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研究生:沈芙慧
研究生(外文):Fu-Hui Shen
論文名稱:以土地利用回歸模式評估細懸浮微粒與二氧化氮短期暴露量與心血管疾病相關性
論文名稱(外文):Assessing the Association between Cardiovascular Diseases and Short-Term Exposure to Particulate Matter and Nitrogen Dioxidewith Land Use Regression Models
指導教授:吳章甫吳章甫引用關係
口試委員:蘇大成陳主智
口試日期:2014-07-25
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:職業醫學與工業衛生研究所
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:2014
畢業學年度:102
語文別:英文
論文頁數:107
中文關鍵詞:土地利用模式細懸浮微粒二氧化氮急性心血管效應
外文關鍵詞:Land use regressionFine particleNitrogen dioxideAcute cardiovascular effects
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前言:
空氣汙染物已在許多研究中證實與心血管疾病的發生與惡化有關,尤其是交通汙染源所產生的汙染物(例如:細懸浮微粒與二氧化氮)。過去進行短期效應的暴露評估時,大多使用空氣品質監測站數據作為居民空氣汙染物暴露程度的指標。本研究在臺北地區發展短期的土地利用回歸模式,探討土地利用與交通排放對於二氧化氮與細懸浮微粒濃度空間分布的貢獻,並將之應用於急性心血管效應與不同暴露評估方法比較。
方法:
本研究於2013年在臺北地區選擇了117位在某金融大樓工作的員工,並在其辦公地點的大樓進行心血管的健康檢查與室內空氣品質監測。在空氣汙染暴露的推估方面使用了三種不同的暴露評估設計:(1)使用土地利用模式去預測受試者住家的室外空氣品質(2)利用土地利用模式結合辦公大樓的室內空氣品質監測(3)最近測站法。在心血管檢查的部分,進行了一般檢查、血樣抽樣以及非侵入性的血管彈性量測。最後我們所選取的心血管效應指標為: 臂踝脈波傳播速率(baPWV) 、踝肱指數(ABI) 以及高敏感度C-反應蛋白(hsCRP)。
結果:
本研究在暴露評估上的方法比較上,顯示當土地利用模式結合辦公大樓的室內空氣品質監測,相較於單純用土地利用模式,對於心血管效應指標的探討有著更顯著的結果,表示室內空氣品質監測對於暴露評估上的應用,有一定的重要性。在交通汙染物與急性心血管效應的關聯上,我們發現PM2.5 以及 NO2和 baPWV有顯著的正相關,而與ABI並沒有顯著的相關。在與hsCRP的關係中,僅發現NO2 與其有顯著的正相關。
結論:
本研究在臺北地區建立了短期的細懸浮微粒與二氧化氮的土地利用模式,進行交通產生的汙染物與急性心血管效應的探討。並在此研究中反映出交通汙染源產生的汙染物會造成急性心血管效應的惡化。


Background:
The study was designed to combine air quality monitoring data with land use data to build land use regression (LUR) models in Taipei Metropolis to predict individualized traffic-related air pollutants exposure levels and linked with cardiovascular endpoints to discuss the association between short-term traffic-related air pollution and acute cardiovascular effects.
Method:
We selected 117 subjects working at a bank to have health examination in February, June, and September in 2013. The health examinations included general medical examination and cardiovascular screening. Additionally, we monitored air quality at the subjects’ workplaces over the period of health examination. We also collected information on the subjects’ home addresses and time-activity patterns to predict individualized PM2.5 and NO2 exposures at the subjects’ home addresses by land use regression models. Three exposure assessment methods were developed to represent personal exposure: (1) LUR models, (2) LUR models combine with indoor air monitoring, and (3) Nearest station. For cardiovascular markers, we used the inflammation maker (High-sensitivity CRP, hsCRP) and the markers for the arterial stiffness (baPWV and ABI) as our health endpoints.
Results:
With regard to the different exposure assessment methods, we found that using LUR models combining with indoor air monitoring data had a better explanation on the relationship with acute cardiovascular effects. With regard to the association between traffic-related air pollutants and cardiovascular endpoints, we found that both PM2.5 and NO2 was significantly associated with baPWV, and NO2 was significantly associated with hsCRP. However, ABI was not found to be associated with traffic-related air pollutants.
Conclusion:
We were able to develop land use regression models by combining air-quality monitoring data with geographic variables to predict personal exposure in Taipei Metropolis. These LUR models were applied to link with the subjects’ health data. It was found that acute cardiovascular effects were significantly associated with short-term traffic-related air pollution.


