Abstract
Abstract. Following a continuous increase in the surface ozone (O3) level from 2013 to 2019, the overall summertime O3 concentrations 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 (relative to 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 due to the unexpected surface O3 enhancements. We first use the XGBoost machine learning method to correct the GEOS-Chem model–measurement O3 discrepancy over the SCB. The relative contributions of meteorology and anthropogenic emission changes to the unexpected surface O3 enhancements are then quantified with a 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 mortalities are estimated. The results show that changes in anthropogenic emissions caused a 0.9 ± 0.1 ppbv O3 reduction, whereas changes in meteorology caused an 11.1 ± 0.7 ppbv O3 increase in May–June 2020 relative to 2019. The meteorology-induced surface O3 increase is mainly attributed to an increase in temperature and decreases in precipitation, specific humidity, and cloud fractions over the SCB and surrounding regions in May–June 2020 relative to 2019. These changes in meteorology combined with the complex basin effect enhance biogenic emissions of volatile organic compounds (VOCs) and nitrogen oxides (NOx), speed up O3 chemical production, and inhibit the ventilation of O3 and its precursors; therefore, they 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 relative to 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 correcting the errors in all O3 predictors, the GEOS-Chem-XGBoost model significantly improves the prediction of surface O3 concentrations in all cities over the Sichuan Basin (SCB) compared with GEOS-Chem (Fig. S8 in the Supplement)
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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