交通對臺北市學校空氣品質影響評估研究

Study on traffic-related air pollution in public schools of Taipei City

黃姿榕(Tzu-Jung Huang)

指導教授 ： 詹長權

國立臺灣大學/公共衛生學院/職業醫學與工業衛生研究所/碩士(2011年)

https://doi.org/10.6342/NTU.2011.02028

摘要

背景：
許多研究指出交通污染源對於都市空氣污染惡化的貢獻，但是以交通空氣污染的量化指標來研究其對空氣品質衝擊程度仍然不多，本研究嘗試用主要道路長度及交通流量兩個指標來探討交通污染對臺北市公立學校空氣品質惡化的影響。

方法：
本研究使用兩個交通指標分別為主要道路長度及主要道路車流量進行臺北市學校空氣品質與交通流量之關係進行探討。首先利用監測站半徑100公尺內主要道路長度佔所有道路長度50％為交通衝擊高低區分之交通指標，將位於臺北市內環保署所架設之中山測站（新興國中）及松山測站（松山國小）分為traffic site（大於50%），士林測站（文林國小）分為urban site（小於50%），陽明山鞍部站為background site，使用1994-2008年空氣污染物監測資料分析不同空氣污染物年平均濃度受交通衝擊高低的影響。其次利用車輛監測器（VD）資料探討主要道路車流量和空氣污染物之間的關係，針對中山測站空氣污染物逐時濃度和其半徑100公尺內之主要道路（民權東路一段）逐時車流量進行迴歸分析。另外於環保署所架設之中山測站、松山測站及士林測站各站半徑3.3公里內選取之13所學校，於2009年9月及12月進行為期3天的二氧化氮（NO2）及懸浮微粒（PM2.5）採樣所取得的日平均濃度，結合該學校半徑100公尺內主要道路上VD資料之每日車流量進行迴歸分析，以探討交通與學校空氣污染物日平均濃度之關係。

結果：
Traffic site的中山和松山測站的CO、NO2、PM10、PM2.5、SO2年平均濃度皆顯著高於urban site的士林測站及background site的陽明山鞍部測站，且污染物traffic site/urban site比值介於1.28∼1.58間，traffic site/background site 的比值介於1.77∼7.60間。在中山測站逐時濃度與逐時車流量分析結果發現在校正風速因子後CO及NO2濃度與主要道路上大客車、小客車及機車各別之逐時車流量線性關係良好，其複迴歸線R2值分別為0.54及0.42。複迴歸模式預估大客車逐時車流量每增加100輛其CO逐時濃度會增加0.22 ppm，NO2逐時濃度會增加6.55 ppb。經剔除受開放性環境、建築工地及高架道路影響的採樣點後臺北市學校NO2（9所）及PM2.5（8所）的日平均濃度，且在加入採樣點距主要道路之距離變項後，與鄰近該校主要道路上大客車、小客車及機車三種車輛總和之每日總車流量線性關係良好，複迴歸線R2值分別為0.62及0.72。總車流量每日增加1,000 輛會使NO2日平均濃度增加0.10 ppb，PM2.5日平均濃度增加0.13 μg/m3，此外採樣點距主要道路之距離每增加1公尺會使NO2日平均濃度減少0.13 ppb，PM2.5日平均濃度減少0.08 μg/m3。

結論：
主要道路長度可作為評估交通污染會導致學校內CO、NO2、PM10、PM2.5、SO2等污染物長期空氣品質惡化（逐年）的量化指標，主要道路車流量可作為評估交通污染對學校內CO、NO2、PM2.5短期空氣品質惡化（逐時、逐日）的量化指標。

關鍵詞：空氣污染、二氧化氮NO2、懸浮微粒PM2.5、車流量、主要道路長度

關鍵字

空氣污染 ； 二氧化氮NO2 ； 懸浮微粒PM2.5 ； 車流量 ； 主要道路長度

並列摘要

Background:
Although many studies have found that traffic-related air pollution caused deterioration of urban air quality, few of them used quantitative indicators to estimate its impact on air quality. This study tried to use to indicators of traffic emissions, i.e. length and traffic flow of main traffic roads, to quantify contribution of traffic emissions to air pollution of public schools in Taipei City.

Methods:
In this study, we use two traffic-related indicators to evaluate the traffic contribution to air pollutant concentrations among 13 schools in Taipei City. One is the length of major roads within 100 m radius buffer zone of each sampling site, and the other is the traffic counts of the major roads. First, we selected ambient air quality monitoring stations of three public schools in the Taipei city, i.e. Jhongshan, Songshan and Shihlin, and classified both Jhongshan and Songshan as traffic sites and Shihlin as an urban site, by using 50% of total length of all roads being major roads in the buffer zones. One monitoring station in Yang-ming Mountain was designated as the background site of Taipei City with limited traffic emissions. And we also used the air monitoring data air monitoring data between 1994 and 2008 to evaluate the impact of traffic emissions on annual air pollution levels in these schools. The Jhongshan station’s hourly air monitoring measurements were further regressed against hourly traffic counts of its nearest major road in order to estimate the impact of traffic emissions on hourly air pollution levels. Furthermore, we conducted 3-day air monitoring campaigns of NO2 and PM2.5 on September and December in 2009 at 13 public schools within 3.3 km buffer zones of either Jhongshan, Songshan or Shihlin monitoring stations and combined with total daily traffic counts of their nearest major roads to evaluate the relationship between the traffic contributions to daily air pollution levels among these schools in Taipei city.
Results:
Annual air pollution levels of traffic sites (Jhongshan,Songshan) were found significantly higher than urban site (Shihlin) and background site (Yangming) for CO, NO2, PM10, PM2.5, and SO2 .And their traffic site/urban site concentration ratios were between 1.28~1.58, while traffic site/background site concentration ratios were between 1.77~7.60 . After adjusting for wind speed, hourly pollution levels of CO and NO2 at the Jhongshan station were positively associated with hourly traffic counts of either buses, cars or motorcycles on the major road, with R2=0.54 for CO and R2=0.42 for NO2. Our multiple regression model predicted hourly increase in 0.22 ppm of CO and 6.55 ppb of NO2 per 100 buses increase per hour. After excluding significant local influence on air monitoring and adjusting for the distance to major roads, our multiple regression models showed daily sum of all traffic counts were associated with daily averaging concentrations of NO2 in 9 schools (R2=0.62) and PM2.5 in 8 schools(R2=0.72). Our models predicted daily increase in 0.10 ppb of NO2 and 0.13 μg/m3 of PM2.5 per 1000 vehicles increase per day. Besides that, our model also predicted that daily decrease in 0.13 ppb of NO2 and 0.08 μg/m3 of PM2.5 for every 1 m increase between ajor roads and monitoring stations.
Conclusions:
The length of major roads can be used to quantify impact of traffic emissions on long-term (annually) levels of CO, NO2, SO2, PM10, and PM2.5, while the traffic flows on major roads can quantify short-term (hourly and daily) levels of CO, NO2, and PM2.5 for public schools in Taipei.

Key words:
air pollution, nitrogen dioxide (NO2), particulate matter (PM2.5), traffic flows, major road length

並列關鍵字

air pollution ； nitrogen dioxide (NO2) ； particulate matter (PM2.5) ； traffic flows ； major road length

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