環保專案成果報告查詢系統

專案計畫編號	EPA-104-L102-02-103
經費年度	104
計畫經費	15,800 千元
專案開始日期	2015/07/01
專案結束日期	2016/12/31
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	國立中央大學環境工程研究所
專案分類	環境監測及檢測
中文關鍵字	PM2.5化學成分監測、PM2.5化學成分時間與空間分布特徵、推估PM2.5污染來源及能見度影響因子
英文關鍵字	PM2.5 chemical speciation monitoring, spatial and temporal characteristics of PM2.5 chemical speciation, PM2.5 source apportionment and visibility influencing factors
協同主持人	周崇光；張士昱；蕭大智
共同主持人
計畫聯絡人	黃秋華
計畫聯絡電話	03-4226551#
計畫聯絡信箱	cilia0110@gmail.com

中文摘要	本計畫於2015年7月開始在北、中、南部環保署空氣品質監測站執行PM2.5化學成分檢測，至2016年12月共完成6個季節採樣每季連續7次3站同步採樣，18個月常規採樣每月1次3站同步採樣，32次任務型採樣包括台東背景地區採樣5站次，斗六高污染地區16站次，嘉義高污染地區11站次。整體PM2.5質量濃度與環保署監測站季節變化趨勢有一致性，顯示採樣結果在採樣地區具有季節代表性。各站四季PM2.5質量濃度變化，大致呈現夏季最低，秋季升高，冬、春和秋季比較，互有高低。採樣過程PM2.5化學成分會有揮發及吸附，若僅使用單張濾紙水溶性無機離子將會低估11%~37% NH4+濃度、低估21%~91% NO3-濃度，Cl-也會低估41%~90%的濃度。碳成分若不透過第二張與第三張濾紙修正，將有0%~19%有機碳(OC)質量濃度被高估。各站各季節PM2.5質量濃度占比最高化學成分，在夏季為SO42-，秋季為SO42-或OC，冬季較為分歧，春季為SO42-；南部地區冬季嘉義、小港和前金站質量濃度占比最高成分為NO3-。採樣期間PM2.5低濃度(19 μg m-3, n=154)相對於高濃度(52 μg m-3, n=59)樣本，PM2.5 NO3-占比從7% 增大到16%，其他成分則縮小或不變，顯示PM2.5 NO3-前驅NOx排放源的管制應該加強。各站PM2.5金屬元素成分濃度最高兩個元素為Na和K，顯示受海風、塵土逸散、生質燃燒影響大。計算各站金屬元素富集因子，顯示地殼、燃煤、交通排放為重要排放源。彙整各次採樣PM2.5濃度大於35 μg m-3事件污染來源，指出大部分事件都是區域傳輸、擴散不佳、發生光化學反應所造成，少數來自境外傳輸。綜合北、中、南部三個測站PMF受體模式推估結果，｢二次氣膠｣是最主要污染類別，在板橋、忠明、小港站PM2.5濃度占比分別為30.7%、28.6%、32.3%。三個測站大氣能見度多元迴歸模式模擬顯示NH4+代表大氣PM2.5硝酸銨和硫酸銨，在三個地區能見度迴歸模式都是主要自變數，氣象因子中相對濕度和溫度分別將降低以及增高能見度；以PMF因子取代PM2.5成分則指出｢二次氣膠｣和｢化石燃料鍋爐與二次硫酸鹽｣是主要自變數。考量高濃度事件硝酸根離子的增益，積極管制當地污染源減少前驅物排放將是達成PM2.5空氣品質標準的有效策略。在國際間PM2.5化學成分採樣檢測經驗與最新技術發展方面，本計畫彙整並解讀PM2.5化學成分濃度增益、PM2.5化學成分監測技術、灰霾觀測、PM2.5化學成分受體模式解析污染來源四個議題共30篇文獻。對於PM2.5化學分析資料標準格式，本計畫提出規劃，在第一層表格將建議納入最基本的PM2.5化學成分，在第二層表格為使用特定採樣方法檢測出更多PM2.5化學成分或將第一層數據進行加值運算結果。對於PM2.5採樣及化學成分分析技術規範手冊，本計畫提出建議內容以供建置標準方法。
中文關鍵字	PM2.5化學成分監測、PM2.5化學成分時間與空間分布特徵、推估PM2.5污染來源及能見度影響因子

