Mapping of background air pollution at a fine spatial scale across the European Union

Sci Total Environ. 2009 Mar 1;407(6):1852-67. doi: 10.1016/j.scitotenv.2008.11.048. Epub 2009 Jan 18.


Background: There is a need to understand much more about the geographic variation of air pollutants. This requires the ability to extrapolate from monitoring stations to unsampled locations. The aim was to assess methods to develop accurate and high resolution maps of background air pollution across the EU.

Methods: We compared the validity of ordinary kriging, universal kriging and regression mapping in developing EU-wide maps of air pollution on a 1x1 km resolution. Predictions were made for the year 2001 for nitrogen dioxide (NO(2)), fine particles <10 microm (PM(10)), ozone (O(3)), sulphur dioxide (SO(2)) and carbon monoxide (CO) using routine monitoring data in Airbase. Predictor variables from EU-wide databases were land use, road traffic, population density, meteorology, altitude, topography and distance to sea. Models were developed for the global, rural and urban scale separately. The best method to model concentrations was selected on the basis of predefined performance measures (R(2), Root Mean Square Error (RMSE)).

Results: For NO(2), PM(10) and O(3) universal kriging performed better than regression mapping and ordinary kriging. Validation of the final universal kriging estimates with results from all validation sites gave R(2)-values and RMSE-values of 0.61 and 6.73 microg/m(3) for NO(2); 0.45 and 5.19 microg/m(3) for PM(10); and 0.70 and 7.69 microg/m(3) for O(3). For SO(2) and CO none of the three methods was able to provide a satisfactory prediction.

Conclusion: Reasonable prediction models were developed for NO(2), PM(10) and O(3) on an EU-wide scale. Our study illustrates that it is possible to develop detailed maps of background air pollution using EU-wide databases.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Air Pollutants / analysis*
  • Data Interpretation, Statistical
  • Environmental Monitoring / methods
  • European Union
  • Geographic Information Systems
  • Humans
  • Models, Statistical*
  • Nitrogen Dioxide / analysis
  • Ozone / analysis
  • Particulate Matter / analysis
  • Regression Analysis


  • Air Pollutants
  • Particulate Matter
  • Ozone
  • Nitrogen Dioxide