Abstract
The estimation of human exposure to air pollution underlies well-known methodological challenges. Two of the major challenges are (1) the application of population activity and (2) the consideration of outdoor pollutant concentrations infiltrating indoor environments. These two aspects are frequently ignored in current exposure assessments at regional scales and introduce BIAS, which leads to non-representative exposure estimates.
Consequently, in this study we developed a method for regional exposure estimates by taking into account population activity, as well as infiltration of air pollutants to indoor environments. Therefore, we applied the time-microenvironment-activity concept in combination with different datasets for spatial and temporal distribution of the European population. The applied datasets are mainly JRC ENACT datasets for day and night time populations for the whole of Europe, Copernicus Monitoring Service land use information, the GHSL data package 2022 and Eurostat population statistics. The result is a framework that allows for the whole of Europe to create grids of population activity in different microenvironments with a spatial resolution of 1x1 km2 and a temporal resolution of 1 hour. Additionally, each microenvironment is assigned to seasonal dependent literature based outdoor to indoor infilatration factors. The resulting dynamic population grids can be applied to mapped air pollutant concentrations derived with any air quality model, for any European domain or period.
Finally, we applied (1) the developed dynamic exposure model and (2) a static residential addresses dataset to air pollutant concentrations to regions in Europe and compared the resulting exposure estimates. The results show differences in population exposure to PM2.5, NO2 and O3. In general, the results indicate that the exposure derived with established approaches is lower compared to the developed approach, which means that the adverse health effects might currently be underestimated for the European population.
