[ Back to EurekAlert! ] Public release date: 22-Aug-2013
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Contact: Natasha Pinol
npinol@aaas.org
202-326-6440
American Association for the Advancement of Science

New risk model highlights arsenic risk in China's groundwater

This news release is available in French, Spanish, German and Chinese.

AUDIO: In this podcast, researcher Luis Rodriguez-Lado is interviewed about his work to build a model used to predict the risk of groundwater contamination by arsenic in China.

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A new model to predict the risk of contaminants in groundwater will save those who use it significant time and money, highlighting drinking water quality issues in the process. This model is currently being implemented in China to determine the spread of groundwater contamination by arsenic.

"Arsenic poisoning due to the use of contaminated drinking water is a major health problem in many parts of the world," explained Dr. Luis Rodriguez-Lado, a researcher on the model design team.

Cases of chronic arsenic poisoning are particularly well-known in Southeast Asian countries like Bangladesh. And since the 1990s, reports continue to reveal new regions of the globe, including Central Europe, South America, Mongolia and some parts of the United States, in which groundwater is also contaminated with this chemical.

In China, the focus of efforts by Rodriguez-Lado and his team, arsenic poisoning from contaminated groundwater was first diagnosed in the late 1970s. This happened in a part of China that is very arid, and where the population is extremely dependent on deep groundwater aquifers (bodies of rock that contain water) for the water they drink. In these aquifers, sedimentary deposits from volcanic rocks and other sources can contain naturally occurring arsenic in a readily available form that is dangerous for consumption.

IMAGE: This is a partial view of the predictive map of high levels of arsenic in groundwater resources in China (blue, low probability; red, high probability; width, 600 kilometers; resolution, 1...

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Long-term exposure to arsenic is a major health risk. It is shown to cause hyperpigmentation of the skin, disorders of liver and kidney function, and various types of cancer.

In 1994, as Chinese people continued to report these symptoms, the country's government declared arsenic poisoning an endemic disease and created a committee of experts to evaluate the situation. The Chinese Ministry of Health conducted a massive screening campaign to sample individual wells. Called the "Chinese National Survey Program", and lasting from 2001 to 2005, this effort tested about 445,000 wells for arsenic contamination in approximately 12% of China's counties. Since that time, the screening of wells has continued, costing the government millions. China is so massive, however, that it could take decades to survey the counties that remain.

It was in this setting, and recognizing this problem that Rodriguez-Lado and his colleagues began to think about building a predictive tool for groundwater contamination.

Their idea was also motivated by a map of locations of known arsenic contamination released by the World Bank in 2005. "Many areas in this map were blank," Rodriguez-Lado explained. "We thought that instead of being surprised by new occurrences of arsenic contamination, it would be very useful if we could develop a model to predict regions where contamination was possible."

His team's desire to build a predictive model coincided with the growth of freely available geospatial information -- about wetness, soil salinity and topography, for example. Because this information can serve as a proxy for where arsenic contamination is likely to be high, the team was able to use it to make predictions about arsenic contamination in areas they did not visit.

Their model combined this geospatial information with data from the Chinese National Survey Program. Using population data and the World Health Organization's standard threshold for arsenic concentration (10 g per liter), which, as of recently, is also the Chinese standard, they categorized areas of China as low-risk and high-risk.

The researchers note that, when talking about risky levels of arsenic, there is still some debate.

"We know that the higher the concentration of arsenic, the more quickly the effects will appear," said Rodriguez-Lado, "but effects highly depend on factors such as age, nutritional status, and general health."

Their results indicated that an estimated 19,580,000 people in China live in high-risk areas, mainly in Xinjiang, Inner Mongolia, Henan, Shandong and Jiangsu provinces.

Critically, the model identified known areas of high-risk and also new areas, including provinces in the North China Plain and the central part of the province of Sichuan. "In these locations," Rodriguez-Lado explained, "arsenic risk is coincident with the presence of a high population density; thus groundwater here should be tested for arsenic as soon as possible."

This model is not limited in use to China alone. "It may also be appropriate for use in other parts of the world," he continued, citing arid regions such as the Southwestern United States, where high arsenic concentrations have been reported. The model isn't limited to arsenic either. "In our opinion, predictive modeling is a promising technique for the development of risk maps for any kind of pollutant."

The authors emphasize that while their approach has several advantages over traditional groundwater screening methods, it is not a substitute for these methods. "The variability of arsenic concentrations is very high at short distances and our predictive model has a limited spatial resolution of one kilometer squared. This means that the screening methods implemented by the Chinese authorities at local scales are still necessary."

Rodriguez-Lado and his colleagues hope that, in China, their work can be used to support the well monitoring program currently in place, highlighting areas of particular risk to authorities.

"On a global scale," he continued "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, especially in developing countries."

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This study was funded by the Sino-Swiss Science and Technology Cooperation Program of the Swiss State Secretariat for Education and Research and by the External Cooperation Program of the Chinese Academy of Sciences.

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