Improved isoprene emission estimates from MEGAN and comprehensive modeling of BVOCs driven aerosol dynamics in the Boreal forests

Accurate representation of biogenic volatile organic compound (BVOC) emissions is critical for understanding their role in atmospheric chemistry and secondary organic aerosol (SOA) formation. In this study, we present an improved framework for modeling biogenic emissions, using the latest version of the Model of Emissions of Gases and Aerosols from Nature (MEGAN). We used domain-specific tree cover data, species distributions (retrieved from the Natural Resources Institute Finland website), and species-specific emission factors, and we recalculated isoprene emission factors tailored to the Finnish boreal region. These modifications were implemented in MEGAN and integrated into the WRF-CHIMERE chemistry transport model, enabling a more accurate simulation of biogenic emissions. We perform simulations over the summer period for the year 2017, 2018, and 2019.These simulations reveal a significant reduction in bias for both isoprene emissions as well as concentrations when compared to observations at the Hyytiälä and Pallas stations. Additionally, we introduced a detailed canopy correction (sensitivity simulation) to account for the effects of forest canopy on the vertical and horizontal transport of BVOCs. These adjustments additionally reduced the bias in modeled isoprene concentrations when compared to observations. The enhanced representation of BVOC emissions and the effects of canopy on dispersion resulted in improvements in the modeled dynamics of SOA formation and transportation, emphasizing the importance of ecosystem-specific modifications in emission models and the inclusion of forest canopy correction in chemical transport models.Our findings show that the vanilla versions of MEGAN version 3.2 without modification is insufficient to accurately represent isoprene emissions, at least in the European boreal forest ecosystem. High-resolution, domain-specific vegetation data are essential to capture the variability in tree cover, species distribution, and emission factors, ensuring the reliability of modeled biogenic emissions and their impacts on atmospheric chemistry.