Impact of Multiple Vegetation Inputs on Biogenic VOC Emissions over the Korean Peninsula

Jang, Y., Woo, J.-H., Kim, Y. ORCID: https://orcid.org/0000-0002-5053-5068, Kim, J., Kim, J., Park, M., & Kim, B. (2020). Impact of Multiple Vegetation Inputs on Biogenic VOC Emissions over the Korean Peninsula. In: AGU Fall Meeting 2020, 1-17 December 2020, San Francisco, USA.

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Abstract

Biogenic Volatile Organic Compounds (BVOCs) emissions from natural environments, such as forest, are a major precursor to ozone, secondary fine particles, and act as climate pollutants. In addition, estimating vegetation emissions correctly is a very important factor in air quality modeling and analysis on climate change and atmospheric environment, as more than 80% of global VOCs emissions are generated by vegetation sources. This study was conducted to understand the effect of using different PFT and LAI datasets as vegetation input data in a BVOCs emission model, when trying to quantify realistic BVOC emission estimates for the Korean Peninsula. The effect of meteorological modeling performance as an input to MEGAN v2.1 emissions model was also discussed. We had used the MEGAN (Model of Emissions of Gases and Aerosols from Nature) BVOCs emissions model to estimate BVOCs emissions. We have tested BVOCs emissions estimation over the Korean Peninsula using multiple PFTs and LAIs. Two different datasets from PFTs (MODIS vs. Local) and LAIs(MODIS vs. STARFM) combination 4 cases were used.
The emission estimate amounts change by case and by species. When the LAI changes from MODIS to Local, the fraction of vegetation area and needleleaf trees were increased. These inputs changed BVOCs emission modelling results; isoprene emissions decreased by 20 %, but monoterpenes emission is increased by 60%. When the PFT changed from MODIS to STARFM, emissions of both isoprene and monoterpenes were increased (by 50% for isoprene and 70% for monoterpenes). When compared with other BVOCs emission inventory, including inverse emissions from GlobEmission, the emissions using Local PFT and STARFM LAI turned out to be the best estimates among 4 cases. Correlation analysis and monthly inter-comparison also indicate that our bottom-up emissions represent reasonable spatial and temporal variations using Local PFT and STARFM LAI, but generally overestimated the amount of BVOCs by 30%. The main reason of this overestimation was because of solar radiation from WRF model, which constantly overestimated by 10~20%.

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