• Journal of Korean Society for Atmospheric Environment
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• v.29 no.6
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• pp.838-848
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• 2013

The Community Multiscale Air Quality (CMAQ) model is capable of providing high quality atmospheric chemistry profiles through the utilization of high-resolution meteorology and emissions data. However, it cannot simulate air quality accurately if input data are not appropriate and reliable. One of the most important inputs required by CMAQ is the air pollutants emissions, which determines air pollutants concentrations during the simulation. For the CMAQ simulation of Korean peninsula, we, in general, use the Korean National Emission Inventory data which are estimated by Clean Air Policy Support System (CAPSS). However, since they are not provided by model-ready emission data, we should convert CAPSS emissions into model-ready data. The SMOKE is the emission model we used in this study to generate CMAQ-ready emissions. Because processing the emissions data is very monotonous and tedious work, we have developed CAPSS2SMOKE program to convert CAPSS emissions into SMOKE-ready data with ease and effective. CAPSS2SMOKE program consists of many codes and routines such as source classification code, $PM_{10}$ to $PM_{2.5}$ ratio code, map projection conversion routine, spatial allocation routine, and so on. To verify the CAPSS2SMOKE program, we have run SMOKE using the CAPSS 2009 emissions and found that the SMOKE results inherits CAPSS emissions quite well.

• Journal of Environmental Health Sciences
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• v.49 no.4
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• pp.190-200
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• 2023

Background: Gunsan has been constantly affected by pollutants generated by the Saemangeum development and the construction industry since the completion of the Saemangeum seawall on April 27, 2010. However, there are limitations to its study, such as taking into consideration weather conditions, geographical factors, and foreign inflows. Objectives: In this study, we compared the Existing-CAPSS emissions of Gunsan with Recalculated-CAPSS emissions data to analyze the differences in emissions characteristics by year (2016~2019). Methods: Using Existing data on CAPSS emissions (2016~2019) and Recalculated-CAPSS emissions (2016~2019) for Gunsan, which were Recalculated following the improvement of emissions calculations for 2020, we organized CO, NO_X, SO_X, PM₁₀, VOC_S, and NH₃ emissions by substance and investigated the differences and characteristics of the Recalculated emissions by year. Results: For Re-CO and Re-PM₁₀, the emission characteristics of CO were examined as energy industry combustion and PM₁₀ emission characteristics were examined as ship cargo from non-road transportation sources, as ship leisure sources were excluded from non-road transportation source emissions. Conclusions: Comparing the emissions of Existing-CAPSS and Recalculated-CAPSS in Gunsan, the emissions of Recalculated-CAPSS by substance decreased by 39.76% for CO, 9.98% for PM₁₀, 5.53% for VOC_S, and 9.24% for NH₃, while Re-NO_X increased by 2.86% and Re-SO_X increased by 1.97%. On the other hand, when comparing the emissions characteristics of Existing-CAPSS and Recalculated-CAPSS in Gunsan, Jeonju, and Iksan, the emission characteristics of Re-NO_X, Re-SO_X, Re-VOC_S and Re-NH₃ were similar to those of Ex-NO_X, Ex-SO_X, Ex-VOC_S, and Ex-NH₃. As such, Gunsan, Iksan, and Jeonju, showed differences in the comparison of different emission characteristics due to the geographical characteristics of the region (population, area, topography, weather factors) and the characteristics of the industrial complex (metal, petrochemical).

• Journal of Korean Society for Atmospheric Environment
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• v.25 no.5
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• pp.410-419
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• 2009

Generally, air pollutant emission at workplace is estimated by two methods: indirect methods using emission factors and direct methods based on CEMS (Continuous Emission Monitoring System). CAPSS (Clean Air Policy Support System) is a representative indirect method and the national air pollutant database of Korea. However, characteristics of some workplaces may create a gap between CAPSS and CEMS data. For improving of emission data accuracy, emission data of CEMS (named CleanSYS) equipped at 138 target workplaces were compared with those of CAPSS. As a result, $SO_x$ and $PM_{10}$ emission levels obtained by CAPSS were lower than those of CleanSYS. $SO_x$ and $PM_{10}$emission ratios were 61.5% and 71.2% lower respectively, showing the biggest gaps. On the other hand, $NO_x$ emission of CAPSS was higher by 10.4%. $SO_x$ showed the biggest difference in 'Energy industry combustion' and $NO_x$ did in 'Production Process' within the SCC category. $PM_{10}$ presented a large gap in 'Manufacturing industry combustion.' The differences in $SO_x$ between the two systems occurred because some large-size facilities lack pollution controllers or efficient pollution controllers. Based on this study, CAPSS emission database of Korea will improve accuracy through adopting CEMS emission system, which enables more efficient national atmospheric policies and workplace management.

• Journal of Korean Society for Atmospheric Environment
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• v.30 no.1
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• pp.26-36
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• 2014

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.

