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姓名	陳誼(Yi Chen)  查詢紙本館藏	畢業系所	大氣科學學系
論文名稱	整合無人機與光達觀測解析斗六地區空污事件之演變過程 (Dissecting critical factors of PM2.5 deterioration in Douliu area, Taiwan using UAV and lidar observations)
相關論文	★ 鹿林山背景站大氣輻射及氣膠輻射驅動力之研究 ★ 中南半島生質燃燒氣膠濃度分布之年際變化與其對區域環境衝擊研究 ★ 中壢地區光達消光散射比之長期分析與污染物關聯性研究 ★ 臺灣大氣背景PM2.5質量濃度之推估 ★ 雲林斗六PM2.5濃度變化與氣膠光學特性及氣象條件之關聯性研究 ★ Mapping Surface Solar Radiation with Satellite Data over Taiwan ★ 開發適用於大氣邊界層觀測的無人機系統 ★ 利用AERONET資料解析中南半島地區氣膠種類及成分 ★ 氣膠對臺灣北部暖雲微物理和毛雨的影響 ★ Characteristics and Corrections of Thermal Offset for Secondary Standard Pyranometers ★ 氣膠對臺灣中部平原夏季降水日變化之影響 ★ 中南半島生質燃燒氣膠傳送動力機制及區域氣候反饋 ★ 2019年春季泰國北部無人機觀測實驗: 邊界層特徵與氣膠垂直分布之研究 ★ Investigating hygroscopic cloud-seeding effects in liquid-water clouds in northern Taiwan: in-situ measurements and model simulation ★ 氣膠光學及微物理反演法開發：以鹿林山大氣背景站應用為例 ★ 利用向日葵8號衛星及單層輻射模式反演地面輻射量
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摘要(中)	空氣污染問題不僅受污染源排放、大氣化學反應影響，氣象條件以及地形效應的影響也是造成高污染事件發生的重要因素。過去模式研究指出，臺灣在高壓迴流或是弱綜觀的天氣型態時，會有利於高PM2.5濃度事件發生，雖然位於雲林縣山麓地帶的斗六地區並非臺灣中部PM2.5濃度排放量最高的地區，但斗六地區空氣品質卻經常性不佳。為解析斗六地區空污事件之演變過程，本研究利用新建構的無人機觀測系統與氣膠光達於2020年11月16日至20日，在斗六地區進行高時間解析度的氣象場與氣膠剖面觀測，以提供過去少有的高時間頻率大氣垂直剖面資訊，並透過一系列的資料處理流程與驗證程序，來確保無人機的觀測品質，同時搭配地面空氣品質測站資料和WRF模式模擬的再分析場，整合分析空污事件之演變過程。 研究結果顯示，弱綜觀天氣條件下，斗六地區污染物的傳輸與擴散特徵與海陸風環流、背風渦旋及逆溫結構有關。個案期間夜晚風速皆小於1 m s-1，整體水平擴散條件類似，但在17、18日晚間高污染事件發生時，伴隨雙層逆溫結構，分別為近地面的厚度約200公尺的輻射逆溫與800-1000公尺左右的沉降逆溫，沉降作用同時導致高空環境乾燥無雲，有利加劇夜間地表的輻射冷卻，使地面逆溫強度增強，相較於較低污染之 16、19日夜晚的2.6 ℃ km-1與4.5 ℃ km-1，17、18日逆溫強度分別高達8.0 ℃ km-1與12.1 ℃ km-1，此惡劣的垂直擴散條件使斗六空品站於17日和18日晚間分別測得最高PM2.5濃度達70 μg m-3和99 μg m-3。海風環流約於上午9時建立，將潮濕氣團與沿岸污染物往內陸傳輸，18日下午1時可觀測到明顯的海風鋒面抵達，造成斗六比濕與PM2.5濃度同時上升，而光達觀測顯示1300公尺內NRB數值明顯增加，表示海風鋒面結構超過1000公尺。夜間隨著陸風環流發展，將內陸污染物帶離，斗六高污染濃度在17、18日分別於18時與21時之後下降。模式結果顯示個案期間臺灣海峽區域共出現三個背風渦旋，其中兩個為氣旋式環流，一個為反氣旋式環流，渦旋發展主要受大環境風場影響，而有不同的移動路徑與結構變化，其中氣旋式的背風渦旋會導致近地面污染物北傳，而反氣旋式環流的北風則伴隨沉降所造成的乾空氣移入斗六地區，呼應前述18日夜間高污染發生的條件。整體而言，本研究透過無人機與光達高時間解析的垂直剖面觀測進行分析，從污染物與氣象場空間分布演變探討高污染事件成因，並確立此觀測技術能應用於將來大氣剖面研究。
摘要(英)	Douliu is one of the most air-polluted areas in Taiwan. Factors that caused the deterioration of air quality in this area might attribute to emissions, chemical reactions, meteorology, and geographical distribution, etc. Recent modeling studies point out that high PM2.5 concentration events occurred are usually associated with favorite weather conditions (i.e., high-pressure peripheral circulation and weak synoptic weather). To make a statement on the deterioration of PM2.5 events, we employed a new technology, Unmanned Aerial Vehicle (UAV), and an aerosol lidar to observe the high resolution of meteorological and aerosol profiles at Douliu during November 16th-20th, 2020. A series of data validation procedures had applied to ensure the UAV data quality. As a result, the UAV measurements (i.e., meteorological parameters and PM2.5 concentration) are reliable and show in good agreement with vertical structure obtained from lidar observation. In addition to observations (i.e., UAV, lidar, surface air quality, and meteorology data), we also use a WRF model to run a reanalysis for this case study. According to our results, the roles of land-sea breeze, temperature inversion layer, and leeside vortex in aerosol transportation and dispersion over Douliu during the weak synoptic weather were discovered. The horizontal dispersion condition did not change much during the night of the observation, which was accompanied by the wind speed smaller than 1 m s-1. On November 17th and 18th when the high pollution event occurred, there were two temperature inversion layers