中文摘要

本計畫於2015年7月開始在北、中、南部環保署空氣品質監測站執行PM2.5化學成分檢測，至2016年12月共完成6個季節採樣每季連續7次3站同步採樣，18個月常規採樣每月1次3站同步採樣，32次任務型採樣包括台東背景地區採樣5站次，斗六高污染地區16站次，嘉義高污染地區11站次。整體PM2.5質量濃度與環保署監測站季節變化趨勢有一致性，顯示採樣結果在採樣地區具有季節代表性。各站四季PM2.5質量濃度變化，大致呈現夏季最低，秋季升高，冬、春和秋季比較，互有高低。採樣過程PM2.5化學成分會有揮發及吸附，若僅使用單張濾紙水溶性無機離子將會低估11%~37% NH4+濃度、低估21%~91% NO3-濃度，Cl-也會低估41%~90%的濃度。碳成分若不透過第二張與第三張濾紙修正，將有0%~19%有機碳(OC)質量濃度被高估。各站各季節PM2.5質量濃度占比最高化學成分，在夏季為SO42-，秋季為SO42-或OC，冬季較為分歧，春季為SO42-；南部地區冬季嘉義、小港和前金站質量濃度占比最高成分為NO3-。採樣期間PM2.5低濃度(19 μg m-3, n=154)相對於高濃度(52 μg m-3, n=59)樣本，PM2.5 NO3-占比從7% 增大到16%，其他成分則縮小或不變，顯示PM2.5 NO3-前驅NOx排放源的管制應該加強。各站PM2.5金屬元素成分濃度最高兩個元素為Na和K，顯示受海風、塵土逸散、生質燃燒影響大。計算各站金屬元素富集因子，顯示地殼、燃煤、交通排放為重要排放源。彙整各次採樣PM2.5濃度大於35 μg m-3事件污染來源，指出大部分事件都是區域傳輸、擴散不佳、發生光化學反應所造成，少數來自境外傳輸。綜合北、中、南部三個測站PMF受體模式推估結果，｢二次氣膠｣是最主要污染類別，在板橋、忠明、小港站PM2.5濃度占比分別為30.7%、28.6%、32.3%。三個測站大氣能見度多元迴歸模式模擬顯示NH4+代表大氣PM2.5硝酸銨和硫酸銨，在三個地區能見度迴歸模式都是主要自變數，氣象因子中相對濕度和溫度分別將降低以及增高能見度；以PMF因子取代PM2.5成分則指出｢二次氣膠｣和｢化石燃料鍋爐與二次硫酸鹽｣是主要自變數。考量高濃度事件硝酸根離子的增益，積極管制當地污染源減少前驅物排放將是達成PM2.5空氣品質標準的有效策略。在國際間PM2.5化學成分採樣檢測經驗與最新技術發展方面，本計畫彙整並解讀PM2.5化學成分濃度增益、PM2.5化學成分監測技術、灰霾觀測、PM2.5化學成分受體模式解析污染來源四個議題共30篇文獻。對於PM2.5化學分析資料標準格式，本計畫提出規劃，在第一層表格將建議納入最基本的PM2.5化學成分，在第二層表格為使用特定採樣方法檢測出更多PM2.5化學成分或將第一層數據進行加值運算結果。對於PM2.5採樣及化學成分分析技術規範手冊，本計畫提出建議內容以供建置標準方法。

