Abstract
Traffic congestion and smog are hot topics in recent years. This study analyzes the impacts of road traffic characteristic parameters on urban air quality quantitatively based on aerosol optical thickness (AOD) and geographical weighted regression (GWR) models, including the road network density, road area occupancy, intersection number, and bus network density as main factors. There are some major research findings. Firstly, there exists a strong positive correlation between the peak congestion delay index (PCDI) and air quality, the correlation has R2 values of up to 0.4962 (R 0.70). Secondly, GWR refines the local spatial changes in the AOD and the road parameters, and the correlation R2 based GWR model all above 0.6. The correlation between AOD and the road area occupancy was the highest, and the correlations with the bus network density and the intersections number were higher than that with the road network density. Thus, bus route planning, bus emission reduction, road network planning, and signal timing (at intersections) have a greater impact on air quality than other policy, especially in areas with traffic jams. The results of this study could provide theoretical support for traffic planning and traffic control, and is promising in practice.
Highlights
As the largest developing country, China’s air quality has always been a focus of research
Previous studies have shown that there is a strong 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 r esearch27–29 (Sathe et al 2019; Tao et al 2012; Wei et al 2021)
The objective of this study is to quantitatively analyze the impacts of road traffic characteristics on urban air quality based on their spatial heterogeneity
Summary
As the largest developing country, China’s air quality has always been a focus of research. The contribution rate of vehicle exhaust pollution to air quality will continuously increase (Liu et al 2018; Huang et al 2020a). 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 s tudied9–17(Abdull et al 2020; Bae et al 2018; Beddows et al 2020; Jeong et al 2019; Huang et al 2020b; Li et al 2018; Lin et al 2019; Liu et al 2019; Pratama et al 2019; Zhang et al 2021). The objective of this study is to quantitatively analyze the impacts of road traffic characteristics on urban air quality based on their spatial heterogeneity
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