環保專案成果報告查詢系統

專案計畫編號	EPA-105-U101-02-A272
經費年度	105
計畫經費	8,500 千元
預算科目	科技發展-環境科學及技術之研究
專案開始日期	2016/10/06
專案結束日期	2017/10/31
專案主持人	周崇光
主辦單位	監資處
承辦人	陳香宇
執行單位	中央研究院環境變遷研究中心
專案分類	環境監測及檢測
中文關鍵字	細懸浮微粒、同位素、污染來源鑑識
英文關鍵字	PM2.5, Isotope, Pollution Source Identification
協同主持人
共同主持人
計畫聯絡人	黃譯樘
計畫聯絡電話	02-23117722#2328
計畫聯絡信箱	yitang@gate.sinica.edu.tw

中文摘要	本計畫主要目的為建立碳與鉛同位素的分析技術，並透過大氣與特定污染源的PM2.5同位素特徵和受體模式的推估結果，深入探討雲林-彰化-嘉義-南投地區PM2.5的污染來源，及評估同位素和受體模式在PM2.5污染源鑑識的可應用性。本計畫結果顯示：碳和鉛同位素分析技術具有足夠的準確性與精確性，應用於細懸浮微粒污染來源解析中確實可提供重要的參考資訊。周界PM2.5的採樣與化學組成分析結果顯示，PM2.5的主要成分包括：硝酸離子(NO3-)、硫酸離子(SO42-)、銨離子(NH4+)和碳 (Carbon，包括有機碳及元素碳) 等，這四項主要成分對於各測站PM2.5的質量貢獻分別佔了15% (2-30%) (NO3-)、17% (12-27%) (SO42-)、10% (8-14%) (NH4+) 和20% (11-34%) (Carbon)。本計畫採用碳同位素的分析結果將碳進一步區分為“現代碳 (Modern Carbon)”和“化石碳 (Fossil Carbon)”，結果顯示兩者對各測站PM2.5的質量濃度貢獻分別為6-17%和5-17%，顯示“現代碳”和“化石碳”對雲林-彰化-嘉義-南投地區PM2.5的貢獻度大約相當。透過高低PM2.5事件日之化學組成濃度差異的分析，本計畫發現PM2.5事件日期間的二次氣膠和現代碳之濃度明顯上升，反映光化學反應和生質燃燒行為的影響。為建立本土重要微粒污染源的同位素特徵，以供污染源鑑定，本計畫於今年度執行期間，完成4個本土重要微粒污染源的採樣和同位素及傳統化學組成的分析，結果顯示：1.) 燃煤電廠排放的PM2.5主要成分為硫酸鹽，佔PM2.5排放質量濃度的36%，主要的特徵元素包括Se、Zn、Mo、Sb、Ge、Sn和As等，生成的碳以化石碳為主 (percent Modern Carbon (pMC) 比例：36~44%)，206Pb/207Pb比值範圍為1.1604~1.2229，208Pb/207Pb比值範圍為2.4141~2.4806；2.) 