Clean Air Policy Support System Research Articles

Physicochemical characteristics and seasonal variations of PM2.5 in urban, industrial, and suburban areas in South Korea

Among countries that are a part of the Organization for Economic Co-operation and Development, South Korea is the most exposed to PM2.5. Despite the country having implemented various strategies to limit PM2.5 emissions, its concentrations are still high enough to pose serious environmental and health concerns. Herein, we monitored various physiochemical properties of PM2.5 across different regions in South Korea from January 1 to December 31, 2021. Specifically, the study area consisted of the city center, industrial complexes, and suburban areas. Before analyzing dynamics of emissions specific to each site, the Clean Air Policy Support System data for the three areas were compared to elucidate their respective primary emission sources. The particle concentrations for the three areas were 21.8–26.44 µg/m3, with the highest concentrations being observed in March. All the three areas exhibited high ratios of NO3− across all seasons. The particle number concentrations in the three sites were 1.3–1.5 × 107, and the peak points of the concentrations were different in every site: city center (40 nm), industrial complexes (60 nm), and suburban areas (80 nm). We also conducted potential source contribution function and conditional bivariate probability function analyses. These analyses were conducted to determine the inflow direction of the pollution sources for high PM2.5 episodes. For the episodes that occurred in spring and winter, there were no differences in the PM2.5 concentrations between the three sites. Overall, the insights gained from this study offer a framework for developing air-quality management policies in South Korea, specifically in the context of PM2.5 emissions.

Long-term historical trends in air pollutant emissions in South Korea (2000–2018)

This study aims to understand the impact of previous air quality improvement policies on historical emission changes by examining long-term emission trends in Korea. Annual emissions from 2000 to 2018 were estimated using Korea’s official emissions inventory, the Clean Air Policy Support System (CAPSS). To ensure a consistent comparison, standardization of the method for calculating emissions and unification of the reported emission sectors were conducted each year. Furthermore, Korea’s emissions history was compared with that of neighboring countries, such as China and Japan. The annual emissions of these countries were acquired from the HTAPv3 emissions inventory, an international long-term emission trend study. For comparison, the emission source classification of Korea was matched with that of HTAPv3. As a result of the analysis, NOx and SOx emissions in Korea have shown decreasing trends, whereas VOCs (volatile organic compounds) have indicated a gradual increasing trend since 2000. Compared to the previous period of implementing South Korea’s air quality improvement policy, changes in NOx and SOx emissions, which are combustion-related pollutants, showed a relationship with the policy’s timeline. However, non-combustion-related pollutants such as VOCs did not exhibit such a relationship. It was concluded that the related policies were not as effective in reducing VOCs as planned in the policy. By comparing the emission trends of Japan, Korea, and China, it was confirmed that Japan was the first country to experience a decrease in combustion-related pollutants emissions, followed by Korea and China. Additionally, combustion-related pollutants decreased in all three countries, whereas VOCs decreased only in Japan. VOC is a precursor material generating secondary PM2.5 and Ozone; considering that, if relevant policies are additionally implemented to control future PM2.5 concentrations, and to reduce emissions efficiently and effectively, Japan’s VOC reduction policies can be applied to Korea’s emission reduction policies. These results are expected to serve as important references when establishing future air quality improvement policies in Korea.

A Study on the Investigation of Air Pollutant Emissions (2015~2019) in Gunsan

Objectives : In this study, the emission status by year was analyzed using the air pollutant emission data of Gunsan for 5 years from 2015 to 2019. In addition, it is intended to identify the emission characteristics of (Jeonju and Iksan).Methods : Using the air pollutant emission data of the Clean Air Policy Support System(CAPSS) provided by the Ministry of Environment's National Fine Dust Information Center, the status of air pollutant emissions(CO, NOx, SOx, PM10, VOCs, NH3) in Gunsan was analyzed.Results and Discussion : In Gunsan, CO(car registration number), PM10(cargo transport volume) NH3(farmland using fertilizer, manure management) emissions increased. NOx(production of glass products) SOx{glass (carbon removal process)} and VOCs(whiskey, etc) emissions were reduced. In the emission characteristics, CO and NOx are mainly road transport and non-road transport pollutants(leisure, cargo) and SOx is production process{glass(carbon removal process)} and energy industry combustion(private power plant). PM10 is a non-road moving pollutant(ship, cargo), scattering dust(road scattering dust, construction work, agriculture), and VOCs are food and beverage processing(whiskey, etc) and organic solvent in the production process painting facility (ship manufacturing). NH3 is agricultural(fertilized cropland, manure management) and production processes(ammonia consumption). In the comparison of emission characteristics, Gunsan(CO, NOx, SOx, VOCs), Jeonju(PM10), Iksan(NH3) were high.Conclusion : The emission rate of air pollutants in Gunsan was in the order of VOCs > NOx > CO > SOx > NH3 > PM10. In the comparison of emissions characteristics, Gunsan, Jeonju and Iksan showed the same in NOx and NH3, but different in CO, SOx, PM10 and VOCs characteristics. This case is considered to be influenced by the size of the industrial complex, geographical characteristics of the region, economic structure, and population.

