Unprecedented decline in summertime surface ozone over eastern China in 2020 comparably attributable to anthropogenic emission reductions and meteorology

Hao Yin, Xiao Lu*, Youwen Sun*, Ke Li, Meng Gao, Bo Zheng, Cheng Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

China’s nationwide monitoring network initiated in 2013 has witnessed continuous increases of urban summertime surface ozone to 2019 by about 5% year-1, among the fastest ozone trends in the recent decade reported in the Tropospheric ozone assessment report. Here we report that surface ozone levels averaged over cities in eastern China cities decrease by 5.5 ppbv in May–August 2020 compared to the 2019 levels, representing an unprecedented ozone reduction since 2013. We combine the high-resolution GEOS-Chem chemical model and the eXtreme Gradient Boosting (XGBoost) machine learning model to quantify the drivers of this reduction. We estimate that changes in anthropogenic emissions alone decrease ozone by 3.2 (2.9–3.6) ppbv (57% of the total 5.5 ppbv reduction) averaged over cities in eastern China and by 2.5 ~ 3.2 ppbv in the three key city clusters for ozone mitigation. These reductions appear to be driven by decreases in anthropogenic emissions of both nitrogen oxides (NO x ) and volatile organic compounds, likely reflecting the stringent emission control measures implemented by The Chinese Ministry of Environmental and Ecology in summer 2020, as supported by observed decline in tropospheric formaldehyde (HCHO) and nitrogen dioxides (NO2) from satellite and by bottom-up emission estimates. Comparable to the emission-driven ozone reduction, the wetter and cooler weather conditions in 2020 decrease ozone by 2.3 (1.9–2.6) ppbv (43%). Our analyses indicate that the current emission control strategies can be effective for ozone mitigation in China yet tracking future ozone changes is essential for further evaluation. Our study also reveals important potential to combine the mechanism-based, state-of-art atmospheric chemical models with machine learning model to improve the attribution of ozone drivers.

Original languageEnglish
Article number124069
Number of pages10
JournalEnvironmental Research Letters
Volume16
Issue number12
DOIs
Publication statusPublished - Dec 2021

Scopus Subject Areas

  • Renewable Energy, Sustainability and the Environment
  • Environmental Science(all)
  • Public Health, Environmental and Occupational Health

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