| 利用多元线性回归方法评估气象条件和控制措施对APEC期间北京空气质量的影响 |
| 摘要点击 2886 全文点击 991 投稿时间：2018-07-06 修订日期：2018-09-14 |
| 查看HTML全文
查看全文 查看/发表评论 下载PDF阅读器 |
| 中文关键词 多元线性回归方法 相对权重方法 气象条件 控制措施 APEC会议 |
| 英文关键词 multiple linear regression method relative weight method meteorological conditions air pollution control measures the APEC 2014 summit |
| 作者 | 单位 | E-mail | | 李颖若 | 中国气象局北京城市气象研究所, 北京 100089
京津冀环境气象预报预警中心, 北京 100089 | lyr@pku.edu.cn | | 汪君霞 | 北京大学环境科学与工程学院, 北京 100081 | | | 韩婷婷 | 中国气象局北京城市气象研究所, 北京 100089
京津冀环境气象预报预警中心, 北京 100089 | | | 王垚 | 中国气象局北京城市气象研究所, 北京 100089
京津冀环境气象预报预警中心, 北京 100089 | | | 何迪 | 中国气象局北京城市气象研究所, 北京 100089
京津冀环境气象预报预警中心, 北京 100089 | | | 权维俊 | 中国气象局北京城市气象研究所, 北京 100089
京津冀环境气象预报预警中心, 北京 100089 | | | 马志强 | 中国气象局北京城市气象研究所, 北京 100089
京津冀环境气象预报预警中心, 北京 100089 | zqma@ium.cn |
|
| 中文摘要 |
| 气象条件对大气污染物的扩散和传输有重要影响，准确分离和定量气象因素对空气质量的影响是评估大气污染控制政策有效性的前提.本研究利用APEC会议期间及前后（2014-10-15~2014-11-30）北京城区朝阳观测站点SO2、NO、NO2、NOx、CO、PM2.5、PM1和PM10以及气象因素的观测数据，采用多元线性回归分析方法，定量评估了气象条件和空气污染控制措施对APEC期间北京空气质量的影响.在假定排放条件不变的情况下，基于气象因素参数建立的预测污染物浓度的多元线性回归模型模拟效果较为理想，决定系数R2在0.494~0.783之间.控制措施使得APEC控制期SO2、NO、NO2、NOx、CO、PM2.5、PM1和PM10浓度分别降低48.3%、53.5%、18.7%、40.6%、3.6%、34.8%、28.8%和40.6%，气象因素使得APEC控制期SO2、NO、NO2、NOx、CO、PM2.5、PM1和PM10浓度分别降低1.7%、-2.8%、18.7%、4.5%、18.6%、27.5%、30.6%和35.6%.气象因素和控制措施共同作用使得APEC控制期北京空气质量得到了明显改善.控制措施对SO2和氮氧化物浓度的下降起主导作用，气象因素对CO浓度的下降起主导作用，气象因素和控制措施对颗粒物浓度降低的贡献相当.本研究还利用相对权重方法研究了气象因素对污染物浓度影响的贡献，结果表明影响不同污染物浓度的决定性气象因素不同. |
| 英文摘要 |
| Meteorological conditions have important impact on the diffusion and transport of air pollutants, thus separating and quantifying the impact of meteorological factors is a prerequisite for evaluation of air pollution control measures. Using observation data on SO2, NO, NO2, NOx, CO, PM2.5, PM1, and PM10 as well as meteorological factors at the Chaoyang site, an urban site in Beijing, we evaluated the impact of meteorological conditions and control measures on air quality in Beijing during APEC 2014 (from 15 October to 30 November, 2014) by the multiple linear regression method. The simulation performance of a multivariate linear regression model based on the parameters of meteorological factors for predicting pollutant concentration assuming constant emission conditions were ideal, produced a range of determination coefficient (R2) of 0.494-0.783. Our results suggested that air pollution control measures reduced the concentration of SO2, NO, NO2, NOx, CO, PM2.5, PM1, and PM10 by 48.3%, 53.5%, 18.7%, 40.6%, 3.6%, 34.8%, 28.8%, and 40.6%, while meteorological conditions reduced the concentration of SO2, NO, NO2, NOx, CO, PM2.5, PM1, and PM10 by 1.7%, -2.8%, 18.7%, 4.5%, 18.6%, 27.5%, 30.6%, and 35.6%. The combination of meteorological factors and control measures has significantly improved the air quality in Beijing during the APEC period. Control measures played a leading role in the reduction of SO2 and nitrogen oxides, and meteorological factors played a leading role in the reduction of CO. Meteorological factors and control measures made roughly equal contributions to the reduction of particulate matter. We also used the relative weight method to study the contribution of meteorological factors to the pollutant concentration. The results showed that the decisive meteorological factors on the concentrations of different pollutants were different. |
|
|
|
