Risk Maps

The regional or national assessment of groundwater quality is vital when planning to exploit groundwater for drinking purposes. Especially in developing countries, detailed surveys on groundwater quality are often lacking or incomplete. Therefore, there is a demand for tools capable of assessing groundwater quality on larger scales, for example to identify hot-spots for geogenic contamination.

Using GIS methods, it is possible to model the probability of groundwater being contaminated by arsenic or fluoride on large scales (regional and global).

This approach combines raster data containing layers on geology, hydrology, soil types, climate and land use with point measurements of fluoride or arsenic in groundwater to investigate the possible relationship between these parameters.

The results are depicted as risk maps showing the probability of fluoride/arsenic contamination in a certain region.


Dr. Michael BergHead of DepartmentTel. +41 58 765 5078Send Mail
Dr. Michael BergHead of DepartmentTel. +41 58 765 5078Send Mail

In order to make a preliminary assessment of the risk of geogenically contaminated groundwater, we need to identify hot-spots for contamination.

China arsenic probability map
Rodriguez-Lado et al. 2013
Chinese population at risk of drinking arsenic-contaminated water
Rodriguez-Lado et al. 2013
Global fluoride probability map
Amini et al. 2008a
Global arsenic probability map
Amini et al. 2008b
South-East Asia arsenic probability map
Winkel et al. 2008

REMARC - Risk Maps of Arsenic Contamination in Groundwaters of China

Research Background

Around the world arsenic-polluted groundwater, contaminated by natural geogenic sources, is used for drinking water and irrigation purposes. In China it is estimated that over 5 million people are affected to date and arsenicosis has been identified in 9 provinces. Long-term exposure to arsenic can affect human health and is considered to be a significant environmental cause of cancer. Exposure to high levels of arsenic in drinking water has been attributed to the gradual improvement of living standards in rural China. Since the economic reforms began in 1978, many peasants have been able to afford to drill wells 20-30m deep fitted with hand pumps in their homes rather than using microbially contaminated saline, fluoride rich shallow-wells or surface water. The capability of predicting arsenic contamination will greatly simplify the task of identifying arsenic-contaminated groundwaters.

Our Aim

The aim of the research project is to obtain an in-depth understanding of the causes of groundwater contamination with arsenic in the arid regions of China and to develop risk maps that can be used both to rationalise the identification of risk areas and to improve on global arsenic risk maps.

Our Methods

We combine georeferenced groundwater field observations provided by our partners from China with a number of environmental auxiliary variables to understand the mechanisms that underlie the release of arsenic to develop a valuable risk model for China. We compiled up to 26 different environmental auxiliary variables in the form of raster maps at both 1 and 5 Km resolution. They include climatic and topographic variables, satellite images, gravity maps and hydrological and geological information. We use Geographic Information Systems (GIS) coupled to a statistical programming environment (R) to create models explaining the distribution of high arsenic concentrations in groundwaters of the northern provinces of China. At present, field information for the provinces of Inner Mongolia, Gansu and Shanxi has been compiled. These models will allow us to understand the geochemical and hydrological processes that control arsenic mobility in arid regions.

The algorithms already implemented are based on binary models that classify the areas in "at risk"/"not at risk" based on an arsenic threshold pre-defined by the user. By default, this threshold has been set to 10 ppb. At present, the algorithms implemented are:

  • Logistic Regression
  • Point-to-point metrics based on the DOMAIN algorithm
  • Ecological Niche Factor Analysis
  • Random Forests


Rodríguez-Lado, L.; Sun, G.; Berg, M.; Zhang, Q.; Xue, H.; Zheng, Q.; Johnson, C. A. (2013) Groundwater arsenic contamination throughout China, Science, 341(6148), 866-868, doi:10.1126/science.1237484, Institutional Repository
Amini, M.; Mueller, K.; Abbaspour, K. C.; Rosenberg, T.; Afyuni, M.; Møller, K. N.; Sarr, M.; Johnson, C. A. (2008) Statistical modeling of global geogenic fluoride contamination in groundwaters, Environmental Science and Technology, 42(10), 3662-3668, doi:10.1021/es071958y, Institutional Repository
Amini, M.; Abbaspour, K. C.; Berg, M.; Winkel, L.; Hug, S. J.; Hoehn, E.; Yang, H.; Johnson, C. A. (2008) Statistical modeling of global geogenic arsenic contamination in groundwater, Environmental Science and Technology, 42(10), 3669-3675, doi:10.1021/es702859e, Institutional Repository
Winkel, L.; Berg, M.; Amini, M.; Hug, S. J.; Johnson, C. A. (2008) Predicting groundwater arsenic contamination in Southeast Asia from surface parameters, Nature Geoscience, 1, 536-542, doi:10.1038/ngeo254, Institutional Repository