臺灣博碩士論文加值系統

細懸浮微粒(PM2.5)是大氣主要污染物之一，台灣在秋冬季節經常發生高污染事件。前人研究指出，長時間暴露在高濃度細懸浮微粒(PM2.5)的環境下，對於人體健康、氣候變遷以及大氣能見度都有負面的影響。來源解析(Source Apportionment, SA)方法，可分為以觀測資料為基礎與使用排放量資料的三維空品模式為基礎兩種類型，如BFM (Brute Force method)與ISAM (Integrated Source Apportionment Method)，可提供排放源與污染物濃度之間關係的資訊，有助於了解污染物的來源。
雲林位於台灣西部平原之中部地區，坐落於雲林沿海地區的麥寮工業區、農業燃燒以及交通運輸排放皆為雲林境內的主要排放源。大氣污染物濃度亦與氣象環境相關，前人將雲林地區的天氣型態分為六類，其中高壓迴流與弱綜觀天氣型態經常伴隨高PM2.5濃度。本研究挑選三個屬於弱綜觀與高壓迴流伴隨北風環境的高污染事件，使用CMAQ空氣品質模式與來源解析方法，了解雲林之PM2.5主要來源以及與氣象環境之間的關係。
使用WRF(Weather Research and Forecasting Model)3.7.1版與NCEP-FNL再分析場資料進行氣象場模擬，解析度為81/27/9/3公里。在解析度為3公里的網格中進行觀測資料同化(Observation nudging)，亦即每小時在模式第一層加入台灣氣象局與環保署測站的溫度、風速以及風向資料，改善台灣地區風場模擬結果。由於ISAM與BFM計算之排放物對污染物濃度的貢獻量值有高度相關，執行較為容易的ISAM所需之計算時間較少，故本研究使用CMAQ5.0.2版中裝載之ISAM，搭配TEDS9.0排放量資料與WRF模擬之氣象資料，針對雲林境內以及台中都會區之排放源，探討在高污染事件中對雲林地區污染物濃度的貢獻量。
個案一為2015年11月8日至11月9日。2015年11月8日大氣條件屬於弱綜觀環境，微弱風速使得排放物停留在排放源周圍區域，雲林之污染物主要來自於自身排放源，降低雲林各排放源的排放量應可有效降低PM2.5濃度。2015年11月9日受高壓系統影響，微弱東北季風帶來上風處的污染物，使得雲林之污染物主要來自於台中都會區。個案二與個案三發生時間分別為2016年10月27日至10月28日以及2016年11月18日至11月19日。其中10月28日與11月19日之大氣環境主要受高壓迴流影響，台灣西部地區在此時間皆以北風為主。2016年10月28日與2016年11月19日的日平均風速皆較2015年11月9日弱，但常時間處於北風環境下，導致台中都會區之排放源對雲林污染物濃度的影響較高。此外，2016年11月19日的平均風速又高於2016年10月28日，使得台中對雲林污染物濃度的影響範圍擴大，雲林自身排放源的影響亦往南延伸，對自身環境的影響略微下降。在以北風為主的環境下，雲林污染物雖主要來自於台中都會區，仍有部分來自屬於近地表排放的面源與線源；此外，麥寮工業區亦影響著雲林靠海地區之空氣品質。

Yunlin is located in central-southern portion of western Taiwan. The local industrial emissions (Maliao industry), vehicle exhausts, and burnings of agriculture wastes all contribute to the poorer air quality in Yunlin. Besides, the emissions from nearby power plants, Taichung metropolitan area, and Changhua industrial park also contribute to the local air pollution problem in Yunlin County. The local circulation such as the land-sea breeze might transport the air pollutants toward the inland areas and induce high concentrations.
Source apportionment (SA) based on observation data or 3-Dimention air quality model can provide the relationship between emission sources and concentration of air pollutants. This study was conducted to investigate the main emission source that contributes to the PM2.5 concentration in Yunlin County using CMAQ SA technique.
The WRF version 3.7.1 are conducted and the observation nudging technique is applied in WRF modeling to nudge surface observed temperature, wind speed and wind direction data from Taiwan Central Weather Bureau and Environmental Protection Agency to improve the meteorological conditions. This study focused on the emission from Yunlin and Taichung power plants through BFM (Brute Force Method) and ISAM (Integrated Source Apportionment Method) technique. The emission contribution estimated by BFM and ISAM had high correlation with each other. Due to the less computational time from ISAM, CMAQ version 5.0.2 with ISAM SA technique were applied to investigate the contributions of emissions from Yunlin County and Taichung metropolitan area to concentration of pollutants.
Three air pollution episodes were studied. The first episode occurred from Nov 8, 2015 to Nov 9, 2015. Nov 8, 2015 was associated with a weak synoptic weather condition and PM2.5 mainly came from local emissions released within Yunlin County. Therefore, reducing the Yunlin local emissions could decrease concentration of PM2.5. Nov 9, 2015 was affected by a continental anticyclone and weak northeasterly monsoonal flow brings the pollutants from upwind Taichung metropolitan area. The second and third episodes occurred from Oct 27, 2016 to Oct 28, 2016 and Nov 18, 2016 to Nov 19, 2016, respectively. Oct 28, 2016 was slightly affected by the continental anticyclone and the continental anticyclone was moving away to the East of Taiwan on Nov 19, 2016. The average wind speed on Oct 28, 2016 and Nov 19, 2016 were weaker than Nov 9, 2015, but Taichung contributed more to PM2.5 concentration due to the long-lasting northerly wind. Due to the influence of northerly wind flow, there are still small portion of pollutants from near surface emissions from Yunlin and the majority are area and line sources. In addition, the emissions from Mailiao industrial complex could also affect the air quality in coastal area of Yunlin.

摘要 i
Abstract iii
致謝 v
表目錄 ix
圖目錄 x
第一章 緒論 1
1-1 前言 1
1-2 文獻回顧 2
1-3 研究目的 3
第二章 研究方法與實驗設計 4
2-1 模式介紹 4
2-1-1 氣象模式WRF 4
2-1-2 空氣品質模式CMAQ 4
2-2 來源解析方法 5
2-2-1 BFM 5
2-2-2 ISAM 6
2-2-3 BFM與ISAM之比較 7
2-3 資料來源與分析 10
2-3-1 氣象模擬相關資料 10
2-3-2 排放量資料分析 10
2-4 ISAM實驗設計 12
第三章 個案選取與模式設定 14
3-1 個案選取 14
3-1-1 高污染個案一 14
3-1-2 高污染個案二 14
3-1-3 高污染個案三 15
3-2 模式設定 15
第四章 實驗結果與討論 16
4-1 個案一 (2015/11/08-2015/11/09) 16
4-1-1 模式表現 16
4-1-2 ISAM結果 17
4-1-3 個案總結 20
4-2 個案二 (2016/10/28) 20
4-2-1 模式表現 20
4-2-2 ISAM結果 21
4-2-3 個案總結 24
4-3 個案三 (2016/11/19) 24
4-3-1 模式表現 24
4-3-2 ISAM結果 25
4-3-3 個案總結 28
第五章 結論與未來展望 29
5-1 結論 29
5-2 未來展望 30
參考文獻 31
附表 36
附圖 42

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