Effects of Street Trees on PM2.5 and PM10 Pollution in Summer Hangzhou: A Mobile Monitoring Study with LiDAR and Environmental Sensors​

Abstract

With rapid urbanization, street trees have become an important part of urban greening. To investigate the effects of street trees on PM2.5 and PM10 pollution in summer in Hangzhou, 29 urban roads were selected. Vehicle-mounted LiDAR and a mobile environmental monitoring station were used to synchronously measure street tree parameters, road spatial structure, meteorological conditions, and traffic volume. The results showed significant spatiotemporal variations in PM2.5 and PM10 concentrations: Qiantang District (industrial zone) had the most severe pollution, while Xihu District (scenic region) had the lowest. Their temporal variation curves were highly consistent, indicating common sources. Street Trees exerted significant effects under two scenarios: W/H < 0.6 with traffic volume < 1500 vehicles/2h, and W/H > 1.2 with traffic volume > 1500 vehicles/2h. However, their effects had a threshold: under W/H = 0.6-1.2 and traffic volume > 1500 vehicles/2h, the effect was superior to high tree density only when street tree volume was 8000–16000 m³; excessive volume exacerbated pollution. Linear regression models for PM2.5 (F = 21.591, p < 0.001, adjusted R² = 0.290) and PM10 (F = 21.813, p < 0.001, adjusted R² = 0.292) showed that road volume and fuel vehicle traffic were extremely significantly positively correlated with PM concentrations (p < 0.001), while street tree volume and new energy vehicle traffic were extremely significantly negatively correlated (p < 0.001). These findings highlight conditional and threshold effects of street trees on PM pollution, informing evidence-based road design and urban greening to enhance air quality in urbanizing regions.

Keywords: PM, Urban Street Trees, Road Space, Traffic Flow, Effects, LiDAR