Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Surface ozone(O_(3))is influenced by regional background and local photochemical formation under favorable meteorological conditions.Understanding the contribution of these factors to changes in O_(3)is crucial to address the issue of O_(3)pollution.In this study,we propose a novel integrated method that combines random forest,principal component analysis,and Shapley additive explanations to distinguish observed O_(3)into meteorologically affected ozone(O_(3_MET)),chemically formed from local emissions(O_(3_LC)),and regional background ozone(O_(3_RBG)).Applied to three typical stations in Shanghai during the warm season from 2013 to 2021,the results indicate that O_(3_RBG)in Shanghai was 48.8±0.3 ppb,accounting for 79.6%–89.4%at different sites,with an overall declining trend of 0.018 ppb/yr.O_(3_LC)at urban and regional sites ranged from 5.9–9.0 ppb and 8.9–14.6 ppb,respectively,which were significantly higher than the contributions of 2.5–7.4 ppb at an upwind background site.O_(3_MET)can be categorized into those affecting O_(3)photochemical generation and those changing O_(3)dispersion conditions,with absolute contributions to O_(3)ranging from 13.4–19.0 ppb and 13.1–13.7 ppb,respectively.We found that the O_(3)rebound in 2017,compared to 2013,was primarily influenced by unfavorable O_(3)dispersion conditions and unbalanced emission reductions;while the O_(3)decline in 2021,compared to 2017,was primarily influenced by overall favorable meteorological conditions and further emissions reduction.These findings highlight the challenge of understanding O_(3)change due to meteorology and regional background,emphasizing the need for systematic interpretation of the different components of O_(3).