Abstract
Emission inventories are important for modeling studies and policy-making, but the traditional “bottom-up” emission inventories are often outdated with a time lag, mainly due to the lack of accurate and timely statistics. In this study, we developed a “top-down” approach to optimize the emission inventory of sulfur dioxide (SO2) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and a three-dimensional variational (3DVAR) system. The observed hourly surface SO2 concentrations from the China National Environmental Monitoring Center were assimilated and used to estimate the gridded concentration forecast errors of WRF-Chem. The concentration forecast errors were then converted to the emission errors by assuming a linear response from SO2 emission to concentration by grids. To eliminate the effects of modelling errors from aspects other than emissions, a strict data-screening process was conducted. Using the Multi-Resolution Emission Inventory for China (MEIC) 2010 as the a priori emission, the emission inventory for October 2015 over Mainland China was optimized. Two forecast experiments were conducted to evaluate the performance of the SO2 forecast by using the a priori (control experiment) and optimized emissions (optimized emission experiment). The results showed that the forecasts with optimized emissions typically outperformed the forecasts with 2010 a priori emissions in terms of the accuracy of the spatial and temporal distributions. Compared with the control experiment, the bias and root-mean-squared error (RMSE) of the optimized emission experiment decreased by 71.2% and 25.9%, and the correlation coefficients increased by 50.0%. The improvements in Southern China were more significant than those in Northern China. For the Sichuan Basin, Yangtze River Delta, and Pearl River Delta, the bias and RMSEs decreased by 76.4–94.2% and 29.0–45.7%, respectively, and the correlation coefficients increased by 23.5–53.4%. This SO2 emission optimization methodology is computationally cost-effective.
Highlights
IntroductionSulfur dioxide (SO2 ) is a major air pollutant that contributes to poor air quality
Licensee MDPI, Basel, Switzerland.Sulfur dioxide (SO2 ) is a major air pollutant that contributes to poor air quality
The SO2 increment in the Beijing and Chongqing was overall negative during 24 h, implying that the forecast SO2 concentrations was overestimated by Weather Research and Forecasting (WRF)-Chem with the a priori emissions
Summary
Sulfur dioxide (SO2 ) is a major air pollutant that contributes to poor air quality. It has a significant impact on the environment and climate [1,2,3]. A major proportion of anthropogenic SO2 emissions come from power plants and industries, accounting for more than 70% of the total SO2 emissions [4]. It is challenging to predict SO2 concentrations using regional air quality models because of many factors.
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