Abstract:
Periodicity correlations between roadside PM2.5 concentration and traffic volume are difficult to explore due to the complicated process of production and diffusion of PM...Show MoreMetadata
Abstract:
Periodicity correlations between roadside PM2.5 concentration and traffic volume are difficult to explore due to the complicated process of production and diffusion of PM2.5: a reaction of vehicle emission with atmosphere, dependence with wind speed, pavement width, vehicle speed, vehicle type, etc. We propose a framework to explore the periodicity correlation between roadside PM2.5 concentration and traffic volume using wavelet transform. The framework utilizes wavelet spectrum to obtain the characteristic periods of PM2.5 concentration and traffic volume, and utilizes cross-wavelet spectrum to explore the hysteresis response of PM2.5 concentration with respect to traffic volume. Different from traditional research work which handle pollutants and traffic data of macro scales, our study examines the micro-scale relationship of PM2.5 concentration and traffic volume. We conduct experiments from four urban streets of Beijing, and reveal two micro-scale rules: (a) the characteristic period of PM2.5 concentration approximates the characteristic period of traffic volume, and both of them are significantly affected by the traffic signal cycle; (b) PM2.5 concentration exhibits a time delay within 0.3-0.9 minutes with respect to traffic volume. The research will benefit the further study on the evolution of PM2.5 concentration and traffic volume, and provide a reference for the mitigation of PM2.5 pollution by traffic control as well as practical traffic pollutant regulations in metropolises.
Published in: IEEE Transactions on Vehicular Technology ( Volume: 68, Issue: 11, November 2019)