臺灣大型點源與交通移動源污染排放對PM2.5濃度影響

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林怡成(Yi-Cheng Lin) 查詢紙本館藏

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大氣科學學系

論文名稱

臺灣大型點源與交通移動源污染排放對PM2.5濃度影響
(Impact of major point sources and mobile emissions on PM2.5 concentrations in Taiwan)

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摘要(中)

臺灣PM2.5高污染事件常發生於秋冬時期，長期暴露於高濃度PM2.5中，人體呼吸系統將受到嚴重破壞，故空氣污染議題備受民眾關注。高污染事件主要受氣象環境與本地排放源的高排放量影響，臺灣氮氧化物(NOX)年排放量以移動源廢氣排放為主、硫氧化物(SOX)年排放量則以工業活動和電力業居多，其中火力發電廠更占全國點源近四成排放量。根據經濟部能源局2018年資料指出，臺灣有近81%的發電量來自化石燃料，53%屬於傳統的燃煤技術，燃燒後的污染物不僅影響周圍環境，亦隨著風場結構帶往下風處。近年來，臺灣政府致力於實現高發電效率和低排放的目標，引進超超臨界（Ultra Super Critical, USC）燃煤技術應用於林口火力發電廠中，以增加發電量並減少氮氧化物和硫氧化物的排放，此研究的目的是評估USC技術對臺灣空氣污染的影響。
林口火力發電廠位於林口台地西側近海處，為了解林口周圍環境污染物特性，參考前人群集分類結果（Hsu and Cheng, 2019）將污染物依天氣型態分類，並使用環保署測站的歷年成份資料與陽明交通大學蔡春進教授提供2020年2月逐時PM2.5資料進行分析與討論。結果顯示，群集3（弱綜觀天氣型態）PM2.5與前驅物質(NOx與SO2)的濃度值最高，其次為群集2（高壓迴流天氣型態）與群集6（副熱帶高壓壟罩天氣型態），PM2.5成份以硫酸鹽與硝酸鹽為主。
使用氣象模擬WRF3.8.1版本與臺灣空氣污染物排放量清冊資訊TEDS10的輸出結果，納入CMAQv5.2進行空氣污染物模擬，個案一時間點為2020年2月12日至13日、個案二為2020年2月24日至25日，此時臺灣氣象環境處於弱綜觀的穩定天氣型態，由於西半部區域位於背風區、空氣擴散條件差，導致該區域空氣品質不佳。為了進一步探討林口發電廠對下風處空氣品質的影響，使用Brute Force Method（BFM）將林口火力發電廠的排放量調整為零（NoLKP組），並與Base組進行比較。林口電廠主要影響雙北、桃園和新竹地區，PM2.5小時平均貢獻量值約0.5~1.5μg/m3。同時，針對USC技術對於周圍環境空氣品質改善效益，使用2013年林口電廠亞臨界(Subcritical, SC)舊機組排放資料進行空品模擬，計算與Base組之間的差異。結果顯示，北部地區空氣品質改善效益較好，中部地區較差。
當臺灣西部地區高濃度PM2.5事件發生時，其他重工廠與交通移動源的污染排放量高於林口火力電廠許多，欲探討各大型污染源PM2.5貢獻，經由BFM計算各污染源貢獻量，再依照群集結果進行分類。當臺灣處於弱綜觀天氣型態下，各污染源主要影響各自所在的空品區，點源貢獻量占比約1~2%，以台中火力發電廠影響最為顯著；交通移動源貢獻相較於點源PM2.5貢獻量較高，占比約16~23%。

摘要(英)

