• 제목/요약/키워드: $PM_{2.5}$ domestic contribution

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충주시 초미세먼지 (PM2.5)의 배출원 기여도 추정에 관한 연구 (Source Apportionment of Fine Particulate Matter (PM2.5) in the Chungju City)

  • 강병욱;이학성
    • 한국대기환경학회지
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    • 제31권5호
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    • pp.437-448
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    • 2015
  • The purpose of this study is to present the source contribution of the fine particles ($PM_{2.5}$) in Chungju area using the CMB (chemical mass balance) method throughout the four seasons in Korea. The Chungju's annual average level of $PM_{2.5}$ was $48.2{\mu}g/m^3$, which exceeded two times higher than standard air quality. Among these particles, the soluble ionic compounds represent 54.2% of fine particle mass. Additionally, the OC concentration in Chungju stayed similar to other domestic cities, while the EC concentration decreased significantly compared to other domestic/international cities. The concentration of sulfur represented the highest composition (8%) among the fine particle compounds. According to the CMB results, the general trend of the $PM_{2.5}$ mass contributors was the following: secondary aerosols (50.5%: ammonium sulfate 26.5% and ammonium nitrate 24.0%) > gasoline vehicle (18.3%) > biomass burning (11.0%) > industrial boiler (6.0%) > diesel vehicles (4.4%). The contribution of the secondary aerosols was the main cause than others. This impact is assumed to be emitted from air pollutants of urban cities or neighbor countries such as China.

2018년 봄철 제주지역 고농도 PM2.5에 대한 배출량 및 물리·화학적 공정 기여도 분석 (Contributions of Emissions and Atmospheric Physical and Chemical Processes to High PM2.5 Concentrations on Jeju Island During Spring 2018)

  • 백주열;송상근;한승범;조성빈
    • 한국환경과학회지
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    • 제31권7호
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    • pp.637-652
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    • 2022
  • In this study, the contributions of emissions (foreign and domestic) and atmospheric physical and chemical processes to PM2.5 concentrations were evaluated during a high PM2.5 episode (March 24-26, 2018) observed on the Jeju Island in the spring of 2018. These analyses were performed using the community multi-scale air quality (CMAQ) modeling system using the brute-force method and integrated process rate (IPR) analysis, respectively. The contributions of domestic emissions from South Korea (41-45%) to PM2.5 on the Jeju Island were lower than those (81-89%) of long-range transport (LRT) from China. The substantial contribution of LRT was also confirmed in conjunction with the air mass trajectory analysis, indicating that the frequency of airflow from China (58-62% of all trajectories) was higher than from other regions (28-32%) (e.g., South Korea). These results imply that compared to domestic emissions, emissions from China have a stronger impact than domestic emissions on the high PM2.5 concentrations in the study area. From the IPR analysis, horizontal transport contributed substantially to PM2.5 concentrations were dominant in most of the areas of the Jeju Island during the high PM2.5 episode, while the aerosol process and vertical transport in the southern areas largely contributed to higher PM2.5 concentrations.

2014년 2월 서울의 고농도 미세먼지 기간 중에 CMAQ-DDM을 이용한 국내외 기여도 분석 (Analysis of Domestic and Foreign Contributions using DDM in CMAQ during Particulate Matter Episode Period of February 2014 in Seoul)

  • 김종희;최대련;구윤서;이재범;박현주
    • 한국대기환경학회지
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    • 제32권1호
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    • pp.82-99
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    • 2016
  • This study was carried out to understand the regional contribution of Particulate Matter (PM) emissions from East Asia ($82^{\circ}{\sim}149^{\circ}E$, $18^{\circ}{\sim}53^{\circ}N$) to Seoul during high concentration period in February 2014. The Community Multi-scale Air Quality (CMAQ) version 5.0.2 with Decoupled Direct Method (DDM) was used to analyze levels of contributions over Seoul. In order to validate model performance of the CMAQ, predicted PM and its chemical species concentrations were compared to observations in China and Seoul. Model predictions could depict the daily and hourly variations of observed PM. The calculated PM concentrations, however, had a tendency of underestimation. The discrepancies are due to uncertainties of meteorological data, emission inventories and CMAQ model itself. The high PM concentration in Seoul was induced by stationary anticyclone over the West Coast of Korea during 24 to 27 February. The DDM in CMAQ was used to analyze the contributions of emissions from East Asia on Seoul during this PM episode. $PM_{10}$ concentration in Seoul is contributed by 39.77%~53.19% from China industrial and urban region, 15.37%~37.10% from South Korea, and 9.03%~18.05% North Korea. These indicate that $PM_{10}$ concentrations in Seoul during the episode period are dominated by long-range transport from China region as well as domestic sources. It was also found that the largest contribution region in China were Shandong peninsula during the PM event period.

