Adjusting elemental carbon emissions in Northeast Asia using observed surface concentrations of downwind area and simulated contributions

Abstract

In this study, we developed a practical approach to augment elemental carbon (EC) emissions to improve the reproducibility of the most recent air quality with photochemical grid modeling in support of source-receptor relationship analysis. We demonstrated the usefulness of this approach with a series of simulations for EC concentrations over Northeast Asia during the 2016 Korea-United States Air Quality study. Considering the difficulty of acquiring EC observational data in foreign countries, our approach takes two steps: (1) augmenting upwind EC emissions based on simulated upwind contributions and observational data at a downwind EC monitor considered as the most representative monitor for upwind influences and (2) adjusting downwind EC emissions based on simulated downwind contributions, including the effects of updated upwind emissions from the first step and observational data at the downwind EC monitors. The emission adjustment approach resulted in EC emissions 2.5 times higher than the original emissions in the modeling domain. The EC concentration in the downwind area was observed to be 1.0 μg m−3 during the study period, while the simulated EC concentration was 0.5 μg m−3 before the emission adjustment. After the adjustment, the normalized mean error of the daily mean EC concentration decreased from 48 % to 22 % at ground monitor locations. We found that the EC simulation results were improved at high altitudes, and the contribution of the upwind areas was greater than that of the downwind areas for EC concentrations downwind with or without emission adjustment. This implies that collaborating with upwind regions is essential to alleviate high EC concentrations in downwind areas. The developed emission adjustment approach can be used for any upwind or downwind area when transboundary air pollution mitigation is needed because it provides better reproducibility of the most recent air quality through modeling with improved emission data.