Content
口試委員會審定書 I
誌謝 II
摘要 IV
ABSTRACT V
CHAPTER 1 INTRODUCTION 1
1.1 BACKGROUND 1
1.2 LAND USE REGRESSION 3
1.3 ACUTE CARDIOVASCULAR EFFECTS 5
1.4 THE EXPOSURE ASSIGNMENTS WITH AIR POLLUTANT LINK WITH ACUTE CARDIOVASCULAR EFFECTS 6
1.5 OBJECTIVE 7
CHAPTER 2 MATERIALS AND METHODS 9
2.1 STUDY DESIGN 9
2.2 STUDY AREA 10
2.3 HEALTH DATA COLLECTION 10
2.4 CONFOUNDER VARIABLES 11
2.5 ENVIRONMENTAL MONITORING 12
2.5.1 Indoor PM2.5 measurement 12
2.5.2 Indoor NO2 measurement 13
2.5.3 Time-activity pattern questionnaire 13
2.6 LUR MODEL BUILDING 14
2.6.1 Data analysis tools 14
2.6.2 Spatial analysis 14
2.6.3 24-hour average PM2.5 and NO2 concentration calculation 15
2.6.4 Independent variables 15
2.6.5 Model building and validation 17
2.6.6 The solution of outlier 17
2.7 EXPOSURE ASSIGNMENT 18
2.7.1 Method 1: LUR model 18
2.7.2 Method 2: LUR model + Indoor air monitoring 19
2.7.3 Method 3: Nearest station method 19
2.7.4 Lag effect 20
2.8 STATISTICS METHOD 20
CHAPTER 3 RESULTS 22
3.1 SUMMARY STATISTICS 22
3.1.1 Geographic predictors 22
3.1.2 Time-activity patterns 23
3.1.3 Regression indoor PM2.5 and NO2 concentrations 23
3.1.4 PM2.5 and NO2 concentrations 24
3.2 PM2.5 LAND USE REGRESSION MODEL IN METHOD 1 AND METHOD 2 26
3.3 NO2 LAND USE REGRESSION MODEL IN METHOD 1 AND METHOD 2 27
3.4 PM2.5 AND NO2 INDIVIDUALIZED EXPOSURE LEVELS IN DIFFERENT DESIGN OF EXPOSURE ASSESSMENT 28
3.5 GENERAL CHARACTERISTICS OF STUDY POPULATION AND THE CARDIOVASCULAR DISEASE MARKER 30
3.6 THE ASSOCIATION BETWEEN TRAFFIC-RELATED AIR POLLUTANT AND CARDIOVASCULAR MARKERS 31
3.6.1 The effects of cardiovascular endpoints with single-pollutant model 31
3.6.2 The effects of cardiovascular endpoints with two-pollutant model 33
CHAPTER 4 DISCUSSION 36
4.1 EXPOSURE ASSIGNMENTS 36
4.1.1 Cardiovascular marker with exposure to traffic-related air pollutants in different exposure assessment methods 36
4.1.2 Sensitivity analysis 37
4.1.3 Conclusion 38
4.2 THE ASSOCIATION BETWEEN PM2.5 AND NO2 AND CARDIOVASCULAR EFFECTS 38
4.2.1 Endothelial function - baPWV 39
4.2.2 Endothelial function - ABI 41
4.2.3 Vascular inflammations 43
4.2.4 Mechanisms explaining the association between air pollution and cardiovascular effects 45
CHAPTER 5 CONCLUSION 47
REFERENCE 49
APPENDIX 86
APPENDIX A TIME ACTIVITY QUESTIONNAIRE 86
APPENDIX B THE DESCRIPTION OF GEOGRAPHIC VARIABLES LAYER AND TYPE OF EUROPE AND TAIWAN LAND USE LABEL 89
APPENDIX C THE DEFINITION OF PREDICTOR VARIABLES INCLUDED IN LUR MODELS 90
APPENDIX D THE DEVELOPMENT OF MODEL BUILDING 91
APPENDIX E THE NUMBERS OF HEALTH EXAMINATIONS BY DATE 93
APPENDIX F SUMMARY STATISTICS OF PM2.5 AQMS GEOGRAPHIC PREDICTORS 94
APPENDIX G SUMMARY STATISTICS OF NO2 AQMS GEOGRAPHIC PREDICTORS 95
APPENDIX H SUMMARY STATISTICS OF SUBJECTS'' ADDRESS GEOGRAPHIC PREDICTORS 96
APPENDIX I THE PREDICTOR VARIABLES AND R2 VALUE OF DAILY PM2.5 LUR MODEL IN HEALTH EXAMINATION SESSION 97
APPENDIX J THE PREDICTOR VARIABLES AND R2 VALUE OF DAILY NO2 LUR MODEL IN HEALTH EXAMINATION SESSION 100
APPENDIX K DESCRIPTIVE STATISTIC OF EXPOSURE ASSESSMENT 103
APPENDIX L INTERQUARTILE RANGE OF EXPOSURE ASSIGNMENTS AT DIFFERENT LAG-DAYS 104
APPENDIX M THE ASSOCIATION BETWEEN PM2.5 AND NO2 AT DIFFERENT-LAG-DAYS AND CARDIOVASCULAR ENDPOINT 105
APPENDIX N THE ASSOCIATION BETWEEN PM2.5 AND NO2 AT D DIFFERENT-LAG-DAYS AND CARDIOVASCULAR ENDPOINT WITH CONFOUNDER MODEL INCLUDED LDL 108



