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研究生:戴吉莉
研究生(外文):Gerelmaa
論文名稱:亞洲地區氣膠特徵的衛星遙測
論文名稱(外文):SATELLITE REMOTE SENSING OF AEROSOL CHARACTERISTICS OEVR ASIAN REGION
指導教授:劉振榮劉振榮引用關係
指導教授(外文):Prof. Gin-Rong Liu
學位類別:博士
校院名稱:國立中央大學
系所名稱:大氣物理研究所
學門:自然科學學門
學類:大氣科學學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:149
中文關鍵詞:AerosolDust and pollutionRemote Sensing of environmentMODISPollution transportAerosol sources
外文關鍵詞:AerosolDust and pollutionRemote Sensing of environmentMODISPollution transportAerosol sources
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本論文聚焦於應用衛星遙測技術探討亞洲地區氣膠特徵。本研究分析2002至2009年間搭載於Aqua與Terra衛星上的中級解析度成像分光輻射度計(MODIS)所收集到的氣膠光學厚度資料,而地面氣膠監測網 (AERONET)觀測資料則用於驗證MODIS所得氣膠光學厚度,由MODIS 與AERONET所得之Angstrom指數與細微粒氣膠比例等資料則用之探討氣膠粒子的大小、類型與可能來源。另外,美國國家環境預測中心及國家大氣研究中心 (NCEP-NCAR)的再分析資料及全球降雨氣候中心 (GPCC) 資料則用於獲取研究區域的氣候參數。
本研究詳細分析氣膠光學厚度與Angstrom指數間的相關性,以驗證MODIS資料與16個AERONET測站資料所反演之結果,也分析MODIS與AERONET在不同季節的相關性,以瞭解不同氣膠類型在不同季節下此兩種觀測系統的差異。結果顯示:MODIS與AERONET的氣膠光學厚度具很高相關性(> 0.9),然而其對應的Angstrom指數則相關性不佳,若只考慮氣膠光學厚度值> 0.5的Angstrom指數部份,則有較佳相關性。
本文進一步探討亞洲地區氣膠的持久源區,研究方法是將多年的氣膠光學厚度分佈圖就不同季節加以平均,即能顯現出主要的氣膠源區,而氣膠傳輸路徑地區或較短期的氣膠源區則會被平均掉。結果揭露出四個主要氣膠源區:(1)中國北部及西北部,及蒙古南部, (2)中國東部,(3)印度半島東北部,及(4)泰國、越南及印尼等三國部份區域。本研究並分析氣膠源區內的平均氣膠光學厚度隨季節的趨勢變化,以及與氣象參數變化的關係。結果顯示源區氣膠的生成排放於觀測資料期間有增加的趨勢。
為瞭解這些主要氣膠源區對周圍地區的影響,本研究選取北京、釜山、大阪,台北、泰國莫拉限市、越南北江市做為試區,進行氣膠觀測資料的詳細分析。分析項目包括氣膠光學厚度的日變化、月變化、季變化及年際變化,以及與NCEP-NCAR及GPCC氣象參數間的變化關係;並分析Angstrom指數與細微粒氣膠比例等值以探討氣膠粒子大小與類型特徵;及分析季節風型態以瞭解氣膠粒子從氣膠源區傳輸到試區測站的過程。研究結果顯示:當北京的日平均氣膠光學厚度>2.0時有最大的氣膠濃度。所選取的城市中,釜山及台北兩地在大部份季節裡有最小的氣膠光學厚度,其值小於0.5。莫拉限與北江兩地的氣膠光學厚度詳細特徵則是首次被探討。北京、大阪、釜山等地於春季沙塵佔氣膠成份較重要,某種程度上北江市也是如此,然而對所有選取城市而言夏季與春季期間人為污染及生質燃燒等都是貢獻氣膠的來源之一。另外,前述中國北部、西北部、及蒙古南部氣膠源區氣膠光學厚度的增加趨勢似乎也是造成大阪、釜山、北京等地氣膠光學厚度昇高之原因。

