PMID: 22260208

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Project description:Long-term exposure to trace levels of arsenic (As) in shallow groundwater used for drinking and irrigation puts millions of people at risk of chronic disease. Although microbial processes are implicated in mobilizing arsenic from aquifer sediments into groundwater, the precise mechanism remains ambiguous. The goal of this work was to target, for the first time, a comprehensive suite of state-of-the-art molecular techniques in order to better constrain the relationship between indigenous microbial communities and the iron and arsenic mineral phases present in sediments at two well-characterized arsenic-impacted aquifers in Bangladesh. At both sites, arsenate [As(V)] was the major species of As present in sediments at depths with low aqueous As concentrations, while most sediment As was arsenite [As(III)] at depths with elevated aqueous As concentrations. This is consistent with a role for the microbial As(V) reduction in mobilizing arsenic. 16S rRNA gene analysis indicates that the arsenic-rich sediments were colonized by diverse bacterial communities implicated in both dissimilatory Fe(III) and As(V) reduction, while the correlation analyses involved phylogenetic groups not normally associated with As mobilization. Findings suggest that direct As redox transformations are central to arsenic fate and transport and that there is a residual reactive pool of both As(V) and Fe(III) in deeper sediments that could be released by microbial respiration in response to hydrologic perturbation, such as increased groundwater pumping that introduces reactive organic carbon to depth.IMPORTANCE The consumption of arsenic in waters collected from tube wells threatens the lives of millions worldwide and is particularly acute in the floodplains and deltas of southern Asia. The cause of arsenic mobilization from natural sediments within these aquifers to groundwater is complex, with recent studies suggesting that sediment-dwelling microorganisms may be the cause. In the absence of oxygen at depth, specialist bacteria are thought able to use metals within the sediments to support their metabolism. Via these processes, arsenic-contaminated iron minerals are transformed, resulting in the release of arsenic into the aquifer waters. Focusing on a field site in Bangladesh, a comprehensive, multidisciplinary study using state-of-the-art geological and microbiological techniques has helped better understand the microbes that are present naturally in a high-arsenic aquifer and how they may transform the chemistry of the sediment to potentially lethal effect.

Project description:Approximately one million people in the UK are served by private water supplies (PWS) where main municipal water supply system connection is not practical or where PWS is the preferred option. Chronic exposure to contaminants in PWS may have adverse effects on health. South West England is an area with elevated arsenic concentrations in groundwater and over 9000 domestic dwellings here are supplied by PWS. There remains uncertainty as to the extent of the population exposed to arsenic (As), and the factors predicting such exposure. We describe a hazard assessment model based on simplified geology with the potential to predict exposure to As in PWS. Households with a recorded PWS in Cornwall were recruited to take part in a water sampling programme from 2011 to 2013. Bedrock geologies were aggregated and classified into nine Simplified Bedrock Geological Categories (SBGC), plus a cross-cutting "mineralized" area. PWS were sampled by random selection within SBGCs and some 508 households volunteered for the study. Transformations of the data were explored to estimate the distribution of As concentrations for PWS by SBGC. Using the distribution per SBGC, we predict the proportion of dwellings that would be affected by high concentrations and rank the geologies according to hazard. Within most SBGCs, As concentrations were found to have log-normal distributions. Across these areas, the proportion of dwellings predicted to have drinking water over the prescribed concentration value (PCV) for As ranged from 0% to 20%. From these results, a pilot predictive model was developed calculating the proportion of PWS above the PCV for As and hazard ranking supports local decision making and prioritization. With further development and testing, this can help local authorities predict the number of dwellings that might fail the PCV for As, based on bedrock geology. The model presented here for Cornwall could be applied in areas with similar geologies. Application of the method requires independent validation and further groundwater-derived PWS sampling on other geological formations.

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Can arsenic occurrence rates in bedrock aquifers be predicted?

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Can arsenic occurrence rates in bedrock aquifers be predicted?

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