Addressing this challenge, a recent study by the Institute of Atmospheric Physics, published in the Environmental Science & Technology Letters and featured as a supplementary cover of the journal, has proposed an innovative emission update scheme tailored for air quality forecasting. According to the first author Dr. WU Huangjian, the strength in the new approach lies in that, in contrast to conventional methodologies, the new approach significantly reduces computational demands by an impressive 84%, making ensemble-based emission inversion cost-effective and practical for operational air quality forecasting.
The study is featured on the supplementary cover of Environmental Science & Technology Letters.
Co-author Prof. TANG Xiao explained the essence of the approach: "Our methodology builds upon the ChemDAS data assimilation system and takes a significant step forward by decoupling ensemble simulations and forecasts required for emission inversion. This enables the daily estimation of emissions for major pollutants in urban areas." This is echoed by Dr. KONG Lei, another contributor to the study, "Our technique estimates emissions for key pollutants like CO, SO2, NOx, VOCs, and PM2.5 and PM10 precursors by assimilating observed concentrations of CO, SO2, NO2, O3, PM2.5, and PM10, respectively."
The innovative method has already been successfully implemented at the China National Environmental Monitoring Centre (CNEMC), where it facilitates online inversion and updates of emission inventories for operational forecasting. "Our approach not only enhances forecast accuracy but also enables timely assessments of changes in atmospheric pollutant emissions." emphasized Prof. WANG Zifa, the corresponding author of the study.
In testing conducted between January and February 2022, the new method led to a notable 7.1% reduction in root mean square errors in 7-day PM2.5 forecasts and significantly improved predictions for pollutants like O3. Moreover, the updated emission data revealed significant reductions in nitrogen oxide emissions during the 2022 Beijing Winter Olympics, with Beijing experiencing a 53.5% reduction, Zhangjiakou a 42.7% reduction, and Hebei Province a 48.6% reduction.
This approach could advance air quality forecasting by providing timely, accurate, and cost-effective emission updates, contributing to the goal of healthier air for all.
Reference:
Wu, H., Kong, L., Tang, X., Zhu, L., Zhu, J., & Wang, Z. (2023). Air Quality Forecasting with Inversely Updated Emissions for China. Environmental Science & Technology Letters, 10(8), 655-661. https://doi.org/10.1021/acs.estlett.3c00266.
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