배출량 목록에 따른 수도권 PM10 예보 정합도 및 국내외 기여도 분석 (Impact of Emission Inventory Choices on PM10 Forecast Accuracy and Contributions in the Seoul Metropolitan Area)

  • 배창한;김은혜;김병욱;김현철;우정헌;문광주;신혜정;송인호;김순태
    • 한국대기환경학회지
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    • 제33권5호
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    • pp.497-514
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    • 2017
  • This study quantitatively analyzes the effects of emission inventory choices on the simulated particulate matter (PM) concentrations and the domestic/foreign contributions in the Seoul Metropolitan Area (SMA) with an air quality forecasting system. The forecasting system is composed of Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Community Multi-Scale Air Quality (CMAQ). Different domestic and foreign emission inventories were selectively adopted to set up four sets of emissions inputs for air quality simulations in this study. All modeling cases showed that model performance statistics satisfied the criteria levels (correlation coefficient >0.7, fractional error <50%) suggested by previous studies. Notwithstanding the apparently good model performance of total PM concentrations by all emission cases, annual average concentrations of simulated total PM concentrations varied up to $20{\mu}g/m^3$ (160%) depending on the combination of emission inventories. In detail, the difference in simulated annual average concentrations of the primary PM coarse (PMC) was up to $25.2{\mu}g/m^3$ (6.5 times) compared with other cases. Furthermore, model performance analyses on PM species showed that the difference in the simulated primary PMC led to gross model overestimation in general, which indicates that the primary PMC emissions need to be improved. The contribution analysis using model direct outputs indicated that the domestic contributions to the annual average PM concentrations in the SMA vary from 44% to 67%. To account for the uncertainty of the simulated concentration, the contribution correction factor method proposed by Bae et al. (2017) was applied, which resulted in converged contributions(from 48% to 57%). We believe this study shows that it is necessary to improve the simulated concentrations of PM components in order to enhance the accuracy of the forecasting model. It is deemed that these improvements will provide more accurate contribution results.

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
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    • 제33권5호
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    • pp.445-457
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    • 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.

동아시아 지역의 계절별 기상패턴에 따른 우리나라 PM2.5 농도 및 기여도 특성 분석: 2015년 집중측정 기간을 중심으로 (Analysis of PM2.5 Concentration and Contribution Characteristics in South Korea according to Seasonal Weather Patternsin East Asia: Focusing on the Intensive Measurement Periodsin 2015)

  • 남기표;이대균;장임석
    • 환경영향평가
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    • 제28권3호
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    • pp.183-200
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    • 2019
  • 본 연구에서는 지상 $PM_{2.5}$ 측정 자료와 일기도 자료, WRF 및 CMAQ 모델을 활용하여 동북아시아 지역의 계절별 $PM_{2.5}$ 거동특성을 분석하였으며, 대기질 모델에 BFM을 적용하여 우리나라 $PM_{2.5}$ 농도에 대한 계절별 국내외 기여도를 평가하였다. 일기도 자료를 기반으로 국내 $PM_{2.5}$ 측정 자료 및 대기질 모사결과를 통해 $PM_{2.5}$의 거동특성을 분석한 결과, 동북아 지역에서의 $PM_{2.5}$는 장거리 수송된 대기오염 물질의 유입 및 대기정체 현상에 기인한 농도의 증가 또는 깨끗한 공기의 유입에 따른 농도의 감소 등의 특징이 계절별 종관기상 특성에 따라 상이하게 나타났다. 대기질 모델에 BFM (Brute-Force Method)을 적용하여 우리나라 6개 집중측정소 지점의 $PM_{2.5}$ 농도에 대한 국내외 기여도 평가를 수행한 결과, 백령도 지역은 낮은 자체 배출량과 동시에 중국으로부터 인접한 지리적 특성으로 인해 국외로부터의 기여가 지배적인 영향을 미치는 것으로 나타났다. 반면, 서울, 울산과 같이 높은 자체 배출량 특성을 나타내는 지역의 경우, $PM_{2.5}$에 대한 국외 기여도는 타 지역에 비해 상대적으로 낮게 나타남과 동시에 계절에 따른 기여도의 표준편차는 상대적으로 높게 나타나는 특징을 보였다. 본 연구는 우리나라를 중심으로 계절별 기상조건 변화에 따른 동북아 지역의 $PM_{2.5}$ 거동특성을 분석하여 국내 대기오염물질 현상에 대한 이해를 증진함과 동시에, 지역 배출특성에 따라 $PM_{2.5}$ 농도에 대한 국내외 기여도는 상이할 수 있음을 알려 향후 대기질 개선 대책 수립시 기초자료로 활용될 수 있을 것으로 기대된다.

