• 제목/요약/키워드: CAPSS

검색결과 58건 처리시간 0.025초

기상-대기질 모델을 활용한 2010~2014년 우리나라 PM10 변동 특성 분석: 기상 요인을 중심으로 (Analysis on the Characteristics of PM10 Variation over South Korea from 2010 to 2014 using WRF-CMAQ: Focusing on the Analysis of Meteorological Factors)

  • 남기표;이대균;박지훈
    • 환경영향평가
    • /
    • 제27권5호
    • /
    • pp.509-520
    • /
    • 2018
  • 본 연구에서는 기상조건 변화에 따른 우리나라 $PM_{10}$ 농도변화 범위를 정량적으로 산정하기 위하여, 2010년에서 2014년까지(5년간) 모델의 입력자료인 국내외 배출량을 동일하게 가정하였을 때 기상조건에 따른 우리나라 $PM_{10}$ 농도변화 범위를 분석하였다. 본 분석에 사용된 모델은 WRF(ver.3.8.1)과 CMAQ(ver.5.0.2)이며, 기상 입력자료는 NCEP FNL $1^{\circ}{\times}1^{\circ}$ 자료, 국외 배출량 목록은 MIX 2010, 국내 배출량 목록은 CAPSS 2010을 이용하였다. 모델 모사결과는 2010년의 전국 일평균 $PM_{10}$ 농도에 대해 측정값과 0.82의 R값을 보이며 실제 $PM_{10}$ 농도의 증감경향을 잘 나타냈지만, 모델은 실제 $PM_{10}$ 농도와 비교하여 과소모의 하는 것으로 나타났다. 기상 및 대기질 모델을 통해 모사된 우리나라 연평균 $PM_{10}$ 농도는 기상조건의 변화로 인해 2010년 대비 평균적으로 약 $2.6{\mu}g/m^3$의 농도변화를 나타내었으며, 계절별로는 봄, 여름, 가을, 겨울에 대해 각각 $4.8{\mu}g/m^3$, $1.7{\mu}g/m^3$, $1.7{\mu}g/m^3$, $4.2{\mu}g/m^3$의 표준편차를 나타내며 봄철과 겨울철에 상대적으로 큰 $PM_{10}$ 농도 차이를 나타냈다. 전국 18개 권역을 대상으로한 지역별 분석 결과에서는 기상조건의 변화로 인해 모든 지역에서 연평균 $PM_{10}$ 농도가 $1.0{\mu}g/m^3$ 이상의 표준편차를 나타냈으며, 특히 서울과 경기북부, 경기남부, 강원영서, 충북 지역의 경우 $2.0{\mu}g/m^3$ 이상으로 타 지역에 비해 상대적으로 높은 차이를 나타냈다.

수도권 초미세먼지 농도모사: (III) 관측농도 대비 모사농도 비율 적용에 따른 기여도 변화 검토 (PM2.5 Simulations for the Seoul Metropolitan Area: (III) Application of the Modeled and Observed PM2.5 Ratio on the Contribution Estimation)

  • 배창한;유철;김병욱;김현철;김순태
    • 한국대기환경학회지
    • /
    • 제33권5호
    • /
    • pp.445-457
    • /
    • 2017
  • In this study, we developed an approach to better account for uncertainties in estimated contributions from fine particulate matter ($PM_{2.5}$) modeling. Our approach computes a Concentration Correction Factor (CCF) which is a ratio of observed concentrations to baseline model concentrations. We multiply modeled direct contribution estimates with CCF to obtain revised contributions. Overall, the modeling system showed reasonably good performance, correlation coefficient R of 0.82 and normalized mean bias of 2%, although the model underestimated some PM species concentrations. We also noticed that model biases vary seasonally. We compared contribution estimates of major source sectors before and after applying CCFs. We observed that different source sectors showed variable magnitudes of sensitivities to the CCF application. For example, the total primary $PM_{2.5}$ contribution was increased $2.4{\mu}g/m^3$ or 63% after the CCF application. Out of a $2.4{\mu}g/m^3$ increment, line sources and area source made up $1.3{\mu}g/m^3$ and $0.9{\mu}g/m^3$ which is 92% of the total contribution changes. We postulated two major reasons for variations in estimated contributions after the CCF application: (1) monthly variability of unadjusted contributions due to emission source characteristics and (2) physico-chemical differences in environmental conditions that emitted precursors undergo. Since emissions-to-$PM_{2.5}$ concentration conversion rate is an important piece of information to prioritize control strategy, we examined the effects of CCF application on the estimated conversion rates. We found that the application of CCFs can alter the rank of conversion efficiencies of source sectors. Finally, we discussed caveats of our current approach such as no consideration of ion neutralization which warrants further studies.

