國小上下課期間之交通移動污染源特徵分析-以屏東市為例

本研究探討國小上下課期間之交通移動污染源特徵，於2018年2月至9月期間，以屏東市轄內商業地區（仁愛國小）、住宅地區（鶴聲國小）及市郊地區（公館國小）等三種不同區域內，挑選就學人數最多之學校作為研究對象，於上下課期間家長接送學童時之交通移動污染源及車輛怠速污染行為進行採樣作業。研究期間三所學校各進行8次採樣（平日上午3次，平日下午3次及背景上下午2次），總計24次採樣數據，分析大氣懸浮微粒（含PM10與PM2.5）、總揮發性有機物（TVOC）和一氧化碳（CO）之濃度，探討商業地區、住宅地區及市郊地區之國小上下課期間之交通移動污染源特徵。研究結果顯示：（a）商業地區及住宅地區型國小交通源污染特徵以TVOC及PM10最明顯，而市郊地區型國小交通源污染特徵則以PM10及PM2.5較為明顯。各校校門口濃度分布。（b）各校採樣點位濃度進行比較得知，商業地區型國小及住宅地區型國小於R2採樣點（正校門區域）所測濃度值較R1及R3採樣點（校門左右兩側區域）所測濃度高；而市郊地區型國小則是以R2及R3採樣點（正校門及校門左側區域）所測濃度值較R1採樣點（校門右側區域）所測濃度高。整體來說各校R2採樣點（正校門區域）所測濃度都有較高趨勢，這結果顯示負責交通導護的老師及義工們，更應該配戴個人口罩及長袖衣物以減少呼吸道系統及皮膚接觸過多交通污染物。（c）通過平時上課日與假日背景之濃度值探討，無論是商業地區國小、住宅地區國小或市郊地區國小，TVOC及CO、PM10及PM2.5濃度值都是屬於平日濃度大於假日濃度之趨勢。（d）針對採樣環境因子進行Pearson相關係數比對，結果發現TVOC及CO方面呈現顯著相關性（Pearson相關係數= 0.603）；PM10及PM2.5均與CO呈現顯著相關性（Pearson相關係數分別=0.589、0.595）；而PM2.5也與TVOC及PM10呈現顯著相關性（Pearson相關係數分別=0.379、0.936）；在氣象因子部分，大氣壓力與TVOC、CO、PM10及PM2.5均呈現相關性顯著（Pearson相關係數分別=0.526、0.741、0.678、0.697）。（e）不同區域車流調查結果顯示，不同區域交通源種類（機車、小客車及大客(貨)車）比例，各別為商業地區型國小總車輛比例約為63%：36%：1%。住宅地區型國小總車輛比例約為55%：38%：7%。市郊地區型國小總車輛比例約為85%：14%：1%。以車輛數比例來看，機車車流數以市郊地區佔85%相較商業地區及住宅地區都來得高；小客車車流數則以商業地區及住宅地區36%及38%最多；大客(貨)車流數則以住宅地區7%佔相較商業地區及市郊地區都來得高。

關鍵字

國小上下課；交通移動污染源特徵；怠速污染；屏東市

並列摘要

The opening and closing hours of school are the hours during which parents send children to school and pick them up school, respectively. We investigated the characteristics of traffic-led pollution during the opening and closing hours of elementary schools from Feb. to Sept., 2018. We targeted schools in three different districts of Pingtung City, namely, business districts within the jurisdiction (Ren-Ai Elementary School), residential district (Heh Sheng Elementary School), and suburban district (Gongguan Elementary School). We selected the schools with the highest enrollment and collected samples of traffic pollution sources and vehicle idling pollution during the opening and closing hours of the schools. We conducted eight collections in three schools (three times on weekday mornings, three times on weekday afternoons, and two times in the mornings and afternoons of holidays to serve as the background data). A total 24 collection data were collected to analyze particulate matters in the atmosphere (including PM10 and PM2.5), and concentrations of total volatile organic compounds (TVOC) and carbon monoxide. The aim was to examine the characteristics of traffic movement pollution sources in business, residential, and suburban districts during the opening and closing hours of elementary schools. The study results were as follows: (a) TVOC and PM10 were the most noticeable characteristics of traffic pollution sources that affect elementary schools in business and residential districts. By contrast, PM10 and PM2.5 were the most noticeable characteristics of traffic pollution affecting elementary schools in suburban districts. Each concentration distribution was measured at the gate of each school. (b) A comparison of sampling location concentrations at each school indicated that elementary schools in business and residential districts had higher concentration values at the R2 sampling location (the main school gate area) than for the R1 and R3 sampling locations (the left and right sides of the school gate area). Elementary schools in suburban areas had higher concentration values for the R2 and R3 sampling locations (the main school gate area and its left side area) than for the R1 sampling location (the right side of the school gate area). Overall, a trend of relatively high concentration was measured at the R2 sampling locations of schools (main school gate area). This suggested that teachers and volunteers who are responsible for tariff supervision should wear a surgical mask and long-sleeve clothes to minimize their exposure to traffic pollutants. (c) Concentration values during usual school days were significantly different from holiday background values. Specifically, a trend of higher concentration values of CO, PM10, and PM2.5 was found during weekdays in the elementary schools of all districts (business, residential, and suburban). (d) Comparisons between the environmental factors were conducted using Pearson correlation coefficients. The result suggested a significant correlation between TVOC and CO (p value = 0.603); significant correlations were also found between PM10 and CO, and PM2.5, and CO (p values were 0.589 and 0.595, respectively); additionally, significant correlations between PM2.5 and TVOC and between PM2.5 and TVOC were noted (p values were 0.379 and 0.936, respectively); in terms of climatic factors, significant correlations were found between atmospheric pressure and each of the four factors namely, TVOC, CO, PM10, and PM2.5. Therefore, four significant correlations existed (p values=0.526, 0.741, 0.678, and 0.697). (e) Investigation of traffic flows in different areas suggested that the percentages of different types of transportation vehicles (scooters, passenger cars, and large passenger vehicles or trucks) were as follows. Specifically, the percentages of cars travelling to and from elementary schools in business districts were 63%, 36%, and 1%. The percentages of cars travelling to and from elementary schools in residential districts were 55%, 38%, and 7%. The percentages of cars travelling to and from elementary schools in suburban districts were 85%, 14%, and 1%. Car percentage analysis suggested that the highest traffic flow was in suburban districts (85%) and the percentage was higher than those of business districts and residential districts. Business districts and residential districts had the highest percentages of passenger cars; they were 36% and 38%, respectively. The highest percentage of large passenger cars (7%) was found in residential districts, and the percentage was higher than those of residential districts and suburban districts.