The Effect of Land Use Classification on the Gas-Phase and Particle Composition of the Troposphere: Tree Species Versus Forest Type Information

Abstract

Relationships between vegetation and air quality are intricate and still not fully understood. For regional air quality assessments, a better understanding of the diverse feedback mechanisms is crucial. The present article investigates the impact of land use data set detailedness on air quality predictions. Therefore, two different land use data sets were applied for simulations with COSMO-MUSCAT for Germany in May 2014. One data set includes detailed information about tree species while the second one obtains generalized widely applied land use classes including mixed and coniferous forests. Moreover, we examined the role of agricultural NO soil emissions, agricultural biomass density enhancements, and model resolution. For a more comprehensive implementation of the secondary organic aerosol (SOA) formation, the SOA module was extended considering additional biogenic volatile organic compound (BVOC) precursor groups from isoprene, α-pinene, limonene, and sesquiterpene oxidations. The model studies showed substantial differences in BVOC emission patterns between the two land use data sets. The application of detailed tree species information leads to complex BVOC emission patterns with high emission spots. In contrast, coarser forest information lead to standardized comprehensive emissions which result in 50% higher BVOC emissions. These differences affect both the atmospheric oxidizing potential and the production rates of SOA precursors. Land use induced regional differences (tree species minus forest information) in NOx (±2.5%), ozone (−2.5%), OH (±50%), NO3 radical (+70%) concentrations, and SOA (−60%) mass are modeled. Overall, the simulations demonstrate that detailed land use information, extended organic chemistry treatment, and high spatial resolution are mandatory for air quality assessments.