煉油廠排放的PM2.5主要成分為硫酸鹽，佔PM2.5排放質量的51%，特徵元素包括Se、Sb、Zn、Mo、La、Na、Ni和V等，生成的碳以化石碳為主 (pMC：3~24%)，δ13C範圍為-28.5至-27.9‰，206Pb/207Pb比值範圍為1.1528~1.2021，208Pb/207Pb比值範圍為2.4145~2.4425；3.) 稻草燃燒產生的PM2.5之主要成分為有機碳 (OC)，次要組成包括元素碳 (EC)、Cl- 離子和K+ 離子等，特徵元素包括K、As、Se、Cd、Na、Cu和Zn等，生成的碳以現代碳為主 (pMC：97~98%)，δ13C範圍為-35.0至-30.1‰；4.) 交通排放產生的PM2.5主要化學組成為EC，其次為OC、硫酸離子(SO42-)和銨離子(NH4+)等，特徵元素包括Se、Sb、Zn、Mo、Cu、Pb和Cr等，生成的碳以化石碳為主 (pMC：7~21%)，δ13C範圍為-28.6至-26.3‰，206Pb/207Pb比值範圍為1.1329~1.1508，208Pb/207Pb比值範圍為2.4078~2.4294。本計畫嘗試將上述汙染源的同位素特徵應用在周界PM2.5的污染源分析，結果顯示本研究地區中的PM2.5在秋季、冬季和夏季的δ13C平均值及其範圍分別為-27.8‰ (-30.4至-25.6‰)、-28.7‰ (-34.7至-26.6‰) 和-32.0‰ (-38.5至-27.8‰)。14C的分析結果發現秋季、冬季和夏季的pMC比例分別為50% (33~64%)、50% (35~61%) 和50% (39~65%)，代表就整個區域而言，化石燃料和非化石燃料燃燒所貢獻的碳相當。綜合其他指標的分析結果推論，夏季以二次氣膠為主要的碳貢獻源，工業排放、交通排放的貢獻在秋季和冬季較為顯著，且冬季亦有生質燃燒的貢獻。在鉛同位素部分，秋季、冬季和夏季的206Pb/207Pb之平均比值的分別為1.1457 (1.1247~1.1728)、1.1534 (1.1407~1.1710) 和1.1465 (1.1194~1.1635)，208Pb/207Pb之比值的分別為2.4303 (2.4025~2.4577)、2.4323 (2.4168~2.4513) 和2.4208 (2.3936~2.4599)。由於鉛同位素的污染源特徵分析資料相對有限，本期計畫所獲得之污染來源分析結果不確定度仍高，有待進一步研究。針對不同測站的污染源分析結果發現，交通排放和工業排放為彰化站PM2.5的主要碳污染源；臺西站和麥寮站的碳以工業排放為主；斗六、竹山、二林、崙背、嘉義和新港站的碳均以生質燃燒的貢獻較高。PMF推估結果指出交通排放和工業排放為彰化站PM2.5的主要污染源；斗六站和竹山站以生質燃燒為主，其次為工業排放；臺西站和麥寮站以工業排放為主，其次為二次氣膠，第三貢獻量為交通排放；工業排放、交通排放、二次氣膠和生質燃燒對二林站、崙背站、嘉義站和新港站的貢獻相近。兩種污染源鑑定方法顯示，碳同位素和PMF所鑑定出含碳的優勢污染源一致，但鉛同位素比值和PMF所推估含鉛的優勢污染源有差異，顯示需更多資料來提高污染源的鑑識能力。
中文關鍵字	細懸浮微粒、同位素、污染來源鑑識

