Impact analysis of traffic-related air pollution based on real-time traffic and basic meteorological information.
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.
School of Traffic and Transportation, Beijing Jiaotong University, Haidian District, Beijing 100044, China. Electronic address: 14114257@bjtu.edu.cn.
Long
L
Pan
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. Electronic address: enjyao@bjtu.edu.cn.
Enjian
E
Yao
School of Traffic and Transportation, Beijing Jiaotong University, Haidian District, Beijing 100044, China. Electronic address: 11114240@bjtu.edu.cn.
Yang
Y
Yang
D000393:Air Pollutants / Q000009:adverse effects / Q000032:analysis; D000397:Air Pollution / Q000009:adverse effects / Q000032:analysis*; D000068476:Beijing; D002248:Carbon Monoxide / Q000009:adverse effects / Q000032:analysis*; D004781:Environmental Exposure / Q000032:analysis*; D006801:Humans; D008962:Models, Theoretical; D011634:Public Health; D015203:Reproducibility of Results; D001335:Vehicle Emissions / Q000032:analysis*; D014887:Weather
D000393:Air Pollutants; D001335:Vehicle Emissions; D002248:Carbon Monoxide
Air quality modeling; Dispersion; Motor vehicle emissions; Population exposure; Traffic-related air pollution