The Effect of Land Use Classification on the Gas‐Phase and Particle Composition of the Troposphere: Tree Species Versus Forest Type Information
DOI: https://doi.org/10.1029/2021JD035305
Persistent URL: http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9959
Persistent URL: http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/9959
Luttkus, M. L.; Hoffmann, E. H.; Poulain, L.; Tilgner, A.; Wolke, R., 2022: The Effect of Land Use Classification on the Gas‐Phase and Particle Composition of the Troposphere: Tree Species Versus Forest Type Information. In: Journal of Geophysical Research: Atmospheres, Band 127, 7, DOI: 10.1029/2021JD035305.
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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. Plain Language Summary:
Trees are associated with being the lungs of the atmosphere as they filter out harmful substances from the air, they store CO2, and produce oxygen via photosynthesis. Other by‐products of photosynthesis are biogenic volatile organic compounds (BVOCs). BVOCs are chemical substances with a high vapor pressure already at room temperatures, so they quickly evaporate from the leaves into the surrounding air and are responsible for the characteristic forest smell. The amount and composition of BVOC emissions strongly depend on the tree species. Every plant has its own distinct emission properties. The chemical degradation of BVOCs impacts the chemical composition of the troposphere and is connected to ground level ozone production and the formation of secondary organic aerosols (SOA), contributing substantially to particulate matter (PM). On a global scale, standardized BVOC emission information on forest levels are often used, but for regional air quality assessments detailed plant specific information is crucial, but still often lacking. Therefore, two different land use data sets were applied in the present study to investigate the impact of standardized forest versus detailed tree‐species information for Germany in May 2014. The study reveals changes in NOx (±2.5%), ozone (−2.5%), OH (±50%), NO3 radical (+70%), and SOA (−60%) concentrations. Key Points:
Detail of land use data sets crucial for biogenic volatile organic compound emission strength and composition.
Composition and concentration variation of these organic compounds induce changes in regional air quality predictions.
Detailed land use information, extended organic matter treatment, and high‐resolution simulations are mandatory for air quality assessments.
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