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An informatics approach to surface data could allow geologists and environmental scientists to identify regions of the world where people are at high risk of exposure to arsenic in their drinking water without the need for widespread sampling to be undertaken. SpectroscopyNOW has reported previously on an insidious environmental disaster that is affecting millions of people across Asia, and elsewhere, through the contamination of drinking water with arsenic. Contamination occurs when bedrock containing insoluble arsenic salts is exposed to the air by drought or excessive drawing of water for irrigation. The oxidised arsenic salts are rendered soluble and when the rains next arrive, they quickly dissolve in the groundwater supply. The solubilised arsenic causes severe and chronic toxicity problems as well as increasing the risk of cancer in those exposed to the element over the long-term. Identiying areas at risk of arsenic contamination without costly sampling programmes is difficult. Now, researchers at Eawag, the Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, in Switzerland, have developed a new model that allows vulnerable areas to be pinpointed using existing data on geology and soil properties. This model has already allowed the researchers to identify new high-risk areas in regions where groundwater studies had not previously been carried out, including Myanmar and on Sumatra. The model confirms predictions about at-risk regions made by Peter Ravenscroft of the Geography Department at the University of Cambridge and colleagues who presented a predictive model of arsenic occurrence in the tropics previously. The team, led by geologist Lenny Winkel and environmental chemist Michael Berg, compiled existing geological data from Bangladesh, Myanmar, Thailand, Cambodia, Vietnam and Sumatra (Indonesia) to produce a uniformly classified map. Writing in Nature Geoscience, the researchers describe how a model derived from this data based only on surface sediments and soil properties could offer an accurate prediction as to the chemical and physical conditions of the underlying groundwater. They then determined the statistical relationship between thirty surface parameters (geological, hydrological and climate data) and arsenic concentrations, and then incorporated the eight most relevant variables into a logistic regression model. They demonstrated that young river deposits with organic rich sediments proved to be indicators of groundwater arsenic contamination, for instance. This is apparent from the maps in which the probabilities calculated for elevated arsenic concentrations are presented in a graphical form. The next step was to verify the model using more than 1750 available groundwater data points from the Bengal, Mekong and Red River deltas. The data matched their predictions well, although Berg points out that even in "low risk" areas arsenic may not be entirely absent from groundwater. "There is no such thing as zero risk," Berg explains. He suggests that a refined version of the model that includes additional data from deeper rock strata should be possible but will not substitute for conventional analysis of water samples. Nevertheless, "Thanks to the maps, governments, local authorities or aid agencies can tell very quickly where it might be problematic to sink a well." The researchers have identified several new high-risk areas on Sumatra and in Myanmar. The new model is of particular interest for regions where no groundwater measurement data are yet available. The maps also indicate an increased risk of elevated arsenic concentrations in groundwater in the Irrawaddy delta (Myanmar) and along the Chao Phraya river north of Bangkok (Thailand). The latest findings from Southeast Asia are part of the Water Resource Quality (WRQ) project, an Eawag research programme studying the occurrence of geogenic contaminants in groundwater worldwide. Other contaminants being studied in this programme include fluoride, selenium and uranium. In parallel work, other researchers are developing methods to enable those populations affected by such contaminants to treat their contaminated water. Related links:
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Mapping arsenic exposure probabilities in Asia |
