UFORE-D Modeling of Urban Tree Influence on Particulate Matter Concentrations in a High-Altitude Latin American Megacity

Urban trees reduce particulate matter (PM) concentrations through dry deposition, interception, and modifying wind patterns, improving air quality and saving public health expenses in urban planning. The main objective of this article is to present an analysis of the influence of urban trees on PM10 and PM2.5 concentrations in a high-altitude Latin American megacity (Bogotá, Colombia) using UFORE-D modeling. Six PM monitoring stations distributed throughout the megacity were used. Hourly climatic and PM data were collected for seven years, along with dendrometric and cartographic analyses within 200 m of the monitoring stations. Land cover was quantified using satellite imagery (Landsat 8) in order to perform a spatial analysis. The results showed that the UFORE-D model effectively quantified urban forest canopy area (CA) impact on PM10 and PM2.5 removal, showing strong correlations (R2 = 0.987 and 0.918). PM removal increased with both CA and ambient pollutant concentrations, with CA exhibiting greater influence. Sensitivity analysis highlighted enhanced air quality with increased leaf area index (LAI: 2–4 m2/m2), particularly at higher wind speeds. PM10 removal (1.05 ± 0.01%) per unit CA exceeded PM2.5 (0.71 ± 0.09%), potentially due to resuspension modeling. Model validation confirmed reliability across urban settings, emphasizing its utility in urban planning. Scenario analysis (E1–E4, CA: 8.30–95.4%) demonstrated a consistent positive correlation between CA and PM removal, with diminishing returns at extreme CA levels. Urban spatial constraints suggested integrated green infrastructure solutions. Although increased CA improved PM removal rates, the absolute reduction of pollutants remained limited, suggesting comprehensive emission monitoring.