The input-optimized and supplemented MEGAN improves biogenic volatile organic compounds estimation accuracy in China

Abstract

To reduce the uncertainty in biogenic volatile organic compound (BVOC) emission estimates by MEGAN model, this study introduces targeted optimizations in the input conditions driven by plant functional types (PFTs), emission factors (EFs), and leaf area index (LAI), and supplements the missing BVOC emissions from urban green spaces (U-BVOC emissions). To verify the optimization effect of the input-improved MEGAN, the study conducted a comparative validation of the estimated BVOC emission levels in representative regions of northern and southern China using formaldehyde vertical column densities and accumulation-based estimates through stock volume conversion. The results indicate that the input-improved MEGAN can provide more reasonable BVOC estimates with a more notable improvement in the southern region, and the estimated isoprene emissions show better spatiotemporal correlation with formaldehyde vertical column densities. The optimization of PFT + EF and LAI input conditions both effectively improve the model's estimation accuracy. Although the U-BVOC emissions are significantly lower than the BVOC emissions from non-urban environment, the newly added U-BVOC emission inventory is expected to help improve the performance of existing air quality models in simulating O₃ and particulate matter pollution in urban areas.