• Journal of Environmental Impact Assessment
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• v.24 no.5
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• pp.456-471
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• 2015

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, $NO_2$, $SO_2$ and $PM_{10}$ concentrations were compared with simulations and the 'scaling factors' of emissions for CO, $NO_2$, $SO_2$, and $PM_{10}$ were suggested in in aggregative and quantitative manner. The results showed that CAPSS-2003 showed no critical discrepancies of CO and $NO_2$ observations with simulations, while $SO_2$ was overestimated by a factor of more than 12, while $PM_{10}$ was underestimated by a factor of more than 20 times. However, CAPSS-2010 case showed that $SO_2$ and $PM_{10}$ emission were much more improved than CAPSS-2003. However, $SO_2$ was still overestimated by a factor of more than 2, and $PM_{10}$ underestimated by a factor of 5, while there was no significant improvement for CO and $NO_2$ emission. The estimated factors identified in this study can be used as'scaling factors'for optimizing the emissions of air pollutants, particularly $SO_2$ and $PM_{10}$ for the realistic air quality simulation in Ulsan.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2009.10a
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• pp.280-282
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• 2009

• Journal of Climate Change Research
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• v.2 no.2
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• pp.69-77
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• 2011

While increased use of energy and fossil fuel in the recent years could worsen air quality and climate change, only few studies have been conducted on estimation of greenhouse gas emissions and characterization of emission types by sectors and regions in Korea. In this study, greenhouse gases emissions based on resions(Si, Gun, Gu) and emitted sectors(industry, transport, cemmercial and institutional, residential, waste, agriculture, others) were investigated using GHG-CAPSS(Greenhouse GasClean Air Policy Support System) developed to support to national and regional greenhouse gases reduction strategies. GHG-CAPSS follows IPCC(Intergovernmental Panel on Climate Change) Guideline methodology to categorize the emission sources and estimation of greenhouse gases using bottom-up approach. Estimated total greenhouse gases emissions were 588,011 thousand tons as $CO_2$ equivalent. Industry(50.1%) sector exhibited the highest portion followed by transport(17.6%), commercial and institutional(12.6%), residential(12.6%), waste(2.6%), agriculture(2.5%). Based on regional estimation, Gyeonggi(14.9%) demonstrated the highest emitted greenhouse gases among big cities followed by Jeonnam(12.4%), Gyeongbuk(11.0%), Ulsan(9.2%) and Seoul(8.9%).

• Journal of Climate Change Research
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• v.2 no.3
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• pp.203-219
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• 2011

Greenhouse gas(GHG) inventories were reported recently in various fields. It, however, has been rarely to mention about the accuracy and reliability of the GHG inventory results. Some reliable assessment methods were introduced to judge the accuracy of the GHG inventory results. It is, hence, critical to develop an evaluation methodology. This project was designed 1) to develop evaluation methodology for reliability of inventory results by GHG-CAPSS, 2) to check the feasibility of the developed evaluation methodology as a result of applying this methodology to two emission sources: liquid fossil fuel and landfill, and 3) to construct the technical roadmap for future role of GHG-CAPSS. Qualitative and quantitative assessment methodologies were developed to check the reliability and accuracy of the inventory results. Qualitative assessment methodology was designed to evaluate the accuracy and reliability of estimation methods of GHG emissions from emission and sink sources, activity data, emission factor, and quality management schemes of inventory results. On the other hand, quantitative assessment methodology was based on the uncertainty assessment of emission results. According to the results of applying the above evaluation methodologies to two emission sources, those seem to be working properly. However, it is necessary to develop source-specific rating systems because emission and sink sources exhibit source-specific characteristics of GHG emissions and sinks.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.3
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• pp.284-299
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• 2008

Emission input modules was developed to produce emission input data and change some profiles for Sparse Matrix Operator Kernel Emissions (SMOKE) using Clean Air Policy Support System (CAPSS)'s activities and previous studies. Specially, this study was focused to improve chemical speciation and temporal allocation profiles of SMOKE. At first, SCC cord mapping was done. 579 SCC cords of CAPSS were matched with EPA's one. Temporal allocation profiles were changed using CAPSS monthly activities. And Chemical speciation profiles were substituted using Kang et al. (2000) and Lee et al. (2005) studies and Kim et al. (2005) study. Simulation in Seoul Metropolitan Area (Seoul, Incheon, Gyeonggi) using MM5, SMOKE and CMAQ modeling system was done for effect analysis of changed input modules of SMOKE. Emission model results adjusted with new input modules were slightly changed as compared to using EPA's default modules. SMOKE outputs shows that aldehyde emissions were decreased 4.78% after changing chemical profiles, increased 0.85% after implementing new temporal profiles. Toluene emissions were decreased 18.56% by changing chemical speciation profiles, increased 0.67% by replacing temporal profiles as well. Simulated results of air quality were also slightly elevated by using new input modules. Continuous accumulation of domestic data and studies to develop input system for air quality modeling would produce more improved results of air quality prediction.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.6
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• pp.656-665
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• 2012

Recently, yearly production of SRF (Solid Recovered Fuel) as an alternative fuel has been rapidly increasing because of the limited waste disposal, rise in oil prices and reduction of greenhouse gas emission. However, SRF using facilities are excluded from the National Air Pollutant Emission Estimation because SRF using facilities are not yet included among the SCC (Source Classification Code). The purpose of this research was to estimate the emission and emission factor of SRF using facilities' PM and $NO_x$, in order to investigate whether or not they are included in the National Air Pollutant Emission Estimation. The emission factors of SRF using facilities' PM and $NO_x$ are calculated as 0.216 kg/ton, and 3.970 kg/ton, and the emission was estimated based on the yearly total SRF usage of 2011. The results above was 18.7% for PM and 12.8% for $NO_x$ emissions from combustion facility (SCC2) in manufacturing industry combustion (SCC1) of CAPSS. If CAPSS estimate the emission by adding SCC on unlisted SRF in case of Boiler (SCC3) fuel, both PM and $NO_x$'s emissions would increase by 15.8% and 11.3% compare to the emissions for the existing combustion facility. As a result, emissions caused by SRF should be considered when calculating the National Air Pollutant Emission Estimation. In addition, further researches to develop emission factor and improve subdivided SCC should be done in the future, for the accurate and reliable estimation of National Emission.