Impact of Aviation Emissions and its Changes Due to the COVID-19 Pandemic on Air Quality in South Korea

This study analyzed the impact of aviation emissions based on the 2017 CAPSS (Clean Air Policy Support System) data. We focused on major airports in South Korea and examined the concentration of NO2 and PM2.5 by the WRF-SMOKE-CMAQ modeling system. Furthermore, the number of flights in Korea greatly declined in response to the COVID-19 pandemic. To assess the impact of COVID-19 on aviation emissions, time resolution data were newly derived and air pollutant emissions for 2020 were calculated. Additional BAU (Business as Usual) emissions were calculated as well for comparison. Among airports in Korea, RKSI (Incheon International Airport) had the greatest impact on air quality in nearby areas. Changes in emissions due to COVID-19 showed a large deviation by airports for domestic emissions while international emissions had a consistent decrease. The reduced emissions had the strongest impact on air quality in the RKSI area as well. By analyzing aviation emissions due to COVID-19, this study confirmed the notable relationship with the pandemic and air quality. We conclusively recommend that policymakers and industry take note of trends in aviation emissions while establishing future atmospheric environment plans.

Airborne estimation of SO2 emissions rates from a coal-fired power plant using two top-down methods: A mass balance model and Gaussian footprint approach

In this study, two top-down methods—mass balance and Gaussian footprint—were used to determine SO2 emissions rates via three airborne sampling studies over Korea's largest coal power plant in October 2019 and 2020. During the first two flights in October 2019, mass balance approaches significantly underestimated the SO2 emissions rates by 75 % and 28 %, respectively, as obtained from the real-time stack monitoring system. Notably, this large discrepancy accounted for the insufficient number of transects altitudes and high levels of background SO2 along the upwind side. Alternatively, the estimated SO2 emissions rates of the third flight (October 2020) displayed a difference of <10 % from rea-time monitoring data (630 vs. 690 kg·hr−1), owing to the enhanced vertical resolution with increased transects and lower background SO2 levels. In contrast to the mass balance method, Gaussian footprints offered significantly improved accuracy (relative error: 41 %, 32 %, and 2 % for Flights 1, 2, and 3, respectively). This relatively good performance was attributed to prior emissions knowledge via the Clean Air Policy Support System (CAPSS) emissions inventory and its unique ability to accurately estimate stack-level SO2 emissions rates. Theoretically, the Gaussian footprint was less prone to sparse transects and upwind background levels. However, it can be substantially influenced by atmospheric stability and consequently by effective stack heights and dispersion parameters; basically, all factors with minimal-to-no influence on the mass balance approach. Conversely, the mass balance method was the only plausible approach to estimate unidentified source emissions rates when well-defined prior emission information was unknown. Here, the footprint approach supplemented the mass balance method when the emission inventories were known, and employing both strategies approaches greatly enhanced the integrity of top-down emissions inventories from the power plant sources, thus, supporting their potential for ensuring operational compliance with SO2 emissions regulation.

The Comprehensive Automobile Research System (CARS) – a Python-based automobile emissions inventory model

