環保專案成果報告查詢系統

專案計畫編號
經費年度	107
計畫經費	3,888 千元
專案開始日期	2018/05/23
專案結束日期	2019/05/22
專案主持人	呂鴻毅
主辦單位	屏東縣政府環境保護局
承辦人	馬子評
執行單位	立境環境科技股份有限公司
專案分類	大氣空氣
中文關鍵字	細懸浮微粒、受體模式、網格模式、硫酸鹽、硝酸鹽
英文關鍵字	PM2.5, CMB model, CMAQ, sulfate, nitrate
協同主持人	林聖倫、蔡德明
共同主持人
計畫聯絡人	羅國泉
計畫聯絡電話	07-3228761#320
計畫聯絡信箱	xji6eji6fm06@mail.lget.com.tw

中文摘要	(一) 空氣品質現況 107年本縣空品指標AQI>100比例為23.5%，創下歷年最佳紀錄，且自103年30.9%下降至107年23.5%，改善率達23.9%，其中對所有族群不健康(紅色)等級自103年10.6%下降至107年4.8%，另良好(綠色)等級則從103年41.1%增加至107年46.3%，顯示本縣空氣品質高污染濃度已獲得控制，且逐年持續改善，而良好等級也逐年增加中。 107年本縣PM2.5濃度(自動測站)年平均值為19.5µg/m3，日平均值為37.0µg/m3，亦為歷年最佳，相對於96年長期改善率分別達到43.0%、52.3%，長期呈現改善趨勢，顯示本縣空污防制工作具有相當之成效。 (二) 空品不良季節採樣分析成果本計畫在空品不良季節期間依據環保署空品預報、氣象局風場預報及境外預測等評估於PM2.5事件日提前於屏東測站、潮州測站及鹽洲測站部署採樣儀器，採樣天氣類型分別有高壓出海、高壓迴流、微弱東北季風及峰前暖區等，並根據境外移入擴散模式判斷有無境外污染。本計畫採樣分析包含質量濃度及成分組成分析，根據化學質量平衡模式(CMB)研究結果，本計畫於採樣期間(高污染季節)大氣中PM2.5的污染來源衍生性硫酸鹽(SO42-)約29.74%~32.15%及衍生性硝酸鹽(NO3-)約17.02%~21.48%之影響較多，其次依序大致為，交通源(16.56~20.98%)、土壤揚塵(4.39~ 8.23%)、石化工業(2.33~7.48%)及鋼鐵業(2.82~4.67%)。再利用硫氧化率(SOR)及氮氧化率(NOR)計算得知，屏東縣於高污染事件日時，污染物多數是來自較遠距離物種轉化與長程傳輸的影響。 (三) 空氣品質模式模擬結果本計畫使用空氣品質模式Models-3/CMAQ v5.1版結合天氣研究和預報模式(WRF)，以環保署最新公告之排放清冊(102年-TEDs 9.0)，包含人為排放及生物源排放，而非臺灣地區則使用東亞排放資料，進行春(4月)、夏(7月)、秋(10月)、冬(1月)四季各代表性月份的模擬，分析臺灣境外、臺灣其他縣市與屏東縣本身對於本縣細懸浮微粒濃渡之影響。空氣品質模式模擬結果，本縣PM2.5濃度全年受臺灣境外污染影響平均約17.4%、臺灣其他縣市影響平均約45.0%，本縣自身的影響平均約37.6%；從研究結果得知，本縣PM2.5污染來源，一部分來自臺灣境外，大部分來自上風處縣市，另外一部分則是本身自己的污染源所造成。雖全年平均受到臺灣其他縣市影響約45.0%，若僅模擬高污染季節期間，以冬季(1月)案例為例，則外縣市影響將高達63.5%，本縣自身的影響將會下降到20.9%，顯示高污染事件日多數還是來自上風處對本縣的傳輸影響。另外，從模式結果得知，空品不良季節時，NOX衍生污染物增加最為明顯，污染濃度越高，NOX衍生污染物濃度就越高。從研究結果顯示，要改善本縣空品不良季節PM2.5污染，除加強管制本地原生PM外，建議優先管制NOX，並透過新修訂的空污法之好鄰居條款，與上風處縣市共同合作推動跨區域空污管制策略。
中文關鍵字	細懸浮微粒、受體模式、網格模式、硫酸鹽、硝酸鹽

