環保專案成果報告查詢系統

專案計畫編號
經費年度	109
計畫經費	1,980 千元
預算科目	空氣品質監測
專案開始日期	2020/01/01
專案結束日期	2020/12/31
專案主持人	陳正平教授
主辦單位	監資處
承辦人	蔡宜君
執行單位	國立臺灣大學大氣資源與災害研究中心
專案分類	大氣空氣、環境影響評估
中文關鍵字	空氣品質預報、動力模式、中國大陸沙塵與本土河川揚塵
英文關鍵字	air-quality forecast, dynamic numerical model, Mainland China and local riverside mineral dust
協同主持人	蔡宜君助研究員
共同主持人
計畫聯絡人	蔡佩君
計畫聯絡電話	02-33663937#
計畫聯絡信箱	zoeytra@gmail.com

中文摘要	本計畫針對臺灣及離島的空氣品質預報作業進行系統維護，並提供作業上必要的協助。主要工作內容包括中尺度氣象動力模式、東亞大氣沙塵動力模式(TAQM/KOSA)、本土河川沙塵動力模式(TAQM/TWKOSA)以及第三代空氣品質動力化學模式(CMAQ)的維運及提供預報產品查詢網頁。資料分析的部分完成動力模式的預報輸出結果與監測站的監測資料，包括WRF氣象模式的氣象變數輸出、空氣品質模式的臭氧、懸浮微粒與細懸浮微粒濃度進行分析。為提高空氣品質預報的能力，也進行空氣品質模式預報觀測校驗工作，找到動力模式可改進的部分，未來可回饋於修改模式的參考依據。現行模式敏感因子分析，以環保署測站進行臭氧、懸浮微粒與細懸浮微粒濃度分析。針對一般污染分析完成環保署測站及七大空品區模式模擬與觀測污染物濃度日變化序列校驗。並探討模式在近１年季節變化特性分析，及彙整空氣品質不良案例，以天氣型態分類統計空品不良之影響，高污染個案分析完成一起本土擴散不良影響臺灣空氣品質事件之個案。並分析模式參數調整結果，包含最低邊界層高度門檻測試及臺灣本土起沙分布調整測試(WRFDUST)。在協助環保署進行預報的部分，包括派有計畫專案人員協助整理氣象局供應之資料，整合分析沙塵及空氣品質預報資訊，提供各種預報決策參考所需的資料。計畫主持人於東亞沙塵事件日支援提供專家諮詢意見並且參加檢討會議。
中文關鍵字	空氣品質預報、動力模式、中國大陸沙塵與本土河川揚塵

中文摘要

本計畫針對臺灣及離島的空氣品質預報作業進行系統維護，並提供作業上必要的協助。主要工作內容包括中尺度氣象動力模式、東亞大氣沙塵動力模式(TAQM/KOSA)、本土河川沙塵動力模式(TAQM/TWKOSA)以及第三代空氣品質動力化學模式(CMAQ)的維運及提供預報產品查詢網頁。資料分析的部分完成動力模式的預報輸出結果與監測站的監測資料，包括WRF氣象模式的氣象變數輸出、空氣品質模式的臭氧、懸浮微粒與細懸浮微粒濃度進行分析。為提高空氣品質預報的能力，也進行空氣品質模式預報觀測校驗工作，找到動力模式可改進的部分，未來可回饋於修改模式的參考依據。現行模式敏感因子分析，以環保署測站進行臭氧、懸浮微粒與細懸浮微粒濃度分析。針對一般污染分析完成環保署測站及七大空品區模式模擬與觀測污染物濃度日變化序列校驗。並探討模式在近１年季節變化特性分析，及彙整空氣品質不良案例，以天氣型態分類統計空品不良之影響，高污染個案分析完成一起本土擴散不良影響臺灣空氣品質事件之個案。並分析模式參數調整結果，包含最低邊界層高度門檻測試及臺灣本土起沙分布調整測試(WRFDUST)。在協助環保署進行預報的部分，包括派有計畫專案人員協助整理氣象局供應之資料，整合分析沙塵及空氣品質預報資訊，提供各種預報決策參考所需的資料。計畫主持人於東亞沙塵事件日支援提供專家諮詢意見並且參加檢討會議。

