Impacts of improved modeling resolution on the simulation of meteorology, air quality, and human exposure to PM2.5, O3 in Beijing, China

Abstract High resolution modeling provides a more detailed representation of spatial gradient, which is critical for air quality and human exposure estimation studies. However, to date, no experimental investigations have directly addressed the question of whether high-resolution modeling can increase performance with respect to reproducing meteorological conditions in general; and to spatial distribution of air pollution specifically. In this study, we established a high-resolution Weather Research and Forecasting Model-Community Multiscale Air Quality Model with 1 km × 1 km horizontal resolution in the Beijing area. Compared with the 3 km resolution (Case 1), the more detailed land use and land cover in the 1 km resolution (Case 2) result in the improvement of 2-m temperature (T2), while the improvement of 10-m wind speed (WS10) mainly stemmed from implementation of the single urban canopy effect. The more accurate T2 and WS10 jointly lead to a decrease of Planetary Boundary Layer Height in the city. With a better spatially-resolved meteorological field and updated emission inventory, the urban PM2.5 monthly average in Case 2 surpasses Case 1 and the increment of PM2.5 increased alongside the pollution levels, especially above the heavy pollution level. Moreover, the differences in O3 and NOx monthly average between Case 2 and Case 1 negatively correlate across the space. In July, the temporal trend of O3 in Case 2 is closer to observation due to the improvement of T2. Correspondingly, compared to the Case 1, the population weighted PM2.5 concentrations are 2.33% higher while population weighted O3 concentrations are 1.99% lower in Case 2. Overall, the results of Case 2 better capture and reproduce the temporal trends and magnitudes of meteorological conditions and air quality.