中文摘要

(一) 空氣品質現況 107年本縣空品指標AQI>100比例為23.5%，創下歷年最佳紀錄，且自103年30.9%下降至107年23.5%，改善率達23.9%，其中對所有族群不健康(紅色)等級自103年10.6%下降至107年4.8%，另良好(綠色)等級則從103年41.1%增加至107年46.3%，顯示本縣空氣品質高污染濃度已獲得控制，且逐年持續改善，而良好等級也逐年增加中。 107年本縣PM2.5濃度(自動測站)年平均值為19.5µg/m3，日平均值為37.0µg/m3，亦為歷年最佳，相對於96年長期改善率分別達到43.0%、52.3%，長期呈現改善趨勢，顯示本縣空污防制工作具有相當之成效。 (二) 空品不良季節採樣分析成果本計畫在空品不良季節期間依據環保署空品預報、氣象局風場預報及境外預測等評估於PM2.5事件日提前於屏東測站、潮州測站及鹽洲測站部署採樣儀器，採樣天氣類型分別有高壓出海、高壓迴流、微弱東北季風及峰前暖區等，並根據境外移入擴散模式判斷有無境外污染。本計畫採樣分析包含質量濃度及成分組成分析，根據化學質量平衡模式(CMB)研究結果，本計畫於採樣期間(高污染季節)大氣中PM2.5的污染來源衍生性硫酸鹽(SO42-)約29.74%~32.15%及衍生性硝酸鹽(NO3-)約17.02%~21.48%之影響較多，其次依序大致為，交通源(16.56~20.98%)、土壤揚塵(4.39~ 8.23%)、石化工業(2.33~7.48%)及鋼鐵業(2.82~4.67%)。再利用硫氧化率(SOR)及氮氧化率(NOR)計算得知，屏東縣於高污染事件日時，污染物多數是來自較遠距離物種轉化與長程傳輸的影響。 (三) 空氣品質模式模擬結果本計畫使用空氣品質模式Models-3/CMAQ v5.1版結合天氣研究和預報模式(WRF)，以環保署最新公告之排放清冊(102年-TEDs 9.0)，包含人為排放及生物源排放，而非臺灣地區則使用東亞排放資料，進行春(4月)、夏(7月)、秋(10月)、冬(1月)四季各代表性月份的模擬，分析臺灣境外、臺灣其他縣市與屏東縣本身對於本縣細懸浮微粒濃渡之影響。空氣品質模式模擬結果，本縣PM2.5濃度全年受臺灣境外污染影響平均約17.4%、臺灣其他縣市影響平均約45.0%，本縣自身的影響平均約37.6%；從研究結果得知，本縣PM2.5污染來源，一部分來自臺灣境外，大部分來自上風處縣市，另外一部分則是本身自己的污染源所造成。雖全年平均受到臺灣其他縣市影響約45.0%，若僅模擬高污染季節期間，以冬季(1月)案例為例，則外縣市影響將高達63.5%，本縣自身的影響將會下降到20.9%，顯示高污染事件日多數還是來自上風處對本縣的傳輸影響。另外，從模式結果得知，空品不良季節時，NOX衍生污染物增加最為明顯，污染濃度越高，NOX衍生污染物濃度就越高。從研究結果顯示，要改善本縣空品不良季節PM2.5污染，除加強管制本地原生PM外，建議優先管制NOX，並透過新修訂的空污法之好鄰居條款，與上風處縣市共同合作推動跨區域空污管制策略。

中文關鍵字

細懸浮微粒、受體模式、網格模式、硫酸鹽、硝酸鹽

類型	檔名	檔案大小	說明	最新版公開日期
期末報告	107年度屏東縣空品不良季節空氣污染成因分析計畫-上傳.pdf	5MB		2020/3/12 上午 12:00:00

摘要	(一) Air Quality around Pingtung County The number of days that Air Quality Index (AQI) was over 100 only represents 23.5% of 2018, which has been 23.9% reduced from those recorded in 2014 (30.9%). This is the best annul air quality record ever. Additionally, the day fractions of red (Unhealthy) status are significantly reduced from 10.6% in 2014 to 4.8% in 2018, while the percentages of green (Good) status day increases from 41.1% in 2014 to 46.3% in 2018, indicating the air quality is being improved by highly concern and undergoing control strategies. The annual and daily averages of atmospheric PM2.5 are 19.5 and 37.0 µg/m3, respectively, in 2018 by auto-monitoring EPA stations. This is also the best record ever, representing 43.0% and 52.3% reductions, respectively, from 2007. This result again supports the right direction and efficiency of air pollutant control strategies in Pingtung County. (二) Characteristics of Atmospheric PM2.5 during Air Pollution Events The air pollution event was predicted by the comprehensively consideration of air quality and wind field forecasts by EPA and CWB, as well as the forecasting simulation of international pollutant transport during the seasons with bad quality in general. The sampling sites were setup in Pingtung, Chaozhou, and Yanjou stations of EPA. The weather styles during pollution events include off-shored high pressure, high pressure reflux, weak northeast monsoon, and frontal passage. PM2.5 are analyzed for their mass concentrations and chemical compositions, and further using chemical mass balance (CMB) model to evaluate the contributions of potential sources in the current study. The major contributors of atmospheric PM2.5 were secondary sulfate (SO42-, 29.74~32.15%) and nitrate (NO3-, 17.02~21.48%), being followed by traffic sources (16.56~20.98%), soil dust (4.39~8.23%), petrochemical industry (2.33~7.48%), and steel industry (2.82~4.67%). On the other hand, the PM2.5 around sampling sits in Pingtung during pollution event were mainly derived from the precursors in the upwind area according to the analyses of sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR). (三) Air Quality Modeling Model-3/CMAQ v5.1 coupled with Weather Research and Forecasting (WRF) model is employed to simulate the air quality during the desired period in the current study. The input emission data is the latest Taiwan Emission Data System (TEDS 9.0) based on the emission inventory collected in 2013 and developed by Taiwan EPA. The emission data includes anthropogenic and natural sources, while the emission database of East Asia is also included. There are four simulated periods, including spring (April), summer (July), autumn (October), and winter (January), to estimate the contributions (%) of international, cross-county, and self-emissions on the atmospheric PM2.5 around Pingtung County. Results show that the averaging contribution of international pollutant transport is 17.4% on total ambient PM2.5 mass concentration in Pingtung, as well as the contributions of self-emission and the other city/county are 37.6% and 45.0%, respectively. In other words, the atmospheric PM2.5 are mainly transported from the upwind counties/cities and followed by self-emissions and international transports. Notably, the contributions on PM2.5 during low-air quality season are much higher than the average level. For example, the inter-county transport dominates 63.5% of atmospheric PM2.5 in Pingtung during January, while the self-contribution reduces to 20.9%, emphasizing the importance of the emissions from upwind area during pollution events. Additionally, the increases of NOx-derived pollutants are the most significant during low-air quality season. Consequently, the control strategies for reducing atmospheric PM2.5 in Pingtung County should focus not only on primary PM2.5 but NOx emissions. Nevertheless, the cross-county/city cooperation on emission control should be continuously operated for sustainable air quality improvement.
英文關鍵字	PM2.5, CMB model, CMAQ, sulfate, nitrate