수도권 지역의 대기환경관리 시행계획 추진결과 평가를 위한 대기질 모델링 적용 방법 (Methodology of Application to Air Quality Model to Evaluate the Results of the Enforcement Plan in Seoul Metropolitan Area)

  • 유철;이대균;이용미;이미향;홍지형;이석조
    • 한국환경과학회지
    • /
    • 제20권12호
    • /
    • pp.1647-1661
    • /
    • 2011
  • The Government had devised legislation of Special Act and drew up guidelines for improving air quality in Seoul Metropolitan area. In 2007 local government of Seoul, Incheon and Gyeonggi conducted the results of application policy by reduced air pollutants emission for the first time. Although there was reduction of air pollutant emission in each local government, it was ineffective as expected using air pollution monitoring database. Therefore we worked out a way to prepare modeling input data using the results of enforcement plan. And we simulated surface $NO_2$ and PM10 before and after decrease in air pollutants emission and examine reduction effects of air pollution according to enforcement regulation except other influence, by using MM5-SMOKE-CMAQ system. Each local government calculated the amount of emission reduction under application policy, and we developed to prepare input data so as to apply to SMOKE system using emission reduction of enforcement plan. Distribution factor of emission reduction were classified into detailed source and fuel codes using code mapping method in order to allocate the decreased emission. The code mapping method also included a way to allocate spatial distribution by CAPSS distribution. According to predicted result using the reduction of NOx emission, $NO_2$ concentration was decreased from 19.1 ppb to 18.0 ppb in Seoul. In Gyeonggi and Incheon $NO^2$ concentrations were down to 0.65 ppb and 0.68 ppb after application of enforcement plan. PM10 concentration was reduced from 18.2 ${\mu}g/m^3$ to 17.5 ${\mu}g/m^3$ in Seoul. In Gyeonggi PM10 concentration was down to 0.51 ${\mu}g/m^3$ and in Incheon PM10 concentration was decreased about 0.47 ${\mu}g/m^3$ which was the lower concentration than any other cities.

수도권지역에서의 권역간 대기오염물질 상호영향 연구 (A Regional Source-Receptor Analysis for Air Pollutants in Seoul Metropolitan Area)

  • 이용미;홍성철;유철;김정수;홍지형;박일수
    • 한국환경과학회지
    • /
    • 제19권5호
    • /
    • pp.591-605
    • /
    • 2010
  • This study were to simulate major criteria air pollutants and estimate regional source-receptor relationship using air quality prediction model (TAPM ; The Air Pollution Model) in the Seoul Metropolitan area. Source-receptor relationship was estimated by contribution of each region to other regions and region itself through dividing the Seoul metropolitan area into five regions. According to administrative boundary, region I and region II were Seoul and Incheon in order. Gyeonggi was divided into three regions by directions like southern(region III), northern(IV) and eastern(V) area. Gridded emissions ($1km{\times}1km$) by Clean Air Pollicy Support System (CAPSS) of National Institute of Environmental Research (NIER) was prepared for TAPM simulation. The operational weather prediction system, Regional Data Assimilation and Prediction System (RDAPS) operated by the Korean Meteorology Administration (KMA) was used for the regional weather forecasting with 30km grid resolution. Modeling period was 5 continuous days for each season with non-precipitation. The results showed that region I was the most air-polluted area and it was 3~4 times more polluted region than other regions for $NO_2$, $SO_2$ and PM10. Contributions of $SO_2$ $NO_2$ and PM10 to region I, II and III were more than 50 percent for their own sources. However region IV and V were mostly affected by sources of region I, II and III. When emissions of all regions were assumed to reduce 10 and 20 percent separately, air pollution of each region was reduced linearly and the contributions of reduction scenario were similar to those of base case. As input emissions were reduced according to different ratio - region I 40 percent, region II and III 20 percent, region IV and V 10 percent, air pollutions of region I and III were decreased remarkably. The contributions to region I, II, III were also reduced for their own sources. However, region I, II and III affected more regions IV and V. Shortly, graded reduction of emission could be more effective to control air pollution in emission imbalanced area.

