機車通勤族空氣污染個體暴露評估

Personal Exposure Assessment to Air Pollution for Motorcycle Commuters

王子瑄(Tzu-Hsuan Wang)

指導教授 ： 莊振義

國立臺灣大學/理學院/地理環境資源學研究所/碩士(2018年)

https://doi.org/10.6342/NTU201801998

摘要

都會區內車輛污染排放對於通勤族而言是極為重要且待解決的議題，然而近幾年的研究主要關注於不同通勤模式的差異，但卻鮮少探討天氣型態與街道內建成環境對於污染濃度的影響，因此除了需透過量化方式計算實際暴露濃度之外，了解街道尺度中環境因子所扮演的角色亦相當重要。故本研究目的有二，第一，針對台北污染濃度最高之通勤路段，實地量測機車通勤族暴露PM2.5、PM10、CO與PAHS的濃度並推估出實際暴露量；第二，分析氣象因子、建成環境與污染物的關聯。
不同於過去研究以質性方式選擇通勤路線，本研究以量化方式，分析大台北地區環保署空氣品質監測站之歷史資料決定採樣時間，並透過一般克利金內插法找出污染濃度最高之通勤路線。實地移動監測從2017年七月至2018年四月，針對傍晚交通尖峰時間 (17:30~18:30) 進行街道尺度量測，通勤路線從台灣大學出發到三重，總路線長約10公里。
研究結果發現，污染事件日PM10與PM2.5暴露量 (2.2 μg km-1、1.8 μg km-1) 分別比平日 (1.1 μg km-1、0.3 μg km-1) 高出2.0、6.0倍，pPAHS與CO平日暴露量 (7.9 ng km-1、0.5 mg km-1) 分別比假日 (6.7 μg km-1、0.2 mg km-1) 高出1.2、2.5倍。相關矩陣的結果顯示在冷峰與高壓迴流的天氣型態下，PM10與PM2.5的濃度與車速呈負相關；而在高壓迴流的天氣型態下，街寬樓高比 (aspect ratio) 與PM10呈正相關；但在冷峰主導的天氣型態並沒有觀察到相似的結果。再者，機車排放量在兩種天氣型態下都與CO濃度呈正相關，然而由於資料尺度較粗所以上述結果並不顯著。此外，污染濃度在移動與停等的差異甚大，透過獨立樣本T檢定顯示，PM10與PM2.5停等時的平均濃度 (65.3 μg m-3、14.2 μg m-3) 顯著 (p<0.05) 高於移動時濃度 (45.2 μg m-3、12.5 μg m-3)。針對人行道與車道上的濃度進行比較，CO濃度在車道中濃度與人行道濃度高出1.1~30.0倍，然而PM10與PM2.5並沒有相似的結果。本研究結果指出環境因子對污染濃度有重要的影響，結果不僅能提供公衛領域做後續風險分析，亦可供街道空氣品質管理做為重要參考。

關鍵字

街道空氣品質 ； 一般克利金 ； PM2.5 ； pPAHS ； 微氣候 ； 建成環境

並列摘要

Air pollution emitted from vehicles is recognized as one critical environmental issue for commuters in many metropolitans in recently years. However, previous studies focused more on emission sources but seldom considered the influences of the weather patterns and urban microenvironment. Thus, it is important to quantify the personal exposure during commuting and further estimate the effects of environmental factors. The objectives of this study were to measure the exposure of motorcycle commuters in the Taipei metropolitan area to target air pollutants, including PM2.5, PM10, CO, and pPAHS, and further evaluate the influences of the meteorological patterns and the local built environment.
This study used a quantitative method to choose sampling periods and route, which is different from previous qualitative studies. The sampling periods were decided by statistically analyzing air pollution data from Taiwan EPA’s air quality monitoring stations, in addition, the ordinary kriging was used to find the most polluted area and determine the appropriate sampling route. Real-time mobile monitoring on street scale was conducted during the evening traffic rush hour (17:30~18:30) from July 2017 to April 2018. The chosen sampling route starts from National Taiwan University from a park in Sangchong, and the total length is around 10 km.
The exposure doses for PM10 and PM2.5 in the event day (2.2 μg km-1, 1.8 μg km-1) were 2.0 and 6.0 times higher than the averages of weekday (1.1 μg km-1, 0.3 μg km-1), respectively. In addition, the average exposure doses for pPAHS and CO in weekdays (7.9 ng km-1; 0.5 mg km-1) were 1.2 and 2.5 times higher than weekends (6.7 μg km-1; 0.2 mg km-1), respectively. Correlation matrix results show that under both the cold front and high pressure reflux weather patterns, the concentrations of PM10 and PM2.5 was negatively affected by the driving speed of motorcycle, although the result was not statistically significant. Besides, under the high pressure reflux, the aspect ratio was positively correlated with PM10, whereas, the effect of aspect ratio under the cold front wasn’t shown. The number of motorcycle positively correlated with CO under two weather patterns, but due to the coarse resolution of the data, the result was not statistically significant either. Additionally, the exposure concentrations are strongly affected by the stop-and-go movement. By using one-tailed independent sampling t test, the mean concentration of PM10 and PM2.5 (65.3 μg m-3, 14.2 μg m-3) while waiting for the traffic light was significantly higher (p<0.05) than moving (45.2 μg m-3, 12.5 μg m-3). Comparing the concentrations between sidewalk and on-road, the on-road CO concentration was around 1.1~30.0 times higher than those of sidewalk during each mobile-monitoring, whereas, PM10 and PM2.5 didn’t shown the similar results. These results demonstrate the important contributions of environmental effects to pollutant concentration on street scale, and thus the results can not only provide information for risk analysis in the field of public health, but also serve as an important reference for air quality management.

並列關鍵字

roadside air quality ； ordinary kriging ； PM2.5 ； pPAHS ； microclimate ； built environment

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