Abstract. The Comprehensive Automobile Research System (CARS) is an open-source Python-based automobile emissions inventory model designed to efficiently estimate high-quality emissions from motor vehicle emission sources. It can estimate air pollutant, greenhouse gas, and air toxin criteria at any spatial resolution based on the spatiotemporal resolutions of input datasets. The CARS is designed to utilize local vehicle activity data, such as vehicle travel distance, road-link-level network geographic information system (GIS) information, and vehicle-specific average speed by road type, to generate an automobile emissions inventory for policymakers, stakeholders, and the air quality modeling community. The CARS model adopted the European Environment Agency's on-road automobile emissions calculation methodologies to estimate the hot exhaust, cold start, and evaporative emissions from on-road automobile sources. It can optionally utilize average speed distribution (ASD) of all road types to reflect more realistic vehicle speed variations. In addition, through utilizing high-resolution road GIS data, the CARS can estimate the road-link-level emissions to improve the inventory's spatial resolution. When we compared the official 2015 national mobile emissions from Korea's Clean Air Policy Support System (CAPSS) against the ones estimated by the CARS, there is a significant increase in volatile organic compounds (VOCs) (33 %) and carbon monoxide (CO) (52 %) measured, with a slight increase in fine particulate matter (PM2.5) (15 %) emissions. Nitrogen oxide (NOx) and sulfur oxide (SOx) measurements are reduced by 24 % and 17 %, respectively, in the CARS estimates. The main differences are driven by different vehicle activities and the incorporation of road-specific ASD, which plays a critical role in hot exhaust emission estimates but was not implemented in Korea's CAPSS mobile emissions inventory. While 52 % of vehicles use gasoline fuel and 35 % use diesel, gasoline vehicles only contribute 7.7 % of total NOx emissions, whereas diesel vehicles contribute 85.3 %. However, for VOC emissions, gasoline vehicles contribute 52.1 %, whereas diesel vehicles are limited to 23 %. Diesel buses comprise only 0.3 % of vehicles and have the largest contribution to NOx emissions (8.51 % of NOx total) per vehicle due to having longest daily vehicle kilometer travel (VKT). For VOC emissions, compressed natural gas (CNG) buses are the largest contributor at 19.5 % of total VOC emissions. For primary PM2.5, more than 98.5 % is from diesel vehicles. The CARS model's in-depth analysis feature can assist government policymakers and stakeholders in developing the best emission abatement strategies.

Vertical Characteristics of Secondary Aerosols Observed in the Seoul and Busan Metropolitan Areas of Korea during KORUS-AQ and Associations with Meteorological Conditions

In this study, the chemical components of aerosols observed at ground level and in upper layers during the Korea–United States Air Quality (KORUS-AQ) campaign were analyzed in two representative metropolitan areas of Korea: the Seoul metropolitan area (SMA) and the Busan-containing southeastern metropolitan area (BMA). First, we characterized emissions using the Clean Air Policy Support System (CAPSS) emission statistics, and compared them with both ground- and aircraft-based measurements obtained during the KORUS-AQ campaign. The emission statistics showed that the SMA had higher NOx levels, whereas BMA had significantly higher SO2 levels. Ground-level observations averaged for the summer season also showed SMA–nitrate and BMA–sulfate relationships, reflecting the CAPSS emission characteristics of both areas. However, organic carbon (OC) was higher in BMA than SMA by a factor of 1.7, despite comparable volatile organic compound (VOC) emissions in the two areas. DC-8 aircraft-based measurements showed that, in most cases, nitrogen-rich localities were found in the SMA, reflecting the emission characteristics of precursors in the two sampling areas, whereas sulfur-rich localities in the BMA were not apparent from either ground-based or aircraft observations. KORUS-AQ measurements were classified according to two synoptic conditions, stagnant (STG) and long-range transport (LRT), and the nitrate-to-sulfate (N/S) ratio in both ground and upper layers was higher in the SMA for both cases. Meanwhile, organic aerosols reflected local emissions characteristics in only the STG case, indicating that this stagnant synoptic condition reflect local aerosol characteristics. The LRT case showed elevated peaks of all species at altitudes of 1.0–3.5 km, indicating the importance of LRT processes for predicting and diagnosing aerosol vertical distributions over Northeast Asia. Other chemical characteristics of aerosols in the two metropolitan areas were also compared.

The Characteristics of PM2.5 Pollution and Policy Implications in Chungcheong Region

