An observation-based adjustment method of regional contribution estimation from upwind emissions to downwind PM2.5 concentrations

We propose a method to adjust contributions from upwind emissions to downwind PM2.5 concentrations to account for the differences between observed and simulated PM2.5 concentrations in an upwind area. Emissions inventories (EI) typically have a time lag between the inventory year and the release year. In addition, traditional emission control policies and social issues such as the COVID-19 pandemic cause steady or unexpected changes in anthropogenic emissions. These uncertainties could result in overestimation of the emission impacts of upwind areas on downwind areas if emissions used in modeling for the upwind areas were larger than the reality. In this study, South Korea was defined as the downwind area while other regions in Northeast Asia including China were defined as the upwind areas to evaluate applicability of the proposed adjustment method. We estimated the contribution of emissions released from the upwind areas to PM2.5 concentrations in South Korea from 2015 to 2020 using a three-dimensional photochemical model with two EIs. In these two simulations for 2015-2020, the annual mean foreign contributions differed by 4.1-5.5 mu g/m(3). However, after adjustment, the differences decreased to 0.4-1.1 mu g/m(3). The adjusted annual mean foreign contributions were 12.7 and 8.8 mu g/m(3) during 2015-2017 and 2018-2020, respectively. Finally, we applied the adjustment method to the COVID-19 pandemic period to evaluate the applicability for short-term episodes. The foreign contribution of PM2.5 during the lock down period in China decreased by 30% after adjustment and the PM2.5 normalized mean bias in South Korea improved from 15% to-4%. This result suggests that the upwind contribution adjustment can be used to alleviate the uncertainty of the emissions inventory used in air quality simulations. We believe that the proposed upwind contribution adjustment method can help to correctly understand the contributions of local and upwind emissions to PM2.5 concentrations in downwind areas.