CEPU/PPRFS (2023.A2.059.23C)What are the implications of COVID-19 restrictions for ozone pollution

Regional photochemical smog characterized by continuously rising levels of surface ozone (O3) has long been an air quality problem in Hong Kong (HK). Numerous studies attempted to address the combined effects of local chemistry and transboundary transport on O3 pollution in HK. However, a wide range of regional contributions were determined depending on many factors, such as weather patterns, and the estimates were based on assumed emission scenarios of air pollutants. The COVID-19 restrictions, especially the strict lockdowns in mainland China, provide us with a rare opportunity to examine the air quality impacts of human activities. While related work has been extensively carried out, the effects of mainland’s lockdown on O3 formation regime and sensitivity in HK remain to be unraveled. In addition, HK imposed flexible restrictions to ease the epidemic. It is unknown how the measures were different from the strict lockdown in affecting O3 pollution. Further, derived from the air quality impacts of COVID-19 restrictions is a deeper question: Will persistent O3 attainment be achieved even if all local emissions of anthropogenic air pollutants are eliminated? The answer lies in the level of regional background O3. Moreover, O3 pollution in HK is very sensitive to weather. O3 control by reducing anthropogenic emissions must be designed for extreme weather conditions, which however has not been fully understood. In this project, we will i) reveal the changes in O3 and its precursors in HK during the pandemic; ii) compare the impacts of different levels of COVID-19 restriction on O3 pollution, photochemistry, and transboundary transport; iii) determine the regional background O3 and its variations over time; and iv) explore effective solutions to exacerbated O3 pollution under extreme weather conditions. Observations available in air quality monitoring networks will be used to train the explainable machine learning and to constrain and verify model simulations. We will work around the COVID-19 restrictions, i.e., a series of large-scale control experiments, and simulate for extended scenarios. The new knowledge generated from this project will be backed by real data and easily translated into O3 pollution control policies. We expect the findings will support coordinated control of photochemical smog in the Greater Bay Area (GBA), through providing insights on mainland-HK differential control strategies, significance of non-combustion sources, and special intervention for anthropogenic emissions under weather extremes.

Regional photochemical smog characterized by continuously rising levels of surface ozone (O3) has long been an air quality problem in Hong Kong (HK). Numerous studies attempted to address the combined effects of local chemistry and transboundary transport on O3 pollution in HK. However, a wide range of regional contributions were determined depending on many factors, such as weather patterns, and the estimates were based on assumed emission scenarios of air pollutants. The COVID-19 restrictions, especially the strict lockdowns in mainland China, provide us with a rare opportunity to examine the air quality impacts of human activities. While related work has been extensively carried out, the effects of mainland’s lockdown on O3 formation regime and sensitivity in HK remain to be unraveled. In addition, HK imposed flexible restrictions to ease the epidemic. It is unknown how the measures were different from the strict lockdown in affecting O3 pollution. Further, derived from the air quality impacts of COVID-19 restrictions is a deeper question: Will persistent O3 attainment be achieved even if all local emissions of anthropogenic air pollutants are eliminated? The answer lies in the level of regional background O3. Moreover, O3 pollution in HK is very sensitive to weather. O3 control by reducing anthropogenic emissions must be designed for extreme weather conditions, which however has not been fully understood. In this project, we will i) reveal the changes in O3 and its precursors in HK during the pandemic; ii) compare the impacts of different levels of COVID-19 restriction on O3 pollution, photochemistry, and transboundary transport; iii) determine the regional background O3 and its variations over time; and iv) explore effective solutions to exacerbated O3 pollution under extreme weather conditions. Observations available in air quality monitoring networks will be used to train the explainable machine learning and to constrain and verify model simulations. We will work around the COVID-19 restrictions, i.e., a series of large-scale control experiments, and simulate for extended scenarios. The new knowledge generated from this project will be backed by real data and easily translated into O3 pollution control policies. We expect the findings will support coordinated control of photochemical smog in the Greater Bay Area (GBA), through providing insights on mainland-HK differential control strategies, significance of non-combustion sources, and special intervention for anthropogenic emissions under weather extremes.