空氣污染問題除了人為排放影響外，氣象條件則在空氣污染的擴散、稀釋及傳輸上扮演著重要角色。過去研究主要關注綜觀至中小尺度天氣系統對空污問題的影響，而大尺度氣象環境對臺灣空污問題的影響則較少被討論。因此本研究藉由2018-2020十二月至隔年三月冬春季的觀測資料分析與WRF-CMAQ模式進行固定人為排放量的數值實驗，聚焦探討多尺度氣象環境對年際間PM2.5濃度的影響，目的在於增進對東亞大尺度氣象環境與臺灣空氣品質關聯的了解並提升中長期空品預報能力。
根據NOAA公布的ONI index，2018/19、2019/20及2020/21分別為聖嬰年、正常年與反聖嬰年。2020/21年因反聖嬰現象下西太平洋的相對低壓環流結構特徵明顯使臺灣綜觀東風分量增加，造成靜風天數增加、風速下降。同時東亞地區氣溫偏冷、水氣偏少，進而在探空觀測結果也顯示出大氣垂直結構穩定、邊界層不易發展的特性。2020/21年臺灣PM2.5濃度相較於過去兩年聖嬰與正常年時呈現高峰期遲滯且濃度增加的現象。由於模擬氣象場能有效掌握氣象特性，將空品模擬資料針對不同天氣型態進行分類討論後，其結果證明若三年間的排放量相同，則不同的氣象環境能顯著影響PM2.5濃度差異。若不考慮臺灣境內排放的話，臺灣冬春季期間PM2.5濃度的平均背景值約為4-6微克/立方公尺。2020/21年時，由於反聖嬰現象時中國東南方污染物濃度較高，伴隨著增強的氣壓梯度，使得臺灣在東北季風天氣下境外污染貢獻較過去2年多達約6微克/立方公尺。境內污染方面，模式中模擬的氣溫偏低、東風分量增加、低風速、邊界層高度低等穩定的大氣環境說明了在所有的天氣型態下境內PM2.5濃度累積也有較過去2年增加的現象。

In addition to the anthropogenic emissions, the meteorological conditions also play an important role in the diffusion, dilution and transport of air pollutants. Previous studies mainly focused on the relationship between meso-scale weather systems and air pollution problems. However, the influence of large-scale circulation systems to air pollution in Taiwan is less discussed. Therefore, the study focused on discussing the impact of multi-scale meteorological environments to inter-annual variability of PM2.5 concentration in Taiwan during the winter-spring season (DJFM) of 2018-2020 by data analysis and WRF-CMAQ modeling with fixed anthropogenic emissions. The objectives of the study are to gain a deeper understanding of the connections to large-scale environment and air quality and to enhance the ability of mid-term air quality forecast.
According to Ocean Nino index from NOAA, the winter seasons in 2018/19, 2019/20, and 2020/21 were affected by El Nino, neutral and La Nina circulations, respectively. In 2020/21, cyclonic anomaly was obviously found in 2020/21 winter in area of Taiwan which leads to frequent occurrence of the easterly prevailing wind. Moreover, weaker wind speed, cooler environmental temperature and more stable atmospheric structure were observed in western Taiwan. The observation air quality data revealed a worsened PM2.5 concentration in February and March of 2021 instead in January like past years shown. After classifying the simulated data according to different synoptic weather types, the results prove that different meteorological environments indeed influence the PM2.5 concentration under the same anthropogenic emission. Without considering the domestic emission, the average background PM2.5 concentration is approximately 4-6 μg/m3. Under the La Nina, the long-range-transport pollutants from China which accumulated more along the coast transported with the north-south orientation moved airmass to Taiwan under northeasterly synoptic weather type. The long-range transported PM2.5 concentration increased 6 μg/m3 than past two years. Meanwhile, the domestic PM2.5 contribution was also degraded in winter-spring season of 2021 due to the increased easterly wind, weakened wind flow and enhanced atmospheric stability under all synoptic weather types influenced by La Nina environment.

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