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基于机器学习量化气象和排放对2014~2023年杭州城乡臭氧年际变化的影响

Impact of Meteorological Conditions and Emissions on the Evolution of Ozone in Urban and Rural Hangzhou from 2014 to 2023 Based on Machine Learning

  • 摘要: 自2013年国务院开始执行空气质量改善行动计划以来,我国空气质量得到全面改善,但臭氧(O3)污染凸显。分离气象和排放对杭州O3污染演变的影响,评估减排措施的管控成效,对制定精准的防治政策具有重要意义。本文利用2014~2023年暖季(4~10月)杭州15个监测站点的观测数据,综合分析了与O3相关的多个指标的城乡演变规律,并采用基于随机森林算法的去气象方法,剥离O3长期演变趋势中气象和排放的各自贡献,定量评估了O3污染治理效果。结果显示,杭州MDA8 O3(O3日8小时平均浓度最大值)和Ox年增长率分别为1.4和0.07 μg m−3 a−1,其中城区增幅和浓度值均明显高于乡镇,城乡O3和Ox差异持续增大,NOx减排未能有效抑制臭氧上升。从2018年起气象条件由不利O3生成转为有利,且气象对O3浓度最高的5月和9月影响显著,整体O3年际上升趋势中64%由于气象条件导致,36%为管控不利导致。剔除气象影响后,长期管控措施有效降低了城市和乡镇地区的大气氧化能力,遏制了乡镇地区O3浓度的增加,但对城市O3以及局部光化学生成的管控未见成效。上述结果显示在不利气象条件下(重点为5月和9月)继续加强人为管控是抑制杭州臭氧污染的关键。

     

    Abstract: Since the State Council implemented the Air Quality Improvement Action Plan in 2013, China’s overall air quality has significantly improved. However, ozone (O3) pollution has become increasingly prominent. Therefore, disentangling the impacts of meteorological conditions and emissions and evaluating the effectiveness of emission reduction measures are crucial for formulating precise prevention and control policies in Hangzhou. This study considers observational data from 15 monitoring stations in Hangzhou during the warm season from 2014 to 2023, comprehensively analyzes the evolution of multiple O3-related indicators, and adopts a de-meteorologization method based on the random forest algorithm to quantify the respective contributions of meteorological conditions and emissions to long-term O3, thereby assessing the effect of O3 pollution control. The results show that the annual growth rates of MDA8 O3 and Ox in Hangzhou as a whole are 1.4 μg m−3 a−1 and 0.07μg m−3 a−1, respectively. Both the rate of increase and the concentration levels are significantly higher in urban areas than in rural areas. The urban–rural gap in O3 and Ox has widened over time, and reductions in NOx emissions have not effectively suppressed the rise of O3. Since 2018, meteorological conditions have changed from being unfavorable to favorable for O3 generation, and this change has been most significant in May and September—the months with the most severe pollution. Generally, 64% of the overall interannual upward trend in O3 pollution is caused by meteorological conditions, and the rest is due to ineffective management and control. When meteorological effects are removed, long-term management measures effectively reduce atmospheric oxidation capacity in both urban and rural areas and curb the rise in O3 concentration in rural areas. However, these measures have had limited effect on O3 levels in urban areas or on local photochemical production. The above results indicate that strengthening anthropogenic control measures under unfavorable meteorological conditions (especially in May and September) is key to suppressing O3 pollution in Hangzhou.

     

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