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Impact of Emission Inventory Choices on PM10 Forecast Accuracy and Contributions in the Seoul Metropolitan Area

배출량 목록에 따른 수도권 PM10 예보 정합도 및 국내외 기여도 분석

  • Received : 2017.09.02
  • Accepted : 2017.10.16
  • Published : 2017.10.31

Abstract

This study quantitatively analyzes the effects of emission inventory choices on the simulated particulate matter (PM) concentrations and the domestic/foreign contributions in the Seoul Metropolitan Area (SMA) with an air quality forecasting system. The forecasting system is composed of Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Community Multi-Scale Air Quality (CMAQ). Different domestic and foreign emission inventories were selectively adopted to set up four sets of emissions inputs for air quality simulations in this study. All modeling cases showed that model performance statistics satisfied the criteria levels (correlation coefficient >0.7, fractional error <50%) suggested by previous studies. Notwithstanding the apparently good model performance of total PM concentrations by all emission cases, annual average concentrations of simulated total PM concentrations varied up to $20{\mu}g/m^3$ (160%) depending on the combination of emission inventories. In detail, the difference in simulated annual average concentrations of the primary PM coarse (PMC) was up to $25.2{\mu}g/m^3$ (6.5 times) compared with other cases. Furthermore, model performance analyses on PM species showed that the difference in the simulated primary PMC led to gross model overestimation in general, which indicates that the primary PMC emissions need to be improved. The contribution analysis using model direct outputs indicated that the domestic contributions to the annual average PM concentrations in the SMA vary from 44% to 67%. To account for the uncertainty of the simulated concentration, the contribution correction factor method proposed by Bae et al. (2017) was applied, which resulted in converged contributions(from 48% to 57%). We believe this study shows that it is necessary to improve the simulated concentrations of PM components in order to enhance the accuracy of the forecasting model. It is deemed that these improvements will provide more accurate contribution results.

Keywords

Air quality forecasting;$PM_{10}$;$PM_{2.5}$;Emissions inventories;Relative contribution

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Cited by

  1. Review of Shandong Peninsular Emissions Change and South Korean Air Quality vol.34, pp.2, 2018, https://doi.org/10.5572/KOSAE.2018.34.2.356

Acknowledgement

Supported by : 국립환경과학원