中文摘要

本計畫主要目的為建立碳與鉛同位素的分析技術，並透過大氣與特定污染源的PM2.5同位素特徵和受體模式的推估結果，深入探討雲林-彰化-嘉義-南投地區PM2.5的污染來源，及評估同位素和受體模式在PM2.5污染源鑑識的可應用性。本計畫結果顯示：碳和鉛同位素分析技術具有足夠的準確性與精確性，應用於細懸浮微粒污染來源解析中確實可提供重要的參考資訊。周界PM2.5的採樣與化學組成分析結果顯示，PM2.5的主要成分包括：硝酸離子(NO3-)、硫酸離子(SO42-)、銨離子(NH4+)和碳 (Carbon，包括有機碳及元素碳) 等，這四項主要成分對於各測站PM2.5的質量貢獻分別佔了15% (2-30%) (NO3-)、17% (12-27%) (SO42-)、10% (8-14%) (NH4+) 和20% (11-34%) (Carbon)。本計畫採用碳同位素的分析結果將碳進一步區分為“現代碳 (Modern Carbon)”和“化石碳 (Fossil Carbon)”，結果顯示兩者對各測站PM2.5的質量濃度貢獻分別為6-17%和5-17%，顯示“現代碳”和“化石碳”對雲林-彰化-嘉義-南投地區PM2.5的貢獻度大約相當。透過高低PM2.5事件日之化學組成濃度差異的分析，本計畫發現PM2.5事件日期間的二次氣膠和現代碳之濃度明顯上升，反映光化學反應和生質燃燒行為的影響。為建立本土重要微粒污染源的同位素特徵，以供污染源鑑定，本計畫於今年度執行期間，完成4個本土重要微粒污染源的採樣和同位素及傳統化學組成的分析，結果顯示：1.) 燃煤電廠排放的PM2.5主要成分為硫酸鹽，佔PM2.5排放質量濃度的36%，主要的特徵元素包括Se、Zn、Mo、Sb、Ge、Sn和As等，生成的碳以化石碳為主 (percent Modern Carbon (pMC) 比例：36~44%)，206Pb/207Pb比值範圍為1.1604~1.2229，208Pb/207Pb比值範圍為2.4141~2.4806；2.) 煉油廠排放的PM2.5主要成分為硫酸鹽，佔PM2.5排放質量的51%，特徵元素包括Se、Sb、Zn、Mo、La、Na、Ni和V等，生成的碳以化石碳為主 (pMC：3~24%)，δ13C範圍為-28.5至-27.9‰，206Pb/207Pb比值範圍為1.1528~1.2021，208Pb/207Pb比值範圍為2.4145~2.4425；3.) 稻草燃燒產生的PM2.5之主要成分為有機碳 (OC)，次要組成包括元素碳 (EC)、Cl- 離子和K+ 離子等，特徵元素包括K、As、Se、Cd、Na、Cu和Zn等，生成的碳以現代碳為主 (pMC：97~98%)，δ13C範圍為-35.0至-30.1‰；4.) 交通排放產生的PM2.5主要化學組成為EC，其次為OC、硫酸離子(SO42-)和銨離子(NH4+)等，特徵元素包括Se、Sb、Zn、Mo、Cu、Pb和Cr等，生成的碳以化石碳為主 (pMC：7~21%)，δ13C範圍為-28.6至-26.3‰，206Pb/207Pb比值範圍為1.1329~1.1508，208Pb/207Pb比值範圍為2.4078~2.4294。本計畫嘗試將上述汙染源的同位素特徵應用在周界PM2.5的污染源分析，結果顯示本研究地區中的PM2.5在秋季、冬季和夏季的δ13C平均值及其範圍分別為-27.8‰ (-30.4至-25.6‰)、-28.7‰ (-34.7至-26.6‰) 和-32.0‰ (-38.5至-27.8‰)。14C的分析結果發現秋季、冬季和夏季的pMC比例分別為50% (33~64%)、50% (35~61%) 和50% (39~65%)，代表就整個區域而言，化石燃料和非化石燃料燃燒所貢獻的碳相當。綜合其他指標的分析結果推論，夏季以二次氣膠為主要的碳貢獻源，工業排放、交通排放的貢獻在秋季和冬季較為顯著，且冬季亦有生質燃燒的貢獻。在鉛同位素部分，秋季、冬季和夏季的206Pb/207Pb之平均比值的分別為1.1457 (1.1247~1.1728)、1.1534 (1.1407~1.1710) 和1.1465 (1.1194~1.1635)，208Pb/207Pb之比值的分別為2.4303 (2.4025~2.4577)、2.4323 (2.4168~2.4513) 和2.4208 (2.3936~2.4599)。由於鉛同位素的污染源特徵分析資料相對有限，本期計畫所獲得之污染來源分析結果不確定度仍高，有待進一步研究。針對不同測站的污染源分析結果發現，交通排放和工業排放為彰化站PM2.5的主要碳污染源；臺西站和麥寮站的碳以工業排放為主；斗六、竹山、二林、崙背、嘉義和新港站的碳均以生質燃燒的貢獻較高。PMF推估結果指出交通排放和工業排放為彰化站PM2.5的主要污染源；斗六站和竹山站以生質燃燒為主，其次為工業排放；臺西站和麥寮站以工業排放為主，其次為二次氣膠，第三貢獻量為交通排放；工業排放、交通排放、二次氣膠和生質燃燒對二林站、崙背站、嘉義站和新港站的貢獻相近。兩種污染源鑑定方法顯示，碳同位素和PMF所鑑定出含碳的優勢污染源一致，但鉛同位素比值和PMF所推估含鉛的優勢污染源有差異，顯示需更多資料來提高污染源的鑑識能力。

