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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳章甫(Chang-Fu Wu) | |
| dc.contributor.author | Cheuk Wai Yip | en |
| dc.contributor.author | 葉卓衛 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:43:32Z | - |
| dc.date.available | 2021-07-20 | |
| dc.date.available | 2022-11-24T03:43:32Z | - |
| dc.date.copyright | 2021-07-20 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-19 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81329 | - |
| dc.description.abstract | 空氣中的細懸浮微粒 (PM2.5)被認為與不良健康結果有關。近年來，台灣的PM2.5濃度雖逐漸下降，但目前仍高於世界衛生組織訂定的空氣品質標準10 μg/m3。由於暴露於PM2.5的人數眾多，因此此議題須更加受到重視。基於多年的研究基礎，科學界對PM2.5已有充分的認識，研究指出特定的 PM2.5成分，如鈣、鎘、鎳、釩和鋅，對人體的危害比其他成分更大。因此本研究的目的之一為基於台灣六個空氣品質監測站的監測資料透過統計模型建模的方式推估PM2.5以及PM成分濃度。 本研究選擇六處空氣品質監測站（板橋、忠明、斗六、嘉義、小港、花蓮）的監測資料作為PM2.5和PM成分模型建立基礎，這六處測站每六日進行二十四小時採樣。利用來自 Himawari-8 衛星的大氣頂反射率與其他變項以Stepwise AIC的方式建立迴歸模型。本研究中使用四種不同模型情境，包括添加氣象變項、氣體污染物與加入主成分分析的應用。 本研究結果顯示，在所有四種方法中，六個空氣品質監測站的 PM2.5模型都一致顯現出使用大氣頂反射率波段、氣象變項、氣體污染物、與主成分分析為最佳模型（本研究的方法 4 ）。小港測站的PM2.5濃度推估模型預測能力較強（CV R2 = 0.70），其餘測站濃度推估模型則有中等偏強的預測能力（CV R2 >= 0.50）。 在PM成分預測模型中，方法4也顯示出最佳的模型表現，各測站以小港和忠明測站的預測模型表現較佳，而花蓮測站預測模型表現較差。銅、鎘、鐵、錳和鋅，在不同測站的預測模型中多有良好的表現，這些成分都有至少三至四個測站的CV R2 >= 0.40。本研究也呈現透過模型預測2017至2020年各測站的錳推估濃度，以達到在無量測日期填補推估值之目的。 本研究發現大氣頂反射率、氣體污染物和氣象變項有助於推估 PM 成分濃度，大氣頂反射率波段可用於不同預測模型中。但本研究的模型未能把推估模型應用在六處測站以外的測站和地區，未來研究可考慮在更多地點及更頻繁地量測台灣不同地區的PM 成分濃度，讓大氣頂反射率推估模型能預測和了解台灣不同地方的PM 成分濃度。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:43:32Z (GMT). No. of bitstreams: 1 U0001-1907202113251600.pdf: 4461514 bytes, checksum: 6fa49e0a6f2d2906f4d4a0287e2cdc64 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "口試委員會審定書 i 摘要 ii Abstract iii Chapter 1 Introduction 1 1.1 Atmospheric particles and health effects 2 1.1.1 Causes of PM 2 1.1.2 Health hazards of PM2.5 2 1.1.3 Air pollutant monitoring situation in Taiwan 3 1.2 Air quality estimations 3 1.3 Importance of PM composition and estimation 4 1.4 Comparison of satellite products 6 1.5 Study objectives 7 Chapter 2 Material and methods 9 2.1 Study area 9 2.2 Measurements of air pollutants 9 2.3 Satellite Himawari-8 products 13 2.4 Data processing and model construction 15 2.4.1 Data processing 15 2.4.2 Principal component analysis 16 2.4.3 Model building 17 Chapter 3 Results and discussion 19 3.1 Descriptive statistics of PM2.5 concentration and TOAR 19 3.2 TOAR – PM2.5 matched pairs 22 3.3 Prediction models 23 3.3.1 PM2.5 models 23 3.3.2 PCA factor loading interpretation 28 3.3.3 PM composition models 32 3.4 Prediction model across stations 44 3.5 Application of prediction models at individual stations 48 3.6 Prediction models for OC, EC, and ions 56 3.7 Potentials of TOAR models 57 3.8 Comparison with other TOAR models 58 3.9 Study limitations 61 Chapter 4 Conclusion 62 References 63 Appendix 77" | |
| dc.language.iso | en | |
| dc.subject | 主成分分析 | zh_TW |
| dc.subject | PM2.5 | zh_TW |
| dc.subject | PM 成分 | zh_TW |
| dc.subject | 統計模型 | zh_TW |
| dc.subject | 大氣頂反射率 | zh_TW |
| dc.subject | statistical model | en |
| dc.subject | top of atmosphere reflectance | en |
| dc.subject | PM2.5 | en |
| dc.subject | PM composition | en |
| dc.subject | principal component analysis | en |
| dc.title | 以大氣頂反射率應用統計模型預測地面細懸浮微粒與成分濃度 | zh_TW |
| dc.title | Application of Satellite Top of Atmosphere Reflectance with Statistical Modelling for Ground-Level Fine Particulate Matter and Composition Concentration Estimation | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蔡詩偉(Hsin-Tsai Liu),林唐煌(Chih-Yang Tseng) | |
| dc.subject.keyword | PM2.5,PM 成分,統計模型,大氣頂反射率,主成分分析, | zh_TW |
| dc.subject.keyword | PM2.5,PM composition,statistical model,top of atmosphere reflectance,principal component analysis, | en |
| dc.relation.page | 116 | |
| dc.identifier.doi | 10.6342/NTU202101566 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-07-20 | |
| dc.contributor.author-college | 公共衛生學院 | zh_TW |
| dc.contributor.author-dept | 環境與職業健康科學研究所 | zh_TW |
| 顯示於系所單位： | 環境與職業健康科學研究所 | |
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