Pan Long, Yao Enjian, Yang Yang
School of Traffic and Transportation, Beijing Jiaotong University, Haidian District, Beijing 100044, China.
School of Traffic and Transportation, Beijing Jiaotong University, Haidian District, Beijing 100044, China; MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Haidian District, Beijing 100044, China.
J Environ Manage. 2016 Dec 1;183(Pt 3):510-520. doi: 10.1016/j.jenvman.2016.09.010. Epub 2016 Sep 9.
With the rapid development of urbanization and motorization in China, traffic-related air pollution has become a major component of air pollution which constantly jeopardizes public health. This study proposes an integrated framework for estimating the concentration of traffic-related air pollution with real-time traffic and basic meteorological information and also for further evaluating the impact of traffic-related air pollution. First, based on the vehicle emission factor models sensitive to traffic status, traffic emissions are calculated according to the real-time link-based average traffic speed, traffic volume, and vehicular fleet composition. Then, based on differences in meteorological conditions, traffic pollution sources are divided into line sources and point sources, and the corresponding methods to determine the dynamic affecting areas are also proposed. Subsequently, with basic meteorological data, Gaussian dispersion model and puff integration model are applied respectively to estimate the concentration of traffic-related air pollution. Finally, the proposed estimating framework is applied to calculate the distribution of CO concentration in the main area of Beijing, and the population exposure is also calculated to evaluate the impact of traffic-related air pollution on public health. Results show that there is a certain correlation between traffic indicators (i.e., traffic speed and traffic intensity) of the affecting area and traffic-related CO concentration of the target grid, which indicates the methods to determine the affecting areas are reliable. Furthermore, the reliability of the proposed estimating framework is verified by comparing the predicted and the observed ambient CO concentration. In addition, results also show that the traffic-related CO concentration is higher in morning and evening peak hours, and has a heavier impact on public health within the Fourth Ring Road of Beijing due to higher population density and higher CO concentration under calm wind condition in this area.
随着中国城市化和机动化的快速发展,交通相关空气污染已成为空气污染的主要组成部分,不断危害公众健康。本研究提出了一个综合框架,用于利用实时交通和基本气象信息估算交通相关空气污染浓度,并进一步评估交通相关空气污染的影响。首先,基于对交通状况敏感的车辆排放因子模型,根据基于路段的实时平均车速、交通流量和车辆组成计算交通排放。然后,根据气象条件的差异,将交通污染源分为线源和面源,并提出了相应的确定动态影响区域的方法。随后,利用基本气象数据,分别应用高斯扩散模型和烟团积分模型估算交通相关空气污染浓度。最后,将所提出的估算框架应用于计算北京主要区域一氧化碳浓度分布,并计算人口暴露量以评估交通相关空气污染对公众健康的影响。结果表明,影响区域的交通指标(即车速和交通强度)与目标网格的交通相关一氧化碳浓度之间存在一定的相关性,这表明确定影响区域的方法是可靠的。此外,通过比较预测的和实测的环境一氧化碳浓度,验证了所提出估算框架的可靠性。此外,结果还表明,交通相关一氧化碳浓度在早晚高峰时段较高,且由于北京四环路以内区域人口密度较高,静风条件下一氧化碳浓度也较高,因此对公众健康的影响较大。