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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.

Meteorology and emissions play very important roles in the concentrations of air pollutants during severe haze/smog periods. This study compares the impacts of synoptic meteorology and nationwide/local emissions during high PM10 multi-event and non-event days in the Seoul Metropolitan Area (SMA). The multi-event and non-event cases were selected based on daily mean PM10 concentrations in Seoul from January 2014 to March 2019. The multi-event cases in spring and winter were closely associated with weak synoptic winds, while that in autumn was due to the strong winds at the rear side of a strong cold front, which induced the Asian dust event in northeastern China and Korea. The multi-event case in spring was found to be mainly due to series of migratory anticyclones, while winter case was due to the stagnant system after northerly winds. The surrounding low pressure systems as well as high pressure systems could be important to determine whether the synoptic systems would be stagnant or not. The fractional contributions of SMA emissions to the mean PM10 and PM2.5 concentrations were 24%-35% and 22%-35% for the multi-event cases, respectively. The contributions to the maximum PM10 and PM2.5 concentrations were larger than those to the mean concentrations by 16%-23% and 19%-26% for the multi-event cases, respectively.

Understanding how ozone (O3) and fine particulate matter (PM) formation respond to the precursor concentrations in the presence of biogenic emissions (BEs) and thereby changes in health effects can be a key step to design effective air quality management plans. This is particularly true in the Seoul Metropolitan Area (SMA), where future significant controls of anthropogenic sources of O3 and PM2.5 precursors are expected. In this paper, we investigate the effects of BEs on O3 and fine PM (PM2.5) concentrations during a strong photochemical air pollution season in the SMA in Korea. O3 and PM2.5 levels are modeled with and without BEs in June 2008. Further, we perform the health impact assessments (HIA) of O3 and PM2.5 concentration changes due to BEs to seek useful implications for air quality management by utilizing the adjusted exposure concentration fields for O3 and PM2.5 with an observation fusing (OBF) method. With BEs, daily maximum 8-h average O3 (maximum 8-h O3) and secondary organic aerosol (SOA) concentrations in the SMA increase by 17 and 474%, respectively. These increments are associated with significant consumption of photochemical oxidants (Ox), such as a ~ 60% reduction in OH∙ radicals. The reduction in Ox, conversely, lowers the production of secondary inorganic aerosols (SIOAs) by 2.7%. Adjusted O3 and PM2.5 exposure metrics and the subsequent HIA reveal that large mean increments of O3, about 8.43 ppb, due to BEs are responsible for approximately 62 all-cause premature mortalities in the SMA in June. However, mean increment of PM2.5 due to BEs is approximately 0.3 μg m−3 and results in negligible impacts on the all-cause mortality. Significant correlations of O3 and mortality rates (MR) with the VOC/NOx ratios across the SMA suggest that controlling volatile organic compounds (VOCs) from anthropogenic sources can be a priority to reduce O3 levels and population health risks in the SMA. Specifically, linear relationships of log [O3] and log [MR] to log [VOC/NOx] ensure that a 10% decrease in the VOC/NOx ratios through the VOC abatements would lead to a 1.5% decrease in the O3 levels and a 4.3% decrease in the MR on average across the SMA.

A bottom-up emissions inventory is one of the most important data sets needed to understand air quality (AQ) and climate change (CC). Several emission inventories have been developed for Asia, including Transport and Chemical Evolution over the Pacific (TRACE-P), Regional Emission Inventory in Asia (REAS), and Inter-Continental Chemical Transport Experiment (INTEX) and, while these have been used successfully for many international studies, they have limitations including restricted amounts of information on pollutant types and low levels of transparency with respect to the polluting sectors or fuel types involved. To address these shortcomings, we developed: 1) a base-year, bottom-up anthropogenic emissions inventory for Asia, using the most current parameters and international frameworks (i.e., the Greenhouse gas—Air pollution INteractions and Synergies (GAINS) model); and 2) a base-year, natural emissions inventory for biogenic and biomass burning. For 1), we focused mainly on China, South Korea, and Japan; however, we also covered emission inventories for other regions in Asia using data covering recent energy/industry statistics, emission factors, and control technology penetration. The emissions inventory (Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment (CREATE)) covers 54 fuel classes, 201 subsectors, and 13 pollutants, namely SO2, NOx, CO, non-methane volatile organic compounds (NMVOC), NH3, OC, BC, PM10, PM2.5, CO2, CH4, N2O, and Hg. For the base-year natural emissions inventory, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and BlueSky-Asia frameworks were used to estimate biogenic and biomass burning emissions, respectively. Since the CREATE emission inventory was designed/developed using international climate change/air quality (CC/AQ) assessment frameworks, such as GAINS, and has been fully connected with the most comprehensive emissions modeling systems—such as the US Environmental Protection Agency (EPA) Chemical Manufacturing Area Source (CMAS) system—it can be used to support various climate and AQ integrated modeling studies, both now and in the future.

