細懸浮微粒（Fine particulate matters, PM2.5）已被證實為都會區主要空氣污染物之一，許多研究也指出PM2.5會危害人體健康，並且是造成能見度不佳之主因。台灣近年來空氣品質受到PM2.5嚴重影響，因此台灣於2012年5月14日頒布了新的PM2.5空氣品質標準，並於同年4月24日頒布了大氣中PM2.5的標準採樣分析方法（NIEA A205.11C）。為加速改善台灣都會區PM2.5之空氣品質，必須採取適當且有效的管制策略，而各項管制策略則應建立在完整的PM2.5污染特徵分析及排放源解析之基礎上。因此，本研究以臺南都會區為例，分析大氣中PM2.5濃度長期變化趨勢、污染特徵以及不同排放源對空氣品質之影響。
本研究使用箱形圖（box plots）和時間序列分析（time series analysis）來進行長期趨勢分析；於轄區內以PM2.5手動標準方法進行質量濃度採樣及化學成分分析，並運用CMB（Chemical Mass Balance）受體模式進行污染源解析；最後以CMAQ （Community Multiscale Air Quality）模式模擬境外長程傳輸、跨域傳輸、當地污染源與鄰近縣市大型污染源分別對臺南市PM2.5空氣品質之影響分析。
從長期趨勢分析結果顯示，臺南市近十年（2010-2019）來PM2.5年平均濃度（10年平均為29.5 μg/m3）皆不符合15 μg/m3之空氣品質標準，但整體而言是呈現逐年下降之趨勢，從2010年的36.8 μg/m3降至2019年的22.1 μg/m3，十年平均改善率達39.9%。
本研究於2013年在臺南市進行大氣中PM2.5質量濃度的採樣，並分析水溶性離子、碳成分和金屬成分，以評估PM2.5的污染特徵；此外，使用CMB受體模式來解析PM2.5可能來源及其貢獻。根據化學成分分析，衍生性氣膠（NH4+、NO3–及SO42–）在春季和冬季時佔有較高之比例，分別佔PM2.5質量的50％和60％，但夏季時則僅佔40%。根據CMB模式的結果，臺南市PM2.5的主要貢獻來源依序是交通排放（31.5％）、硫酸銨（25.5％）、硝酸銨（12.5％）和地殼元素（11％）。因此，為改善臺南市的PM2.5，優先控制的污染物（或污染源）主要為原生性PM2.5（營建工地和道路揚塵）、NOx（柴油車排放）和SOx（燃料）。
此外，本研究以2010年為基準年，運用MM5-CMAQ模式來模擬境外長程傳輸(台灣境外)、跨域傳輸(臺南轄境外)及當地污染源（點、線和面源）對PM2.5空氣品質之影響。從模式模擬結果顯示，以全年平均而言，臺南市PM2.5最大影響來自鄰近城市的跨域傳輸，約佔34.2％，境外長程傳輸和當地排放源之影響則各佔約32.9％；而就當地排放源而言，影響最大的是面源（18.6％），其次是線源（7.7％）和點源（6.6％）。因此，要控制臺南市本身的PM2.5污染源，重點應放在面源，如營建工地及道路揚塵，其次是線源和點源。
除上述污染源外，本研究再進一步針對臺南市轄內主要排放源及鄰近外縣市大型污染源進行模擬，解析各主要排放源對臺南市PM2.5之污染影響。本研究依據台灣排放量資料庫（TEDS9.0-2013年）篩選臺南市轄內PM2.5排放量較大之9種行業別，分別為：(1)化學材料製造業、(2)鋼鐵業、(3)電力業、(4)使用生煤工廠、(5)柴油車、(6)二行程機車、(7)餐飲業、(8)建築/道路揚塵、(9)露天燃燒；另篩選位於台灣中部地區即臺南市上風區之三個大型污染源，包括：(10)臺中火力發電廠、(11)六輕工業區、以及(12)中龍鋼鐵，運用CMAQ-DDM模式模擬上述重要污染源對臺南市PM2.5空氣品質的影響。從全年的平均模擬結果顯示，柴油車排放的影響量是最高的，為1.06 μg/m3，其次為臺中電廠排放之影響為0.87 μg/m3，再其次為建築/車行揚塵的排放影響為0.80 μg/m3，影響量最低的為露天燃燒排放。這些研究結果可用於後續制定各項空氣品質管制策略之參考。

Fine particulate matters (PM2.5) has been identified as one of the major air pollutants in urban areas. With several studies pointing out a direct link between PM2.5 and the adverse effects on public health, which are also responsible for the deterioration of visibility. In recent years, Taiwan's air quality has been seriously affected by fine particulate matter (PM2.5). Therefore new PM2.5 air quality standards were promulgated in Taiwan on 14th May 2012, as well as the standard sampling and analytical method for atmospheric PM2.5 (NIEA A205.11C) on 24th April 2012. In order to improve the air quality of PM2.5 in the urban area of Taiwan as quickly as possible, the appropriate and effective control strategies must be adopted, which should be based on the comprehensive analyses of characteristics and source apportionment of PM2.5. This study takes Tainan city as researching area to analyze atmospheric PM2.5 characteristics, long-term trends and impacts of various sources on air quality.
