초록 ▼

1. 전지구 3차원 대기화학수송모델 장착 및 운용
- 현존하는 최신의 3차원 전지구 대기화학수송모델인 지오스켐(GEOS-Chem)의 성공적인 장착 및 효율적 운용
2. 모델 모의 및 관측 결과와의 비교 분석
- 동아시아에서 관측된 다양한 자료와 모델 결과와의 비교 분석을 통해 모델의 정확도 검증
3. 인버스 모델링 분석을 위한 모델 오차 및 불확실성 분석
- 전지구 대기화학수송모델과 인공위성 관측값을 이용하여 2001년 동아시아 지표 $PM_{2.5}$와 $PM_{10}$

1. 전지구 3차원 대기화학수송모델 장착 및 운용
- 현존하는 최신의 3차원 전지구 대기화학수송모델인 지오스켐(GEOS-Chem)의 성공적인 장착 및 효율적 운용
2. 모델 모의 및 관측 결과와의 비교 분석
- 동아시아에서 관측된 다양한 자료와 모델 결과와의 비교 분석을 통해 모델의 정확도 검증
3. 인버스 모델링 분석을 위한 모델 오차 및 불확실성 분석
- 전지구 대기화학수송모델과 인공위성 관측값을 이용하여 2001년 동아시아 지표 $PM_{2.5}$와 $PM_{10}$ 에어로졸의 질량 농도를 추정하고 계절별, 지역별에 대한 에어로졸 모의의 불확실성 및 원인 제시
- 시베리아 산불로 인해 발생한 에어로졸이 동아시아 대기질 및 복사강제력에 끼치는 영향을 산정하고 탄소 성분과 먼지 에어로졸의 농도 및 에어로졸 광학두께 모의에 대한 불확실성 제시
- 동아시아를 포함한 북반구에서 오염물질의 배출원-수용지 관계 도출을 위한 전구 모델 intercomparison study를 통해 배출 저감에 따른 수용지에서의 오염 물질 농도의 저감 효과 산정
4. 인버스 모델링을 통한 동아시아 에어로졸 소스 연구
- 2단계에는 1차년도에서 확인한 에어로졸의 특성 및 불확실성을 기반으로 인버스 모델링 분석을 통하여 기존의 bottom-up 방법에 의해 산출된 먼지와 검댕 에어로졸 소스의 초기 자료 정확성 향상을 기할 예정이며, 개선된 모델 결과를 통해 동아시아 일차 에어로졸의 생성 및 특성을 분석할 계획

Abstract ▼

Atmospheric aerosols are one of the critical all pollutants affecting human health and visibility. Their radiative effects of scattering and absorbing solar radiation have an important impact on the Earth's radiation budget and climate. Over Asia those aerosol concentrations have been rapidly increa