충남지역 대형 점오염원이 주변지역 초미세먼지 농도에 미치는 영향 (Impact of Emissions from Major Point Sources in Chungcheongnam-do on Surface Fine Particulate Matter Concentration in the Surrounding Area)

  • 김순태;김옥길;김병욱;김현철
    • 한국대기환경학회지
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    • 제33권2호
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    • pp.159-173
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    • 2017
  • The Weather Research and Forecast (WRF) - Community Multiscale Air Quality (CMAQ) system was applied to investigate the influence of major point sources located in Chungcheongnam-do (CN) on surface $PM_{2.5}$ (Particulate Matter of which diameter is $2.5{\mu}m$ or less) concentrations in its surrounding areas. Uncertainties associated with contribution estimations were examined through cross-comparison of modeling results using various combinations of model inputs and setups; two meteorological datasets developed with WRF for 2010 and 2014, and two domestic emission inventories for 2010 and 2013 were used to estimate contributions of major point sources in CN. The results show that contributions of major point sources in CN to annual $PM_{2.5}$ concentrations over Seoul, Incheon, Gyeonggi, and CN ranged $0.51{\sim}1.63{\mu}g/m^3$, $0.71{\sim}1.62{\mu}g/m^3$, $0.63{\sim}1.66{\mu}g/m^3$, and $1.04{\sim}1.86{\mu}g/m^3$, respectively, depending on meteorology and emission inventory choice. It indicates that the contributions over the surrounding areas can be affected by model inputs significantly. Nitrate was the most dominant $PM_{2.5}$ component that was increased by major point sources in CN followed by sulfate, ammonium, and others. Based on the model simulations, it was estimated that primary $PM_{2.5}$ $(PPM)-to-PM_{2.5}$ conversion rates were 41.3~50.7 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 12.4~18.3 ($10^{-6}{\mu}g/m^3/TPY$) for Seoul, Incheon, and Gyeonggi, respectively. In addition, spatial gradients of PPM contributions show very steep trends. $NO_X$-to-nitrate conversion rates were 7.61~12.3 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 3.94~11.3 ($10^{-6}{\mu}g/m^3/TPY$) for the sub-regions in the SMA. $SO_2$-to-sulfate conversion rates were 4.04~5.28 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 3.73~4.43 ($10^{-6}{\mu}g/m^3/TPY$) for the SMA, respectively.

WRF-CMAQ 모델링 시스템을 활용한 PM2.5 농도변동 원인 분석: 2016년과 2017년의 가을철을 중심으로 (Analysis of the Changesin PM2.5 Concentrations using WRF-CMAQ Modeling System: Focusing on the Fall in 2016 and 2017)

