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).

夏季长三角地区臭氧非线性响应曲面模型的建立及应用

2013年8月上旬,长三角地区发生了一次大范围高浓度臭氧污染事件.本研究基于WRF-CMAQ空气质量模型系统,结合长三角地区大气污染物排放清单,构建了臭氧与其前体物之间的非线性响应曲面模型(Response surface modeling,RSM).基于RSM探讨了长三角地区O_3化学的敏感性特征,分析了上海市O_3的来源并预测不同情景下O_3的变化,提出O_3污染的最优控制方案.研究结果表明,长三角地区夏季O3主控因素区域差别较大,上海环科院、杭州卧龙桥、南京玄武湖等城区点位属于VOC控制型;徐州铜山、合肥肥西、丽水青田等郊区属于NOx控制型.O_3的敏感性特征在不同浓度水平下也呈现较大差异,随着O_3浓度水平的升高,各地区NOx敏感性均有所增加.从区域来源来看,远距离传输对平日上海O_3贡献较大,占50%以上;而在O_3污染日,本地及区域贡献为72.1%,而远距离传输贡献下降至27.9%.快速预测了110组减排情景,表明在本地及区域前体物均减排70%的情况下,2013年8月上海O_3浓度能完全达标.

Design and demonstration of a next-generation air quality attainment assessment system for PM_(2.5)and O_3

Due to the increasingly stringent standards, it is important to assess whether the proposed emission reduction will result in ambient concentrations that meet the standards. The Software for Model Attainment Test-Community Edition （SMAT-CE） is developed for demonstrating attainment of air quality standards of O3 and PM2.5. SMAT-CE improves computational efficiency and provides a number of advanced visualization and analytical functionalities on an integrated GIS platform. SMAT-CE incorporates historical measurements of air quality parameters and simulated air pollutant concentrations under a number of emission inventory scenarios to project the level of compliance to air quality standards in a targeted future year. An application case study of the software based on the U.S. National Ambient Air Quality Standards （NAAQS） shows that SMAT-CE is capable of demonstrating the air quality attainment of annual PM2.5 and 8-hour O3 for a proposed emission control policy.

Development and case study of a science-based software platform to support policy making on air quality

This article describes the development and implementations of a novel software platform that supports real-time, science-based policy making on air quality through a user-friendly interface. The software, RSM-VAT, uses a response surface modeling（RSM） methodology and serves as a visualization and analysis tool（VAT） for three-dimensional air quality data obtained by atmospheric models. The software features a number of powerful and intuitive data visualization functions for illustrating the complex nonlinear relationship between emission reductions and air quality benefits. The case study of contiguous U.S.demonstrates that the enhanced RSM-VAT is capable of reproducing the air quality model results with Normalized Mean Bias 〈 2% and assisting in air quality policy making in near real time.

Aerosol oxalate and its implication to haze pollution in Shanghai,China

A total of 238 samples of PM2.5and TSP were analyzed to study the characteristics,sources,and formation pathways of aerosol oxalate in Shanghai in four seasons of 2007.The concentrations of oxalate were0.07–0.41 lg/m3in PM2.5and 0.10–0.48 lg/m3in TSP,respectively.Oxalate displayed a seasonal variation of autumn[summer[winter[spring in both PM2.5and TSP and was dominantly present in PM2.5in all samples.Correlation between oxalate and K?and high ratio of oxalate/K?suggested that biomass burning was a secondary source of aerosol oxalate in Shanghai,in addition to urban VOCs sources(vehicular and industrial emissions),especially in autumn.Secondary formation accounted for the majority of aerosol oxalate in Shanghai,which was supported by the high correlation of oxalate with nssSO42-,K?and NO3-,proceeding from different mechanisms.Relatively high ambient RH together with high cloud cover was found benefiting the secondary formation of aerosol oxalate.The in-cloud process(aqueous-phase oxidation)was proposed to be likely the major formation pathway of aerosol oxalate in Shanghai,which was supported by the high correlation of oxalate with nss-SO42-and K?,dominant residence of oxalate in droplet mode and result of favorable meteorological condition analysis.High correlation of oxalate and NO3-reflected the OH radical involved oxidation chemistry of the two species in the atmosphere and also suggested that gas-particle surface reactions and the evaporation–condensation process were both possible secondary formation pathways of aerosol oxalate in coarser particle mode([1.0 lm).As a major water-soluble organic compound in aerosols,concentration of oxalate showed a distinct negative correlation to the atmospheric visibility,which implied that aerosol organic compounds could play an important role in haze pollution as well as in air quality in Shanghai.