環保專案成果報告查詢系統

專案計畫編號	EPA-103-FA11-03-A321
經費年度	103
計畫經費	5,560 千元
專案開始日期	2014/10/15
專案結束日期	2015/10/14
專案主持人	吳義林
主辦單位	空保處
承辦人	謝仁碩
執行單位	財團法人成大研究發展基金會
專案分類	大氣空氣、環境監測及檢測
中文關鍵字	成本/效益分析、細懸浮微粒、管制策略
英文關鍵字	cost/benefit analysis, Fine Particle (PM2.5), Control Strategy
協同主持人	王錫豐博士；蔡德明助理教授
共同主持人
計畫聯絡人	niki@cs.ncku.edu.tw
計畫聯絡電話	06-2386764#
計畫聯絡信箱	niki@cs.ncku.edu.tw

中文摘要	本計畫之工作內容包涵蒐集國內外細懸浮微粒之文獻、分析環保署手動測站之水溶性陰陽離子成分、量測衍生性細懸浮微粒化學成分之形成速率、分析各行業別對細懸浮微粒之貢獻量與依成本效益研擬細懸浮微粒管制策略。水溶性陰陽離子成分分析結果顯示全台監測站皆為SO42-佔PM2.5比例最高，皆為20%以上，NH4+與NO3-次之，NH4+所佔PM2.5比例約12%至18%，NO3-全台變動則較大，為4%至20%不等，其餘陰陽離子所佔PM2.5比例大多為5%以下。台灣細懸浮微粒濃度由北至南有漸增之情況，且中南部測站之銨鹽與硝酸鹽所佔比例較北部測站多，而硫酸鹽所占比例較少，但全年平均濃度，各成分濃度皆為南部測站高於北部。各空品區之J值除花東空品區以外均大於一，亦即氨氣為過剩。分析2014年全國所有人工監測結果顯示細懸浮微粒質量濃度為Gamma分布而其分布之形狀參數與尺寸參數分別為9.6與2.496，而達到二十四小時值之98%是35微克/立方公尺、年平均值是15微克/立方公尺之標準時，Gamma分布之形狀參數與尺寸參數分別為3.54與4.24。本計畫於2014年11月19日至11月28日在台南、善化、安南、橋頭、仁武、屏東、大寮、小港及潮州總計九個測站同時進行PM2.5之周界採樣，並進行水溶性離子、碳成分及醣類之分析，並配合WRF模擬三維氣象場、HYSPLIT逆軌跡模式及空間濃度內插分布圖去挑選案例及計算上下風處行進間衍生性氣膠之形成速率，結果顯示硫形成速率、氮形成速率及碳形成速率分別為5.09±3.25%/hr、1.91±1.68%/hr及0.50±0.3%/hr。有機碳來源推估方法為配合醣類分析結果及文獻中有機碳比例係數進行推估，結果顯示為A-SOC最高，其比例為77.91±11.18 %，其次為B-POC：12.19±10.76 %，A-POC：9.28±5.04 %，B-SOC：2.46±2.37 %。因本研究採樣分析季節為冬季，故為異戊二烯排放量較低之時段，因而本計畫推估之B-SOC可能會低估而高估A-SOC。以三維網格模式(Models-3/CMAQ)模擬分析各行業別之細微粒、SO2、NOx與NMHC對細懸浮微粒濃度之影響，並擬訂不同前驅物之管制順序，由模式模擬結果顯示每萬噸PM2.5排放，以餐飲業油煙排放之貢獻比例最高，為10.0%，其次為公車客運車之9.3%與商業燃燒排放之9.1%；每萬噸NOx排放，以成衣業之貢獻比例最高，為0.53%，其次為玻璃業之0.52%與電子器材製造業之0.52%；每萬噸SO2排放，以公車客運車之貢獻比例最高，為1.04%，其次為商業燃燒排放之0.93%與電子器材製造業之0.89%。以污染防制技術分析管制之成本效益，結果顯示必須將各排放源之防制現況納入，而可達到之最大細懸浮微粒濃度降低比例為27%，防制成本為2300億元。
中文關鍵字	成本/效益分析、細懸浮微粒、管制策略