Objectives : This study aims to discuss air quality policy improvement that reflect regional characteristics through analyzing recent PM2.5 concentration, air pollutant emission sources and those contributions to annual PM2.5 concentration in Chungcheong region (Daejeon Metropolitan City, Sejong Metropolitan Autonomous City, the Province of Chungcheongnam-do, and Chungcheongbuk-do) in South Korea. In addition, we identified the characteristics of the PM2.5 pollution at the level of fundamental local government, and demonstrated the number of vulnerable population exposed to high level of PM2.5 concentration in order to propose policy implications in Chungcheong region.Methods : Based on the national emissions estimates (CAPSS: Clean Air Policy Support System) and air quality modelling system, major sectors/sources of air pollutants emission and national contributions of PM2.5 concentrations in Chungcheong region were analyzed. Furthermore, the study identified the number of people exposed to the higher PM2.5 concentrations (&gt;25 µg/m3) by the measurement data and demographics available in 2019.Results and Discussion : The national air pollutants emissions in Chungcheong region were emitted from Chungnam (about 59% of NOx emission volume, 89% of SOx, 70% of NH3, 54% of VOCs, 79% of PM2.5, and 68% of TSP respectively), mainly from industry, domestic, energy, and road sector. According to the results of the air quality modelling, Chungcheong region also had the largest contribution on the average annual PM2.5 concentration in South Korea (27%). Chungnam emitted the largest emission volume of air pollutants, mainly from industry and power generation sectors (especially in Dangjin, Seosan, and Boryeong), while Asan, Yesan, Hongseong, and Cheongyang were classified as the areas with higher PM2.5 concentrations (&gt;25 µg/m3), showing a gap between the areas with large emission volume and high concentration. Chungbuk and Sejong had higher annual PM2.5 concentration due to the influence of external sources and their geographical characteristics. The largest vulnerable population (over 65 years old and under 18 years old) exposed to high PM2.5 concentrations annually lived in Cheongju. Chungbuk had about 40% more air pollutant emission volume than Chungnam, but about 17% more vulnerable population.Conclusions : At the current stage of “master plan” in Chungcheong region, it is important to mitigate air pollutants emissions on the basis of the local emissions characteristic at the level of fundamental local government (such as industry sector in Dangjin, Seosan, and Danyang/ Domestic buring in Cheongju, Cheonan, and Daejeon/power generation in Boryeong, Taean and Dangjin/ road in Daejeon, Cheongju, and Cheoan). In addition, Chungbuk requires management of the areas with higher PM2.5 concentration such as Goesan, Boeun, Okcheon, and Yeongdong located outside “air control zone”. To reduce high level of PM2.5 concentration in Chungcheong region, cooperation with neighboring local governments such as Gyeonggi Province is crucial, and policy solutions are needed between the stakeholders to resolve the disparity issues between areas with larger emission volume and higher PM2.5 concentration.

배출원 기반 대기 중 농도 감축 정책에 대한 제고 : 광주 및 서울시 관측 농도 사례 분석

As particulate matter is of great interest, many mitigation policies related to PM have been established based on Clean Air Policy Support System (CAPSS) as the official statistics for emission rates. CAPSS has been developed for several decades to understand the potential source and receptor regions. The assessment of the air quality impacted from sources, concentrations monitored at the National Monitoring Stations (NMS) is often utilized to understand the evaluation of reduction effects for source control strategies. Although CAPSS should be able to represent air pollution monitoring station data, the limitation can be exist. In this study, their concentration colinearities between CAPSS and NMS for Korean 16 administrartive regions were assessed. As a result, the high rank CAPSS in 2016 is barely uncorrelated with high concentrations at NMS from 2016 to 2019. In addition, the multi-statistical approaches through combinations of time series analysis and correlation matrix based on NMS have been performed for the Seoul and Gwangju areas. The future applications were proposed for their amendments such as a new management plan for the source control.

Fine-Scale Columnar and Surface NOx Concentrations over South Korea: Comparison of Surface Monitors, TROPOMI, CMAQ and CAPSS Inventory

Fine-scale nitrogen oxide (NOx) concentrations over South Korea are examined using surface observations, satellite data and high-resolution model simulations based on the latest emission inventory. While accurate information on NOx emissions in South Korea is crucial to understanding regional air quality in the region, consensus on the validation of NOx emissions is lacking. We investigate the spatial and temporal variation in fine-scale NOx emission sources over South Korea. Surface observations and newly available fine-scale satellite data (TROPOspheric Monitoring Instrument; TROPOMI; 3.5 × 7 km2) are compared with the community multiscale air quality (CMAQ) model based on the clean air policy support system (CAPSS) 2016 emission inventory. The results show that the TROPOMI NO2 column densities agree well with the CMAQ simulations based on CAPSS emissions (e.g., R = 0.96 for June 2018). The surface observations, satellite data and model are consistent in terms of their spatial distribution, the overestimation over the Seoul Metropolitan Area and major point sources; however, the model tends to underestimate the surface concentrations during the cold season.