Atmospheric aerosols are one of the critical all pollutants affecting human health and visibility. Their radiative effects of scattering and absorbing solar radiation have an important impact on the Earth's radiation budget and climate. Over Asia those aerosol concentrations have been rapidly increasing from the booming growth of economic developments in the developing countries such as Chin and India. In particular aerosols from East Asia were reported to be long-range transported across the Pacific and to affect all quality and climate in North America,, Therefore, understanding of the processes in determining temporal and spatial distributions of aerosols concentrations in East Asia is very crucial to accurately assessing their influences.
The purpose of this work is to improve our quantitative understanding on East Asian aerosol sources which are known to have high uncertainties. Aerosol sources with high uncertainties often cause large model errors and make it difficult to accurately assess the effects of aerosols on air quality. In this work we try to decrease those uncertainties with aerosol sources by using an inverse modeling. That is to minimize errors of a priori aerosol sources in the model with observational constraints and to obtain a posteriori sources.
First of all, we applied a 3-D global chemical transport model (GEOS-Chem) to aerosol observations over East Asia in 2001 and 2003 to examine the current status of our scientific understanding for East asian aerosol simulations. We compared the simulated aerosol concentrations against satellite and ground-based aerosol observations for the model evaluation. The model generally underestimated observations especially from the satellite. There are a number of factors contributing to the model bias including the chemical production/loss, wet/dry deposition, transport, and sources. In order to understand those contributions we performed several sensitivity simulations to the model parameters. We found that the low bias in the model is generally due to low estimates of aerosols sources over East Asia.
We then corrected the bias in the simulated aerosol concentrations using the observational constraints from the satellite and computed the best annual mean aerosol concentrations in surface air over East Asia. Values are 14.7 and 71.2 ${\mu}gm^{-2}$ for $PM_{2.5}$ and $PM_{10}$ concentrations, respectively, with relatively high uncertainties of 2.5, 20 ${\mu}gm^{-2}$, respectively. These high uncertainties indicate imitations of both the simulation and the observation of aerosols in East Asia which need to be further investigated.
In 2003 a record-breaking wildfire occurred in Siberia and severely affected East Asia. We quantified the effect of this Siberian fire aerosols on air quality over East Asia. Increases in aerosol concentrations due to fires were up to 90 ${\mu}gm^{-2}$ over Siberia and 5-30 ${\mu}gm^{-2}$ over large down-wind regions on a monthly mean basis. This enhancements account for about 10-60% of annual air quality standard for $PM_{10}$ concentrations in Korea. These aerosols also perturbed solar radiations significantly resulting in -5.8 W $m^{-2}$ of radiative cooling in the surface and -1.5 W $m^{-2}$ at the top of the atmosphere. We find however a large discrepancy in the simulated aerosol optical depth (AOD) against satellite observed AOD despite a relatively good agreement between the simulated and observed $PM_{10}$ concentrations in surface air. This apparent inconsistency surfaced a number of issues regarding aerosol sources and its physical and optical characteristics used in the model and the satellite retrievals.
We also took part in the model intercomparison study for the Hemispheric Transport of Air Pollutants (HTAP) with other 21 institutions worldwide to investigate the source-receptor relationships for air pollutants. For this study we divided four important source regions in the northern hemisphere including East Asia, Europe, North America, and Southeast Asia. Over individual source regions, ozone precursor gas concentrations (NOx, NMVOC, CO) were individually and altogether perturbed by 20% in global chemical transport models which were used to find out how resulting ozone concentrations would be in both source and receptor (other source regions) regions. From this we found that about 0.6-0.8 ppbv ozone concentrations were attributed to the long-range transport. Interestingly a 20% decrease in $CH_4$ concentrations would have similar outcome for the reduction of ozone concentrations as its precursors decreases.
For the first year of this project we quantified the errors and uncertainties associated with the aerosol simulations over East Asia. In addition, there are many issues with aerosol observations especially satellite observations of which quality has never been extensively evaluated over East Asia. Based on the results from our 1st fear work we will first apply inverse modeling techniques to primary aerosols including black carbon and soil dust aerosols to improve their sources over East Asia. The former has recently been in large scrutiny because of its warming climate effect. The latter is one of the most important aerosols in mass concentrations particularly for spring in East Asia. The outcome of our proposed work will then be used to accurately quantifying those aerosols contributions to both air quality and climate over East Asia.