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.

Open biomass burning (excluding biofuels) is an important contributor to air pollution in the Asian region. Estimation of emissions from fires, however, has been problematic, primarily because of uncertainty in the size and location of sources and in their temporal and spatial variability. Hence, more comprehensive tools to estimate wildfire emissions and that can characterize their temporal and spatial variability are needed. Furthermore, an emission processing system that can generate speciated, gridded, and temporally allocated emissions is needed to support air-quality modeling studies over Asia. For these reasons, a biomass-burning emissions modeling system based on satellite imagery was developed to better account for the spatial and temporal distributions of emissions. The BlueSky Framework, which was developed by the USDA Forest Service and US EPA, was used to develop the Asian biomass-burning emissions modeling system. The sub-models used for this study were the Fuel Characteristic Classification System (FCCS), CONSUME, and the Emissions Production Model (EPM). Our domain covers not only Asia but also Siberia and part of central Asia to assess the large boreal fires in the region. The MODIS fire products and vegetation map were used in this study. Using the developed modeling system, biomass-burning emissions were estimated during April and July 2008, and the results were compared with previous studies. Our results show good to fair agreement with those of GFEDv3 for most regions, ranging from 9.7 % in East Asia to 52% in Siberia. The SMOKE modeling system was combined with this system to generate three-dimensional model-ready emissions employing the fire-plume rise algorithm. This study suggests a practicable and maintainable methodology for supporting Asian air-quality modeling studies and to help understand the impact of air-pollutant emissions on Asian air quality.

Air quality modeling is a useful methodology to investigate air quality degradation in various locations and to analyze effectiveness of emission reduction plans. A comprehensive air quality model usually requires a coordinated set of emissions input of all necessary chemical species. We have developed an anthropogenic emissions processing system for Asia in support of air quality modeling and analysis over Asia (named SMOKE-Asia). The SMOKE (Sparse Matrix Operator kernel Emissions) system, which was developed by U.S. EPA and has been maintained by the Carolina Environmental Program (CEP) of the University of North Carolina, was used to develop our emissions processing system. A merged version of INTEX 2006 and TRACE-P 2000 inventories was used as an initial Asian emissions inventory. The IDA (Inventory Data Analyzer) format was used to create SMOKE-ready emissions. Source Classification Codes (SCCs) and country/state/county (FIPS) code, which are the two key data fields of SMOKE IDA data structure, were created for Asia. The 38 SCCs and 2752 FIPS codes were allocated to our SMOKE-ready emissions for more comprehensive processing. US EPA’s MIMS (Multimedia Integrated Modeling System) Spatial Allocator software, along with many global and regional GIS shapes, were used to create spatial allocation profiles for Asia. Temporal allocation and chemical speciation profiles were partly regionalized using Asia-based studies. Initial data production using the developed SMOKE-Asia system was successfully performed. NOx and VOC emissions for the year 2009 were projected to be increased by 50% from those of 1997. The emission hotspots, such as large cities and large point sources, are distinguished in the domain due to spatial allocation. Regional emission peaks were distinguished due to temporally resolved emission information. The PAR (Paraffin carbon bond) and XYL (Xylene and other polyalkyl aromatics) showed the first and second largest emission rate among VOC species. Most of point source emissions are located in layers 3 to 4, which the altitude range reaches 310–550 m AGL. Qualitative inter-comparison between model output and ground/satellite measurement showed good agreements in terms of spatial and temporal patterns. We expect that the result of this study will provide better air quality modeling inputs, which will act as a major step to improve our understanding of Asian air quality.

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.

The objective of this study was to develop and assess a method for estimating the rain scavenging ratios (RSRs) of particle-bound PAHs and PCBs using measured scavenging ratio of particulate matters (PM) and routinely available data of physico-chemical properties of PM. Paired atmospheric and rainwater sampling was conducted for a total of 4 rain events. Assuming equilibrium partitioning in rainwater-gas-PM system, an equation was derived for estimating the RSR of particle-bound chemicals as a function of RSR of PM and three equilibrium partition constants (i.e. dimensionless Henry's law constant, gas-particle partition coefficient, and water-particle partition coefficient). For all PAHs, the model significantly under-predicted the RSR while the model prediction for PCBs agreed with observation mostly within a factor of 5. The RSR model for the chemicals is of limited use as its accuracy critically depends on how close the observed partitioning of the chemicals in the gas-PM-rainwater system is to that estimated under the equilibrium assumption.