中文關鍵字

細懸浮微粒、同位素、污染來源鑑識

類型	檔名	檔案大小	說明	最新版公開日期
期末報告	EPA-105-U101-02-A272 定稿.pdf	12MB		2020/7/16 上午 12:00:00

摘要	The objectives of this study are to develop analytical methods for determination of isotopes in fine particulate matters (PM2.5), identify potential sources of PM2.5 in the central-southern Taiwan with the isotopic fingerprints, and compare the difference between isotopes and PMF model in pollution source identification. δ13C, 14C, and lead isotopes were analyzed by Inductively Coupled Plasms Mass Spectrometer (IRMS) with Gas Chromatograph (GC), Accelerator Mass Spectrometer (AMS), and Inductively Plasma Mass Spectrometer (ICP-MS), respectively. PM2.5 samples in the atmospheres were sampled in Changhua, Erlin, Zhushan, Douliu, Taixi, Mailiao, Lunbei, Chiayi, and Xingang stations in the fall of 2016 and winter and summer of 2017. Moreover, in order to establish isotope fingerprints of specific sources, industrial (coal-fired power plant and oil plant), traffic, and biomass burning emissions were collected and analyzed for water-soluble ions, organic carbon, elemental carbon, metals, and isotopic compositions. Our data showed that δ13C, 14C, and lead isotopes were detected with high precision and accuracy by IRMS with GC, AMS, ICP-MS instruments, respectively. Chemical compositions of PM2.5 in the atmospheres displayed that nitrate, sulfate, ammonium, and total carbon, which accounted for 15%, 17%, 10%, and 20% of the PM2.5 mass, respectively, were the predominant species. Moreover, secondary aerosol (sulfate, nitrate, and ammonium) and modern carbon have higher contributions in high PM2.5 concentration event days. These results implied that photochemical reactions and biomass burning were important contributors of PM2.5 in the central-southern Taiwan. The sulfates were predominant composition of PM2.5 from coal-fired power and oil plants, the fossil carbon was an important carbon source (pMC < 50%), and the 206Pb/207Pb (208Pb/207Pb) ratios were ranged from coal-fired plant and oil plant on 1.1604-1.12229 (2.4141-2.4806) and 1.1528-1.2021 (2.4145-2.4425), respectively. In the part of biomass burning, organic carbon was a predominant component in PM2.5 and the ranges of δ13C and percent Modern Carbon (pMC) were -35 to -30.1‰ and 97 to 98%, respectively. Elemental carbon was a major component of PM2.5 from traffic emission and the ranges of δ13C and pMC were -28.6 to -26.3‰ and 7 to 21%, respectively, and the 206Pb/207Pb (208Pb/207Pb) ratios were ranged on 1.1329-1.1508 (2.4078-2.4294). The average δ13C values in PM2.5 were -27.8‰ (-30.4 to -25.6‰), -28.7‰ (-34.7 to -26.6‰), and -32.0‰ (-38.5 to -27.8‰) in the fall, winter, and summer, respectively. The results of δ13C suggested that industrial and traffic emissions were important carbon sources in the fall and winter, and biomass burning also contributed the carbon level in winter. The secondary aerosol was an important carbon source in summer. Moreover, the pMC results indicated that modern and fossil carbon were nearly equivalent on average in three seasons. The 206Pb/207Pb ratios were 1.1457 (1.1247 to 1.1728), 1.1534 (1.1407 to 1.1710), and 1.1465 (1.1194 to 1.1635) in the fall, winter and summer, respectively, and 208Pb/207Pb ratios were 2.4303 (2.4025 to 2.4577), 2.4323 (2.4168 to 2.4513), and 2.4208 (2.3936 to 2.4599). However, we need more data from pollution sources for more accurately identifying the lead sources in the atmospheres. Finally, this study used both isotopic compositions and PMF model to identify the pollution sources of PM2.5. Our data showed that the carbon sources of PM2.5 were similar from both source identification methods, however, the lead sources were different. These results suggested that more data are necessary from different pollution sources for source identification.
英文關鍵字	PM2.5, Isotope, Pollution Source Identification

