Is the efficacy of satellite-based inversion of SO2 emission model dependent?

Nan Li; Keqin Tang; Yi Wang; Jun Wang; Weihang Feng; Haoran Zhang; Hong Liao; Jianlin Hu; Xin Long; Chong Shi; Xiaoli Su

doi:10.1088/1748-9326/abe829

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Is the efficacy of satellite-based inversion of SO2 emission model dependent?

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Is the efficacy of satellite-based inversion of SO2 emission model dependent?

Nan Li, Keqin Tang, Yi Wang, Jun Wang, Weihang Feng, Haoran Zhang, Hong Liao, Jianlin Hu, Xin Long, Chong Shi, …

Environmental research letters, Vol.16(3), p.35018

03/01/2021

DOI: 10.1088/1748-9326/abe829

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Abstract

Satellite-based inverse modeling has the potential to drive aerosol precursor emissions, but its efficacy for improving chemistry transport models (CTMs) remains elusive because of its likely inherent dependence on the error characteristics of a specific CTM used for the inversion. This issue is quantitively assessed here by using three CTMs. We show that SO2 emissions from global GEOS-Chem adjoint model and OMI SO2 data, when combined with spatial variation of bottom-up emissions, can largely improve WRF-Chem and WRF-CMAQ forecast of SO2 and aerosol optical depth (in reference to moderate resolution imaging spectroradiometer data) in China. This suggests that the efficacy of satellite-based inversion of SO2 emission appears to be high for CTMs that use similar or identical emission inventories. With the advent of geostationary air quality monitoring satellites in next 3 years, this study argues that an era of using top-down approach to rapidly update emission is emerging for regional air quality forecast, especially over Asia having highly varying emissions

Details

Title: Subtitle: Is the efficacy of satellite-based inversion of SO2 emission model dependent?
Creators: Nan Li
Keqin Tang
Yi Wang
Jun Wang
Weihang Feng
Haoran Zhang
Hong Liao
Jianlin Hu
Xin Long
Chong Shi
Xiaoli Su
Resource Type: Journal article
Publication Details: Environmental research letters, Vol.16(3), p.35018
DOI: 10.1088/1748-9326/abe829
ISSN: 1748-9326
eISSN: 1748-9326
Grant note: name: the Major Research Plan of National Social Science Foundation, award: 18ZDA052; DOI: 10.13039/501100001809, name: the National Natural Science Foundation of China, award: 41705128; name: the National Key R&D Program of China, award: 2018YFC0213802
Language: English
Date published: 03/01/2021
Academic Unit: Civil and Environmental Engineering; Iowa Technology Institute; Physics and Astronomy; Chemical and Biochemical Engineering
Record Identifier: 9984104808902771

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