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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/84429


    Title: 桃園市一般道路車輛排放和空氣品質監測站污染物監測濃度相關性研究
    Authors: 李思賢;Li, Ssu-Hsien
    Contributors: 環境工程研究所在職專班
    Keywords: 空氣污染物濃度與一般道路排放量;迴歸分析;相關性;Air pollutant concentrations and vehicle emissions from general roads;Regression analysis;Correlations
    Date: 2020-07-23
    Issue Date: 2020-09-02 19:39:55 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 交通車輛排放是都會區主要空氣污染源,本文探討2014年至2018年環保署設立於桃園市六座空氣品質監測站的懸浮微粒、細懸浮微粒、氮氧化物、硫氧化物、一氧化碳年平均濃度,與12條省道機動車輛污染物年排放量相關性。車輛污染物排放量的計算方法,是依照臺灣污染源排放清冊(Taiwan Emission Data System)所列各車種行駛於距離測站半徑2, 4, 6公里範圍內車輛所行駛里程數與排放係數相乘結果,本文期望瞭解測站周圍主要幹道機動車輛污染物排放量與空氣品質的關聯性,以有助於評估移動污染源對空氣品質的影響。
    以2014年為基準年分析後續五年內移動污染源各污染物排放變化趨勢,發現在2018年NOX成長最大,增加13.14%,PM2.5增加7.00%次之。各種污染物(PM10、PM2.5、SOX、CO)以小型車排放最多,除了NOX是以大貨車排放最多。各幹道車輛排放量以台1線貢獻最大,其次為台四線。
    本文進行監測站空氣污染物年平均濃度與測站周圍主要幹道污染物年排放量簡單線性迴歸,得到下列結果:大園和平鎮測站相關性最好的污染物是NOX,桃園和龍潭測站是CO,中壢交通測站是SOX,觀音背景測站則是SOX。移動污染源排放量和測站監測污染物濃度相關性,會隨著與測站距離和各車種車流量變化而改變,然而前述相關性並未隨著距離改變而有規律地變化,本文只是一個探索性的研究,因此沒有考量高速公路車輛排放和固定污染源排放量。
    ;The emissions from traffic vehicles are one of the major air pollution sources in the urban area. This work investigated the correlations of yearly concentrations of particulate matter, fine particulate matter, nitrogen oxides, sulfur dioxide, and carbon monoxide from the six air quality monitoring stations (referred to as AQS in the text) established by the Environmental Protection Administration and annual emissions of mobile vehicles from main artery roads surrounding the AQS in Taoyuan city. The emissions from mobile vehicles were calculated from the products of the traveled kilometers of the various types of vehicles in the radii of 2, 4, and 6 kilometers from the AQS and the emission factors from Taiwan Emission Data System. It aimed to understand the correlations between mobile vehicle emissions on the main artery roads surrounding the AQS and the air quality, and to assess the air quality impacts from the mobile sources.
    The analysis of emission variation trends from five years after the base year in 2014 revealed that the growth of NOx was the highest at 13.14%, followed by PM2.5 at 7.00% in 2018. Small vehicles emitted the most substantial amount of pollutants (PM10, PM2.5, SOX, and CO) except for NOx contributed mostly from heavy-duty trucks. For the emissions from vehicles on the various artery roads, Taiwan Provincial Highway One contributed the highest, and Taiwan Provincial Highway Four was next to it.
    The simple linear regression analysis applied to the annual emissions from main artery roads and yearly average concentrations of pollutants at the AQS disclosed the following results. The correlations were the best for NOx at the Dayuan and Pingzhen AQS, CO at the Taoyuan and Longtan AQS, and SOx both at the Zhongli traffic AQS and Guanyin background AQS. The correlations between the annual emissions from the vehicles on the main artery roads and yearly average concentrations of pollutants at the AQS varied with the distance to the AQS and traffic flows. However, the preceding correlations did not vary regularly with the distance to the AQS. This work is an exploratory study without considering emissions from stationary sources and vehicles on the expressways.
    Appears in Collections:[環境工程研究所碩士在職專班] 博碩士論文

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