Sensitivities of Ozone Air Pollution in the Beijing-Tianjin-Hebei Area to Local and Upwind Precursor Emissions Using Adjoint Modeling

Xiaolin Wang, Tzung May Fu*, Lin Zhang, Hansen Cao, Qiang Zhang, Hanchen Ma, Lu Shen, Mathew J. Evans, Peter D. Ivatt, Xiao Lu, Youfan Chen, Lijuan Zhang, Xu Feng, Xin Yang, Lei Zhu, Daven K. Henze

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Effective mitigation of surface ozone pollution entails detailed knowledge of the contributing precursors' sources. We use the GEOS-Chem adjoint model to analyze the precursors contributing to surface ozone in the Beijing-Tianjin-Hebei area (BTH) of China on days of different ozone pollution severities in June 2019. We find that BTH ozone on heavily polluted days is sensitive to local emissions, as well as to precursors emitted from the provinces south of BTH (Shandong, Henan, and Jiangsu, collectively the SHJ area). Heavy ozone pollution in BTH can be mitigated effectively by reducing NOx (from industrial processes and transportation), ≥C3 alkenes (from on-road gasoline vehicles and industrial processes), and xylenes (from paint use) emitted from both BTH and SHJ, as well as by reducing CO (from industrial processes, transportation, and power generation) and ≥C4 alkanes (from industrial processes, paint and solvent use, and on-road gasoline vehicles) emissions from SHJ. In addition, reduction of NOx, xylene, and ≥C3 alkene emissions within BTH would effectively decrease the number of BTH ozone-exceedance days. Our analysis pinpoint the key areas and activities for locally and regionally coordinated emission control efforts to improve surface ozone air quality in BTH.

Original languageEnglish
Pages (from-to)5752-5762
Number of pages11
JournalEnvironmental Science and Technology
Issue number9
Publication statusPublished - 4 May 2021

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China (41975158, 42011530176, and 41922037), the Shenzhen Science and Technology Innovation Committee (KCXFZ202002011008038), and the Guangdong Basic and Applied Basic Research Fund Committee (2020B1515130003). H.C. and D.H. acknowledge support from NASA (80NSSC18K0689 and NNX16AQ26G). Computational resources were provided by the Center for Computational Science and Engineering at the Southern University of Science and Technology.

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