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研究生:周祈炫
研究生(外文):Chi-HsuanChou
論文名稱:居民PM2.5長期暴露與健康風險的整體評估
論文名稱(外文):An integrated approach for conducting long-term PM2.5 exposure and health risk assessment for residents
指導教授:蔡朋枝蔡朋枝引用關係
指導教授(外文):Perng-Jy Tsai
學位類別:碩士
校院名稱:國立成功大學
系所名稱:環境醫學研究所
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:2016
畢業學年度:104
語文別:英文
論文頁數:102
中文關鍵詞:定點測站移動式量測平台PM2.5健康風險評估
外文關鍵詞:Stationary measurementMobile measurementPM2.5Health risk assessment
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現今居民空氣品質暴露評估大多採用環保署定點監測數據,唯該資料無法充分的描述特定區域居民之暴露實態,本研究分析移動式量測平台、固定式測站及環保署空氣品質測站數據相關性,以有效描述粒狀物在區域內時間及空間上的特徵與變異情形,並建立居民之長期暴露資料庫,進行居民暴露與健康風險評估。本研究以沙鹿地區為研究對象在環保署測站旁設置一PM2.5固定式測站,除此之外建構一移動式測站同時監測區域內PM2.5濃度,採樣期間皆為2013秋季至2014年夏季,各季之採樣期程為一個月,移動式測站每日採樣時間為早上(7:00-10:00 AM)與晚上(18:00-21:00 PM),所測得之數值與對應定點測站測值之相關性,及定點之測值與環保署測站測值之相關性在排除離群值及極端值後,建構兼具時間及空間變化之當地居民長期暴露資料庫,並對照環保署PM2.5空氣品質標準(STD24hr)進行暴露評估,最後再利用該年之實測值與十年之長期暴露測值,運用貝氏決策分析推估居民之暴露與健康危害風險。
結果顯示大多數白天之PM2.5濃度顯著高於夜晚,其原因可能因白天交通流量、車流量等較高所致,比較移動式量測平台與定點測站的量測結果發現,移動式量測平台的數值皆高於定點測站,這顯示移動式之監測高度和範圍均較接近當地來源,因此採用環保署定點測站數值恐有低估居民暴露之虞。此外,冬季濃度相對其他季節來的最高,可能受冬季大氣不利擴散所致,暴露評估結果發現移動式測站與固定式測站以及環保署定點監測數據之年平均值分別為29.14、27.15 、20.05 (μg/m3)超過環保署之PM2.5空氣品質標準15 (μg/m3),顯示居民暴露情形超過法定標準,運用貝式決策分析沙鹿地區居民長期暴露實態,落在2.5至5倍、5至10倍與大於10倍STD24hr之機率分別為74、25.6與0.4%,增量致癌風險落於不可接受範圍(大於10-4)之機率為99.9%;增量心血管疾病風險落於暴露區間5*10-5-5*10-4、5*10-4-1.25*10-3、1.25*10-3-2.5*10-3與大於2.5*10-3之機率分別為16.3、63.1、3.1與17.6%;增量氣喘風險落於區間5*10-4-1.25*10-3、1.25*10-3- 2.5*10-3與大於2.5*10-3之機率分別為0.3、64.3與35.4%。本研究顯示結合環保測站、定點與移動式測站數據,將可有效描述當地PM2.5 時間空間變異,並且能建立長期居民暴露資料庫進行暴露與健康風險評估,暴露及健康風險評估結果皆為不可接受之風險,促使在未來需要透過鑑別PM2.5主要汙染源以啟用適當減量控制策略。

SUMMARY
In this study, the relationship of PM2.5 data sets obtained from the mobile monitoring station (MMS), stationary monitoring station (SMS) and Air Quality monitoring station of the Environmental Protection Agency monitoring station (AQMS) were established in order to describe the spatial and temporal variations of PM2.5 of the Shalu area, and to build a long term databank for conducting exposure and health risk assessment for residents’ exposures to PM2.5. A stationary PM2.5 monitoring station was built next to EPA monitoring station. In addition, a mobile monitoring station was used to measure PM2.5 of the area simultaneously in 2013-2014. Samplings were performed during both the daytime and nighttime on both weekdays and weekends for one month per season. Results show that most of daytime air pollution levels were significant higher than that of the nighttime due to the higher traffic flow and traffic density of the former. Comparing the results between Mm and Sm indicating that using the data of AQMS might cause underestimation for assessing residents’ exposures.
Exposure assessment results show that annual mean value of SMS, MMS and AQMS are 29.14, 27.15 and 20.05 μg/m3, respectively, which is exceed PM2.5 air quality annual standard (15μg/m3) regulated by the EPA. High coefficient of determinations (R2) were found between AQMS and SMS, and between SMS and MMS, and hence an exposure databank of residents characterized with both spatial and temporal variations was established. The obtained long term exposure profile of residents, and the estimated incremental risks (IR) of the lung cancer, cardiovascular disease, and asthma are found to be unacceptable, which urges the needs for identifying main PM2.5 pollution sources for initiating proper control strategies in the future.

