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北京市PM2.5-O3复合污染数值响应解析
摘要点击 1974  全文点击 1702  投稿时间:2022-03-21  修订日期:2022-05-07
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中文关键词  PM2.5|臭氧( O3)|复合污染|数值响应|滞后效应
英文关键词  PM2.5|ozone(O3)|compound pollution|numerical response|delayed response
作者单位E-mail
刘添强 成都理工大学地球物理学院, 地球勘探与信息技术教育部重点实验室, 成都 610059 liutianqiang@stu.cdut.edu.cn 
中文摘要
      为分析PM2.5-O3复合污染数值响应关系,基于2015~2020年北京市空气质量数据、气象资料和新冠疫情数据,分析PM2.5-O3复合污染事件在多尺度下的变化趋势.同时提出一种复合污染指数,在广义相加模型下分析数值响应趋势,并进一步引入分布滞后非线性模型,分别解析复合污染指数、复合污染事件和影响因素间的滞后响应关系.结果表明,北京市PM2.5-O3复合污染事件逐年减少,具有明显的季节效应、星期效应和节假日效应.复合污染指数与降雨量无明显相关性,与O3和空气温度呈线性正相关,与其余解释变量均为非线性相关.同时大气污染物和气象条件对复合污染指数有明显滞后效应,滞后影响主要集中在1~3 d.高值的PM2.5、PM10、O3、SO2和空气温度明显增加复合污染风险,中值段的CO (1~6 mg·m-3)、NO2(38~118 μg·m-3)、相对湿度(54%~87%)和低风速都会增加复合污染风险.复合污染事件在数值响应中表现出多天连续污染的趋势,相较于PM2.5和PM10,复合污染事件更依赖于O3,高值区复合污染率在30.7%~47.5%.CO和相对湿度对复合污染事件影响较小,空气温度影响最大,复合污染事件中84.7%发生在20~30℃.
英文摘要
      The multi-scale variation trend of PM2.5-O3 compound pollution events was analyzed based on air quality data, meteorological data, and COVID-19 data in Beijing from 2015 to 2020. For the threshold of compound pollution, a compound pollution index was proposed, and the numerical response trend was evaluated based on the generalized additive model. A distributed lag nonlinear model was introduced to analyze the risk response relationship between compound pollution and influencing factors. The results showed that the events of PM2.5-O3 compound pollution in Beijing decreased annually. At the same time, due to the influence of pollutant emissions and meteorological conditions, there were obvious seasonal effects, week effects, holiday effects, and epidemic effects. The composite pollution index had no correlation with rainfall but had a linear positive correlation with O3 and air temperature and a nonlinear correlation with other explanatory variables. Air pollutants and meteorological conditions had obvious lag effects on the composite pollution index, and the lag effects were mainly concentrated in 1-3 d. PM2.5, PM10, O3, SO2, and air temperature in high-value areas significantly increased the risk of compound pollution. The CO (1-6 mg·m-3), NO2 (38-118 μg·m-3), and relative humidity (54%-87%) in the median section would also increase the risk of compound pollution, as would low wind speed. The compound pollution events showed a trend of multi-day continuous pollution in the numerical response. Compared with PM2.5 and PM10, compound pollution events were more dependent on O3, and the compound pollution rate in high-value areas was 30.7%-47.5%. CO and relative humidity had little effect on compound pollution events. The air temperature had the greatest impact, and 84.7% of the composite pollution incidents occurred at 20-30℃.

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