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研究生:吳欣育
研究生(外文):Shin-Yu Wu
論文名稱:利用受體模式評估都會區細懸浮微粒上多環芳香烴濃度與來源貢獻垂直分布
論文名稱(外文):Evaluating vertical variation and source apportionment of PM2.5-bound polycyclic aromatic hydrocarbons in an urban area with receptor models
指導教授:吳章甫吳章甫引用關係
口試委員:蔡詩偉邱嘉斌
口試日期:2018-11-08
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:環境衛生研究所
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2018
畢業學年度:107
語文別:英文
論文頁數:64
中文關鍵詞:PM2.5上的PAHs來源分析有效變異數化學質量平衡(EV-CMB)主成分分析(PCA)風險排序
DOI:10.6342/NTU201804341
相關次數:
  • 被引用被引用:1
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細懸浮微粒(PM2.5)為空氣中普遍存在的污染物,且由無機物與有機物所構成。在所有的組成當中,多環芳香烴(Polycyclic aromatic hydrocarbons, PAHs)被證實具有致突變與致癌的特性。過去的研究顯示,在台北都會區,約有超過半數的居民居住在高樓建築中。然而,目前探討垂直變異的研究非常的稀少。因此,本研究的研究目標為 (1) 辨別並量化PM2.5上的PAHs其垂直變異與來源貢獻 (2) 評估在不同樓層中PM2.5上的PAHs 的潛在健康風險
本研究於台北市某大樓進行研究並選定其中三層樓 (低、中、高) 來探討垂直變異。於2018年3月16日至2018年5月13日在三個不同樓層 (低樓層:2nd (6m), 中樓層:6th (24m), 高樓層:11th (44m) 進行PM2.5的樣本收集,共收集33筆的PM2.5每日樣本。除此之外,使用直讀式空氣品質偵測器進行PM2.5連續樣本的收集。本研究使用熱脫附氣相層析質譜儀 (Thermal Desorption Gas Chromatography/Mass Spectrometry, TD-GC/MS) 進行石英濾紙上31種PAHs的化學分析,並利用診斷比(Diagnostic ratio, DR)、有效變異數化學質量平衡 (Effective Variance Chemical Mass Balance, EV-CMB) 與主成分分析 (Principal Component Analysis, PCA) 將所得的資料進行來源分析。因PAHs在大氣中會進行光解並改變污染源資訊,因此在進行EV-CMB模式時將考慮PAHs的降解特性 (降解因子, decay factor)。
從研究結果來看,中樓層具有較高的PM2.5濃度,其次為低樓層與高樓層。 PM2.5上PAHs的平均濃度為0.68±0.30 ng/m3 且低樓層具有最高的PAHs濃度 (0.77±0.28 ng/m3)。從來源分析的結果來看,考慮PAHs降解會增強模式模擬效果。汽油排放、柴油排放與烹飪油煙為本研究區域的主要貢獻污染源。在風險排序方面,低樓層具有較高的潛在致癌風險 (3.03*10-6)。
本研究顯示PM2.5與PM2.5上的PAHs 在都會地區的垂直變異情況。瞭解台北地區PM2.5上PAHs的特定污染源的貢獻與致癌風險排序有助於設計有效的污染防治策略。
Fine particulate matter (PM2.5) is a ubiquitous mixture of pollutant that contains organic and inorganic species. Among these species, polycyclic aromatic hydrocarbons (PAHs) are proved to be mutagenic and carcinogenic. In Taipei metropolis, more than 50% of residents lived in high-rise buildings. However, studies considered vertical variation are limited. The objectives of this study are: (1) to identify and quantify source contributions to PM2.5-bound PAHs and their vertical variations, and (2) to assess potential health risks of PM2.5-bound PAHs at different floor levels.
A total of 33 daily PM2.5 samples were collected on quartz-fiber filters at three different floor levels (low: 2nd floor (6m), middle: 6th floor (24m), and high: 11th floor (44m)) of a building from March 16th to May 13th in 2018. Additionally, continuous measurements of PM2.5 mass concentrations were obtained using real-time air quality monitors. Thirty-one PAH compounds on the quartz-fiber filters were analyzed using thermal desorption system coupled with gas chromatography/mass spectrometry (TD-GC/MS). Different approaches, including diagnostic ratio, effective variance chemical mass balance (EV-CMB), and principal component analysis (PCA), were used for source identification. Considering degradation of PAHs with time due to photochemical reactions, decay factors were estimated and applied in the EV-CMB modeling.
The highest average PM2.5 concentration was observed at the middle level, followed by low and high floors. The average concentration of PM2.5-bound PAHs was 0.68±0.30 ng/m3, showing the highest at the low level (0.77±0.28 ng/m3). Source apportionment results indicated that considering PAHs degradation enhanced model fitting. Gasoline emission, diesel emission, and cooking sources were major contributors in the study area. With regard to the cancer risk prioritizations, it was the highest (3.03*10-6) at the low level.
This study explored the vertical variations of PM2.5 and PM2.5-bound PAHs in an urban area. Understanding vertical distributions of source-specific contributions and cancer risk prioritizations of PM2.5-bound PAHs could be helpful in designing effective control strategies in Taipei city.
CONTENTS v
Chapter 1 Introduction 1
Chapter 2 Materials and Methods 5
2.1 Descriptions of the study area 5
2.2 Sampling strategies 6
2.3 Chemical analysis of PM2.5-bound PAHs 7
2.4 Analytical processes of PM2.5-bound PAHs 7
2.5 Data quality management 8
2.5.1 Real-time PM2.5 calibration 8
2.5.2 PM2.5-bound PAHs calibration curves 8
2.5.3 Quality Assurance and Quality Control (QA/QC) 9
2.5.4 Method detection limit 10
2.6 Receptor model 10
2.6.1 Effective Variance Chemical Mass Balance (EV-CMB) 10
2.6.2 Matlab for model improvement 12
2.6.3 PAHs with the EV-CMB 13
2.6.4 Model optimization (source profile modification) 13
2.6.5 Principal component analysis (PCA) 15
2.7 Risk prioritization 16
Chapter 3 Results 18
3.1 Vertical variation 18
3.1.1 Concentration of PM2.5 18
3.1.2 Concentration of PM2.5-bound PAHs 18
3.2 Source apportionment 20
3.2.1 Diagnostic ratio 20
3.2.2 Model optimization 21
3.2.3 EV-CMB results 21
3.2.4 PCA results 22
3.3 Risk prioritization 23
Chapter 4 Discussion 25
4.1 PM2.5 concentration 25
4.2 PM2.5-bound PAHs 25
4.3 Source apportionment 26
4.4 Risk prioritization 27
4.5 Study limitations 28
Chapter 5 Conclusion and Recommendation 29
Chapter 6 Reference 45
Chapter 7 Appendix 53
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