博碩士論文 102022604 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:9 、訪客IP:54.198.28.114
姓名 夢明玉(Mong Thi Ngoc)  查詢紙本館藏   畢業系所 遙測科技碩士學位學程
論文名稱
(The spatial correlation of satellite-estimated PM2.5 and epidemiological diseases in Taiwan)
檔案 [Endnote RIS 格式]    [Bibtex 格式]    至系統瀏覽論文 (2017-10-31以後開放)
摘要(中) 近來的研究指出,空氣汙染對於環境以及人體健康造成了顯著的影響。PM2.5是一個直徑小於2.5 µm 的微小顆粒,因為 PM2.5的粒徑很小,所以散佈在空氣中很容易被人體所吸收,並造成呼吸系統上的疾病。為了解PM2.5 對於人體健康最的重要性,很多地區的地面站開始架設儀器來測量PM2.5,本研究使用了NASA的 MODIS衛星資料,因為它具有高覆蓋率及高空間解析度的優點,所以用來推估 PM2.5是個不錯的選擇。在本篇研究中,PM2.5是基於MODIS L2 AOD反演產品的關係所估而得,並使用統計方式去分析台灣各個城市的過敏性鼻炎患者與MODIS所推估的PM2.5兩者間的關係。我們初步的結果顯示,在MODIS AOD與地面站的量測到的PM2.5間,其相關係數為0.4。此研究也指出了,在春秋兩季,特定年齡族群的過敏性鼻炎患者跟 PM2.5有一個顯著的正相關,本篇的重點在於衛星的觀測對於空氣汙染的高潛在地區提供了有用的資訊,尋找高汙染地區有益於為呼吸疾病的患者做出一些保護性的預防工作,在未來,其他可能造成呼吸疾病的環境因素也能以類似方式做一個很好的評估
摘要(英) Air pollution has significant impacts on the environment and human health. Recent studies have been focusing on analyzing the separate impacts of each air pollutants to effectively mitigate the potential air pollution related health risks. Fine particles, with diameter less than 2.5 μm (PM2.5), are one of the pollutants attract a lot of interest in current researches. The PM2.5 particles are very small in size, therefore they remain suspended in the air and easily gets into human body through inhalation, and consequently cause respiratory diseases to humans. To understand the importance of PM2.5 to human health, many ground stations have been established in some regions to measure PM2.5 concentration. However, existing sparse in situ systems may limit their capability to observe detailed PM2.5 concentration and distribution at regional scale. Hence, using NASA’s spaceborne MODIS data, which has the advantages of a wide coverage and high spatial resolution, is seen as one of the optimal choices to derive PM2.5 concentration. In this study, PM2.5 is estimated based on its relationship with the Aerosol Optical Depth (AOD), the Level-2 product derived from space-borne MODIS observation. The correlation between the MODIS-estimated PM2.5 and the statistical allergic rhinitis patients from various cities in Taiwan was analysed by using statistical methods. Our preliminary result suggests a relationship between MODIS AOD and ground-based PM2.5 with a correlation coefficient of about 0.692. This model could explain up to 78.3% training samples within one standard deviation margin. This research also pointed out the positive impacts of PM2.5 to allergic rhinitis patient in adults (aged 18-65) and preschoolers/teenagers (aged 3-18) groups and as well as the significant positive correlation particular in both spring and fall. The key finding is that satellite observations and data may provide valuable information to imply the potential risk regions. The addressing of the health risk region could be useful for affected people to find their way to protect themselves from the unexpected impacts from air pollutants. In future, the additional environmental factors, which might be other causes of respiratory diseases, will be considered for better assessment.
關鍵字(中) ★ PM2.5
★ 氣膠光學厚度
★ 中解析度成像分光輻射計
★ 流行病學
關鍵字(英) ★ PM2.5
★ aerosol optical depth
★ MODIS
★ epidemiology
論文目次 Chinese Abstract ................................................................................................. 1
English Abstract .................................................................................................. 3
Acnoleagement .................................................................................................... 5
Table of content ................................................................................................... 7
List of Figures ..................................................................................................... 9
List of Tables ..................................................................................................... 11
Explanation of Abbreviations .......................................................................... 12
I. INTRODUCTION .................................................................................... 1
1.1. Air Pollution and Its Effects ................................................................... 1
1.2. PM2.5 Status and Health Impacts in Taiwan .......................................... 3
1.2.1. What is PM2.5? ................................................................................ 3
1.2.3. How to measure PM2.5 .................................................................... 7
1.3. How MODIS retrieve AOD from satellite images ................................ 10
1.3.1. Dark Target algorithm .................................................................... 11
1.3.2. Deep blue algorithm ....................................................................... 15
1.4. Research Motivation ........................................................................... 16
1.5. Research Objectives ............................................................................ 17
II. BACKGROUND ..................................................................................... 18
2.1. Study Area ........................................................................................... 18
2.1.1. Geographic Location ..................................................................... 18
2.1.2. Typical Climate Status .................................................................. 19
2.1.3. Aerosol and PM2.5 Status ............................................................ 19
2.2. Datasets ............................................................................................... 20
2.2.1. Satellite Data ................................................................................. 20
2.2.2. In-situ PM2.5 Data ........................................................................ 20
2.2.3. Long-term Health Data Records ................................................... 23
III. METHODOLOGY AND DATA PROCESSING ................................ 24
3.1. Methodology ......................................................................................... 24
3.1.1. Overall Design .............................................................................. 24
3.1.2. Building a Model to Estimate PM2.5 ........................................... 24
3.1.3. Assessing the Correlation between PM2.5 and Health Data ........ 27
IV. RESULTS AND DISCUSSION ............................................................. 28
4.1. The Relationship Between Ground-based PM2.5 Concentration and
MODIS AOD ............................................................................................... 28
4.2. Correlation Between MODIS-estimated PM2.5 and Allergic Rhinitis
Diseases ....................................................................................................... 29
4.3. Mapping the Suitable Habitat for Patients .......................................... 37
V. CONCLUSION ....................................................................................... 38
Bibliographies ................................................................................................... 41
參考文獻 Chu, D.A. et al., 2015. Regional characteristics of the relationship between columnar AOD and surface PM 2.5: Application of lidar aerosol extinction profiles over Baltimore–Washington Corridor during DISCOVER-AQ. Atmospheric Environment, 101: 338-349.
