111年度細懸浮微粒(PM2.5)化學成分監測及分析計畫
| 中文摘要 | 本計畫執行2022年臺灣六個測站PM2.5採樣並分析質量及化學成分濃度，2022年截至11月底各採樣日PM2.5質量及化學成分濃度從臺灣東部經北部往中、南部逐漸增高，發生PM2.5高濃度事件日數最高前三站依序為嘉義、小港、斗六站，解析的化學成分特徵持續彰顯出NOx污染源管制的重要性。PM2.5高濃度事件日發生的原因多為環境擴散不佳導致污染物累積，雖然有些採樣日受境外污染傳輸影響。2022年各站金屬元素濃度總和，小港、嘉義、斗六三站都高於忠明、板橋、花蓮三站，且多項金屬元素出現高濃度，顯示污染源眾多；其中，濃度相對高的金屬元素多具有燃煤、鋼鐵業及車輛排放貢獻特徵。 各測站2022年相較2017年的PM2.5質量和主要化學成分都有減量，以EC削減比例最高，其次為SO42-和NH4+，OC減量比例最低，因此在PM2.5占比提升。近六年各測站PM2.5質量和主要化學成分季度空間分布顯示：季度平均濃度以Q1為最高，其次多為Q4，再其次多為Q2。近六年Q1~Q3各測站金屬元素濃度總和最高的年度多為2021年，Q4則多為2019年。2017年至2022年各測站PMF受體模式污染因子季度貢獻變化顯示Q1的最高平均濃度，主要受到「硝酸鹽」的貢獻，其次是「硫酸鹽」和「車輛排放」；說明除了NO3-，SO42-和OC也需要進行管制。近六年忠明（含）以南的四個測站PM2.5高濃度事件日發生時，最高平均濃度的化學成分都是NO3-，因此，NO3-前趨污染源的管制對PM2.5高濃度事件日的減少非常重要。近六年分析指出中、南部要有效改善大氣能見度，需對硝酸鹽及其前驅污染源進行管制，硫酸鹽的減量則對東部及北部的大氣能見度改善較明顯。2021年PM2.5質量濃度增高，斗六、嘉義和小港站部分原因是受Q1乾旱影響。PM2.5、SO42-和OC在斗六、嘉義、小港測站有高空間相似度，可能受區域性污染影響；NO3-在各測站的空間相似度較低，應以在地污染影響為主。 總結來說，本計畫解析2022年臺灣六個測站PM2.5質量及化學成分濃度時間和空間分布，結合2017~2022年數據，釐清六年來PM2.5質量及化學成分濃度及影響因子變化趨勢，研究成果同時提供了污染源管制方向。 | ||
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| 中文關鍵字 | PM2.5化學成分，時間與空間分布，PM2.5污染源，大氣能見度 | ||
基本資訊
| 專案計畫編號 | 經費年度 | 111 | 計畫經費 | 14940 千元 | |
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| 專案開始日期 | 2021/12/09 | 專案結束日期 | 2022/12/31 | 專案主持人 | 李崇德 |
| 主辦單位 | 監資處 | 承辦人 | 黃健瑋 | 執行單位 | 國立中央大學 |
成果下載
| 類型 | 檔名 | 檔案大小 | 說明 |
|---|---|---|---|
| 期末報告 | 111年度細懸浮微粒(PM2.5)化學成分監測及分析計畫成果報告.pdf | 23MB |
The 2022 Project of Chemical Speciation Monitoring and Analysis of Fine Particulate Matter (PM2.5)
| 英文摘要 | This project collected PM2.5 to analyze mass and chemical speciation concentrations at the six stations in Taiwan. The PM2.5 mass and chemical speciation concentrations increased from the east through the north to the central and south of Taiwan for the sampling days ended by November 2022. The three most frequently occurring high PM2.5 concentration days for stations were in the order of the Chiayi, Xiaogang, and Douliu stations. The resolved chemical speciation characteristics persistently revealed the importance of controlling NOx pollution sources. The high PM2.5 concentration days were largely due to bad environmental ventilation causing pollutant accumulations, although some sampling days were influenced by transboundary transport. The summed metal elemental concentration of each station at Xiaogang, Chiayi, and Douliu was higher than that of each station at the Zhongming, Banqiao, and Hualien stations, with various high-concentration metal elements indicating multiple sources. Among relatively high-concentration metal elements were those characterized by the emissions from coal burning, iron and steel manufacturing, and vehicles. Comparing 2022 to 2017 for all stations, the PM2.5 mass and major chemical speciation concentrations were all reduced, with the highest reduction fraction in EC followed by SO42- and NH4+, and the lowest in OC, and thus a rise of OC in PM2.5 proportion. The quarterly spatial distribution showed that Q1 was the highest quarter of the PM2.5 mass and major chemical speciation concentrations followed by Q4 and Q2 in most stations for the most recent six years. The highest summed metal elemental concentrations in Q1~Q3 were frequently in 2021 and Q4 in 2019 for the most recent six years. The quarterly contributions of pollution factors of PMF receptor modeling showed that the highest concentration in Q1 was predominantly contributed by “Nitrate” followed by “Sulfate” and “Vehicle Emissions” in all stations from 2017 to 2022. This fact implies that SO42- and OC must also be controlled in addition to NO3-. For the high PM2.5 concentration days, the highest average concentration of chemical species was NO3- for the four stations south of the Zhongming (included) in the most recent six years. Therefore, controlling the precursor pollution sources of NO3- is crucial to reducing the high PM2.5 concentration days. A rise in PM2.5 mass concentration in 2021 can be partly accounted for by the influence of dry weather in Q1 at the Douliu, Chiayi, and Xiaogang stations. The high spatial similarity of PM2.5, SO42-, and OC at the Douliu, Chiayi, and Xiaogang stations may indicate an influence of regional pollution sources. In contrast, the low spatial similarity of NO3- among all stations implies the predominant influence of local pollution sources. In summary, this project analyzed the temporal and spatial distributions of the PM2.5 mass and chemical speciation concentrations. It summarized 2017~2022 data to clear out the variation trends of the influential factors and PM2.5 mass and chemical speciation concentrations. The direction for controlling pollution sources is also provided in the study results. | ||
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| 英文關鍵字 | PM2.5 chemical speciation, Temporal and spatial distributions, PM2.5 pollution sources, Atmospheric visibility | ||