Sub-grid Variability and Its Impact on Air Quality Exposure Assessment

Abstract

AbstractLong term exposure estimates over large areas can be made using a combination of air quality models and population density data. However, the grid resolution of such models is often limited to 25–50 km and there may be a significant level of unresolved variability within the grids that will impact on the exposure estimates. In this paper the sub-grid variability is assessed using air quality monitoring (AirBase) and population data, concentrating on the covariance of concentration and population, which is the defining term in estimating sub-grid population exposure. The error that occurs when calculating the urban background exposure is assessed. The assessment shows that the error made in the exposure calculation for all of Europe is small for typical CTM resolutions of 50 km. The error is largest for NO2, where the average European urban background exposure is underestimated by 16%. Particulate matter is also underestimated, but only by 6%. Conversely, estimates of ozone exposure (SOMO35) are overestimated by a factor of 15%.KeywordsSub-grid variabilityExposureAir quality modelling