Biogenic volatile organic compound (BVOCs) emissions are the largest VOC emission source globally, and are precursors to ozone and secondary organic aerosols, both of which are strong, short-lived climate pollutants. BVOC emissions are usually estimated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN), which requires Plant Functional Types (PFTs) and Leaf Area Indexes (LAIs) as inputs. Herein, the effects of refined input data on regional BVOC emission estimates are analyzed. For LAIs, lower resolution MODerate-resolution Imaging Spectroradiometer (MODIS), and higher spatio-temporal resolution Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) LAI were generated. For PFTs, local land cover maps were developed, in addition to MODIS PFT. In South Korea, annual emissions of isoprene and monoterpenes in 2015 were estimated as 384 and 160 Gg/year, respectively, using STARFM LAI and Local PFT (Case 4). For North Korea, 340 Gg/year isoprene and 72 Gg/year monoterpenes emissions were estimated using STARFM LAI and MODIS PFT. These estimates were 14–110% higher than when using MODIS LAI and MODIS PFT (Case 1). Inter-comparison with satellite-based inverse isoprene emission estimates from GlobEmission shows 32% (North Korea) to 34% (South Korea) overestimation in bottom-up data. Our new vegetation inputs improve MEGAN performance and resulting BVOC emission estimations. Performance of Weather Research and Forecasting (WRF) meteorological modeling requires improvement, especially for solar radiation, to avoid overestimation of isoprene emissions.

When predicting the health impacts of PM_2.5 from future air quality changes, it is crucial to consider both air quality improvements and population aging. This study divided future emission scenarios into a base and control scenario to project air quality from 2015 to 2030 and assess health outcomes. The GUIDE model, an Integrated Assessment Model (IAM), was used to estimate future emissions, while the CMAQ (Chemical Transport Model) and BenMAP (Health Impact Model) evaluated health impacts resulting from changes in air quality in Korea. The study focused on the impact of population aging on future health outcomes. Both scenarios showed improved PM_2.5 concentrations, with the control scenario showing more substantial improvements due to stronger policy measures. When applying current age patterns, health impacts decreased as PM_2.5 concentrations decreased. However, when considering future population aging, health impacts increased despite improved air quality. The results excluding aging show that the number of premature deaths due to cardiovascular disease and all other causes caused by PM_2.5 is 18,413 in the base year, while in the future control scenario, the number decreases to 11,729. In contrast, when aging is taken into account, the number of premature deaths increases to 23,037. This finding suggests that, although PM_2.5 concentrations are expected to decline, the increasing proportion of elderly individuals will exacerbate health risks. Therefore, accounting for aging population trends is essential when studying the health impacts of future air quality changes.