과수원에서 사과 및 배 재배 시 복합비료 시용에 따른 암모니아 배출계수 평가 (Evaluation of Ammonia Emission Coefficient according to the use of Compound Fertilizers when Cultivating Apples and Pears in Orchards)

  • 김민욱;홍성창;유선영;김진호
    • 한국환경농학회지
    • /
    • 제40권4호
    • /
    • pp.366-372
    • /
    • 2021
  • BACKGROUND: Ammonia is known as a precursor to fine particulate matter, and according to CAPSS, annual ammonia emissions in the agricultural sector were 249,777 tons as of 2018, accounting for about 79.0% of Korea's total ammonia emissions. In particular, ammonia emissions from agricultural land increased by 19,566 tons (10.2%) compared to the previous year. The Ministry of Environment is setting emission statistics using the ammonia emission coefficient developed in Korea in 2008, but researchers in the agricultural field regard it as a coefficient that does not reflect the reality of Korea's agricultural environment. Accordingly, in order to develop ammonia emission coefficients from the cultivation of apples and pears, Korea's representative fruit type, test agricultural land was set in Iksan, Jeollabuk-do. METHODS AND RESULTS: This study attempted to obtain the ammonia emission coefficient by the treatment of the composite fertilizer (N-P2O5-K2O=12-7-9), and the flux was measured using a dynamic flow-through chamber method. As for the chamber, a total of 12 chambers were installed repeatedly in 4 zones and used to develop emission coefficients. Using compound fertilizers during fruit tree cultivation, the ammonia emission coefficient was evaluated as 10.4 kg NH3/ton for pears and 15.3 kg NH3/ton for apples. The reason why the ammonia emission coefficient according to the use of composite fertilizers was calculated higher for apple cultivation is believed to be due to the relatively high pH concentration of apple orchard soil. CONCLUSION(S): This study may provide basic data for upgrading the ammonia emission coefficient when using composite fertilizers in agricultural land. In the future, it might be necessary to upgrade the calculation of emissions through the development of ammonia and fine particulate matter emission coefficients considering the agricultural environment of Korea.

WRF-CMAQ 모델을 이용한 한반도 CH4 배출의 기여농도 추정 및 검증 (Verification and Estimation of the Contributed Concentration of CH4 Emissions Using the WRF-CMAQ Model in Korea)

  • 문윤섭;임윤규;홍성욱;장은미
    • 한국지구과학회지
    • /
    • 제34권3호
    • /
    • pp.209-223
    • /
    • 2013
  • 이 연구의 목적은 한반도에서 $CH_4$ 농도의 수치모의 검증을 통하여 $CH_4$ 배출원의 기여 농도를 추정하는 것이고, 이 수치모의에 사용된 $CH_4$ 배출량을 상자모델로부터 추정된 $CH_4$ 배출량과 비교하는 것이다. 한반도에서 2010년 4월 1일부터 8월 22일까지 $CH_4$의 평균 농도를 추정하기 위해 WRF-CMAQ 모델이 사용되었다. 모델에서 $CH_4$ 배출량은 전지구 배출량인 EDGAR와 한국에서의 온실기체 배출량인 GHG-CAPSS로부터 인위적 배출 인벤토리와 전지구 자연적 인벤토리인 MEGAN이 적용되었다. 이들 $CH_4$ 배출량은 안면도 및 울릉도에서 측정된 $CH_4$ 농도와 모델링 농도 자료를 비교함으로써 검증되었다. 울릉도에서 국내 배출원으로부터 추정된 $CH_4$의 기여 농도는 약 20%로 나타났고, 이것은 한반도 내 농장(8%), 에너지 기여 및 산업공정(6%), 일반폐기물(5%), 생체 및 토지이용(1%) 등 $CH_4$ 배출원으로부터 기원하였다. 그리고 중국으로부터 수송된 $CH_4$의 기여 농도는 약 9%였고, 나머지 배경농도는 약 70%로 나타났다. 박스모델로 추정된 $CH_4$ 배출량은 WRF-CMAQ 모델에서 사용한 $CH_4$ 배출량과 유의미한 결과를 얻었다.

