109年臺中市細懸浮微粒(PM2.5)成分分析及空品預報計畫
| 中文摘要 | 本計畫調查臺中市PM2.5污染特徵與貢獻來源，包括環境微粒成分調查、管道PM2.5檢測、能見度量測，以及高PM2.5排放工廠對本市空氣品質的影響。 PM1對人體健康的危害更甚於PM2.5，本市PM2.5中以PM1以下的小顆粒居多，PM1占PM2.5的80%左右。微粒成分元素碳及有機碳危害健康程度高於硫酸鹽及硝酸鹽，大里地區PM2.5中元素碳和有機碳總和占47.1%，PM1達51.7%，以維護健康角度而言，大里應優先管制汽油車排放。管制措施朝減少車輛數、推廣電動車、加嚴汽油車排放標準三方面進行。 本市對3座電弧爐排放管道PM2.5調查結果，FPM介於0.29 mg/Nm3～1.23 mg/Nm3，CPM介於0.09 mg/Nm3～2.00 mg/Nm3，PM2.5/TSP值介於0.12～1.23。FPM化學成分以鐵（Fe）跟鋅（Zn）占比較高，此與廢鋼中以鍍鋅鋼板類為最大宗原料來源有關。 以HYSPLIT模式分析105年的5個海風案例，海風案例日的軌跡分析結果，台中電廠煙流受海風影響時，有三種類型出現：一、煙流軌跡越過大肚山進入臺中市；二、煙流軌跡由大肚山及八卦山谷地進入東側山區；三、煙流軌跡沿八卦山西側往東南。第一種類型對臺中市的空氣品質影響最大。選擇105年為模擬之年份，基於最新TEDS10排放量係以民國105年為基準年計算。105年的海風5案例日PM2.5最大小時貢獻量模擬結果，台中電廠對臺中市PM2.5貢獻最大地點是沙鹿，小時貢獻量可達21.7μg/m3，中龍鋼鐵最大貢獻地點亦在沙鹿，小時貢獻量可達9.0μg/m3。本案例篩選高污染日做代表，非代表所有空品情形。現今，台中電廠減煤24%，相對地PM2.5貢獻量亦會降低。 空品預報AQI值以環保署預測為主，輔以機器學習及類神經網絡方法優化，對於本市分區AQI>100以上之預報準確率皆達83%以上，整體而言，AQI= 101～150預報準確率平均為88%，AQI>150預報準確率平均為92%。 | ||
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| 中文關鍵字 | 管道過濾性微粒、管道凝結性微粒、HYSPLIT模式、CMAQ模式 | ||
基本資訊
| 專案計畫編號 | 經費年度 | 109 | 計畫經費 | 11500 千元 | |
|---|---|---|---|---|---|
| 專案開始日期 | 2020/02/14 | 專案結束日期 | 2020/12/31 | 專案主持人 | 程萬里 |
| 主辦單位 | 臺中市政府環境保護局 | 承辦人 | 張詠雅 | 執行單位 | 台灣綠仕科技有限公司 |
成果下載
| 類型 | 檔名 | 檔案大小 | 說明 |
|---|---|---|---|
| 期末報告 | 期末報告全文(定稿).pdf | 16MB | 期末報告全文 |
Composition Analysis of PM2.5 and Air Quality Forecasting for Taichung City in 2020
| 英文摘要 | This project investigates the characteristics of PM2.5 and the sources of its emissions in Taichung City. This study includes a component analysis of ambient PM2.5, stack emission monitoring of PM2.5, measurement of visibility, and an investigation on the air quality impacts by the largest PM2.5 industrial emitters. The health impacts of PM1 are more detrimental than that of PM2.5. We find that PM2.5 in Taichung is primarily composed of particulate matter smaller than 1μm, with PM1 accounting for about 80% of PM2.5. In terms of the constituents of PM2.5, elemental carbon and organic carbon have more severe health impacts than sulfates and nitrates. Elemental carbon and organic carbon together account for 47.1% of PM2.5, whereas they account for 51.7% of PM1 in the Dali area. From the perspective of public health, the regulation of emissions from gasoline-powered vehicles in Dali should be a priority. Control measures should aim to reduce the number of vehicles, increase the use of electric vehicles, and tighten regulatory emission standards for gasoline-powered vehicles. Investigation of PM2.5 from discharge pipes of three electric arc furnaces in the City shows that FPM was between 0.29 mg/Nm3～1.23 mg/Nm3, CPM was between 0.09 mg/Nm3～2.00 mg/Nm3, and PM2.5/TSP was in the range of 0.12～1.23. Iron (Fe) and zinc (Zn) were the most abundant components in FPM, the reason being that galvanized steel (zinc-coated steel) accounted for the most substantial proportion of scrap steel. Using HYSPLIT with data from 2016, we generated five scenarios under the influence of the sea breeze. The trajectory analysis indicates the emissions from the Taichung Power Plant might travel via three paths: 1.Traveling across the Dadu Plateau and entering the Taichung City; 2.Entering the eastern mountain area, via the Dadu Plateau and the Bagua Valley; and 3.Traveling along the eastern side of the Bagua Mountain towards the southeast. Emissions have the most considerable impact on the air quality of Taichung City when they travel via the first path. Selecting 2016 as the year of simulation is based on the calculation of the latest TEDS10 emissions using 2016 as the base year. Simulation results of the five scenarios in 2016 from CMAQ indicate that the maximum hourly contributions of PM2.5 by the Taichung Power Plant and the Dragon Steel Corporation are the highest in Shalu, among all Taichung City locations, reaching as high as 21.7μg/m3 and 9.0μg/m3. In these case, high-pollution days are chosen as the examples, which are not covered all air quality status. The Taichung Power Plant has reduced coal by 24% now, at the same time, the contribution of PM2.5 will also be reduced. We used forecasted AQI values by the Environmental Protection Administration. With the aid of machine learning and a neural network-like method for optimization, we calculated the accuracy of forecasted AQI>100 to be over 83% for all individual locations in Taichung. Overall, for AQI= 101～150, the average accuracy was 88%, and for AQI>150, the average accuracy was 92%. | ||
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| 英文關鍵字 | Filterable Particulate Matter (FPM), Condensable Particulate Matter (CPM), Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT), The Community Multiscale Air Quality Modeling System (CMAQ) | ||