The high PM2.5 pollution incidents often happen during autumn and winter in Taiwan. Long-term exposure to high concentrations of PM2.5, the human respiratory system will be destroyed severely, therefore, the issue of air pollution is of great concern to the public. High pollution incidents are mainly affected by the meteorological environment and high local emissions. The annual emissions of nitrogen oxides (NOX) and sulfur oxides (SOX) are dominated by mobile source exhaust emissions, industrial activities and the electric power industry. Nearly 40% of the emissions from all factories in Taiwan come from power plants. According to the data from Bureau of Energy in 2018, nearly 81% of Taiwan’s electricity generation comes from fossil fuels, and 53% of fossil fuels are using traditional coal-burning technologies. Pollutants not only affect the surrounding environment, but also are blown to downwind area. In recent years, the government has been committed to achieving the goals of high power generation efficiency and low emissions. It has introduced a new technology Ultra Super Critical (USC) coal-burning technology which is installed in the Linkou Power Plant to increase power generation and reduce the emissions of nitrogen oxides and sulfur oxide. The purpose of this study is to evaluate the influence of USC technology on Taiwan’s air pollution.
The results of WRF v3.8.1 and Taiwan Emission Data System (TEDS10) were incorporated into CMAQv5.2 for air quality simulation. Case 1 is February 12-13, 2020, and case 2 is February 24 to 25, 2020, at this time, Taiwan’s meteorological environment was in a weak synoptic weather. As western Taiwan was located in the leeside area and the pollutants diffusion conditions were poor, air quality in that region was bad. To further explore the influence of Linkou Power Plant on air quality of downwind area, using Brute Force Method (BFM) to adjust the emissions of Linkou Power Plant to zero (NoLKP) and compared with the Base simulation. Linkou Power Plant mainly affects Taipei, Taoyuan and Hsinchu areas, the hourly mean contribution of PM2.5 is about 0.5~1.5μg/m3. At the same time, in view of the efficiency of how USC technology improving the ambient air quality, calculate the difference Base and Subcritical(SC) which using 2013 Linkou Power Plant emission data into CMAQ model. The results show that the efficiency of air quality improvement in the northern and southern regions are better.
To explore the contribution of other high emission industries and transportation sources in the western Taiwan when high PM2.5 concentration event happened, the contribution of each pollution source is calculated by BFM. When Taiwan is in a weak synoptic weather, each pollution source mainly affects its air quality area, and the factories contribute about 1 to 2%. The Taichung power plant has the most significant impact, and the mobile source contribution affected nearly 16~23%.

關鍵字(中)

★ 細懸浮微粒
★ 超超臨界技術
★ 火力發電廠
★ 交通移動源

關鍵字(英)

★ CMAQ
★ Brute Force Method

論文目次

摘要…………………………………………………………………………………i
Abstract…………………………………………………………………iii
致謝…………………………………………………………………………………v
表目錄……………………………………………………………………………ix
圖目錄……………………………………………………………………………xi
第一章緒論………………………………………………………………1
1-1 前言………………………………………………………………………1
1-2 文獻回顧………………………………………………………………2
1-3 研究目的………………………………………………………………4
第二章污染物特性分析…………………………………………5
2-1 北臺灣污染物特性………………………………………….………………5
2-2 PM2.5成份特性…………………………………..…………………………6
第三章研究方法………………………………………………………..……………8
3-1 模式介紹…………………………………………………….………………8
3-1-1 氣象模式………………………………………………..……………8
3-1-2 排放資料………………………………………………..…...…………9
3-1-3 空氣品質模式………………………………………..…………..……9
3-2 Brute Force Method…………………………………………………..…..…10
第四章個案選取與實驗設計…………………………………………………...……11
4-1 個案選取………………………………………………..……………………11
4-1-1 高污染事件個案一………………………………………………...…11
4-1-2 高污染事件個案二…………………………………………………...12
4-2 模式設定………………………………………………..……………………12
4-3 實驗設計………………………………………………………………….…13
第五章實驗結果與討論………………………………………………………..……14
5-1 氣象與空品模擬結果統計校驗指數………………………………………..14
5-2 個案一(2020/02/12 – 2020/02/13) …………………………………….……14
5-2-1 氣象模式表現……………………………………………..………….14
5-2-2 空品模式表現……………………………………………………...…15
5-3 個案二(2020/02/24 – 2020/02/25) …………………………………………16
5-3-1 氣象模式表現…………………………………………………….…16
5-3-2 空品模式表現……………………………………………..………..17
5-4 林口火力發電廠對周界環境影響…………………………………………18
5-4-1 個案一(2020/02/12 – 2020/02/13) …………………………………18
5-4-2 個案二(2020/02/24 – 2020/02/25) …………………………………20
5-5 USC技術對空氣品質改善效益……………………………………………21
5-5-1 個案一(2020/02/12 – 2020/02/13) …………………………………21
5-5-2 個案二(2020/02/24 – 2020/02/25) …………………………………22
5-6 臺灣西半部地區各大型污染源之貢獻量……………………………..…23
第六章結論與未來展望……………………………………………………………26
6-1 結論……………………………………………………………………….26
6-2 未來展望……………………………………………..………………….27
參考文獻……………………………………………..……………………………28
附表……………………………………………..…………………………………33
附圖……………………………………………..…………………………………54

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指導教授

鄭芳怡(Fang-Yi Cheng)

審核日期

2021-8-25

推文