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研究生:謝明村
研究生(外文):Ming-Tsun Hsieh
論文名稱:整合網格模型和健康影響評估於多環芳香烴: 城市空氣品質的綜合方法
論文名稱(外文):Integrating Grid-Scale Models and Health Impact Assessments for Polycyclic Aromatic Hydrocarbon: Approaches in Urban Air Quality
指導教授:李宗霖李宗霖引用關係林俊宏林俊宏引用關係
指導教授(外文):Lee, Chon-LinLin,Chun-Hung
學位類別:博士
校院名稱:國立中山大學
系所名稱:海洋環境及工程學系研究所
學門:工程學門
學類:環境工程學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:中文
論文頁數:269
中文關鍵詞:網格模型(GSM)都市空氣污染和多環芳香烴(PAHs)曝露源分配PCA-APCS環境管理流行病學影響
外文關鍵詞:Grid-Scale Modeling (GSM)Urban Air Pollution and Polycyclic Aromatic Hydrocarbons (PAHs) ExposureSource Apportionment PCA-APCSEnvironmental ManagementEpidemiological Impact
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城市空氣污染是一重大公共衛生挑戰,細懸浮微粒(PM2.5)及其上多環芳香烴(PAHs)因其不良健康影響而被特別關注。為克服數據稀缺,本論文擴展先前每小時PAHs網格尺度建模(GSM)估算,提出一系列改進GSM應用。首先,利用光化測站收集的苯、甲苯、乙苯和二甲苯(BTEX)濃度,以類似方法開發一每小時GSM模型。利用微型氣相色譜系統進行驗證,具顯著相關性(n = 121, r = 0.566**, p < 0.01),並應用覆蓋於約72%臺灣人口地區。隨後驗證GSM模型在不同年份適用性,全面評估高雄長期時空效應的PAH變化。從估計數據庫中,PM2.5-PAH年均值從2005年的3.18 ng m-3降至2018年的1.78 ng m-3。總體下降44%,對大眾運輸改善(如捷運通車)和加強工業法規(如調整空污費)密切相關。此外,結合主成分分析-絕對主成分得分(PCA-APCS)方法,廣泛研究臺北和高雄PAH來源。汽車排放占近一半兩城市總PAH量,而臺北化石燃料燃燒和高雄固定排放與生物質燃燒約占總PAH量30%。模型估算與觀測間具顯著相關性(r>0.78**),這表明模型捕捉到測量值季節行為並給出可接受結果。利用高雄汽車尾氣排放GSM所估算臺北數據將殘差從80.8%降至52.8%,特別是在交通流量高地區,並開發一通用模型,其可調參數適應臺灣不同地區特徵。最後通過結合干預設計和使用空氣淨化器,評估成年哮喘患者家中PM2.5特徵組成及其室內外比例(I/O比率)。結果顯示室內和室外PM2.5和PAH水平之間存在顯著相關性。在為期三天干預中,PM2.5和PM2.5-PAH的濃度分別減少了37%(29±9%)和53%(35±15%),而PM2.5上金屬的減少範圍為37%至89%(59±15%)。這項全面分析不僅凸顯GSM模型在流行病學研究中潛力,還促進空氣質量管理政策和實踐的發展,旨在減輕空氣污染對健康不良影響。
Urban air pollution is a major public health challenge, with fine particle matter (PM2.5) and bounded polycyclic aromatic hydrocarbons (PAHs) being of particular concern due to their adverse health effects. To address PAH data scarcity, this thesis extended the previous hourly grid-scale model (GSM) and proposed a series of improved GSM applications. First, benzene, toluene, ethylbenzene and xylene (BTEX) data were collected from photochemical monitoring stations and a similar concept was used to develop an hourly GSM model, which was applied to approximately 72% of the Taiwanese population. Validation was performed using portable micro-gas chromatography systems and shows significant correlation (n = 121, r = 0.566**, p < 0.01). Subsequent validation was performed over different years to assess the long-term spatiotemporal effects on PAH variation in Kaohsiung. From the estimated PAH database, the annual mean PM2.5-PAH decreased from 3.18 ng m-3 in 2005 to 1.78 ng m-3 in 2018, a total decrease of 44%, which was associated with improvements in public transportation, e.g. operation of Mass Rapid Transit System, and tightened industrial regulations, e.g. Air Pollution Control Fee. In addition, the combination of Principal Component Analysis-Absolute Principal Component Scores (PCA-APCS) was used to investigate source apportionment in Taipei and Kaohsiung. Vehicle exhaust accounted for almost half of the total PAHs in both cities, while fossil fuel combustion in Taipei and stationary emissions in Kaohsiung accounted for about 30% of the total PAHs. The model estimates showed a significant correlation with the observations (r > 0.78**), indicating that the results were acceptable in capturing the seasonal behavior. Using the Kaohsiung vehicle exhaust GSM to estimate the Taipei data reduced the residual from 80.8% to 