New Predictive Method Pinpoints Arsenic Hotspots
European and Chinese researchers have built a model to predict the presence of arsenic groundwater contamination in China and elsewhere where millions are at risk, according to work published Thursday.
The technique can be applied to any region where the problem affects large populations and can also be applied to other pollutants, according to research that appeared in the U.S. journal Science.
Moreover, the method promises to save officials who track the dangerous chemical time and money.
Arsenic poisoning has been widely documented in Southeast Asian countries such as Bangladesh.
It was first found in China in 1970 and authorities there declared it an endemic disease in 1994.
The new model builds off a massive and costly screening campaign conducted by the Chinese government, which tested individual wells from 2001 to 2005.
The predictive model created by researcher Luis Rodriguez-Lado and his team combines the well-screening data with geospatial information on wetness, soil salinity and topography.
The model then takes into account population data and a threshold for arsenic concentration in order to classify areas as high-risk or low-risk.
The researchers emphasized that while the predictive model is more efficient and less costly than screening individual wells, it is not a substitute. Groundwater must still be tested on-location.
The researchers have pinpointed areas already identified as high-risk zones in addition to new locations including some North China Plain provinces and the central part of Sichuan province.
Some 19.6 million Chinese people are at risk of consuming arsenic-contaminated groundwater beyond the maximum threshold set by the World Health Organization, the researchers estimate.
Long-term exposure to arsenic can cause skin hyperpigmentation, liver and kidney disorders, and cancer.
Groundwater contamination occurs when arsenic leaches into deep aquifers via sedimentary deposits and volcanic rock far underground, making the water dangerous for consumption.
In addition to arsenic, the researchers believe their model can be applied to detect other pollutants as well.
"We hope our work can serve to highlight that drinking water quality is an important issue, and that this kind of study can help to implement prevention policies to improve the wellness of millions of people," Rodriguez-Lado said.