  • 남기표;임용재;박지훈;김덕래;이재범;김상민;정동희;최기철;박현주;이한솔;장임석;김정수
    • 환경영향평가
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    • 제27권2호
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    • pp.215-231
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    • 2018
  • 본 연구에서는 지상 기상 및 $PM_{2.5}$ 농도, GOCI 위성의 AOD 등 다양한 관측 자료와 WRF-CMAQ 모델링을 통해 2016년과 2017년의 우리나라 가을철 $PM_{2.5}$ 농도변화 원인을 분석하였다. 지상에서 관측된 2017년 전국 평균 $PM_{2.5}$ 농도는 2016년에 비해 약 12.3% ($3.0{\mu}g/m^3$) 감소한 것으로 나타났다. 두 해간 $PM_{2.5}$ 농도 차이는 10월과 11월의 두 사례(사례1: 10월 11일~10월 20일, 사례2: 11월 15일~19일) 기간에 주로 발생하였으며, 2017년의 기상조건이 2016년에 비하여 국외로부터 대기오염물질의 장거리 수송이 어렵고, 국내의 대기환기 효과를 증가시키는 방향으로 변화한 것이 주요한 원인으로 분석되었다. WRF-CMAQ 모델링 시스템을 이용하여 기상조건 변화가 $PM_{2.5}$ 농도에 미치는 정량적인 영향을 평가한 결과, $PM_{2.5}$ 모의농도는 2016년 대비 2017년의 사례1 기간에는 64.0% ($23.1{\mu}g/m^3$) 감소, 사례2 기간에는 35.7% ($12.2{\mu}g/m^3$) 감소한 것으로 나타나, 관측 농도 기반 감소율인 53.6% (사례1)와 47.8% (사례2)에 상응하는 감소율을 보였다. 따라서 기상조건 변화가 우리나라 가을철 $PM_{2.5}$ 농도 변화에 큰 영향을 미치는 것으로 분석되었다. 기상조건 변화로 인한 우리나라 $PM_{2.5}$ 농도 감소에 미친 국내외 기여율은 사례1 기간에 국외로부터의 장거리 수송영향이 52.8% 그리고 대기환기 효과에 따른 국내영향이 47.2% 로 국내외 영향이 유사하게 나타나지만, 사례2 기간에는 국외영향이 66.4% 그리고 국내영향이 33.6%로서 국외영향의 감소효과가 더 크게 나타났다.

주암호 외남천 유역 하천수의 질소농도와 동위원소비 분석을 이용한 오염원 평가 (Estimation of Pollution Sources of Oenam Watershed in Juam Lake using Nitrogen Concentration and Isotope Analysis)

  • 최유진;정재운;최우정;윤광식;최동호;임상선;정주홍;임병진;장남익
    • 한국물환경학회지
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    • 제27권4호
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    • pp.467-474
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    • 2011
  • In an effort to investigate water pollution characteristics of Juam lake, water samples were collected from three sites (Sites A, B, and C) of Oenam stream which is a typical tributary of rural watershed in the lake and analyzed for N concentration and the corresponding isotope ratio (${\delta}^{15}N$) of ${NO_3}^-$. Concentrations of ${NO_3}^-$ were not dramatically different among the sites; $0.8{\pm}0.2mgNL^{-1}$ (range: $0.0{\sim}4.3mgNL^{-1}$) for Site A, $1.1{\pm}0.2mgNL^{-1}$ ($0.0{\sim}4.3mgNL^{-1}$) for Site B, and $1.1{\pm}0.1mgNL^{-1}$ ($0.1{\sim}2.6mgNL^{-1}$) for Site C. Meanwhile, ${\delta}^{15}N$ tended to decrease with river flow; it was highest for Site A ($45.5{\pm}5.3$‰) followed by Site B ($19.7{\pm}2.0$‰) and Site C ($8.7{\pm}1.5$‰). Such high ${\delta}^{15}N$ values of ${NO_3}^-$ in Site A suggested that ${NO_3}^-$ derived from livestock feedlot (specifically livestock excrete of which ${\delta}^{15}N$ is higher than 10‰) is the predominant pollution sources despite mountainous area occupied the most of land-use in the watershed. Using the two-sources isotope mixing model, it was estimated that the contribution of cropping activities (i.e. fertilization) became greater in down-stream area (Sites B and C) due to the higher agricultural land-use than the up-stream area (Site A). Particularly, during the active cropping season, the low contribution of organic pollution sources indicated that domestic sewage was not the predominant pollution source. Therefore, it was suggested that agricultural sources such as livestock farming and cropping rather than mountainous and residential are the dominant sources of water pollution in the study area. These results could be effectively utilized in elucidating water pollution sources in rural areas and selecting water management practices.