Sub-grid variability and its impact on European wide air quality exposure assessment

Abstract

Estimates of population exposure to air pollution on the European scale are required for policy development and health impact assessment. Long term exposure estimates of this type can be made using spatially distributed air quality and population density data. Gridded chemical transport models (CTMs) are often used for this purpose, however the grid resolution of CTMs that cover entire continents is usually limited to 25–100 km and there may be a significant level of unresolved variability within the grids that will impact on the exposure estimates. In this paper sub-grid variability and its impact on long term exposure estimates is assessed by investigating the covariance of concentration and population, which is shown to be the defining term in estimating the average sub-grid population exposure. A parameterisation of the sub-grid covariance is described, based on the sub-grid covariance of other proxy data, and this is applied using EMEP model results for all of Europe. The study shows that the error made in the exposure calculations for all of Europe is significant for the typical CTM resolution of 50 km. The error is largest for NO 2 , where the average European urban background exposure using CTMs is underestimated by 44 ± 4%. Particulate matter with a diameter less than 10 μm (PM 10 ) is also underestimated, but only by 15 ± 4%. Calculation of the sub-grid covariance for the ozone health indicator SOMO35 (Sum of Ozone 8 h running Means Over 35 ppb) was not carried out for all of Europe as the sub-grid parameterisation was considered too uncertain for useful application. However, estimates based on observations alone show that population weighted SOMO35 concentrations are overestimated by around 13% when using model grid resolutions of 50 km. ► We investigate the impact of sub-grid variability on air quality exposure. ► We use the sub-grid covariance of concentration and population for this. ► We use observed concentrations for the assessment. ► We develop a parameterisation that makes use of high resolution proxy data. ► We apply this to all of Europe.