Key words: Stationary measurement, Mobile measurement, PM2.5, Health risk assessment

INTRODUCTION
To date, Environmental Protection Agency monitoring station measurements (AQMS) are widely used for characterizing air quality data, but simply using AQMS could be inadequate to characterize residents’ exposures of the specific area. Our study analyzes the correlation of PM2.5 data sets of the mobile measurements (MMS), stationary measurements (SMS) and AQMS in order to describe the spatial and temporal variations of PM2.5 in the area, and to build a long term databank for conducting exposure and health risk assessment for residents’ exposures to PM2.5.

MATTERALS AND METHODS
The Shalu area was chosen as the target area. A stationary PM2.5 monitoring station was built next to EPA monitoring station. In addition, a mobile monitoring station was used to measure PM2.5 of the area simultaneously in 2013-2014. Samplings were performed during daytime (7:00-10:00 AM) and nighttime (18:00-21:00 PM) on both weekdays and weekends for one month per season. After eliminated high leverage value and outliers, the correlations of MMS, SMS and AQMS were established, and spatial and temporal variations of PM2.5 in the area were assessed, and finally a long term PM2.5 databank was constructed. The Bayesian decision analysis (BDA) were used for conducting long term exposure and health risk assessment of residents by comparing with EPA PM2.5 air quality standards (STD24hr).


RESULTS AND DISCUSSION
Results show that most of daytime air pollution indicators were significant higher than that of the nighttime due to the higher traffic flow and traffic density for the former. Comparing the results between MMS and SMS, the former are higher than that of the latter mainly due to their monitoring site is closer to local emission sources. Therefore, using the data of AQMS might cause underestimation for assessing residents’ exposures. Moreover, high concentrations were found in winter which may be affected by its intrinsic unfavorable atmospheric dispersion. Exposure assessment results show that annual mean value of SMS, MMS and AQMS are 29.14, 27.15 and 20.05 μg/m3, respectively, which is exceed PM2.5 air quality annual standard (15 μg/m3) regulated by the EPA. The coefficient of determination (R2) between AQMS and SMS are found to be 62.0% in spring, 75.6% in summer, 61.8% in fall, and 85.6% in winter. The R2 between SMS and MMS are 50.2% in spring, 64.3% in summer, 65.2% in fall, and 73.0% in winter. The above results suggest the possibility for effectively building an exposure databank of residents characterized with both spatial and temporal variations by combining the data of AQMS, MMS and SMS. Long term exposure profile of residents at Shalu area obtained by the BDA shows that residents' exposure rating (ER) most probability (i.e., 74%) falls to ER2 (i.e., 2.5 to 5 STD24hr). Using the same data sets, the increment risk (IR) of lung cancer (46.2%) falls to ER4 (i.e., ≥5*10-4), cardiovascular disease most probability (63.1%) falls to ER2 (i.e., 5*10-4 to 1.25*10-3), and asthma most probability (64.3%) falling to ER4 (i.e., 1.25*10-3ꟷ 2.5*10-3).


CONCLUSION
Our results suggest that simply using the data of AQMS might cause underestimation for assessing residents’ exposures. Judging from the obtained R2 between AQMS and SMS, and that obtained between SMS and MMS, the present study suggests the possibility for effectively building an exposure databank of residents characterized with both spatial and temporal variations by combining the data of AQMS, MMS and SMS. The obtained long term exposure profile of residents, and the estimated IR of the lung cancer, cardiovascular disease, and asthma are found to be unacceptable, which urges the needs for identifying main PM2.5 pollution sources for initiating proper control strategies in the future.


Chapter 1. Introduction 1
1-1 Research background 1
1-2 Research questions 3
1-3 Research purposes 3
Chapter 2. Literature review 4
2-1 Particulate matter (PM) 4
2-1-1 Definitions of PM 4
2-1-2 Sources of PM 4
2-1-3 PM2.5 caused human health effects 5
2-2 Current PM2.5 exposures in Taiwan and other areas 7
2-3 Techniques for PM monitoring 8
2-4 Exposure assessment techniques 11
2-5 Health risk assessment techniques 15
2-6 Application of Bayesian decision analysis in risk assessments 17
Chapter 3. Research methods 19
3-1 Research framework 19
3-1-1 Selected AQMS 21
3-1-2 The installed SMS and its sampling plan 22
3-1-3 The installed MMS and its sampling plan 23
3-2 PM sampling instruments 25
3-2-1 PM sampling instruments used in MMS 25
3-2-2 PM Sampling instruments used in SMS 28
3-2-3 PM Sampling instruments used in AQMS 29
3-3 Establishment of long-term exposure databank 30
3-4 Data analysis 31
3-4-1 Statistical analysis 31
3-4-2 Bayesian Decision Analysis 31
3-5 The Assessment of health risk posed on residents 33
Chapter 4. Research results and discussion 35
4-1 Weather and air quality conditions 35
4-2 Temporal variation of PM 35
4-2-1 Day and night variation of PM 36
4-2-2 Seasonal variation of PM 37
4-2-3 PM2.5 mass concentration conversion process 38
4-3 Correlation between measurements and AQMS 42
4-4 PM2.5 spatial variations in different type of area 43
4-5 Exposure and health risk assessments 44
4-5-1 Exposure assessment for the annual mean PM2.5 concentration 44
4-5-2 Non-cancer risk 45
4-5-3 Long term Exposure and Health hazard risk assessment 46
Chapter 5. Research limitations, conclusions, and recommendations 93
5-1 Research limitations 93
5-2 Research conclusions and recommendations 93
References 95


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