in vertical. One was the surface radiative inversion whose depth was about 200 m, and another was the elevated subsidence inversion which was located at around 800-1000 m. The subsidence of the air also led to the dry condition in the sky and was beneficial for radiative cooling at the surface at night. Therefore, the strength of the temperature inversion near the surface increased, compared with the strength of 2.6 ℃ km-1 on the night of 16th and 4.5 ℃ km-1 on the night of 19th, the inversion strength on 17th and 18th were up to 8.0 ℃ km-1 and 12.1 ℃ km-1, respectively. Therefore, these worse vertical dispersion conditions caused the maximum of hourly PM2.5 concentration to be up to 70 μg m-3 in the evening of 17th and 99 μg m-3 in the evening of 18th. The sea breeze circulation developed at about 9 a.m., and the specific humidity and PM2.5 concentration increased at the same time at noon, because the sea breeze transported the pollutant and water vapor from the coastal area to Douliu. The structure of sea breeze front was distinct on November 18th at 1 p.m., and the leading edge of the sea breeze front reached a height greater than 1000 m which was demonstrated from the lidar NRB data. When the land breeze gradually developed, the air quality improved after 18 p.m. on 17th and 21 p.m. on 18th. The result of the numerical simulation indicated there were three leeside vortexes during this case, two were associated with the cyclonic circulation and one was associated with the anticyclonic circulation. The location, structure, as well as direction of rotation with respect to the leeside vortex showed differently is due to the variation of the synoptic winds and how they interacted with the terrain. The cyclonic vortex led northward of the pollutant near the surface, and the anticyclonic vortex brought the dry air of subsidence with the northerly wind over Douliu. Overall, this study used the high temporal resolution vertical observation data from UAV and aerosol lidar to dissect critical factors of PM2.5 deterioration in Douliu area. This work also highlights that the high temporal resolution of UAV and lidar observations can serve as a powerful tool to improve our understanding of the relationship between atmospheric vertical profile and air quality.
關鍵字(中)	★ 無人機 ★ 微脈衝光達 ★ 空氣污染 ★ 大氣邊界層 ★ 海陸風環流	關鍵字(英)	★ Unmanned aerial vehicle ★ Micro pulse lidar ★ Air pollution ★ Atmospheric boundary layer ★ Land-sea breeze circulation
論文目次	摘要 i Abstract iii 誌謝 v 目錄 vi 圖目錄 viii 表目錄 xi 一、前言 1 1-1 研究動機 1 1-2 研究目的 2 二、文獻回顧 4 2-1 海陸風對於污染物的相關研究 4 2-2 邊界層與逆溫結構對於污染物的相關研究 7 2-3 地形對於污染物的相關研究 9 2-4 臺灣過去局地環流垂直剖面觀測 10 2-5 無人機於污染物垂直剖面的觀測應用 12 2-6 光達於污染物垂直剖面的觀測應用 13 三、研究方法 15 3-1 無人機觀測時間與地理條件 17 3-2 垂直剖面觀測資料 19 3-2-1 無人機系統架構與觀測方法 19 3-2-2 微脈衝光達 21 3-2-3 高空氣球探測儀 24 3-3 地面觀測資料 24 3-3-1 移動式氣象站 24 3-3-2 環保署空氣品質監測網 25 3-3-3 氣象局自動氣象站 26 3-4 無人機資料處理流程 27 3-4-1溫濕度觀測反應時間修正 28 3-4-2 PM2.5濃度吸濕性修正 33 3-4-3 資料品保品管與分級 36 3-4-4 大氣穩定度與逆溫強度計算 37 3-5 WRF模式資料說明 38 3-5-1 模式設定 39 3-5-2 HYSPLIT軌跡模式 40 四、觀測資料特性與模式性能評估 41 4-1 無人機資料驗證 41 4-1-1 無人機反時間修正結果 41 4-1-2 無人機觀測與地面觀測資料比對 42 4-1-3 無人機觀測特性與誤差來源評估 46 4-2 模式性能評估 46 4-3 模式模擬結果與無人機觀測差異 51 五、結果與討論 54 5-1 個案期間綜觀天氣型態與全臺空品 54 5-2 斗六地面氣象場與空品時序分析 57 5-3 斗六垂直氣象場與污染分布 59 5-4 邊界層發展對於污染物垂直分布的影響 66 5-5 逆溫強度對於污染事件的影響 71 5-6 海陸風對於污染傳送的影響 76 5-7 模式資料輔助說明局部環流 80 5-7-1 HYSPLIT探討高污染濃度來源 80 5-7-2 背風渦旋對於污染傳送的影響 84 5-8 污染累積與消散機制 90 六、總結與未來展望 91 6-1 總結 91 6-2 未來展望 93 參考文獻 95
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指導教授	王聖翔(Sheng-Hsiang Wang)	審核日期	2021-8-3
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