Evaluation of regional isoprene emission factors and modeled fluxes in California

Abstract

Accurately modeled biogenic volatile organic compound (BVOC) emissions are an essential input to atmospheric chemistry simulations of ozone and particle formation. BVOC emission models rely on basal emission factor (BEF) distribution maps based on emission measurements and vegetation land-cover data but these critical input components of the models as well as model simulations lack validation by regional scale measurements. We directly assess isoprene emission-factor distribution databases for BVOC emission models by deriving BEFs from direct airborne eddy covariance (AEC) fluxes (Misztal et al., 2014) scaled to the surface and normalized by the activity factor of the Guenther et al. (2006) algorithm. The available airborne BEF data from approx. 10 000 km of flight tracks over California were averaged spatially over 48 defined ecological zones called ecoregions. Consistently, BEFs used by three different emission models were averaged over the same ecoregions for quantitative evaluation. Ecoregion-averaged BEFs from the most current land cover used by the Model of Emissions of Gases and Aerosols from Nature (MEGAN) v.2.1 resulted in the best agreement among the tested land covers and agreed within 10% with BEFs inferred from measurement. However, the correlation was sensitive to a few discrepancies (either overestimation or underestimation) in those ecoregions where land-cover BEFs are less accurate or less representative for the flight track. The two other land covers demonstrated similar agreement (within 30% of measurements) for total average BEF across all tested ecoregions but there were a larger number of specific ecoregions that had poor agreement with the observations. Independently, we performed evaluation of the new California Air Resources Board (CARB) hybrid model by directly comparing its simulated isoprene area emissions averaged for the same flight times and flux footprints as actual measured area emissions. The model simulation and the observed surface area emissions agreed on average within 20 %. We show that the choice of model land cover input data has the most critical influence on model measurement agreement and the uncertainty in meteorology inputs has a lesser impact at scales relevant to regional air quality modeling.