Air quality modeling with WRF-Chem v3.5 in East Asia: sensitivity to emissions and evaluation of simulated air quality

We conducted simulations using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) version 3.5 to study air quality in East Asia at a spatial resolution of 20 km x 20 km. We find large discrepancies between two existing emissions inventories: the Regional Emission Inventory in ASia version 2 (REAS) and the Emissions Database for Global Atmospheric Research version 4.2 (EDGAR) at the provincial level in China, with maximum differences of up to 500% for CO emissions, 190% for NO, and 160% for primary PM10. Such discrepancies in the magnitude and the spatial distribution of emissions for various species lead to a 40-70% difference in surface PM10 concentrations, 16-20% in surface O-3 mixing ratios, and over 100% in SO2 and NO2 mixing ratios in the polluted areas of China. WRF-Chem is sensitive to emissions, with the REAS-based simulation reproducing observed concentrations and mixing ratios better than the EDGAR-based simulation for July 2007. We conduct additional model simulations using REAS emissions for January, April, July, and October of 2007 and evaluate simulations with available ground-level observations. The model results illustrate clear regional variations in the seasonal cycle of surface PM10 and O-3 over East Asia. The model meets the air quality model performance criteria for both PM10 (mean fractional bias, MFB <= +/- 60 %) and O-3 (MFB <= +/- 15 %) at most of the observation sites, although the model underestimates PM10 over northeastern China in January. The model predicts the observed SO2 well at sites in Japan, while it tends to overestimate SO2 in China in July and October. The model underestimates observed NO2 in all 4 months. Our study highlights the importance of constraining emissions at the provincial level for regional air quality modeling over East Asia. Our results suggest that future work should focus on the improvement of provincial-level emissions especially estimating primary PM, SO2, and NOx