In this study, a box plot and time series analysis was used for long-term trend analysis of PM2.5 concentration; the atmospheric PM2.5 manual standard sampling method was performed for mass concentration and chemical composition analysis, while using Chemical Mass Balance (CMB) receptor model for pollution source apportionment. Finally, Community Multi-Scale Air Quality (CMAQ) model was used to simulate the impacts of PM2.5 various sources on air quality, including long-range and trans-boundary transport, native sources, as well as large-scale pollution sources from neighboring counties and cities of Tainan City.
The long-term trend analysis shows that the levels of PM2.5 (averaged at 29.5 μg m-3) in Tainan City for ten years (2010-2019) were above the yearly average standards of 15 μg m-3, showing non-attainment status. Overall, the results show a decreasing trend which was from 36.8 μg m-3 in 2010 to 22.1 μg m–3 in 2019 in the levels of PM2.5 in Tainan atmosphere in the period of ten years. Over the past decade, the average concentration improvement rate reached 39.9%.
The characteristics of PM2.5 in Tainan City during 2013 were evaluated by measuring the mass concentration of PM2.5 and analyzing the water-soluble ionic, carbon, and metal components. Additionally, CMB receptor model was used to identify possible sources of PM2.5 and their contributions. According to chemical composition analysis, secondary aerosols (NH4+, NO3–, and SO42–) contributed approximately 50% and 60% of PM2.5 mass in spring and winter respectively; but were responsible about 40% by mass in summer. From the results of CMB model, the main contribution sources to the PM2.5 in Tainan are traffic emissions (31.5%), ammonium sulfate (25.5%), ammonium nitrate (12.5%), and crustal elements (11%). Consequently, to improve PM2.5 of Tainan City, the priority control pollutants (or sources) are primary PM2.5 (construction sites and road dust by vehicles), NOx (diesel vehicle emissions), and SOx (fuels).
In addition, this study used a coupled MM5-CMAQ (Community Multiscale Air Quality Model coupled with Mesoscale Modeling System) modeling system to simulate the impacts of long-range transport (outside of Taiwan), trans-boundary transport (outside of Tainan) and local pollution sources (point, line and area sources) on air quality of PM2.5 in Tainan City. The results of simulation indicated that the highest contribution on PM2.5 in Tainan City was from trans-boundary pollution from neighboring cities (34.2%), while long-range transport and local emissions from Tainan each contributed a fraction of approximately 32.9%. In terms of local sources, the highest influence is from area sources (18.6%), followed by line sources (7.7%) and point sources (6.6%). Thus, to control local PM2.5 in Tainan City, the focus should be on area sources, such as construction and road dust, followed by line and point sources.
Other than the sources above, this study further distinguished various sources of PM2.5, and focused on analysis of PM2.5 sources within Tainan City in an effort establish the contribution of large-scale pollution sources within the city as well as those from neighboring counties and cities. During this study, the nine important emission sources in Tainan City were investigated from TEDS9.0 (Taiwan Emission Data System, 2013): (1) the Chemical manufacturing industry, (2) the iron and steel industry, (3) the power industry, (4) manufacturing of coal-based products, (5) diesel vehicles, (6) two-stroke scooters, (7) catering, (8) construction/road dust, and (9) open burning. Three large-scale pollution sources in the central region of Taiwan were investigated as well, including: (10) the Taichung Power Plant, (11) the Formosa Petrochemical Corporation (FPCC) Sixth Naphtha Cracking Industry, and (12) the Dragon Steel Company, all of which are located on the windward side of Tainan City. The CMAQ-DDM (Community Multi-scale Air Quality with Decoupled Direct Method) model was used to simulate the impact of the mentioned sources on Tainan’s air quality. The results for the monthly contributions from the different sources averaged over a one year period indicated that diesel vehicles are the largest source, effect up to 1.06 μg m–3, followed by the Taichung Power Plant, which had 0.87 μg m–3, the construction industry and road dust emissions, with 0.80 μg m–3, and with open burning of waste having the lowest contribution. These results can be applied to facilitate the development of follow-up air quality control strategies.

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林佳薇，2015，台灣南部地區細懸浮微粒之特徵，碩士論文，國立成功大學，環境工程學系，臺南市。
楊智翰，2013，境外不同區域長程傳輸對臺灣空氣品質影響之模擬研究，碩士論文，國立雲林科技大學，環境與安全衛生工程系，雲林。