Chu, D.A. et al., 2003. Global monitoring of air pollution over land from the Earth Observing System-Terra Moderate Resolution Imaging Spectroradiometer (MODIS). Journal of Geophysical Research: Atmospheres, 108(D21): n/a-n/a.
Fuertes, E. et al., 2013. Childhood allergic rhinitis, traffic-related air pollution, and variability in the GSTP1, TNF, TLR2, and TLR4 genes: results from the TAG Study. Journal of Allergy and Clinical Immunology, 132(2): 342-352. e2.
Gugamsetty, B. et al., 2012. Source characterization and apportionment of PM10, PM2. 5 and PM0. 1 by using positive matrix factorization. Aerosol Air Qual. Res, 12: 476-491.
Hosono, T., Su, C.-C., Siringan, F., Amano, A., Onodera, S.-i., 2010. Effects of environmental regulations on heavy metal pollution decline in core sediments from Manila Bay. Marine Pollution Bulletin, 60(5): 780-785.
Jun, L. et al., 2005. Optimal cloud-clearing for AIRS radiances using MODIS. IEEE Transactions on Geoscience and Remote Sensing, 43(6): 1266-1278.
Kampa, M., Castanas, E., 2008. Human health effects of air pollution. Environmental pollution, 151(2): 362-367.
Kumar, N., Chu, A., Foster, A., 2007. An empirical relationship between PM(2.5) and aerosol optical depth in Delhi Metropolitan. Atmospheric environment (Oxford, England : 1994), 41(21): 4492-4503.
Li, J., Carlson, B.E., Lacis, A.A., 2015. How well do satellite AOD observations represent the spatial and temporal variability of PM2.5 concentration for the United States? Atmospheric Environment, 102: 260-273.
Li, J. et al., 2005. Optimal cloud-clearing for AIRS radiances using MODIS. Geoscience and Remote Sensing, IEEE Transactions on, 43(6): 1266-1278.
Li, J., Liu, C.-Y., Zhang, P., Schmit, T.J., 2012. Applications of full spatial resolution space-based advanced infrared soundings in the preconvection environment. Weather and Forecasting, 27(2): 515-524.
Lin, C. et al., 2015. Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2.5. Remote Sensing of Environment, 156: 117-128.
Liu, C.Y. et al., 2016. Retrieval of Atmospheric Thermodynamic State From Synergistic Use of Radio Occultation and Hyperspectral Infrared Radiances Observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(2): 744-756.
Liu, C.Y. et al., 2008. Synergistic use of AIRS and MODIS radiance measurements for atmospheric profiling. Geophysical Research Letters, 35(21).
Liu, C.Y. et al., 2014. Using Surface Stations to Improve Sounding Retrievals from Hyperspectral Infrared Instruments. IEEE Transactions on Geoscience and Remote Sensing, 52(11): 6957-6963.
Perez, L. et al., 2015. Associations of daily levels of PM10 and NO2 with emergency hospital admissions and mortality in Switzerland: Trends and missed prevention potential over the last decade. Environmental Research, 140: 554-561.
Salomonson, V.V., Barnes, W., Maymon, P.W., Montgomery, H.E., Ostrow, H., 1989. MODIS: Advanced facility instrument for studies of the Earth as a system. Geoscience and Remote Sensing, IEEE Transactions on, 27(2): 145-153.
Shon, Z.-H., 2015. Long-term variations in PM2.5 emission from open biomass burning in Northeast Asia derived from satellite-derived data for 2000–2013. Atmospheric Environment, 107: 342-350.
Song, W., Jia, H., Huang, J., Zhang, Y., 2014. A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China. Remote Sensing of Environment, 154: 1-7.
Sorek-Hamer, M. et al., 2015. Assessment of PM2.5 concentrations over bright surfaces using MODIS satellite observations. Remote Sensing of Environment, 163: 180-185.
Spurny, K.R., 1996. Aerosol air pollution its chemistry and size dependent health effects. Journal of Aerosol Science, 27, Supplement 1: S473-S474.
Tsai, T.-C., Jeng, Y.-J., Chu, D.A., Chen, J.-P., Chang, S.-C., 2011. Analysis of the relationship between MODIS aerosol optical depth and particulate matter from 2006 to 2008. Atmospheric Environment, 45(27): 4777-4788.
Zhang, Q., Qiu, Z., Chung, K.F., Huang, S.-K., 2015. Link between environmental air pollution and allergic asthma: East meets West. Journal of thoracic disease, 7(1): 14.
指導教授 劉千義(Chian-Yi Liu) 審核日期 2016-6-13
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