건물 가스에너지소비에 따른 여과성 및 응축성 먼지배출량 예측과 사례분석 연구

The TSP, PM10, PM2.5, which is calculated by NIER(National institute of environmental research)’s clean air policy support system(CAPSS), targets only filterable particulate matter(FPM) consisting of 2.5 to 10μm particles. However, it should be considered together as total particulate matter(TPM) emitted from workplaces, cars, heating and cooling will emit not only filterable PM but also condensable particulate matter(CPM). Therefore, in this study, filterable PM and condensable PM generated by the burning of LNG in buildings were calculated for each building use, using the emission factors announced by NIER. In addition, for the purpose of predicting the effects of PM by buildings, the prediction model of PM emissions was established and the case analysis was performed using machine learning. As a result, particulate matter per unit area of buildings was generally analyzed high in residential and commercial buildings and particularly high in the winter months due to increased LPG use caused by heating. And, since about 98% of the total PM was analyzed to be condensable PM, prevention and reduction measures for condensable PM should be expanded.

Temporal Analysis of OMI-Observed Tropospheric NO2 Columns over East Asia during 2006–2015

The study analyzed temporal variations of Ozone Monitoring Instrument (OMI)-observed NO2 columns, interregional correlation, and comparison between NO2 columns and NOx emissions during the period from 2006 to 2015. Regarding the trend of the NO2 columns, the linear lines were classified into four groups: (1) ‘upward and downward’ over six defined geographic regions in central-east Asia; (2) ‘downward’ over Guangzhou, Japan, and Taiwan; (3) ‘stagnant’ over South Korea; and (4) ‘upward’ over North Korea, Mongolia, Qinghai, and Northwestern Pacific ocean. In particular, the levels of NO2 columns in 2015 returned to those in 2006 over most of the polluted regions in China. Quantitatively, their relative changes in 2015 compared to 2006 were approximately 10%. From the interregional correlation analysis, it was found that unlike positive relationships between the polluted areas, the different variations of monthly NO2 columns led to negative relationships in Mongolia and Qinghai. Regarding the comparison between NO2 columns and NOx emission, the NOx emissions from the Copernicus Atmosphere Monitoring Service (CAMS) and Clean Air Policy Support System (CAPSS) inventories did not follow the year-to-year variations of NO2 columns over the polluted regions. In addition, the weekly effect was only clearly shown in South Korea, Japan, and Taiwan, indicating that the amounts of NOx emissions are significantly contributed to by the transportation sector.

Implication of activity-based vessel emission to improve regional air inventory in a port area

This paper has aimed to evaluate the air pollution emissions from maritime transportation arrive in/depart from Port of Incheon using two different approaches; fuel-based (Tier 1) and activity-based (Tier 3) methodologies. So far, Tier 1 or 2 level of estimation approaches, based on the fuel consumption provided by national fuel supply statistics, have been reasonably supported for environmentalists and policy makers at national level of emission inventory analysis, even for the global analysis. When considering the local air quality such as in a port area, however, the fuel based approach would not be properly estimating the shipping air emission sector due to the characteristic of maritime transportation. In this study, the fuel-based air emission inventory provided by Clean Air Policy Support System (CAPSS), South Korea was compared and analyzed with the activity-based emission estimation produced by Portal Air Quality Management System (PAQman©). As a result, there discovered a significant gap in the air pollution emissions between fuel-based and activity-based estimations. The findings in this study implies the necessity and significance of local/regional level focused implementation for air quality improvement and regulatory framework especially in a port-city region e.g. Incheon. Although this paper still contains some limitations and assumption described, the results are confident enough to emphasize the application of the activity-based shipping emission estimation approach to discover the potential limitation, which has been unknown and not listed on the national air emission inventory using fuel-based approach.

Regional contributions to particulate matter concentration in the Seoul metropolitan area, South Korea: seasonal variation and sensitivity to meteorology and emissions inventory

Abstract. The impact of regional emissions (e.g., domestic and international) on surface particulate matter (PM) concentrations in the Seoul metropolitan area (SMA), South Korea, and its sensitivities to meteorology and emissions inventories are quantitatively estimated for 2014 using regional air quality modeling systems. Located on the downwind side of strong sources of anthropogenic emissions, South Korea bears the full impact of the regional transport of pollutants and their precursors. However, the impact of foreign emissions sources has not yet been fully documented. We utilized two regional air quality simulation systems: (1) a Weather Research and Forecasting and Community Multi-Scale Air Quality (CMAQ) system and (2) a United Kingdom Met Office Unified Model and CMAQ system. The following combinations of emissions inventories are used: the Intercontinental Chemical Transport Experiment-Phase B, the Inter-comparison Study for Asia 2010, and the National Institute of Environment Research Clean Air Policy Support System. Partial contributions of domestic and foreign emissions are estimated using a brute force approach, adjusting South Korean emissions to 50 %. Results show that foreign emissions contributed ∼ 60 % of SMA surface PM concentration in 2014. Estimated contributions display clear seasonal variation, with foreign emissions having a higher impact during the cold season (fall to spring), reaching ∼ 70 % in March, and making lower contributions in the summer, ∼ 45 % in September. We also found that simulated surface PM concentration is sensitive to meteorology, but estimated contributions are mostly consistent. Regional contributions are also found to be sensitive to the choice of emissions inventories.