摘要

The objectives of this study are to develop analytical methods for determination of isotopes in fine particulate matters (PM2.5), identify potential sources of PM2.5 in the central-southern Taiwan with the isotopic fingerprints, and compare the difference between isotopes and PMF model in pollution source identification. δ13C, 14C, and lead isotopes were analyzed by Inductively Coupled Plasms Mass Spectrometer (IRMS) with Gas Chromatograph (GC), Accelerator Mass Spectrometer (AMS), and Inductively Plasma Mass Spectrometer (ICP-MS), respectively. PM2.5 samples in the atmospheres were sampled in Changhua, Erlin, Zhushan, Douliu, Taixi, Mailiao, Lunbei, Chiayi, and Xingang stations in the fall of 2016 and winter and summer of 2017. Moreover, in order to establish isotope fingerprints of specific sources, industrial (coal-fired power plant and oil plant), traffic, and biomass burning emissions were collected and analyzed for water-soluble ions, organic carbon, elemental carbon, metals, and isotopic compositions. Our data showed that δ13C, 14C, and lead isotopes were detected with high precision and accuracy by IRMS with GC, AMS, ICP-MS instruments, respectively. Chemical compositions of PM2.5 in the atmospheres displayed that nitrate, sulfate, ammonium, and total carbon, which accounted for 15%, 17%, 10%, and 20% of the PM2.5 mass, respectively, were the predominant species. Moreover, secondary aerosol (sulfate, nitrate, and ammonium) and modern carbon have higher contributions in high PM2.5 concentration event days. These results implied that photochemical reactions and biomass burning were important contributors of PM2.5 in the central-southern Taiwan. The sulfates were predominant composition of PM2.5 from coal-fired power and oil plants, the fossil carbon was an important carbon source (pMC < 50%), and the 206Pb/207Pb (208Pb/207Pb) ratios were ranged from coal-fired plant and oil plant on 1.1604-1.12229 (2.4141-2.4806) and 1.1528-1.2021 (2.4145-2.4425), respectively. In the part of biomass burning, organic carbon was a predominant component in PM2.5 and the ranges of δ13C and percent Modern Carbon (pMC) were -35 to -30.1‰ and 97 to 98%, respectively. Elemental carbon was a major component of PM2.5 from traffic emission and the ranges of δ13C and pMC were -28.6 to -26.3‰ and 7 to 21%, respectively, and the 206Pb/207Pb (208Pb/207Pb) ratios were ranged on 1.1329-1.1508 (2.4078-2.4294). The average δ13C values in PM2.5 were -27.8‰ (-30.4 to -25.6‰), -28.7‰ (-34.7 to -26.6‰), and -32.0‰ (-38.5 to -27.8‰) in the fall, winter, and summer, respectively. The results of δ13C suggested that industrial and traffic emissions were important carbon sources in the fall and winter, and biomass burning also contributed the carbon level in winter. The secondary aerosol was an important carbon source in summer. Moreover, the pMC results indicated that modern and fossil carbon were nearly equivalent on average in three seasons. The 206Pb/207Pb ratios were 1.1457 (1.1247 to 1.1728), 1.1534 (1.1407 to 1.1710), and 1.1465 (1.1194 to 1.1635) in the fall, winter and summer, respectively, and 208Pb/207Pb ratios were 2.4303 (2.4025 to 2.4577), 2.4323 (2.4168 to 2.4513), and 2.4208 (2.3936 to 2.4599). However, we need more data from pollution sources for more accurately identifying the lead sources in the atmospheres. Finally, this study used both isotopic compositions and PMF model to identify the pollution sources of PM2.5. Our data showed that the carbon sources of PM2.5 were similar from both source identification methods, however, the lead sources were different. These results suggested that more data are necessary from different pollution sources for source identification.

英文關鍵字

PM2.5, Isotope, Pollution Source Identification