對移動污染源及固定污染源的排放強度進行估計:以實際道路車隊及工業區料堆為例

Estimation of emission factors for mobile sources and stationary sources: The cases of real world vehicle fleet emission and coal pile in industrial area

葉日勛(Jih-Hsun Yeh)

指導教授 ： 蕭大智

國立臺灣大學/工學院/環境工程學研究所/碩士(2021年)

https://doi.org/10.6342/NTU202103827

摘要

在台灣，隨著都市化及工業化的發展，越來越多人會居住在道路旁或是工業區附近，這使得民眾對於自身空氣污染物的暴露風險評估也越來越重視。而排放因子作為暴露風險模擬的輸入參數之一，計算最符合實際污染狀況的排放因子就極其重要。
根據污染源本身的特性可以分為固定污染源以及移動污染源，固定污染源主要關注工業區的人為排放，而移動污染源主要就是交通源。過去對工業區料堆的排放因子主要參考EPA AP-42裡面的經驗公式，這個經驗公式只能以近似的狀況計算人為因素產生的揚塵而忽略自然因素，所以必須建立另一種方法來計算料堆排放因子來更符合實際的逸散量。過去對交通源排放因子的研究認為移動量測是最符合實際道路污染狀況的方法，然而，過去多是以單一排氣管量測的結果來換算為整條道路的車隊排放因子或是進行隧道量測這類封閉空間的量測，這些方法與實際道路狀況也有許多差異，所以計算實際開放道路的車隊排放因子也有其必要性。
本研究針對固定污染源利用高雄S工業區內部的固定測站的資料來進行分析，位於上游的A測站作為背景測站，而下游的C測站為受到逸散源影響的測站。從排放因子與風速之間的關係來觀察，PM2.5~10的排放因子具有兩種不同的揚塵行為，兩種都與風速呈指數遞增的關係，於是推測兩種行為皆是由風力所主導的揚起行為。然而，另一種揚起行為只出現在低風速的情況，透過其與CO通量的相關性可以推測這種行為除了自然因素揚起以外，人為擾動也有參與這類的揚起行為，而且相比於同風速的排放因子，這個情況的排放因子明顯較高，所以本研究認為這種行為是造成下游空氣品質劣化的主因。最後，由AP-42估計的排放因子與本研究比較，經驗公式並無法體現與天氣因素的變化，表示經驗公式存在某些限制而有辦法正確的體現實際的排放狀況。
針對移動污染源本研究使用實際的道路移動監測來計算台灣大道的車隊排放因子，並且以蒐集所有車輛排放的方式來對實際道路的車隊排放因子進行研究，並且透過量測的時間序列方法來估計背景濃度。本研究也透過二氧化碳的結果來分辨交通排放的煙流，並分析每縷煙流的黑炭、數目濃度以及NO的車隊排放因子。本研究計算的黑炭排放因子約為100~300 mg/kgfuel、數目濃度為5~32 1014#/kgfuel，NO約為4~17 g/kgfuel，之前的研究通常只分析單一因素與排放因子之間的關係，而本研究將所有可能的因素共同進行因子分析，以期找出造成污染的熱區以及時段的原因。經由因子分析的結果可以看到，所有污染物共同發生的熱區及時段都是在早上的山區，發生的原因也都與卡車這類重污染車輛的上坡行為導致，雖然屬於低機率事件，但是對整個排放因子的影響非常大，所以可以初步評估如果要改善台灣大道的交通排放，可以從大型重污染車輛的駕駛行為著手。

關鍵字

排放因子 ； 交通源 ； 逸散源 ； 盒狀模式 ； 因子分析

並列摘要

Emission factor (EF) is the critical input parameter for modeling air pollution and assessing the related health risks. However, accurately determining these EFs is still a challenging issue for environmental engineers. For example, the emissions of fugitive stationary sources are difficult to control, but the EFs are also hard to retrieve. Currently, the empirical formula in AP-42 Emissions Handbook issued by US EPA is usually applied, while the method can only estimate the anthropogenic emissions from human activity. The EF calculated by AP-42 was not a function of wind speed, and the meteorological effects can not be considered. On the other hand, single tailpipe and tunnel measurements were implemented to estimate the traffic EFs in previous research. Although both methods could reflect real-world traffic emissions more closely, they only explored emissions from a single vehicle or in a confined environment.
In this study, we calculated the fugitive EF using a simple box model. Two different exponentially growth behaviors were found in investigating the relationship between PM2.5~10’s EF and wind speed. One only occurred in low wind speed, and the EFs and CO flux was highly correlated, indicating the emission, in this case, was influenced by human activities. The other majorly occurs during high wind speed and could be related to natural weathering. For mobile sources, the catch-all method is employed to study real-world fleet-averaged EFs by using mobile monitoring. Due to stability and diffusion issues, this study firstly analyzed the CO2 concentrations to identify the plume related solely to traffic emissions. The EFs of black carbon (BC), total number concentration (NTot), NO for each plume were then estimated, and the values are 100~300 mg/kgfuel, 5~32 1014#/kgfuel, and 4~17 g/kgfuel, respectively. Furthermore, the hot spots and periods for the studied traffic artery were investigated. Based on the factor analysis, the high EFs were always observed in the mountain region in the morning, which could be resulted from the heavy-polluting vehicles in uphill driving mode. Therefore, special attention should be paid to the heavy-polluting vehicles in the mountain region, especially in the morning.

並列關鍵字

Emission factor ； Traffic emission ； fugitive emission ； box model ； factor analysis

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