| 基于随机森林的南京市PM2.5和O3对减排的响应 |
| 摘要点击 2070 全文点击 434 投稿时间：2022-09-17 修订日期：2022-11-03 |
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| 中文关键词 细颗粒物(PM2.5) 臭氧(O3) 随机森林 减排情景分析 GEOS-Chem 模式 |
| 英文关键词 fine particulate matter (PM2.5) ozone (O3) random forest emission reduction scenarios GEOS-Chem model |
| 作者 | 单位 | E-mail | | 尚永杰 | 南京信息工程大学环境科学与工程学院, 江苏省大气环境监测与污染控制重点实验室, 江苏省大气环境与装备技术协同创新中心, 南京 210044 | shangyongjieae@163.com | | 茅宇豪 | 南京信息工程大学环境科学与工程学院, 江苏省大气环境监测与污染控制重点实验室, 江苏省大气环境与装备技术协同创新中心, 南京 210044
南京信息工程大学气象灾害教育部重点实验室, 气象灾害预报与评估协同创新中心, 气候与环境变化国际联合研究实验室, 南京 210044 | yhmao@nuist.edu.cn | | 廖宏 | 南京信息工程大学环境科学与工程学院, 江苏省大气环境监测与污染控制重点实验室, 江苏省大气环境与装备技术协同创新中心, 南京 210044
南京信息工程大学气象灾害教育部重点实验室, 气象灾害预报与评估协同创新中心, 气候与环境变化国际联合研究实验室, 南京 210044 | | | 胡建林 | 南京信息工程大学环境科学与工程学院, 江苏省大气环境监测与污染控制重点实验室, 江苏省大气环境与装备技术协同创新中心, 南京 210044 | | | 邹泽庸 | 南京信息工程大学环境科学与工程学院, 江苏省大气环境监测与污染控制重点实验室, 江苏省大气环境与装备技术协同创新中心, 南京 210044 | |
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| 中文摘要 |
| 自2013年《大气污染防治行动计划》实施后,南京市大气污染有所改善,但仍面临着细颗粒物(PM2.5)和臭氧(O3)污染问题.为探究污染物浓度对其前体物减排的响应,获得有效的减排策略,常利用大气化学模式进行多组基于排放扰动的敏感性试验,而这需要消耗大量计算时间和计算资源.应用随机森林算法对2015年大气化学传输模式(GEOS-Chem)模拟结果进行机器学习,高效地预测了南京2019年PM2.5 浓度日均值和日最大8 h 臭氧(MDA8 O3)浓度对不同人为源排放控制情景的响应.随机森林结果表明2019年中国人为排放每减少10%,南京ρ(PM2.5)季节平均值下降2~4 μg ·m-3.当2019年中国人为源减排比例高于20%时,南京ρ(PM2.5)年均值将低于国家二级限值(35 μg ·m-3).若仅对中国地区O3前体物氮氧化物(NOx)和挥发性有机污染物(VOCs)同比例减排,反而可能导致南京MDA8 O3浓度季节平均值上升.2019年中国地区人为排放同等比例减少10%~50%,南京ρ(MDA8 O3)季节平均值在春、秋和冬季分别比基准试验增高约1~3、1~4和3~11 μg ·m-3.而当中国地区NOx 减排10% 且VOCs 减排20%时,南京各季节的ρ(MDA8 O3)平均值均有所下降(3~6 μg ·m-3); 在此基础上,进一步加大VOCs 减排比例(30%),南京ρ(MDA8 O3)年均值将减少7 μg ·m-3.若是仅进行南京本地人为源减排,南京O3浓度年均值将出现增加.因此,为有效缓解南京O3 污染,中国地区NOx和VOCs 减排比需小于1:2.结合随机森林和GEOS-Chem 模式可高效地得到污染物对前体物减排的响应,为大气污染防治策略的制定提供有效的科学支撑. |
| 英文摘要 |
| High levels of fine particulate matter (PM2.5) and ozone (O3) in ambient air affect climate change and also endanger human health and ecosystems. Air pollution in Nanjing has been improving since the implementation of the "Air Pollution Prevention and Control Action Plan" in 2013. However, Nanjing still faces PM2.5 and O3 pollution. Evaluating the response of pollutant concentrations to the reductions in precursor emissions is helpful to obtain effective strategies of emission reduction to improve pollution levels. The sensitive simulations of emission perturbation in atmospheric chemistry models directly demonstrate the response of pollution to the reductions in emissions. Nevertheless, these sensitive simulations are limited in computing time and resources. The random forest algorithm was trained by using the simulation results of the atmospheric chemical transport model (GEOS-Chem) in 2015. The changes in daily PM2.5 and daily maximum eight-hour O3 (MDA8 O3) concentrations in Nanjing in 2019 were efficiently predicted under different reduction scenarios of anthropogenic emissions. The simulations showed that the seasonal average of ρ(PM2.5) in Nanjing would decrease by 2-4 μg·m-3 with the reduction in anthropogenic emissions of 10% in 2019 in China. In the case of controlling only local emissions in Nanjing, the concentrations of PM2.5 in Nanjing decreased significantly without local anthropogenic emissions. Additionally, the simulations showed that the annual average of ρ(PM2.5) in Nanjing could be lower than the national secondary limit (35 μg·m-3) when the anthropogenic emission reduction in China was higher than 20% in 2019. For ozone, the equal proportional emission reductions in nitrogen oxides (NOx) and volatile organic pollutants (VOCs) of O3 precursors in China likely led to the increase in seasonal average concentrations of O3 in Nanjing. For the proportional reduction of anthropogenic emissions by 10%-50% in China, the seasonal average of ρ(MDA8 O3) in Nanjing in 2019 would increase by 1-3 μg·m-3 in spring, 1-4 μg·m-3 in autumn, and 3-11 μg·m-3 in winter, respectively, compared with that in the base simulation. With the reduction in anthropogenic NOx emission by 10% and VOCs by 20%, the seasonal average of ρ(MDA8 O3) in Nanjing would decrease by 3-6 μg·m-3. On this basis, further increasing the proportion (30%) of VOCs emission reduction could reduce the annual average of ρ(MDA8 O3) in Nanjing by 7 μg·m-3. However, the annual average of ρ(MDA8 O3) of Nanjing in 2019 increased by 1 μg·m-3, with the local emission reduction of NOx by 10% and VOCs by 30%. Therefore, this showed that the key to alleviate ozone pollution in Nanjing is a reasonable control ratio of ozone precursor emissions and the implementation of regional joint prevention and control. In order to effectively reduce the O3 pollution in Nanjing, the emission reduction ratio of NOx and VOCs in China should be less than 1:2. The response of pollutant concentrations to reductions in precursor emissions were efficiently obtained by the random forest algorithm and GEOS-Chem model. The simulations would provide the scientific basis for the emission control strategy to alleviate air pollution. |
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