中文關鍵字

PM2.5化學成分監測、PM2.5化學成分時間與空間分布特徵、推估PM2.5污染來源及能見度影響因子

類型	檔名	檔案大小	說明	最新版公開日期
期末報告	PM2.5化學成分監測計畫定稿.pdf	37MB		2017/3/1 上午 12:00:00

摘要	This project collected PM2.5 for chemical speciation at the air quality monitoring stations of Taiwan Environmental Protection Administration (TEPA) located in the northern, middle, and southern part of Taiwan from July 2015 to December 2016. Field work of seasonal collections were accomplished for six seasons with seven consecutive daily collections in each season and regular collections in 18 months with one daily collection in each month both synchronized at the three chosen stations. In addition, 32 mission oriented collections were completed including five in Taitung background area, 16 in Douliu highly polluted area, and 11 in Chiayi highly polluted area. On the whole, the seasonal PM2.5 mass concentrations varied consistently with that of TEPA stations indicating the results were representative in seasons in the study areas. Seasonal variations of PM2.5 mass concentrations across all stations showed that the lowest concentration occurring in summer with subsequent autumn elevation but alternating the roles of low and high among winter, spring, and autumn. Certain PM2.5 chemical components are known to vaporize, while quartz-fiber filter is subject to absorb gas interferences during sample collection. For the usage of a single filter in collecting PM2.5 water-soluble inorganic ions, underestimations of NH4+, NO3-, and Cl- were found ranging from 11%-37%, 21%-91%, and 41%-90%, respectively. In contrast, overestimations ranging from 0%-19% of organic carbon (OC) concentrations were resulted from collections without using the back-up second and third filters for interference corrections. The highest ranking of speciation percentages of PM2.5 across all stations and seasons are SO42- in summer, SO42- or OC in autumn, relatively divided in winter, and back to SO42- in spring. It is noted that NO3- is the highest ranking of PM2.5 speciation percentages at the southern Chiayi, Xiaogang, and Qianjin stations in winter. During the whole PM2.5 collection period, PM2.5 speciation percentage of NO3- was increased from 7% to 16% from the comparison between low (19 μg m-3, n=154) and high PM2.5 concentration samples (52 μg m-3, n=59) and that of the other components were reduced or unchanged. It implies that the sources discharging NOx, NO3- precursor, need to be controlled stringently. Among analyzed concentrations of metal elements across all stations, Na and K are ranked the upper two. It implies that sea breeze, dust suspension, and biomass burning are important sources. The computed values of enrichment factor (EF) across all stations show that contributions from crustal suspended dusts, coal burning, and traffic emissions are major emitting sources. In analyzing all pollution events with PM2.5 concentrations greater than 35 μg m-3, most were found caused by regional transport, bad dispersion, and photochemical reactions. Summarizing positive matrix factorization(PMF) modeling on TEPA monitoring stations in the northern, middle, and southern part of Taiwan, “secondary aerosol” was found the most important pollution category. PM2.5 speciation percentages of “secondary aerosol” at Banqiao, Zhongming, and Xiaogang stations were 30.7%, 28.6%, and 32.3%, respectively. In multiple regression analyses for ambient visibility in the stations of northern, middle, and southern part of Taiwan, NH4+ representing PM2.5 ammonium nitrate and ammonium sulfate is the major independent variable in all three study areas. Meanwhile, relative humidity and ambient temperature of meteorological factors play a role in reducing and increasing ambient visibility, respectively. The “secondary aerosol” and “fossil-fuel boilers and secondary sulfate” become major independent variables when PM2.5 chemical components are replaced by PMF factors in visibility regression modeling. In considering NO3- enhancement in high concentraion events, active control on local sources in reducing precursor emissions will be an effective measure to achieve PM2.5 air quality standard. In the international sampling experience and newly developed techniques of PM2.5 chemical speciation, 30 papers were reviewed to classify into PM2.5 chemical speciation enhancement, PM2.5 chemical speciation monitoring, haze observation, and source apportionment of PM2.5 chemical speciation. For the archived data format of PM2.5 chemical speciation, suggestions are proposed to store basic PM2.5 chemical speciation in the first tier and put extra detected PM2.5 chemical components from other specific methods or computations made on the data of the first tier into the second tier. In addition, a technical guide for the collection and analysis of PM2.5 chemical speciation is also proposed for the establishment of standard method.
英文關鍵字	PM2.5 chemical speciation monitoring, spatial and temporal characteristics of PM2.5 chemical speciation, PM2.5 source apportionment and visibility influencing factors

