| 交通与气象因子对不同粒径大气颗粒物的影响机制研究 |
| 摘要点击 3160 全文点击 1799 投稿时间：2013-03-26 修订日期：2013-05-13 |
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| 中文关键词 PM2.5 PM10 最佳子集分析 交通因子 气象因子 |
| 英文关键词 PM2.5 PM10 best subset analysis traffic factors meteorological factors |
| 作者 | 单位 | E-mail | | 罗娜娜 | 首都师范大学三维信息获取与应用教育部重点实验室, 北京 100048
首都师范大学资源环境与地理信息系统北京市重点实验室, 北京 100048
首都师范大学城市环境过程与数字模拟国家重点实验室培育基地, 北京 100048 | luonana0331@aliyun.cn | | 赵文吉 | 首都师范大学三维信息获取与应用教育部重点实验室, 北京 100048
首都师范大学资源环境与地理信息系统北京市重点实验室, 北京 100048
首都师范大学城市环境过程与数字模拟国家重点实验室培育基地, 北京 100048 | zhaowenji1215@163.com | | 晏星 | 香港理工大学土地测量及地理资讯学系, 香港 | | | 宫兆宁 | 首都师范大学三维信息获取与应用教育部重点实验室, 北京 100048
首都师范大学资源环境与地理信息系统北京市重点实验室, 北京 100048
首都师范大学城市环境过程与数字模拟国家重点实验室培育基地, 北京 100048 | | | 熊秋林 | 首都师范大学三维信息获取与应用教育部重点实验室, 北京 100048
首都师范大学资源环境与地理信息系统北京市重点实验室, 北京 100048
首都师范大学城市环境过程与数字模拟国家重点实验室培育基地, 北京 100048 | |
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| 中文摘要 |
| 为了研究北京市气象因子与车流量、车速等交通因子对 PM2.5、PM10浓度水平的影响,在市区三环主路及居民区选取了 28个采样点,采集滞尘量, PM2.5、PM10浓度、车速、车流量、温度、湿度、风速等数据. 通过 3个月的滞尘质量分析,得出交通源对空气质量的影响是显著的,其中三环主道路两侧采样点和远离交通源对照点滞尘均值分别为 0.284 g和0.016 g. 再由道路口与居民区对比实验(局部实验)得出,居民区采样点测得的 PM2.5和 PM10浓度均低于道路口颗粒物浓度,差值均值分别为 101074 n·(cf)-1和 15386 n·(cf)-1,同时 PM2.5白天浓度一般低于夜间. 最后结合最佳子集预测模型分析得出, PM2.5和 PM10受到湿度和温度的影响最大,车速、车流量、风速次之,其中车速、车流量、低风速对颗粒物 PM2.5的影响比对 PM10的影响更为显著. |
| 英文摘要 |
| To study the effects of meteorological and traffic factors on the PM2.5 and PM10 concentrations, 28 samples were taken in the Third Ring Road of Beijing, and dust fall weight, velocity of vehicle, traffic volume, temperature, humidity, wind speed, PM2.5 and PM10 concentration data were collected for these samples. Analysis of the collected data on dust fall weight indicated that the traffic had a significant impact on the air quality. The average dust fall weights in the road and away from the traffic source were 0.284g and 0.016 g, respectively. The results of the partial experiment indicated that concentrations of PM2.5 and PM10 in residential areas were lower than those in road, furthermore, the PM2.5 at night was often higher than that during daytime, and the mean values of the difference in PM2.5 and PM10 were 101074 n·(cf)-1 and 15386 n·(cf)-1, respectively. Through analysis using the best subset prediction model, it was indicated that PM2.5 and PM10 were both most significantly influenced by temperature and humidity, followed by wind speed, velocity of vehicle and traffic volume. Comparing with PM10, the velocity of vehicle, traffic volume and wind speed had a more significant influence on PM2.5. |
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