摘要

(一) Air Quality around Pingtung County The number of days that Air Quality Index (AQI) was over 100 only represents 23.5% of 2018, which has been 23.9% reduced from those recorded in 2014 (30.9%). This is the best annul air quality record ever. Additionally, the day fractions of red (Unhealthy) status are significantly reduced from 10.6% in 2014 to 4.8% in 2018, while the percentages of green (Good) status day increases from 41.1% in 2014 to 46.3% in 2018, indicating the air quality is being improved by highly concern and undergoing control strategies. The annual and daily averages of atmospheric PM2.5 are 19.5 and 37.0 µg/m3, respectively, in 2018 by auto-monitoring EPA stations. This is also the best record ever, representing 43.0% and 52.3% reductions, respectively, from 2007. This result again supports the right direction and efficiency of air pollutant control strategies in Pingtung County. (二) Characteristics of Atmospheric PM2.5 during Air Pollution Events The air pollution event was predicted by the comprehensively consideration of air quality and wind field forecasts by EPA and CWB, as well as the forecasting simulation of international pollutant transport during the seasons with bad quality in general. The sampling sites were setup in Pingtung, Chaozhou, and Yanjou stations of EPA. The weather styles during pollution events include off-shored high pressure, high pressure reflux, weak northeast monsoon, and frontal passage. PM2.5 are analyzed for their mass concentrations and chemical compositions, and further using chemical mass balance (CMB) model to evaluate the contributions of potential sources in the current study. The major contributors of atmospheric PM2.5 were secondary sulfate (SO42-, 29.74~32.15%) and nitrate (NO3-, 17.02~21.48%), being followed by traffic sources (16.56~20.98%), soil dust (4.39~8.23%), petrochemical industry (2.33~7.48%), and steel industry (2.82~4.67%). On the other hand, the PM2.5 around sampling sits in Pingtung during pollution event were mainly derived from the precursors in the upwind area according to the analyses of sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR). (三) Air Quality Modeling Model-3/CMAQ v5.1 coupled with Weather Research and Forecasting (WRF) model is employed to simulate the air quality during the desired period in the current study. The input emission data is the latest Taiwan Emission Data System (TEDS 9.0) based on the emission inventory collected in 2013 and developed by Taiwan EPA. The emission data includes anthropogenic and natural sources, while the emission database of East Asia is also included. There are four simulated periods, including spring (April), summer (July), autumn (October), and winter (January), to estimate the contributions (%) of international, cross-county, and self-emissions on the atmospheric PM2.5 around Pingtung County. Results show that the averaging contribution of international pollutant transport is 17.4% on total ambient PM2.5 mass concentration in Pingtung, as well as the contributions of self-emission and the other city/county are 37.6% and 45.0%, respectively. In other words, the atmospheric PM2.5 are mainly transported from the upwind counties/cities and followed by self-emissions and international transports. Notably, the contributions on PM2.5 during low-air quality season are much higher than the average level. For example, the inter-county transport dominates 63.5% of atmospheric PM2.5 in Pingtung during January, while the self-contribution reduces to 20.9%, emphasizing the importance of the emissions from upwind area during pollution events. Additionally, the increases of NOx-derived pollutants are the most significant during low-air quality season. Consequently, the control strategies for reducing atmospheric PM2.5 in Pingtung County should focus not only on primary PM2.5 but NOx emissions. Nevertheless, the cross-county/city cooperation on emission control should be continuously operated for sustainable air quality improvement.

英文關鍵字

PM2.5, CMB model, CMAQ, sulfate, nitrate