목차 Contents

• 제 1 장 연구개발 과제의 개요 ...17
• 제 1 절 연구개발의 필요성 ...17
• 제 2 장 국내외 기술개발 현황 ...19
• 제 1 절 세계적 수준 ...19
• 제 2 절 국내 수준 ...19
• 제 3 절 국내.외의 연구 현황 ...20
• 제 3 장 연구개발 수행 내용 및 결과 ...21
• 제 1 절 3차원 전지구 대기화학 모형 국내 장착 및 구동 ...23
• 1. 지오스켐 모델개요 ...23
• 제 2 절 동아시아 에어로졸 모의 및 인공위성 자료를 이용한 지표 에어로졸 농도 예측 개선 연구 ...25
• 1. 서론 ...25
• 2. 자료 및 방법 ...26
• 가. 관측자료 ...26
• 나. 모델모의 ...28
• 다. 방법 ...29
• 3. 결과 및 토의 ...30
• 가. 모델에서 모의된 PM10 농도자료 검증 ...30
• 나. 모디스 AOD를 이용한 Remote-sensed PM10 농도 ...34
• 다. 모디스 AOD와 FMF를 이용한 Remote-sensed PM2.5 농도 ...40
• 라. Remote-sensed PM10 농도의 불확실성 ...44
• 4. 소결론 ...45
• 제 3 절 지상 및 인공위성 관측 자료와 3차원 대기화학 모델을 이용한 시베리아 산불이 동아시아 에어로졸 농도에 끼치는 영향 연구 ...47
• 1. 서론 ...47
• 2. 모델 및 관측자료 ...48
• 3. 모델 검증 ...50
• 4. 산불 배출 고도의 민감도 조사 ...56
• 5. 시베리아 산불이 지상 PM10과 오존 농도에 끼치는 영향 ...58
• 6. 산불에 의한 복사 효과 ...60
• 7. 소결론 ...62
• 제 4 장 목표달성도 및 관련분야에의 기여도 ...64
• 제 5 장 연구개발결과의 활용계획 ...65
• 제 6 장 연구개발과정에서 수집한 해외과학기술정보 ...66
• 제 7 장 참고문헌 ...67
• Fig. 1 Locations of 62 AQS/EANET aerosol monitoring sites used in this study. Crosses, open circles, and closed circles represent PM measuring sites in Korea and Japan, South China, and North China, respectively. $PM_{10}$ data are available at all sites and $PM_{2.5}$ data are available at three sites (50, 54, and 64). The 11 AERONET sites are additionally shown as gray triangles ...27
• Fig. 2 Seasonal mean $PM_{10}$ mass concentrations in surface air for winter (DJF), spring (MAM), summer (JJA) , and fall (SON) of 2001. Observations from the EANET/AQS network are in the top panel, while the simulated and the estimated $PM_{10}$ concentrations with the MODIS AOD are shown in the middle and the bottom panels, respectively. The colour scale is saturated. White in the bottom panel indicates the regions with daily MODIS observations available for fewer than 10% of the days in 2001 ...31
• Fig. 3 Scatterplots of observed versus simulated seasonal mean $PM_{10}$ mass concentrations for the ensemble of sites shown in Figure 1. Different symbols are used for sites in North China (closed circles), South China (open circles), and Korea/Japan (crosses), as shown in Figure 1. Reduced major axis regressions (Hirsch and Gilroy, 1984) for the ensemble of the data (dashed lines) are shown along with the regression equations, R, and the number of samples. The y = x relationships (solid line) are also shown ...33
• Fig. 4 Scatterplots of AERONET versus simulated AOD (circles) and MODIS AOD (stars) in each season. The simulated AODs are averaged for AERONET and MODIS measurement times. The dashed lines indicate the 2:1, 1:1, and 1:2lines. Different colors are used for 11 AERONET sites in East Asia: Anmyon (36N, 126E), Beijing (39N, 116E), Gosan_SNU (33N, 126E), NCU_Taiwan (24N, 121E), Noto (37N, 137E), Okinawa (26N, 127E), Osaka (34N, 135E), Seoul_SNU (37N, 126E), Shirahama (33N, 135E), XiangHe (39N, 116E), Yulin (38N, 109E) ...35
• Fig. 5 Same as in Figure 3 but for the remote-sensed $PM_{10}$ concentrations using MODIS AOD data. EANET/AQS measurements are taken from 1000 LT through 1200 LT during successful overpass measurements ...36
• Fig. 6 Daily mean $PM_{10}$ mass concentrations at eight representative EANET/AQS sites: Beijing, Harbin, Shanghai, Chongqing, Lanzhou, Guangzhou, Happo, and Seoul. Observations are in black. Simulated and remote-sensed values are shown in blue and red, respectively ...38
• Fig. 7 Temporal correlation coefficients between observed and simulated daily mean $PM_{10}$ concentrations (left column) and between observed and remote-sensed daily mean $PM_{10}$ concentrations (right column) for the entire year (top panels) and for individual seasons. Regressions validated to the 85% confidence level by F-test are indicated by black circles. N indicates the number of sites with R > 0.3 ...39
• Fig. 8 Best estimates of annual and seasonal means of $PM_{10}$ and $PM_{2.5}$ mass concentrations averaged over East Asia ($14^{\circ}-56^{\circ}N,\;75^{\circ}-150^{\circ}$E), North China ($30^{\circ}-50^{\circ}N,\;75^{\circ}-125^{\circ}$E), South China ($20^{\circ}-30^{\circ}N,\;75^{\circ}-125^{\circ}$E), and Korea and Japan ($25^{\circ}-50^{\circ}N,\;125^{\circ}-150^{\circ}$E) for 2001. $PM_{2.5}$ values are obtained using both MODIS FMF and AOD data ...41
• Fig. 9 Spatial map of best estimates of season-mean $PM_{2.5}$ mass concentrations using MODIS AOD and FMF in surface air for winter (DJF), spring (MAM), summer (JJA), and fall (SON) of 2001. White indicates the regions with daily MODIS observations available for fewer than 10% of the days in 2001 ...42