摘要

This project collected PM2.5 for chemical speciation at the air quality monitoring stations of Taiwan Environmental Protection Administration (TEPA) located in the northern, middle, and southern part of Taiwan from July 2015 to December 2016. Field work of seasonal collections were accomplished for six seasons with seven consecutive daily collections in each season and regular collections in 18 months with one daily collection in each month both synchronized at the three chosen stations. In addition, 32 mission oriented collections were completed including five in Taitung background area, 16 in Douliu highly polluted area, and 11 in Chiayi highly polluted area. On the whole, the seasonal PM2.5 mass concentrations varied consistently with that of TEPA stations indicating the results were representative in seasons in the study areas. Seasonal variations of PM2.5 mass concentrations across all stations showed that the lowest concentration occurring in summer with subsequent autumn elevation but alternating the roles of low and high among winter, spring, and autumn. Certain PM2.5 chemical components are known to vaporize, while quartz-fiber filter is subject to absorb gas interferences during sample collection. For the usage of a single filter in collecting PM2.5 water-soluble inorganic ions, underestimations of NH4+, NO3-, and Cl- were found ranging from 11%-37%, 21%-91%, and 41%-90%, respectively. In contrast, overestimations ranging from 0%-19% of organic carbon (OC) concentrations were resulted from collections without using the back-up second and third filters for interference corrections. The highest ranking of speciation percentages of PM2.5 across all stations and seasons are SO42- in summer, SO42- or OC in autumn, relatively divided in winter, and back to SO42- in spring. It is noted that NO3- is the highest ranking of PM2.5 speciation percentages at the southern Chiayi, Xiaogang, and Qianjin stations in winter. During the whole PM2.5 collection period, PM2.5 speciation percentage of NO3- was increased from 7% to 16% from the comparison between low (19 μg m-3, n=154) and high PM2.5 concentration samples (52 μg m-3, n=59) and that of the other components were reduced or unchanged. It implies that the sources discharging NOx, NO3- precursor, need to be controlled stringently. Among analyzed concentrations of metal elements across all stations, Na and K are ranked the upper two. It implies that sea breeze, dust suspension, and biomass burning are important sources. The computed values of enrichment factor (EF) across all stations show that contributions from crustal suspended dusts, coal burning, and traffic emissions are major emitting sources. In analyzing all pollution events with PM2.5 concentrations greater than 35 μg m-3, most were found caused by regional transport, bad dispersion, and photochemical reactions. Summarizing positive matrix factorization(PMF) modeling on TEPA monitoring stations in the northern, middle, and southern part of Taiwan, “secondary aerosol” was found the most important pollution category. PM2.5 speciation percentages of “secondary aerosol” at Banqiao, Zhongming, and Xiaogang stations were 30.7%, 28.6%, and 32.3%, respectively. In multiple regression analyses for ambient visibility in the stations of northern, middle, and southern part of Taiwan, NH4+ representing PM2.5 ammonium nitrate and ammonium sulfate is the major independent variable in all three study areas. Meanwhile, relative humidity and ambient temperature of meteorological factors play a role in reducing and increasing ambient visibility, respectively. The “secondary aerosol” and “fossil-fuel boilers and secondary sulfate” become major independent variables when PM2.5 chemical components are replaced by PMF factors in visibility regression modeling. In considering NO3- enhancement in high concentraion events, active control on local sources in reducing precursor emissions will be an effective measure to achieve PM2.5 air quality standard. In the international sampling experience and newly developed techniques of PM2.5 chemical speciation, 30 papers were reviewed to classify into PM2.5 chemical speciation enhancement, PM2.5 chemical speciation monitoring, haze observation, and source apportionment of PM2.5 chemical speciation. For the archived data format of PM2.5 chemical speciation, suggestions are proposed to store basic PM2.5 chemical speciation in the first tier and put extra detected PM2.5 chemical components from other specific methods or computations made on the data of the first tier into the second tier. In addition, a technical guide for the collection and analysis of PM2.5 chemical speciation is also proposed for the establishment of standard method.

英文關鍵字

PM2.5 chemical speciation monitoring, spatial and temporal characteristics of PM2.5 chemical speciation, PM2.5 source apportionment and visibility influencing factors