Spatially heterogeneous influence of street greenery on street-level PM2.5 pollution using mobile monitoring from a three-dimensional perspective

Abstract

Greenery may be effective in mitigating particulate matter (PM) pollution. However, most previous studies have explored the effects of greenery on air quality from a two-dimensional rather than a three-dimensional (3D) perspective. In this study, a geographically weighted regression (GWR) model was constructed to explore the effect of street greenery on street-level PM2.5 concentrations using street view imagery from a 3D perspective. Mobile monitoring of PM2.5 concentrations was conducted in Wuhan. The results demonstrated that GWR is suitable for exploring street-level air quality. From a 3D perspective, street greenery strongly affects the air quality of the surrounding area within 300 m. Moreover, considering spatial nonstationarity, the negative impact of street greenery on air quality depends on the pollution and ventilation conditions. High pollution and canyon effects amplify the negative impact of street greenery on pollution dispersion. Thus, the negative effect of street greenery on air quality is sensitive to the density of second-level roads and of commercial areas, the number of intersections and the distance to rivers. Dense planting trees in these areas must be reconsidered based on the possibility of ventilation loss. These results provide insights into street-level PM2.5 pollution and can contribute to the evidence-based design of urban green infrastructure.