• Fig. 10 Estimated dry mass burned (Tg C $mon^{-1}$) due to the Siberian forest fires in May for 1998-2005 from the GFED2 inventory ...49
• Fig. 11 Sites from the Acid Deposition Monitoring Network in East Asia (EANET; closed circles) and Aerosol Robotic Network (AERONET; closed triangles) in 2003. Boxes indicate the 2$\times$2.5 model grids ...50
• Fig. 12 Time series data of (a) simulated (red line) and observed (black line) hourly $PM_{2.5}$ concentrations at the Rishiri site of the EANET, and (b) simulated (closed circle) and observed (open circle) daily AOD values at Gosan (red), Noto (green), and Shirahama (blue) AERONET sites (For interpretation of the references to color in this figure, the reader is referred to the web version of this article) ...51
• Fig. 13 Monthly mean AOD at 550 nm from the MODIS (left) and the model (right) for May 2003. White areas indicate missing data (For interpretation of the references to color in this figure, the reader is referred to the web version of this article) ...52
• Fig. 14 Scatter plots of the observed and the simulated monthly mean (a) $PM_{10}$ and (c) daytime ozone concentrations (averaged for 1300-1700 local time) and (b) AOD at 550 nm at EANET sites in May 2002 (triangles), May 2003 (closed circles) and May 2004 (open circles). Reduced major axis regressions for the ensemble of the data (thin line) are shown; $R^2$ and regression equations are shown inset. Dashed lines denote a factor of 2 departure ...53
• Fig. 15 Comparisons of the observed (red circles) and the simulated (large bars) monthly mean values for (a) $PM_{10}$ and (c) daytime ozone concentrations, and (b) AOD at 550 nm sampled at EANET sites in May 2003. The $PM_{10}$ and ozone concentrations are in surface air from the EANET and the AOD observations are from the MODIS. Vertical error bars represent one standard deviation with respect to the observed daily mean concentrations, respectively. Simulated contributions of secondary inorganic aerosol (SNA), black carbon (BC), organic carbon mass (OMC), soil dust (DUST), and fine mode sea-salt (FSS) aerosols to $PM_{10}$ concentrations and AOD values are denoted in different colors. OMC aerosols include primary OC with non-carbon mass and SOA (For interpretation of the references to color in this figure, the reader is referred to the web version of this article) ...55
• Fig. 16 Same as in Fig. 15 but with two more sensitivity model results using 3.0 km (CASE1) and 4.5 km (CASE2) injection heights of the Siberian fire emissions. Red circles indicate the observations with the standard deviation (vertical error bars). Green, skyblue, and blue bars indicate the standard, CASE1, and CASE2 sensitivity simulations, respectively. Statistics including the coefficient of determination ($R^2$), reduced major axis regression (Slope), and mean bias (Bias) are shown in the right panel (For interpretation of the references to color in this figure, the reader is referred to the web version of this article) ...57
• Fig. 17 Spatial distributions of the simulated monthly mean (a) $PM_{10}$ and (c) daytime ozone concentrations at the surface from our best simulation with 4.5 km injection height (CASE2 simulation in Section 5). The enhancements in (b) $PM_{10}$ and (d) daytime ozone concentrations due to the Siberian forest fires were computed by subtracting the simulation without the fire emissions from the CASE2 simulation. The domain-averaged values are shown in the upper right corner of each panel ...59
• Fig. 18 Monthly mean surface radiative forcing (W $m^{-2}$) of (a) OC, (b) BC, and (c) ozone from the Siberian forest fires over East Asia. Surface radiative forcing is computed as differences in net downward fluxes at the surface between the models with and without the Siberian forest fires emissions. The bottom right panel shows the sum of those three. The domain minimum and maximum values are given on the upper right corner of each panel; numbers in parentheses represent the mean values of the domains ...61
• Fig. 19 Same as in Fig. 18 but at the TOA ...62
• Table 1. Statistics for the observed (x-axis) versus estimated (y-axis) $PM_{2.5}$ concentrations at Gosan (33.3N, 126.2E), Oki (36.3N, 133.2E), and Rishiri (45.1N, 141.2N) in Korea and Japan. Reduced major axis method [Hirsch and Gilroy, 1984] is used to obtain the regression equations ...43
• Table 2. Comparison of monthly biomass burning emissions for BC, OC, and CO in Siberia [40N-90N, 60E-180E] during May 2003 ...49