104

3,900 千元

2015/11/10

2016/11/09

吳章甫

臺北市政府環境保護局

徐立豪

國立臺灣大學

大氣空氣

低污染排放示範區

Clean Zone

詹長權

吳宗鋼

02-33668096#

pewu76@gmail.com

近年來，由於大眾的環保意識抬頭以及國人對於健康議題的重視，促使大眾運輸系統的普及與公車、自行車通勤族人數的增加。選擇以公車或自行車代步，雖然可能達到整體環保、節能之目的，但卻因鄰近交通污染源之故，可能造成個人空氣污染物的暴露量增加。 本研究針對此相關議題進行實地監測與研究，於臺北市信義路與松江新生南路之公車專用道候車亭上及自行車專用道旁架設儀器，監測細懸浮微粒與揮發性有機化合物濃度，並同時結合相關車流資訊、週遭地理資訊及測站資訊，將監測數據進行統計模式分析，以找出相關之影響因子。 公車專用道候車亭監測期間，分別於2、3月及8、9月進行第一場次與第三場次採樣，而自行車專用道監測期間，於7、8月進行第二場次採樣。公車專用道候車亭與自行車專用道各設置12個監測點，每個監測點量測期間約為一週。PM2.5濃度結果部分，公車站每分鐘平均濃度為20.51 μg/m3（平均值範圍：12.14－33.77 μg/m3），同一週監測之各站濃度平均值差異不超過5 μg/m3。印度德里在公車站量測到120－248 μg/m3，香港量測值則為37－40 μg/m3，西班牙則在公車上量測到25 μg/m3。自行車道每分鐘平均濃度為16.29 μg/m3 （平均值範圍：9.43 －20.99 μg/m3），同一週各監測點差異不超過7 μg/m3。在印度德里則有285 μg/m3、愛爾蘭71.61－88.14 μg/m3、中國49.10 μg/m3、香港39－46 μg/m3與西班牙29 μg/m3、義大利23.1 μg/m3、美國5.24－10.9 μg/m3與比利時3.5－3.8 μg/m3。VOC濃度部分，整體平均來看每週Toluene都具有最高的濃度。整體而言，現場監測結果與環保署周遭鄰近測站平均濃度相比皆有較高的趨勢，顯示測站監測結果與微環境的濃度有一定程度的差異，且尖峰暴露亦不可忽略。 本研究進一步採用混合模式(mixed model)，在考慮環境風速及濕度的狀況下，分析車流量與土地利用變項對環境污染物量測濃度的影響。在公車專用道之多變項模式分析中，鄰近慢車道機車數對於各污染物皆有顯著影響，而大車數、小車數則對於近半數的污染物有顯著影響，其餘土地利用資訊則以道路長度或面積為主要影響因子。而公車到站期間五期排放標準公車對增加Benzene及BTEX暴露濃度影響小於四期排放標準公車，但無法清楚區隔五期與四期排放標準公車對增加PM2.5濃度之影響差異性。在自行車專用道之模式分析中，單一車種變項模式下，鄰近慢車道總車數、小車數、機車數對於各污染物皆有顯著影響，只有鄰近慢車道大車數對PM2.5、Toluene、Ethylbenzene、o-xylene的影響不顯著。而在多重車種模式下，小車數與機車數對多數污染物有顯著正向影響。 本研究結果顯示，公車站亭與自行車道污染物濃度主要影響因子為鄰近車道車輛數，多項污染物關鍵影響因子為機車與小車，推測大車在機慢車道因佔整體比例較低，為對污染物影響多為不顯著的原因。土地利用變項則以道路長度或面積為主要影響因子。另統計模式結果顯示在同時考慮公車到站車數與鄰近車道車流量的狀況下，無法完全區隔四期排放標準與五期排放標準公車對公車站亭濃度之影響。為降低通勤族（公車族與自行車騎乘者）可能的暴露濃度，可透過推行空氣清淨區（Clean Zone）之政策來改善環境空氣品質，另也可考慮透過評估鄰近車道車輛數，建議民眾較佳之搭車或騎車時段與路段。