수도권 초미세먼지 농도모사 : (II) 오염원별 , 배출물질별 자체 기여도 및 전환율 산정

A set of BFM (Brute Force Method) simulations with the CMAQ (Community Multiscale Air Quality) model were conducted in order to estimate self-contributions and conversion rates of PPM (Primary PM2.5), NOx, SO₂, NH₃, and VOC emissions to PM2.5 concentrations over the SMA (Seoul Metropolitan Area). CAPSS (Clean Air Policy Support System) 2013 EI (emissions inventory) from the NIER (National Institute of Environmental Research) was used for the base and sensitivity simulations. SCCs (Source Classification Codes) in the EI were utilized to group the emissions into area, mobile, and point source categories. PPM and PM2.5 precursor emissions from each source category were reduced by 50%. In turn, air quality was simulated with CMAQ during January, April, July, and October in 2014 for the BFM runs. In this study, seasonal variations of SMA PM2.5 self-sensitivities to PPM, SO₂, and NH3 emissions can be observed even when the seasonal emission rates are almost identical. For example, when the mobile PPM emissions from the SMA were 634 TPM (Tons Per Month) and 603 TPM in January and July, self-contributions of the emissions to monthly mean PM2.5 were 2.7 ㎍/㎥ and 1.3 ㎍/㎥ for the months, respectively. Similarly, while NH₃ emissions from area sources were 4,169 TPM and 3,951 TPM in January and July, the self-contributions to monthly mean PM2.5 for the months were 2.0 ㎍/㎥ and 4.4 ㎍/㎥, respectively. Meanwhile, emission-to-PM2.5 conversion rates of precursors vary among source categories. For instance, the annual mean conversion rates of the SMA mobile, area, and point sources were 19.3, 10.8, and 6.6 ㎍/㎥/10⁶TPY for SO₂ emissions while those rates for PPM emissions were 268.6, 207.7, and 181.5 (㎍/㎥/10⁶TPY), respectively, over the region. The results demonstrate that SMA PM2.5 responses to the same amount of reduction in precursor emissions differ for source categories and in time (e.g. seasons), which is important when the cost-benefit analysis is conducted during air quality improvement planning. On the other hand, annual mean PM2.5 sensitivities to the SMA NOx emissions remains still negative even after a 50% reduction in emission category which implies that more aggressive NOx reductions are required for the SMA to overcome ‘NOx disbenefit’ under the base condition.

Intercomparison of NO&amp;lt;sub&amp;gt;&amp;lt;i&amp;gt;x&amp;lt;/i&amp;gt;&amp;lt;/sub&amp;gt; emission inventories over East Asia

Abstract. We compare nine emission inventories of nitrogen oxides including four satellite-derived NOx inventories and the following bottom-up inventories for East Asia: REAS (Regional Emission inventory in ASia), MEIC (Multi-resolution Emission Inventory for China), CAPSS (Clean Air Policy Support System) and EDGAR (Emissions Database for Global Atmospheric Research). Two of the satellite-derived inventories are estimated by using the DECSO (Daily Emission derived Constrained by Satellite Observations) algorithm, which is based on an extended Kalman filter applied to observations from OMI or from GOME-2. The other two are derived with the EnKF algorithm, which is based on an ensemble Kalman filter applied to observations of multiple species using either the chemical transport model CHASER and MIROC-chem. The temporal behaviour and spatial distribution of the inventories are compared on a national and regional scale. A distinction is also made between urban and rural areas. The intercomparison of all inventories shows good agreement in total NOx emissions over mainland China, especially for trends, with an average bias of about 20 % for yearly emissions. All the inventories show the typical emission reduction of 10 % during the Chinese New Year and a peak in December. Satellite-derived approaches using OMI show a summer peak due to strong emissions from soil and biomass burning in this season. Biases in NOx emissions and uncertainties in temporal variability increase quickly when the spatial scale decreases. The analyses of the differences show the importance of using observations from multiple instruments and a high spatial resolution model for the satellite-derived inventories, while for bottom-up inventories, accurate emission factors and activity information are required. The advantage of the satellite-derived approach is that the emissions are soon available after observation, while the strength of the bottom-up inventories is that they include detailed information of emissions for each source category.