中文摘要

本計畫之工作內容包涵蒐集國內外細懸浮微粒之文獻、分析環保署手動測站之水溶性陰陽離子成分、量測衍生性細懸浮微粒化學成分之形成速率、分析各行業別對細懸浮微粒之貢獻量與依成本效益研擬細懸浮微粒管制策略。水溶性陰陽離子成分分析結果顯示全台監測站皆為SO42-佔PM2.5比例最高，皆為20%以上，NH4+與NO3-次之，NH4+所佔PM2.5比例約12%至18%，NO3-全台變動則較大，為4%至20%不等，其餘陰陽離子所佔PM2.5比例大多為5%以下。台灣細懸浮微粒濃度由北至南有漸增之情況，且中南部測站之銨鹽與硝酸鹽所佔比例較北部測站多，而硫酸鹽所占比例較少，但全年平均濃度，各成分濃度皆為南部測站高於北部。各空品區之J值除花東空品區以外均大於一，亦即氨氣為過剩。分析2014年全國所有人工監測結果顯示細懸浮微粒質量濃度為Gamma分布而其分布之形狀參數與尺寸參數分別為9.6與2.496，而達到二十四小時值之98%是35微克/立方公尺、年平均值是15微克/立方公尺之標準時，Gamma分布之形狀參數與尺寸參數分別為3.54與4.24。本計畫於2014年11月19日至11月28日在台南、善化、安南、橋頭、仁武、屏東、大寮、小港及潮州總計九個測站同時進行PM2.5之周界採樣，並進行水溶性離子、碳成分及醣類之分析，並配合WRF模擬三維氣象場、HYSPLIT逆軌跡模式及空間濃度內插分布圖去挑選案例及計算上下風處行進間衍生性氣膠之形成速率，結果顯示硫形成速率、氮形成速率及碳形成速率分別為5.09±3.25%/hr、1.91±1.68%/hr及0.50±0.3%/hr。有機碳來源推估方法為配合醣類分析結果及文獻中有機碳比例係數進行推估，結果顯示為A-SOC最高，其比例為77.91±11.18 %，其次為B-POC：12.19±10.76 %，A-POC：9.28±5.04 %，B-SOC：2.46±2.37 %。因本研究採樣分析季節為冬季，故為異戊二烯排放量較低之時段，因而本計畫推估之B-SOC可能會低估而高估A-SOC。以三維網格模式(Models-3/CMAQ)模擬分析各行業別之細微粒、SO2、NOx與NMHC對細懸浮微粒濃度之影響，並擬訂不同前驅物之管制順序，由模式模擬結果顯示每萬噸PM2.5排放，以餐飲業油煙排放之貢獻比例最高，為10.0%，其次為公車客運車之9.3%與商業燃燒排放之9.1%；每萬噸NOx排放，以成衣業之貢獻比例最高，為0.53%，其次為玻璃業之0.52%與電子器材製造業之0.52%；每萬噸SO2排放，以公車客運車之貢獻比例最高，為1.04%，其次為商業燃燒排放之0.93%與電子器材製造業之0.89%。以污染防制技術分析管制之成本效益，結果顯示必須將各排放源之防制現況納入，而可達到之最大細懸浮微粒濃度降低比例為27%，防制成本為2300億元。

中文關鍵字

成本/效益分析、細懸浮微粒、管制策略

類型	檔名	檔案大小	說明	最新版公開日期
期末報告	EPA-103-FA11-03-A321(公開版)-1.pdf	11MB		2016/2/1 上午 12:00:00