低污染排放示範區

104年度暴露計畫期末報告定稿.pdf

12MB

2017/9/1 上午 12:00:00

In the last decade, numbes of bus and bike commuters increased due to the public concern in environmental and health issues. Because of being close to roadways, bus and bike commuters could be exposed to considerable amount of air pollutants. Therefore, their personal exposure to air pollution could increase even though these transportation modes might improve the overall air quality. This study was conducted through a series of field campaigns to investigate the distribution of air pollutants near the bus and bike lanes in Taipei City. The Bus campaigns were conducted in Feburary to March and August to early September while the Bike campaign was conducted in July to early August. Selected volatile organic compounds (VOCs) and fine particulate matter (PM2.5) mass concentrations were measured at 6 sites on Xinyi Road (H1-H6) and 6 sites on Songjiang Road/Xinsheng South Road (V1-V6). In addition, vehicle detector (VD) and land use data were collected to examine their associations with VOCs and PM2.5 measurements using a mixed effect model. Each site was monitored for one week. The average PM2.5 mass concentration was 20.51 μg/m3 (Range of mean: 12.14－33.77 μg/m3) at bus stops and 16.29 μg/m3 (9.43 －20.99 μg/m3) at bike lanes during the monitoring periods. The differences of average concentrations were less than 5 and 7 μg/m3 among sites monitored in the same week for the bus and bike campaigns. For the bus stops, two studies showed that the PM2.5 measurements ranged from 120 to 248 and 37 to 40 μg/m3 in Delhi and Hong Kong, respectively. There was also a mean result of 25 μg/m3 measured on the bus in Spain. For the measurements on the bike or in bike way, there are several study results: 285 μg/m3 in Deli, 71.61－88.14 μg/m3 in Ireland, 49.10 μg/m3 in China, 39－46 μg/m3 in Hong Kong, 29 μg/m3 in Spain, 23.1 μg/m3 in Italy, 5.24－10.9 μg/m3 in USA, and 3.5－3.8 μg/m3 in Belgium. Among the selected VOCs at all sites, toluene had the highest concentrations. In general, the measurements at bus stops and bike lanes were higher than those measured at the surrounding air quality monitoring stations of Taiwan EPA, indicating that the exposures are different between in atmospheric environment and in microenvironment. In addition, peak exposurs also should not be ignored. We used the mixed models to exame the assocaitons between air pollutants, vehicle fleets and land-use variables after controlling the meteorogical factors. During the bus monitoring periods, number of large vehicle (NL), number of small vehicle (NS), and number of motorcycle (NT) were significantly associated with most of the pollutants while lengh or area of rodas were the main significant land-use variables. Further analysis identified significant effects of emission standards of buses (i.e., number of phase 4 buses > number of phase 5 buses) for Benzene and BTEX. During the bike monitoring period, the single-vehicle type model showed that the number of total vehicle (NTV), NL, NS and NT were siginificantly associated with all the pollutants other than the association between NL and PM2.5, Toluene, Ethylbenzene and o-xylene. In the multi- vehicle type models, the NS and NT were signigcantly and positively associated with most pollutants. This study indicates that the levels of pollutants at the bus stops and along the bike lanes are mainly affected by the number of nearby vehicle fleets, especially by the fleets of motorcycle and small vehicles. The relatively low proportion of NL to NTV may explain its non-significant effects in this study. On the other hand, it was difficault to differentiate the effects between phase 4 and phase 5 buses on PM2.5 levels at bus stops. In order to reduce the exposures and improve the air quality for the comuters, the clean zone plocy might be needed. In addition, by assessing the traffic flows along the bus lanes or bike ways, the proper communication or biking periods could be suggested to the public.

Clean Zone