Improving BVOC emission estimation with high-resolution tree species mapping in urban areas

Biogenic volatile organic compounds (BVOCs) are critical precursors of atmospheric secondary pollutants, posing challenges to urban air quality management and public health. Current methodologies for estimating BVOC emissions, largely based on plant functional types (PFTs), lack the spatial and taxonomic precision required for urban landscapes characterized by diverse and heterogeneous vegetation. This limitation introduces significant uncertainties, particularly in highly developed urban areas. To address this gap, this study proposes the development of a high-resolution tree species classification dataset by utilizing remote sensing, machine learning, or in situ data integration techniques. By integrating this dataset with existing emission models, the research aims to quantify the discrepancies between species-level and PFT-based BVOC emission estimates, with a specific focus on urban areas. The species-level data is observed to yield higher emission estimates, highlighting the importance of fine-scale classification for accurately capturing emission dynamics. Additionally, this study analyzes the spatial distribution of BVOCs across urban and peri-urban gradients and assesses the implications of BVOC emissions on the formation of ozone. The findings are expected to provide suggestions for urban vegetation management, supporting the design of evidence-based strategies to mitigate air pollution and enhance urban sustainability.