Abstract
After a continuous increase in surface ozone (O3) level from 2013 to 2019, the overall summertime O3 concentration across China showed a significant reduction in 2020. In contrast to this overall reduction in surface O3 across China, unexpected surface O3 enhancements of 10.2 ± 0.8 ppbv (23.4 %) were observed in May–June 2020 vs. 2019 over the Sichuan basin (SCB), China. In this study, we use high resolution nested-grid GEOS-Chem simulation, the eXtreme Gradient Boosting (XGBoost) machine learning method and the exposure−response relationship to determine the drivers and evaluate the health risks of the unexpected surface O3 enhancements. We first use the XGBoost machine learning method to correct the GEOS-Chem model-to-measurement O3 discrepancy over the SCB. The relative contributions of meteorology and anthropogenic emissions changes to the unexpected surface O3 enhancements are then quantified with the combination of GEOS-Chem and XGBoost models. In order to assess the health risks caused by the unexpected O3 enhancements over the SCB, total premature death mortalities are estimated. The results show that changes in anthropogenic emissions caused 0.9 ± 0.1 ppbv of O3 reduction and changes in meteorology caused 11.1 ± 0.7 ppbv of O3 increase in May–June 2020 vs. 2019. The meteorology-induced surface O3 increase is mainly attributed to significant increases in temperature and downward potential vorticity, and decreases in precipitation, specific humidity and cloud fractions over the SCB and surrounding regions in May–June 2020 vs. 2019. These changes in meteorology combined with the complex basin effect enhance downward transport of O3 from upper troposphere, enhance biogenic emissions of volatile organic compounds (VOCs) and nitrogen oxides (NOx), speed up O3 chemical production, and inhabit the ventilation of O3 and its precursors, and therefore account for the surface O3 enhancements over the SCB. The total premature mortality due to the unexpected surface O3 enhancements over the SCB has increased by 89.8 % in May–June 2020 vs. 2019.
Highlights
Surface ozone (O3) is largely generated from its local anthropogenic and natural precursors, such as volatile organic compounds (VOCs), nitrogen oxides (NOx), and carbon monoxide (CO), via a chain of photochemical reactions (Cooper, 2019; Sun et al, 2018)
Exposure to ambient O3 pollution evokes a series of health risks including stroke, respiratory disease (RD), hypertension, cardiovascular disease (CVD), and chronic obstructive pulmonary disease (COPD)
After a continuous increase in surface O3 levels from 2013 to 2019 by approximately 5 % yr−1 (Fig. 1d), the MDA8 O3 averaged over all cities outside of the Sichuan Basin (SCB) across China in May–June 2020 relative to 2019 levels showed a significant reduction of 5.3 ± 0.5 ppbv (8.3 %)
Summary
Surface ozone (O3) is largely generated from its local anthropogenic (fossil fuel and biofuel combustions) and natural (biomass burning, BB; lightning; and biogenic emissions) precursors, such as volatile organic compounds (VOCs), nitrogen oxides (NOx), and carbon monoxide (CO), via a chain of photochemical reactions (Cooper, 2019; Sun et al, 2018). Meteorological conditions affect surface O3 variability indirectly through changes in the natural emissions of its precursors or directly via changes in wet and dry removal, dilution, chemical reaction rates, and transport flux Previous studies typically use state-of-the-art chemical transport models (CTMs) with sensitivity simulations to quantify the drivers of O3 variability, e.g. fixed meteorology but varied emission levels to quantify the influences of emission changes or vice versa We use high-resolution nested-grid GEOS-Chem simulation, the eXtreme Gradient Boosting (XGBoost) machine learning method, and the exposure– response relationship to determine the drivers and evaluate the health risks due to the unexpected surface O3 enhancements. In order to assess the health risks caused by the unexpected O3 enhancements over the SCB, total premature mortalities are estimated
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