News: News Archives
Model Highlights Arsenic Risk in China's Groundwater
Quiang Zhang (left) and Jianbo Shi of the Research Center for Eco-Environmental Science, Beijing sample a well for arsenic contamination. [Courtesy of A. Johnson, Eawag, Switzerland]
Researchers have designed a model to predict the risk of contaminants in groundwater that, in addition to highlighting critical drinking water quality issues, will save those who use it significant time and money. The 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 Luis Rodriguez-Lado, a model design team member and a postdoctoral researcher at the University of Santiago de Compostela. He and his colleagues reported on their model in the 23 August issue of the journal Science.
Cases of chronic arsenic poisoning are particularly well-known in Southeast Asian countries like Bangladesh. 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, where groundwater is also contaminated with arsenic.
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 (water-bearing bodies of rock) for the water they drink. In these aquifers, sedimentary deposits can harbor naturally occurring arsenic. Under certain atmospheric conditions, it is released from the sediment in a dissolved form that moves with and contaminates drinking water.
Long-term exposure to arsenic is a major health risk. It can cause hyperpigmentation of the skin, disorders of liver and kidney, 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," it ran from 2001 to 2005 and tested about 445,000 wells.
"Huge financial and human resources were invested during this project," said team member Guifan Sun, a dean and professor at the School of Public Health at China Medical University. Yet, the project only provided data for approximately 12 percent of China's counties. "A faster method to predict the arsenic-contaminated areas was desperately needed," Sun said.
It was in this setting that Sun, Rodriguez-Lado and colleagues began thinking about building a predictive tool for groundwater contamination.
Their idea — a collaboration between the Swiss Federal Institute of Aquatic Science and Technology (known by its Swiss acronym Eawag) and China Medical University — was also motivated by a map released by the World Bank released in 2005, showing locations of known arsenic contamination. "Many areas in this map were blank," explained Rodriguez-Lado. "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. 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 geospatial information with data from the Chinese National Survey Program and population information. Using the World Health Organization's safety guidelines for arsenic concentration in drinking water (10 micrograms per liter), the team categorized areas of China as low- and high-risk.
When talking about risky levels of arsenic, they note, 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 depend highly on factors such as age and general health."
Their results indicate that an estimated 19,580,000 people in China live in high-risk areas, mainly in Xinjiang, Inner Mongolia, Henan, Shandong and Jiangsu provinces.
Annette Johnson, Guifan Sun, Luis Rodriguez-Lado and Michael Berg discuss their findings at a Science press conference in Duebendorf, Switzerland. [AAAS/Natasha D. Pinol]
"The maps from our model are the first step in mitigating the ongoing arsenic contamination problem in these regions," emphasized Michael Berg, senior researcher at Eawag, at a 22 August press briefing at Eawag in Duebendorf, Switzerland.
Critically, the model identified areas not known to be at high-risk from arsenic contamination, 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."
Providing direction with respect to where to allocate resources for testing is a key use of this new model. "The risk maps generated using our model provide visual images that can be easily understood and that we hope decision makers will utilize," said Annette Johnson, team member and a senior Eawag scientist.
This model is not limited in use to China alone. "It may also be appropriate for use in other parts of the world," said Rodriguez-Lado, citing arid regions such as the southwestern United States where high arsenic concentrations have been reported.
The model isn't restricted to arsenic either. "In our opinion, predictive modeling is a promising technique for the development of risk maps for any kind of pollutant," Rodriguez-Lado said.
Fluoride is another naturally-occurring contaminant of global significance (approximately 200 million people are exposed to high levels of fluoride, long-term exposure to which can lead to health issues). "We have already developed a global predictive model for fluoride using similar methodology," Johnson explained.
The authors emphasize that while their predictive modeling 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," said Rodriguez-Lado. "This means that the screening methods implemented by the Chinese authorities at the local scale are still necessary."
The research team hopes 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," Rodriguez-Lado 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."
22 August 2013