摘要	To develop the control strategy for fine particle(PM2.5) in Taiwan, the contents for this project include literature review, analysis of water soluble ionic species for samples from the Taiwan FRM sampling networks, field measurements of sulfate, nitrate and secondary organic aerosol formation rates, simulation of impacts of various kinds of emission sources by using three-dimensional air quality model CMAQ, and cost/benefit assessment for various control scenarios. The dominant ionic species of fine particle is sulfate whose fraction is greater than 20% at all sites; the fraction of ammonia is about 12% to 18% and that for nitrate varies between 4% to 20% from site to site. Except for eastern Taiwan, the values of J are all greater than one, implying the abundance of gaseous NH3. The type of distribution for all mass concentrations in 2014 is Gamma distribution of (9.6,2.496). Therefore, it is (3.54,4.24) to meet the national ambient air quality standard of PM2.5. Field campaigns were conducted at nine different sites in southern Taiwan from 19th to 28th November, 2014 simultaneously in order to study the formation rates of secondary aerosol. The time duration for each sample was six hours in order to differentiate the daytime and nighttime. The three-dimensional meteorological conditions were simulated by using WRF and the back-trajectory analyses were performed by using HYSPLIT. The upwind concentration was determined by interpolation among all measurement sites for each six-hour trajectory and the effects of dispersion and dry deposition were adjusted by using ventilation factor and potassium, respectively. The measurement results show that the formation rates for sulfate, nitrate, and SOA are 5.09±3.25%/hr, 1.91±1.68%/hr, and 0.50±0.30%/hr, respectively. The contributions of various kinds of emissions sources to the ambient fine particle concentrations were simulated by using the three-dimensional grid air quality model CMAQ with the zero-out method. Thirty-one different kinds of emission sources have been simulated with the emission data from TEDS8.1. For primary fine particle, the contribution from cooking and restaurant is the greatest of 10.0% for every ten thousand of PM2.5 emitted, followed by bus of 9.3% and commercial burning of 9.1%. For NOx, the contribution from clothes industry is the greatest of 0.53% for every ten of NOx emitted, followed by glass industry and electronic industry of 0.52% both. For SO2, the contribution from bus is the greatest of 1.1% for every ten thousand of SO2 emitted, followed by commercial burning of 0.93% and electronic industry of 0.89%. Significant differences have been found in the cost/benefit analysis whether the existing control efficiency is taken into account. For adding control measures into the existing emission sources, the maximum reduction efficiency is 27% and the annual cost is 230 billion NT$.
英文關鍵字	cost/benefit analysis, Fine Particle (PM2.5), Control Strategy

摘要

To develop the control strategy for fine particle(PM2.5) in Taiwan, the contents for this project include literature review, analysis of water soluble ionic species for samples from the Taiwan FRM sampling networks, field measurements of sulfate, nitrate and secondary organic aerosol formation rates, simulation of impacts of various kinds of emission sources by using three-dimensional air quality model CMAQ, and cost/benefit assessment for various control scenarios. The dominant ionic species of fine particle is sulfate whose fraction is greater than 20% at all sites; the fraction of ammonia is about 12% to 18% and that for nitrate varies between 4% to 20% from site to site. Except for eastern Taiwan, the values of J are all greater than one, implying the abundance of gaseous NH3. The type of distribution for all mass concentrations in 2014 is Gamma distribution of (9.6,2.496). Therefore, it is (3.54,4.24) to meet the national ambient air quality standard of PM2.5. Field campaigns were conducted at nine different sites in southern Taiwan from 19th to 28th November, 2014 simultaneously in order to study the formation rates of secondary aerosol. The time duration for each sample was six hours in order to differentiate the daytime and nighttime. The three-dimensional meteorological conditions were simulated by using WRF and the back-trajectory analyses were performed by using HYSPLIT. The upwind concentration was determined by interpolation among all measurement sites for each six-hour trajectory and the effects of dispersion and dry deposition were adjusted by using ventilation factor and potassium, respectively. The measurement results show that the formation rates for sulfate, nitrate, and SOA are 5.09±3.25%/hr, 1.91±1.68%/hr, and 0.50±0.30%/hr, respectively. The contributions of various kinds of emissions sources to the ambient fine particle concentrations were simulated by using the three-dimensional grid air quality model CMAQ with the zero-out method. Thirty-one different kinds of emission sources have been simulated with the emission data from TEDS8.1. For primary fine particle, the contribution from cooking and restaurant is the greatest of 10.0% for every ten thousand of PM2.5 emitted, followed by bus of 9.3% and commercial burning of 9.1%. For NOx, the contribution from clothes industry is the greatest of 0.53% for every ten of NOx emitted, followed by glass industry and electronic industry of 0.52% both. For SO2, the contribution from bus is the greatest of 1.1% for every ten thousand of SO2 emitted, followed by commercial burning of 0.93% and electronic industry of 0.89%. Significant differences have been found in the cost/benefit analysis whether the existing control efficiency is taken into account. For adding control measures into the existing emission sources, the maximum reduction efficiency is 27% and the annual cost is 230 billion NT$.

英文關鍵字

cost/benefit analysis, Fine Particle (PM2.5), Control Strategy