수도권 초미세먼지 농도모사 : (II) 오염원별, 배출물질별 자체 기여도 및 전환율 산정 (PM2.5 Simulations for the Seoul Metropolitan Area: (II) Estimation of Self-Contributions and Emission-to-PM2.5 Conversion Rates for Each Source Category)

  • 김순태;배창한;유철;김병욱;김현철;문난경
    • 한국대기환경학회지
    • /
    • 제33권4호
    • /
    • pp.377-392
    • /
    • 2017
  • 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 $PM_{2.5}$), $NO_x$, $SO_2$, $NH_3$, and VOC emissions to $PM_{2.5}$ 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 $PM_{2.5}$ 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 $PM_{2.5}$ self-sensitivities to PPM, $SO_2$, and $NH_3$ 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 $PM_{2.5}$ were $2.7{\mu}g/m^3$ and $1.3{\mu}g/m^3$ for the months, respectively. Similarly, while $NH_3$ emissions from area sources were 4,169 TPM and 3,951 TPM in January and July, the self-contributions to monthly mean $PM_{2.5}$ for the months were $2.0{\mu}g/m^3$ and $4.4{\mu}g/m^3$, respectively. Meanwhile, emission-to-$PM_{2.5}$ 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 $6.6{\mu}g/m^3/10^6TPY$ for $SO_2$ emissions while those rates for PPM emissions were 268.6, 207.7, and 181.5 (${\mu}g/m^3/10^6TPY$), respectively, over the region. The results demonstrate that SMA $PM_{2.5}$ 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 $PM_{2.5}$ sensitivities to the SMA $NO_x$ emissions remains still negative even after a 50% reduction in emission category which implies that more aggressive $NO_x$ reductions are required for the SMA to overcome '$NO_x$ disbenefit' under the base condition.

다중규모 모델을 이용한 도시 지역 흐름과 초미세먼지(PM2.5) 분포 특성 연구: Part II - 도로 배출 영향 (A Numerical Study on the Characteristics of Flows and Fine Particulate Matter (PM2.5) Distributions in an Urban Area Using a Multi-scale Model: Part II - Effects of Road Emission)

  • 박수진;최원식;김재진
    • 대한원격탐사학회지
    • /
    • 제36권6_3호
    • /
    • pp.1653-1667
    • /
    • 2020
  • 본 연구에서는 국지예보시스템(LDAPS)과 전산유체역학(CFD) 모델을 접합하여, 부산 중구 광복동에 소재한 건물 밀집 지역의 상세 흐름과 PM2.5 농도 분포를 조사하였다. 도로 배출이 건물 밀집 지역의 PM2.5 농도에 미치는 영향을 분석하기 위해, PM2.5의 연간 시·군·구별, 배출 원소 별, 연료 별 도로이동오염원·비산먼지 배출량 자료와 월별·일별·시간 별 배출 계수를 이용하여 부산의 단위 면적당 시간별 PM2.5 배출량을 산정하였다. 본 연구에서는 건물 옥상과 도로변에서 수행된 특별 측정 자료를 이용하여 수치 모의 결과를 검증하고, 도로배출 유·무에 따른 PM2.5 농도 분포 특성을 분석하였다. 대상 기간(2020년 06월 22일) 동안 대상 지역에서는 바람이 약하게 나타났다. 새벽 시간에는 북동풍과 북서풍이 불고 주간에는 주로 남동풍이 불었다. 도로 배출을 고려하지 않은 경우에 LDAPS-CFD 접합 모델은 측정 지점(PKNU-AQ Sensor)의 PM2.5 농도를 과소모의 하였으나, 도로 배출을 고려하여 수치 모의한 PM2.5 농도는 도로 배출의 영향으로 PM2.5 농도가 증가하여 측정 결과와 유사하게 나타났다. 2020년 6월 22일 07시와 19시의 유입 풍향은 각각 북동풍과 남동풍이지만, 주변 지형과 건물에 의해 흐름이 변화되어, 두 시각 모두 측정 지점 주변에서는 주로 남풍 계열의 흐름이 나타났다. 07시와 19시의 유사한 흐름에 의해, 두 시각의 PM2.5 농도 분포도 매우 유사하게 나타났다. 건물 옥상 측정 지점에서 수치 모의된 PM2.5 농도는 도로 배출 영향을 크게 받지 않았으나, 도로변 에서는 도로 배출 영향을 상대적으로 크게 받았다. 도로 배출을 고려한 경우, 풍속이 약한 북쪽 도로와 긴 도로 협곡에 위치한 서쪽 도로에서 PM2.5 농도가 높고, 상대적으로 건물의 밀집도가 낮은 동쪽 도로에서는 PM2.5 농도가 낮게 나타났다. LDAPS-CFD 접합모델은 모든 도로에서 배출량이 동일하게 적용되기 때문에, 좁은 골목과 건물 밀도가 낮은 지역의 지형 특성이 반영되어 도로 별 PM2.5 농도 특성이 다양하게 나타났다.