As the largest developing country, China's air quality has always been a focus of research. Air pollution and the processes that generate air pollution exhibit spatial heterogeneity. According to the pollutant analysis results released by Beijing, Jinan, Hangzhou, vehicle exhaust has surpassed coal as the main source of urban air pollution (especially PM2.5). By June 2020, the number of motor vehicles in China had reached 360 million, and traffic congestion had become the norm in many Chinese cities. As a result, the contribution rate of vehicle exhaust pollution to air quality will continuously increase. Its contribution to PM2.5, volatile organic compounds (VOC), etc., were experimentally analyzed (John, 2001; Osada, 2019; Yuanchung, 2019, 2020; Yanzhao, 2019; Wenlong, 2017; Tianzeng, 2018). The influences of the traffic characteristics, traffic sources, traffic flow states, road grade, vehicle type, fuel, terrain, meteorological conditions, and spatial-temporal heterogeneity on exhaust emissions were studied(Abdull, 2020; Ryosuke, 2019; Asokraj, 2018; Jianbing, 2019; Haobing, 2019; Suhong, 2019; Cheol-Heon, 2018; Osada, 2019). Traffic simulations, the OMG volume-source model, cellular automata, sensitivity analysis, and the fault tree model have also been used to study exhaust emissions, diffusion, and their influence on air pollution. (Hitoshi, 1990; Zhao, 2017; Pratama, 2019; Sergio,2020; Fengchuan,2019; Pratama et al., 2019). Few studies on the impacts of road network traffic characteristics (e.g., road density, intersection, and bus network density) on air quality have considered the spatial heterogeneity.
Air pollution and the process of producing air pollution exhibit spatial heterogeneity. The geographically weighted regression (GWR) model considering spatial heterogeneity was used to analyze urban air pollutants, and the results revealed that the GWR is better than regression model (Cheng et al.,2017; Hu et al., 2013; Wang et al.,2016). The multi-scale geographically weighted region (MGWR) model was proposed and used to examining the influences of air quality in China's cities (Fotheringham et al, 2017, 2019). The retrieval of the air pollution status based on remote sensing data not only makes up for the lack of observation data, but also reflects the spatial distribution characteristics of the air pollution, so remote sensing inversion has become an important method for studying air pollution. Previous studies have shown that there is a certain correlation between the aerosol optical thickness (AOD) and the concentration of near surface particles, and the AOD product of MODIS (Moderate Resolution Imaging Spectroradiometer) is the most widely used in air pollution research (Chu et al, 2003; Slater et al, 2004; Wang et al, 2003; Gupta et al,2006; Tao, et al,2013). The objective of this study is to quantitatively analyze the impacts of road traffic characteristics on urban air quality based on their spatial heterogeneity.