Update of SO2 emission inventory in the Megacity of Chongqing, China by inverse modeling. (1st February 2023)

• Record Type: Journal Article
• Authors: Feng, Xiaoxiao; Zhang, Xiaole; Wang, Jing
• Is Part Of: Atmospheric environment. Volume 294(2023)
• Publication Date: 2023-02-01
• Subjects: Emission inventory -- Inverse problem -- CMAQ DDM-3D -- SO2 pollution; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2022.119519
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: Chongqing, a metropolitan with over 32 million residents in southwest China, has suffered from SO2 pollution since 1980s. The emission inventory is an important tool to evaluate the SO2 pollution and to design the effective emission reduction policies. The present work developed a scheme to update the obsolescent SO2 emission inventory in Chongqing obtained from Multi-resolution Emission Inventory for China in 2008 (MEIC2008). The updated emission inventory was estimated by integrating the a priori knowledge of the baseline emissions and the current observations based on Bayesian inference, in which the source-receptor sensitivities were calculated by the Decoupled Direct Method in Three Dimensions in the Community Multiscale Air Quality Modeling System (CMAQ DDM-3D). An analytical solution of the Bayesian theorem was derived based on the linear response assumption and applied to estimate the actual SO2 emissions. The updated emission inventory was comparable with the most recent MEIC emission inventory in 2016 and 2017, and was in line with the decline trend of SO2 emissions in Chongqing in the last decade. The adjustment of the emissions improved the accuracy in predicting SO2 concentrations with the developed method. Highlights: The emission inventory of SO2, one of the causal factors of SO2 pollution, was estimated from the ground observations. The inverse problem was solved by combing air quality models with Bayesian inference. The response of the ambient SO2 to SO2 emissions was quantified as linear. The updated emissions were more accurate in predicting the SO2 pollution than the baseline.