52.8%, especially in heavy traffic areas, and developed a universal model with adjustable parameters tailored to the unique characteristics of different regions in Taiwan. Finally, the characteristics of PM2.5 composition and indoor/outdoor ratio in the asthmatic homes were evaluated, combining the intervention design with the use of air purifiers. The results showed significant correlations between indoor and outdoor PM2.5 and PAH levels. In a three-day intervention, PM2.5 and PM2.5-PAH concentrations were reduced by 37% (29 ± 9%) and 53% (35 ± 15%), respectively, while the PM2.5-bound metal reduction was 59 ± 15% (37% to 89%). These comprehensive analyses not only highlighted the potential of GSM in epidemiological research, but also promoted the development of air quality management policies and practices aimed at mitigating adverse health effects.
論文審定書 i
致謝 ii
摘要 iii
Abstract iv
Table of Contents vi
Table of Figures ix
Table of Tables xiii
Abbreviations xv
Chapter 1 Introduction 1
1.1 Framework 1
1.2 Literature Review 2
1.3 Summery of Chapters 9
Chapter 2 Simulating the spatiotemporal distribution of BTEX with an hourly grid-scale model 12
2.1. Introduction 12
2.2. Methodology 14
2.3. Results and Discussion 20
2.3.1 Establishment of the grid-scale models 20
2.3.2 Validation of the simulation results 21
2.3.3 Demonstration of the developed BTEX hourly grid-scale model 23
2.3.4 Area limitation of the model application 30
2.3.5 Model application in lifetime risk assessment 35
2.3.6 Comparison of the incremental lifetime cancer risk (ILCR) equation and lifetime risk of associated cancer of benzene inhalation exposures 37
Chapter 3 Simulation of polycyclic aromatic hydrocarbons by the grid-scale model _validation and applications for environmental management 42
3.1. Introduction 42
3.2. Methodology 44
3.3. Results and Discussion 48
3.3.1 Comparison of PM2.5-PAH and air quality monitoring data between 2013 and 2014 48
3.3.2 Cross validation of grid-scale model by 2013 and 2014 datasets 50
3.3.3 Estimated PM2.5-PAH concentration in Kaohsiung from 2005 to 2018 53
3.4 Implication in environmental management 60
Chapter 4 A cross-city study for establishing a general grid-scale model to estimate PAH 67
4.1. Introduction 67
4.2. Methodology 70
4.3. Results and Discussion 75
4.3.1 Comparison of spatiotemporal PM2.5-PAH distribution in Taipei and Kaohsiung 75
4.3.2 Cross validation of grid-scale model by Taipei and Kaohsiung datasets 77
4.3.3 Spatiotemporal performance of the grid-scale models 83
4.3.4 Source identification and source apportionment by PCA-APCS 89
4.3.5 Applied PCA-APCS to GSM 105
4.4 Discussion 111
4.4.1 Evaluation of toxicity and cancer risk 115
Chapter 5 Compositional assessment of indoor air pollution and its mitigation by air purifier: An exploratory study 117
5.1. Introduction 117
5.2. Methodology 119
5.2.1 Sampling 119
5.2.2 PAH Extraction and investigation 126
5.2.3 Metals 127
5.2.4 Data analysis 127
5.3. Results and Discussion 127
5.3.1 Single week experiment of PM2.5 and PM2.5-PAH 127
5.3.2 Single week experiments of metals 134
5.3.3 Ratio of indoor and outdoor at each case in single and bi-week experiment 135
5.3.4 Bi-week experiments with air purifier intervention 138
Chapter 6 Conclusion 151
6.1. Suggestion 152
References 154
Appendix 170
Curriculum vitae 252
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