City-scale PM2.5 removal by urban trees: validation of i-Tree eco and biomonitoring of PM2.5-bound trace elements

• Quantified PM 2.5 dry deposition for five tree species across sites and timescales. • i-Tree Eco estimates matched field data within 7% across species, sites, and times. • Leaf-surface PM 2.5 deposition saturated after approximately 442 h of exposure. • Metals such as Fe, Ni, Cu, Zn, and Pb were higher in urban than peri-urban sites. • Findings support i-Tree Eco calibration and urban trees as passive PM 2.5 samplers. Urban trees play a critical role in mitigating fine particulate matter (PM 2.5 ) pollution through dry deposition, yet widely applied models such as i-Tree Eco remain insufficiently validated, often relying on single-species or short-term studies that overlook spatiotemporal variability. This study provides the first multi-species, spatiotemporal empirical validation of i-Tree Eco, based on leaf-surface PM 2.5 dry deposition measurements across five broadleaf tree species, three urban sites, and five time periods in Taiwan. On average, measured deposition was 33.91 µg m −2 h −1 , with i-Tree Eco underestimating measured deposition by an average of 7 %, indicating robust performance for broad-scale, multi-species, and spatiotemporal applications. Under low and stable wind conditions, discrepancies were primarily linked to the limited representativeness of ambient PM 2.5 data for tree-level environments, underscoring the need for localized air quality measurements to refine model accuracy. Species differences also influenced dry deposition fluxes, with Bischofia javanica Bl., characterized by its thick, leathery leaves, exhibiting values closest to the species mean. We further identified a PM 2.5 dry deposition saturation threshold (approximately 442 h), providing insights into removal dynamics not currently represented in i-Tree Eco. Elemental analysis revealed elevated concentrations of Fe, Cu, Zn, Pb, Cr and Ni at urban sites compared to the peri -urban area, despite similar PM 2.5 mass levels, highlighting the importance of PM 2.5 -bound heavy metal assessment and the role of urban trees as biomonitors of atmospheric heavy metals. Collectively, these findings not only provide an empirical basis for calibrating the urban forest air pollution removal model i-Tree Eco, but also demonstrate the potential of urban trees as passive samplers of PM 2.5 and its associated bound pollutants, advancing both ecosystem service quantification and urban air quality management strategies.