울산 지역 대기질 모의능력 개선을 위한 배출량자료 평가

배출량 자료는 대기질 수치모의에 있어서 큰 영향을 주는 요소로서 정확한 대기질 수치모의를 위해서 배출량 자료의 정확성과 신뢰도 향상은 대기질 모델 연구에 있어 필수적이다. 본 연구에서는 울산지역을 대상으로 2003년과 2010년 CAPSS 배출량 자료인 CAPSS-2003과 CAPSS-2010를 입력자료로 하여 WRF-CMAQ 모델을 수행하여 울산지역 배출량 지료의 타당성을 검토하였다. 우선 오염물질의 장거리 수송 영향을 배제하기 위하여 울산지역 자체 내의 배출량 영향이 우세한 종관 기상조건을 가진 사례일을 선정하고 선정된 사례일에 대해서 WRF-CMAQ모델을 수행한 후 그 결과인 CO, <TEX>$NO_2$</TEX>, <TEX>$SO_2$</TEX>, <TEX>$PM_{10}$</TEX> 농도와 관측 값을 비교하여, 과소 혹은 과대 모의되는 정도를 분석하여 모의결과를 보정 할 수 있는 'scaling factor'를 제시하였다. 그 결과 CAPSS-2003을 이용한 모의결과는 CO와 <TEX>$NO_2$</TEX>에 대해서는 관측과 유사한 수준으로 모의하였으나 <TEX>$SO_2$</TEX>는 약 12배 과대모의, <TEX>$PM_{10}$</TEX>은 약 27배나 과소모의하는 결과를 얻었다. 반면 CAPSS-2010을 이용한 모의결과에서는 CO와 <TEX>$NO_2$</TEX>의 모의결과는 유사하였으며, <TEX>$SO_2$</TEX>는 약 2배 과대모의, <TEX>$PM_{10}$</TEX>은 약 5배 과소모의 하여 <TEX>$SO_2$</TEX>와 <TEX>$PM_{10}$</TEX> 배출량이 상당히 개선됨을 확인하였다. <TEX>$SO_2$</TEX>와 <TEX>$PM_{10}$</TEX> 역시 이전 보다는 관측에 가깝게 모의되었으나 현실적 모델링 결과를 도출하기 위해서 배출량은 향후 개선되어야 할 부분으로 판단되었다. 따라서 보다 현실적인 모델링 결과를 도출하기 위해서는 본 연구에서 제시한 울산지역 배출량의 'scaling factor'를 이용하면 보다 안정된 모델링 결과가 도출 될 것으로 판단된다. Emission source term is one of the strong controlling factors for the air quality simulation capability, particularly over the urban area. Ulsan is an industrial area and frequently required to simulate for environmental assessment. In this study, two CAPSS (Clean Air Policy Support System) emission data; CAPSS-2003 and CAPSS-2010 in Ulsan, were employed as an input data for WRF-CMAQ air quality model for emission assessment. The simulated results were compared with observations for the local emission dominant synoptic conditions which had negative vorticities and lower geostrophic wind speed at 850hPa weather maps. The measurements of CO, <TEX>$NO_2$</TEX>, <TEX>$SO_2$</TEX> and <TEX>$PM_{10}$</TEX> concentrations were compared with simulations and the 'scaling factors' of emissions for CO, <TEX>$NO_2$</TEX>, <TEX>$SO_2$</TEX>, and <TEX>$PM_{10}$</TEX> were suggested in in aggregative and quantitative manner. The results showed that CAPSS-2003 showed no critical discrepancies of CO and <TEX>$NO_2$</TEX> observations with simulations, while <TEX>$SO_2$</TEX> was overestimated by a factor of more than 12, while <TEX>$PM_{10}$</TEX> was underestimated by a factor of more than 20 times. However, CAPSS-2010 case showed that <TEX>$SO_2$</TEX> and <TEX>$PM_{10}$</TEX> emission were much more improved than CAPSS-2003. However, <TEX>$SO_2$</TEX> was still overestimated by a factor of more than 2, and <TEX>$PM_{10}$</TEX> underestimated by a factor of 5, while there was no significant improvement for CO and <TEX>$NO_2$</TEX> emission. The estimated factors identified in this study can be used as'scaling factors'for optimizing the emissions of air pollutants, particularly <TEX>$SO_2$</TEX> and <TEX>$PM_{10}$</TEX> for the realistic air quality simulation in Ulsan.