中文關鍵字

空氣品質預報、動力模式、中國大陸沙塵與本土河川揚塵

類型	檔名	檔案大小	說明	最新版公開日期
期末報告	109EPA計畫成果報告(定稿).pdf	22MB		2021/1/10 上午 12:00:00

摘要	The purpose of this project is to maintain the performance of forecasting systems and to facilitate the daily forecast of air quality over Taiwan region. The main work includes the maintenance and operation of the mesoscale numerical forecasting model, the Mainland China dust dynamic numerical model (TAQM/KOSA), the local riverside mineral dust dynamic model (TAQM/TWKOSA), and the Community Multi-scale Air Quality Model (CMAQ), and provides the forecast product query webpage. The characteristics of atmospheric variables and the concentrations of ozone, PM10 and PM2.5 from the model’s outputs and from the observation have been analyzed. To improve the air quality forecasting capability, we have also tried to identify the potential issues leading by the dynamics. In the future, these results can be further used to improve the performance of the dynamic numerical model. Current sensitivity analysis includes comparing the concentrations of O3, PM10 and PM2.5 between the data from EPA monitoring stations and from model simulations. For general pollution analysis, the simulated concentrations, diurnal variations and the seasonal variation of the pollutants are compared with the observation of EPA monitoring stations and seven air quality districts respectively. The poor quality event days in different weather patterns in this past year, and a serious air pollution event due to poor local dispersion, are analyzed. And the adjustment results of the model parameters are also analyzed, including the minimum boundary layer height limit test and the adjustment test of the dust emission distribution in Taiwan (WRFDUST). To help the daily forecasting in EPA, special-duty manpower is assigned to work for organizing and integrating all of the information to support the pre-warning decision. This project also provides the expert opinion from project director during the Mainland China severe dust events forecasting meeting.
英文關鍵字	air-quality forecast, dynamic numerical model, Mainland China and local riverside mineral dust

摘要

The purpose of this project is to maintain the performance of forecasting systems and to facilitate the daily forecast of air quality over Taiwan region. The main work includes the maintenance and operation of the mesoscale numerical forecasting model, the Mainland China dust dynamic numerical model (TAQM/KOSA), the local riverside mineral dust dynamic model (TAQM/TWKOSA), and the Community Multi-scale Air Quality Model (CMAQ), and provides the forecast product query webpage. The characteristics of atmospheric variables and the concentrations of ozone, PM10 and PM2.5 from the model’s outputs and from the observation have been analyzed. To improve the air quality forecasting capability, we have also tried to identify the potential issues leading by the dynamics. In the future, these results can be further used to improve the performance of the dynamic numerical model. Current sensitivity analysis includes comparing the concentrations of O3, PM10 and PM2.5 between the data from EPA monitoring stations and from model simulations. For general pollution analysis, the simulated concentrations, diurnal variations and the seasonal variation of the pollutants are compared with the observation of EPA monitoring stations and seven air quality districts respectively. The poor quality event days in different weather patterns in this past year, and a serious air pollution event due to poor local dispersion, are analyzed. And the adjustment results of the model parameters are also analyzed, including the minimum boundary layer height limit test and the adjustment test of the dust emission distribution in Taiwan (WRFDUST). To help the daily forecasting in EPA, special-duty manpower is assigned to work for organizing and integrating all of the information to support the pre-warning decision. This project also provides the expert opinion from project director during the Mainland China severe dust events forecasting meeting.

英文關鍵字

air-quality forecast, dynamic numerical model, Mainland China and local riverside mineral dust