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Reliable results for the fine particulate matter (PM2.5) predictions are very useful for air policy establishment to reduce air pollution and adverse health effects caused by PM2.5. To this end, it is necessary to establish reliable emission input data for air quality models based on air pollutants' emission inventory data. In this study, we examine the characteristics of major air pollutants emitted from various emission sources in the southeast region in Korea, one of the four regions being recognized as the air pollution is serious, using a gathered information on recent 10-year's air emissions. In addition, we search the points to be carefully considered for the development of the detailed modeling emission inventory necessary for the reliable predictions of PM2.5 in the Southeast region. AcknowledgementsThis research was supported by the FRIEND (Fine Particle Research Initiative in East Asia Considering National Differences) Project through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (Grant No.: 2020M3G1A1114615).Correspondence to: Chang-Keun Song (cksong@unist.ac.kr)

Strong air pollution control policies have been implemented to reduce health damage from heavy air pollution, but the PM2.5 concentration level is still high in Southeast Asia. This study aims to identify the causes of air pollution in East Asia using chemical transport models (CTMs). Using three CTMs (CMAQ, WRF-Chem, and GEOS-Chem), air quality was simulated by season in 2019 in Northeast Asia including China and Korea. The prediction performance of PM2.5 and its major components was evaluated, and the causes affecting the difference between CTMs were analyzed, and ways to improve the prediction performance were considered. The 2019 emission inventory updated to reflect recent changes in air pollution emissions was used in common for all three models.As a result of analyzing the total mass concentration of simulated PM2.5 and major chemical components, the performance of each model was different for each season. CMAQ in January, WRF-Chem in July, and GEOS-Chem in October tended to overestimate the PM2.5 concentrations. For CMAQ, secondary organic aerosol (SOA) was produced by semivolatile/intermediate-volatility organic compounds (S/IVOC), and PM2.5 concentrations was high in winter. This is because CMAQ includes a new pathway for potential combustion secondary organic aerosol (pcSOA). Also, WRF-Chem simulated a particularly high concentration of sulfate because it affected the scavening function according to the aqueous phase chemistry.These multi-model intercomparison of air quality simulations will be helpful in future research to increase understanding of the differences between CTMs in Northeast Asia and to identify the causes of air pollution.

Plant functional type (PFT) distributions affect the results of biogenic emission modeling as well as O3 and particulate matter (PM) simulations using chemistry-transport models (CTMs). This paper analyzes the variations of both surface biogenic volatile organic compound (BVOC) emissions and O3 concentrations due to changes in the PFT distributions in the Seoul Metropolitan Areas, Korea. The Fifth-Generation NCAR/Pennsylvania State Meso-scale Model (MM5)/the Model of Emissions of Gases and Aerosols from Nature (MEGAN)/the Sparse Matrix Operator Kernel Emissions (SMOKE)/the Community Multiscale Air Quality (CMAQ) model simulations were implemented over the Seoul Metropolitan Areas in Korea to predict surface O3 concentrations for the period of 1 May to 31 June 2008. Starting from a performance check of CTM predictions, we consecutively assessed the effects of PFT area deviations on the MEGAN BVOC and CTM O3 predictions, and we further considered the basis of geospatial and statistical analyses. The three PFT data sets considered were (1) the Korean PFT, developed with Korea-specific vegetation database; (2) the CDP PFT, adopted from the community data portal (CDP) of US National Center for Atmospheric Research in the United States (NCAR); (3) MODIS PFT, reclassified from the NASA Terra and Aqua combined land cover products. Although the CMAQ performance check reveals that all of the three different PFT data sets are applicable choices for regulatory modeling practice, noticeable primary data (i.e., PFT and Leaf Area Index (LAI)) was observed to be missing in many geographic locations. Based on the assessed effect of such missing data on CMAQ O3 predictions, we found that this missing data can cause spatially increased bias in CMAQ O3. Thus, it must be resolved in the near future to obtain more accurate biogenic emission and chemistry transport modeling results. Comparisons of MEGAN biogenic emission results with the three different PFT data showed that broadleaf trees (BTs) are the most significant contributor, followed by needleleaf trees (NTs), shrub (SB), and herbaceous plants (HBs) to the total BVOCs. In addition, isoprene from BTs and terpene from NTs were recognized as significant primary and secondary BVOC species in terms of BVOC emissions distributions and O3-forming potentials in the study domain. A geographically weighted regression analysis with locally compensated ridge (LCR-GWR) with the different PFT data (δO3 vs. δPFTs) suggests that addition of BT, SB, and NT areas can contribute to O3 increase, whereas addition of an HB area contributes to O3 decrease in the domain. Assessment results of the simulated spatial and temporal changes of O3 distributions with the different PFT scenarios reveal that hourly and local impacts from the different PFT distributions on occasional inter-deviations of O3 are quite noticeable, reaching up to 13 ppb. The simulated maximum 1 h O3 inter-deviations between different PFT scenarios have an asymmetric diurnal distribution pattern (low in the early morning, rising during the day, peaking at 05:00 p.m., and decreasing during the night) in the study domain. Exponentially diverging hourly BVOC emissions and O3 concentrations with increasing ambient temperature suggest that the use of different PFT distribution data requires much caution when modeling (or forecasting) O3 air quality in complicated urban atmospheric conditions in terms of whether uncertainties in O3 prediction results are expected to be mild or severe.

Aged smoke from a prescribed fire (dominated by conifers) impacted Atlanta, GA on 28 February 2007 and dramatically increased hourly ambient concentrations of PM2.5 and organic carbon (OC) up to 140 and 72 μg m−3, respectively. It was estimated that over 1 million residents were exposed to the smoky air lasting from the late afternoon to midnight. To better understand the processes impacting the aging of fire plumes, a detailed chemical speciation of carbonaceous aerosols was conducted by gas chromatography/mass spectrometry (GC/MS) analysis. Ambient concentrations of many organic species (levoglucosan, resin acids, retene, n-alkanes and n-alkanoic acids) associated with wood burning emission were significantly elevated on the event day. Levoglucosan increased by a factor of 10, while hopanes, steranes, cholesterol and major polycyclic aromatic hydrocarbons (PAHs) did not show obvious increases. Strong odd over even carbon number predominance was found for n-alkanes versus even over odd predominance for n-alkanoic acids. Alteration of resin acids during transport from burning sites to monitors is suggested by the observations. Our study also suggests that large quantities of biogenic volatile organic compounds (VOCs) and semivolatile organic compounds (SVOCs) were released both as products of combustion and unburned vegetation heated by the fire. Higher leaf temperature can stimulate biogenic VOC and SVOC emissions, which enhanced formation of secondary organic aerosols (SOA) in the atmosphere. This is supported by elevated ambient concentrations of secondary organic tracers (dicarboxylic acids, 2-methyltetrols, pinonic acid and pinic acid). An approximate source profile was built for the aged fire plume to help better understand evolution of wood smoke emission and for use in source impact assessment.