A study of spatial resolution in pollution exposure modelling

BACKGROUND This study is part of several ongoing projects concerning epidemiological research into the effects on health of exposure to air pollutants in the region of Scania, southern Sweden. The aim is to investigate the optimal spatial resolution, with respect to temporal resolution, for a pollutant database of NOx-values which will be used mainly for epidemiological studies with durations of days, weeks or longer periods. The fact that a pollutant database has a fixed spatial resolution makes the choice critical for the future use of the database. RESULTS The results from the study showed that the accuracy between the modelled concentrations of the reference grid with high spatial resolution (100 m), denoted the fine grid, and the coarser grids (200, 400, 800 and 1600 meters) improved with increasing spatial resolution. When the pollutant values were aggregated in time (from hours to days and weeks) the disagreement between the fine grid and the coarser grids were significantly reduced. The results also illustrate a considerable difference in optimal spatial resolution depending on the characteristic of the study area (rural or urban areas). To estimate the accuracy of the modelled values comparison were made with measured NOx values. The mean difference between the modelled and the measured value were 0.6 mug/m3 and the standard deviation 5.9 mug/m3 for the daily difference. CONCLUSION The choice of spatial resolution should not considerably deteriorate the accuracy of the modelled NOx values. Considering the comparison between modelled and measured values we estimate that an error due to coarse resolution greater than 1 mug/m3 is inadvisable if a time resolution of one day is used. Based on the study of different spatial resolutions we conclude that for urban areas a spatial resolution of 200-400 m is suitable; and for rural areas the spatial resolution could be coarser (about 1600 m). This implies that we should develop a pollutant database that allows different spatial resolution for urban and rural areas.