Assessing Spatiotemporal Characteristics of Urban PM2.5 Using Fractal Dimensions and Wavelet Analysis

Abstract

Due to rapid urbanization and industrialization, atmospheric fine particulate matter (PM2.5) has become a primary urban pollutant, seriously affecting air quality and resident health. Existing airborne remote sensing and ground sensor monitoring can efficiently collect PM2.5 data. It is urgent yet challenging to fully use these two monitoring modes to analyze the spatial distribution and dynamic changes of PM2.5. This paper proposes a method to analyze the spatiotemporal characteristics of urban PM2.5 concentration using airborne and ground monitoring data. The method utilizes the boundary dimension and the radius dimension to describe the spatial distribution characteristics of PM2.5 and then adopts wavelet analysis to detect the fluctuation periods of PM2.5 concentration. Case study was performed based on the moderate-resolution imaging spectroradiometer (MODIS) data and the daily ground monitoring concentration of PM2.5 from 2014 to 2017 in Beijing. The decrease results of boundary dimension indicate that the changes in PM2.5 concentration tend to be slower over time, and the fluctuation amplitude gradually becomes smaller. The increase results closer to 2.0 in the radius dimensions suggest that the PM2.5 distribution becomes more uniform, indicating that the pollution control in Beijing had achieved initial success. The frequency results of wavelet analysis reveal that PM2.5 concentration in Beijing is mainly subject to periodic variation of 190 days. These findings can help us to gain deeper insight into the complex spatiotemporal characteristics of urban PM2.5.