This thesis focusses on investigating the aerosol characteristics over Asian region using satellite remote sensing. Seven years of aerosol optical thickness (AOT) product from MODerate Imaging Spectroradiometer (MODIS) onboard Aqua and Terra satellites during the years 2002-2008 are analyzed for the study. Ground observations of aerosol robotic network (AERONET) at different stations within the study area are used to validate the MODIS AOT. Angstrom exponent (AE), and fine mode fraction (FMF) from MODIS and AERONET are used to discuss the size, type and possible sources of the particles. National Center for Environmental Prediction, National Center for Atmospheric Research (NCEP-NCAR) reanalysis data and Global Precipitation Climatology Center (GPCC) data are used to obtain the meteorological parameters over the study area.
A detailed correlation analysis of AOT and AE has been carried out to validate the MODIS data using corresponding AERONET measurements over 16 selected stations. Behavior of the MODIS-AERONET correlation in different seasons is investigated to understand the response of the two measurements for varying aerosol types. The results indicate very high (> 0.9) correlation of AOT between MODIS and AERONET, while the corresponding AE correlation is poor, but seems to improve if only those data points with corresponding AOT > 0.5 are considered.
Further analysis is carried out to examine permanent aerosol source regions over Asia. The methodology involves taking average AOT map during the years over the region in different seasons, in which the permanent source regions will appear pronounced whereas the locations influenced by transport or any emissions that last shorter time period will be smoothened. The results reveal four main such source regions: (1) the region at North and North-West of China and South of Mongolia, (2) Eastern part of China, (3) North-East of Indian continent, and (4) Parts of Thailand, Vietnam and Indonesia. The average AOT trends over the source regions in the years are examined in different seasons together with the corresponding variations of meteorological parameters and the results suggest an increase in the emissions.
In order to understand how the permanent source regions influence the surrounding locations, the aerosol properties selected East Asian stations of Beijing, Gosan, Osaka, Taipei, Mukdahan, and Bac-Gaing are further investigated in detail. The daily, monthly, seasonal, and inter-annual variations of the AOT are described along with meteorological parameters from NCEP-NCAR reanalysis and GPCC data. AE and FMF from MODIS and AERONET are used to discuss the size, type of particles. Seasonal wind pattern are used to understand the transport of particles from the source regions to the chosen stations. The results suggest that maximum aerosol loading occur over Beijing with daily mean AOT reaching above 2.0. Gosan and Taipei are among the stations having smallest AOT in most of the seasons with values below 0.5. The detail analysis of the AOT characteristics over Mukdahan, and Bac-Giang are reported for the first time. Dust influence appears to be significant over Beijing, Osaka, as well as Gosan, and to a lesser extent over Bac-Giang in the spring, while pollution, bio-mass burning, etc. contribute in the summer and spring over all the stations. The increasing AOT trend over the permanent source region (1) mentioned above seems to result in the observed increase of AOT trend over Osaka and Gosan, and also over Beijing.

Abstract (Chinese) i
Abstract (English) iii
Acknowledgments v
List of Figures ix
List of Tables xiii

1. Introduction 1
1.1 Aerosols 1
1.2 Sources and Characteristics 2
1.3 Significance 8
1.4 Scope of the Current Research 10

2. Aerosol Measurement 13
2.1 Measuring Aerosols 13
2.2 Advantages and Challenges in Satellite Remote Sensing of Aerosols 15
2.3 Principle of Satellite Aerosol Measurement 17
2.4 Aerosol Remote Sensing from Satellite Measurement 19
2.5 Aqua, Terra, and MODIS 20
2.6 MODIS AOT Retrieval 22
2.7 AERONET 26

3. MODIS and AERONET Correlation 29
3.1 Background 29
3.2 Study Area 31
3.3 Spatial and Temporal Matching 31
3.4 AOT Correlation 33
3.5 AE Correlation 36
3.6 Aerosol Characteristics and AOT, AE Correlation 37

4 Permanent Aerosol Source Regions over Asia 40
4.1 Background 40
4.2 Permanent Source Regions 42
4.3 Aerosol Characteristics over the Source Regions 46
4.4 Trends During 2002-2008 49

5. Aerosol Characteristics over East Asia 54
5.1 Background 54
5.2 Selected Stations 56
5.3 Data and Methodology 56
5.4 Seasonal and Monthly Aerosol Characteristics 57
5.5 Influence of Dust Particles 62
5.6 Smoke and Pollution 64
5.7 Seasonal Variation with Meteorological Parameters 65
Summary and Conclusion 69
Future Work 72
References 74
Tables 90
Figures 96

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