Luo Na-Na, Zhao Wen-Ji, Yan Xing, Gong Zhao-Ning, Xiong Qiu-Lin
Key Laboratory of 3D Information Acquisition and Application of Ministry of Education, Capital Normal University, Beijing, 100048, China.
Huan Jing Ke Xue. 2013 Oct;34(10):3741-8.
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.
为研究气象和交通因素对PM2.5和PM10浓度的影响，在北京三环路采集了28个样本，并收集了这些样本的降尘量、车速、交通流量、温度、湿度、风速、PM2.5和PM10浓度数据。对收集到的降尘量数据进行分析表明，交通对空气质量有显著影响。道路上和远离交通源处的平均降尘量分别为0.284克和0.016克。部分实验结果表明，居民区的PM2.5和PM10浓度低于道路上的浓度，此外，夜间的PM2.5往往高于白天，PM2.5和PM10差值的平均值分别为101074纳克/立方米和15386纳克/立方米。通过使用最佳子集预测模型进行分析表明，PM2.5和PM10均受温度和湿度影响最为显著，其次是风速、车速和交通流量。与PM10相比，车速、交通流量和风速对PM2.5的影响更为显著。