Characteristics of Ozone Precursor Emissions and POCP in the Biggest Port City in Korea

Emissions of ozone precursors (NO x and VOCs) and photochemical ozone creation potentials (POCPs) of VOC emission sources were investigated in the largest port city (i.e., Busan), Korea during the year 2011. This analysis was performed using the Clean Air Policy Support System (CAPSS) national emission inventory provided by the National Institute of Environmental Research (NIER), Korea. For NO x , the emissions from off-road mobile sources in Busan were the most dominant (e.g., 31,202 ton yr -1 ), accounting for about 60% of the total NOx emissions. The emission from shipping of off-road mobile sources (e.g., 24,922 ton yr -1 ) was a major contributor to their total emissions, amounting to 47% of the total NO x emissions due to the port-related activities in Busan. For VOCs, the emission source category of solvent usage was predominant (e.g., 36,062 ton yr -1 ), accounting for approximately 82% of the total VOC emissions. Out of solvent usages, the emission from painting was the most dominant (22,733 ton yr -1 ), comprising 52% of the total emissions from solvent usages. The most dominant VOC species emitted from their sources in Busan was toluene, followed by xylene, butane, ethylbenzene, n-butanol, isopropyl alcohol, and propane. The major emission sources of toluene and xylene were found to be painting of coil coating and ship building, respectively. The value of POCP for the offroad mobile source (61) was the highest in ten major activity sectors of VOC emissions. Since the POCP value of ship transport of off-road mobile source (72) was also high enough to affect ozone concentration, the ship emission can play a significant role in ozone production of the port city like Busan.

화목난로∙보일러와 펠릿난로∙보일러 사용에 의한 대기오염물질 배출량 산정에 관한 연구

Biomass burning is one of the significant emission source of PM and CO, but a few studies are reported in Korea. Air pollutants emission from biomass burning such as wood stove and boiler, and wood-pellet stove and boiler were estimated in this study. Activity levels related to biomass burning such as fuel types, amount of fuel loading, and location and temporal variation were investigated by field survey over Korea. Fuel loadings were 14.9 kg/day for wood stove, 31.3 kg/day for wood boiler, 12.8 kg/day for wood-pellet stove, 32.5 kg/day for wood-pellet boiler during the season of active use. These were mostly burned in winter season from october to april of next year. Estimated annual emissions from wood stove & boiler were CO 76,677, 710, 70, VOC 20,941, TSP 6,605, PM10 2,921, PM2.5 1,851, and NH3 7 ton/yr, respectively. Emissions from wood-pellet stove and boiler were CO 32,798, 1,830, 25, VOCs 5,673, TSP 629, PM10 457, PM2.5 344, and 2 ton/yr, respectively. When the emission estimates are compared with total emissions of the national emission inventory (CAPSS: Clean Air Policy Support System), Those occupy 12.5%, 2.8% of total national emission for CO and PM10, respectively. These results show wood and wood-pellet burning appliances were one of the major source of air pollution in Korea. In future, these types of heaters need to be regulated to reduce air pollution, especially in suburb area.

DARS에 의한 CAPSS 배출자료의 불확도 평가

The uncertainty assessment is important to improve the reliability of emission inventory data. The DARS (Data Attribute Rating System) have recommended as the uncertainty assessment technic of emission inventory by U.S. EPA (Environmental Protection Agency) EIIP (Emission Inventory Improvement Program). The DARS score is based on the perceived quality of the emission factor and activity data. Scores are assigned to four attributes; measurement/method, source specificity, spatial congruity and temporal congruity. The resulting emission factor and activity rate scores are combined to arrive at an overall confidence rating for the inventory. So DARS is believed to be a useful tool and may provide more information about inventories than the usual qualitative grading procedures (e.g. A through E). In this study, the uncertainty assessment for 2009 CAPSS (Clean Air Policy Support System) emission inventory is conducted by DARS. According to the result of this uncertainty assessment, the uncertainty for fugitive dust emission data is higher than other sources, the uncertainty of emission factor for surface coating is the highest value, and the uncertainty of activity data for motor cycle is the highest value. Also it is analysed that the improvement of uncertainty for activity data is as much important as the improvement for emission factor to upgrade the reliability of CAPSS emission inventory.