지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.8
- 수록면
- 377 - 392 (16page)
- DOI
- 10.5572/KOSAE.2017.33.4.377
이용수
초록· 키워드
오류제보하기
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 PM<SUB>2.5</SUB>), NO<SUB>x</SUB>, SO₂, NH₃, and VOC emissions to PM<SUB>2.5</SUB> 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 PM<SUB>2.5</SUB> 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 PM<SUB>2.5</SUB> 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 PM<SUB>2.5</SUB> 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 PM<SUB>2.5</SUB> for the months were 2.0 ㎍/㎥ and 4.4 ㎍/㎥, respectively. Meanwhile, emission-to-PM<SUB>2.5</SUB> 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 PM<SUB>2.5</SUB> 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 PM<SUB>2.5</SUB> sensitivities to the SMA NO<SUB>x</SUB> emissions remains still negative even after a 50% reduction in emission category which implies that more aggressive NO<SUB>x</SUB> reductions are required for the SMA to overcome ‘NO<SUB>x</SUB> disbenefit’ under the base condition.
In this study, seasonal variations of SMA PM<SUB>2.5</SUB> 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 PM<SUB>2.5</SUB> 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 PM<SUB>2.5</SUB> for the months were 2.0 ㎍/㎥ and 4.4 ㎍/㎥, respectively. Meanwhile, emission-to-PM<SUB>2.5</SUB> 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 PM<SUB>2.5</SUB> 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 PM<SUB>2.5</SUB> sensitivities to the SMA NO<SUB>x</SUB> emissions remains still negative even after a 50% reduction in emission category which implies that more aggressive NO<SUB>x</SUB> reductions are required for the SMA to overcome ‘NO<SUB>x</SUB> disbenefit’ under the base condition.
목차
Abstract
1. 서론
2. 연구 방법
3. 결과 및 고찰
4. 결론
References
참고문헌 (36)
참고문헌 신청
Bae, C., C. Yu, B.-U. Kim, H.C. Kim, and S. Kim (2017) PM2.5 Simulations for the Seoul Metropolitan Area: (III) Application of the Modeled and Observed PM2.5 Ratio on the Contribution Estimation, Journal of Korean Society for Atmospheric Environment (Under review).
Byun, D.W. and J.K.S. Ching (1999) Science Algorithms of the EPA Models-3 Community Multi-scale Air Quality (CMAQ) Modeling System, EPA Report, EPA/600/R-99/030, National Exposure Research Laboratory, Research Triangle Park, NC.
Carter, W.P.L. (1999) Documentation of the SAPRC-99 Chemical Mechanism for VOC Reactivity Assessment, Report to California Air Resources Board, Contracts 92-329 and 95-308.
Choi, J.K., J.B. Heo, S.J. Ban, S.M. Yi, and K.D. Zoh (2013) Source apportionment of PM2.5 at the coastal area in Korea, Science of the Total Environment, 447, 370-380.
Cohan, D., J. Boylan, A. Marmur, and M. Khan (2007) An Integrated Framework for Multipollutant Air Quality Management and Its Application in Georgia, Environmental Management, 40, 545-554.
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