List of Tables
Table 1 A review table of health outcome of exposure to air pollutants predicted by short-term LUR models 56
Table 2 The regression model using for estimating the indoor air pollutant concentration of the day without sampling 57
Table 3 The correlation between PM2.5 and NO2 in different exposure assignments and lags 58
Table 4 The correlation between exposure assessment methods at different lag-days in air pollutant 59
Table 5 Descriptive statistics of health data 60
Table 6 A review table of the association between cardiovascular effects and traffic-related air pollutants 61



List of Figures
Figure 1 The flow chart of this study 64
Figure 2 The study area and location of all sampling monitoring station and subjects’ home 65
Figure 3 The timeline of air pollutants monitoring and health examination and the 66
Figure 4 The example of sample scheme 67
Figure 5 The flow chart of model development 67
Figure 6 Distribution of cardiovascular marker 68
Figure 7 The mean value of predictor variables in different buffer radii 69
Figure 8 The locations of subjects before they had health examination 72
Figure 9 The life custom when the subjects were at home 73
Figure 10 The indoor and outdoor concentrations of PM2.5 and NO2 74
Figure 11 The scatter plot between Grimm and Harvard Impactor 75
Figure 12 Counts of predictor variables in the final LUR models for PM2.5 76
Figure 13 R2 and validation R2 of PM2.5 LUR models in each day 77
Figure 14 Counts of predictor variables in the final LUR models for NO2 78
Figure 15 R2 and validation R2 of NO2 LUR models in each day 79
Figure 16 The process of excluding subjects 80
Figure 17 Descriptive statistic of exposure assessment 81
Figure 18 Percent changes in baPWV 83
Figure 19 Percent changes in baPWV 84
Figure 20 Percent changes in hsCRP for interquartile range changes 85


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