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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/66039


    Title: 台灣北、中′南部細懸浮微粒(PM2.5)儀器比對成分分析與來源推估;Fine suspended particles (PM2.5) instrument comparison, component analysis and source apportionment in northern, central, and southern Taiwan.
    Authors: 魏海青;Wei,Hai-ching
    Contributors: 環境工程研究所
    Keywords: 細懸浮微粒(PM2.5);PM2.5化學成分特性;自動和手動儀器PM2.5量測差異;PM2.5化學成分採樣器評估;PMF受體模式;Fine suspended particles (PM2.5);Chemical characterization of PM2.5;The measuring differences between PM2.5 manual collection and automated instruments;Assessments of PM2.5 chemical speciation samplers;PMF receptor model
    Date: 2014-07-14
    Issue Date: 2014-10-15 17:25:46 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 細懸浮微粒(PM2.5)質量和化學成分可用於評估空氣品質、人體健康風險以及污染源管制成效。由於以手動採樣和自動監測方法量測PM2.5質量濃度存有差異,為探討手動和自動儀器量測差異原因,本文於2012年12月17日~2013年8月19日,在台灣北、中、南部環保署空氣品質監測站-新莊、崙背和前鎮站,使用BGI PQ 200 (以下簡稱PQ 200)和Thermo R&P 1405-F FDMS (以下簡稱FDMS)量測PM2.5質量濃度,並使用Thermo R&P 2300 (以下簡稱R&P 2300)量測PM2.5化學成分。在2013年5月20日~6月30日增加Met One Super SASS (以下簡稱Super SASS)和URG 3000N量測PM2.5成分濃度,探討不同手動採樣器採集PM2.5化學成分和修正誤差的差異,對於PM2.5污染來源,本文使用受體模式PMF (Positive Matrix Factorization)並以CPF (Conditional Probability Function)結合污染源貢獻高濃度和風速、風向驗證鄰近污染源影響。
    研究結果顯示新莊和前鎮站PQ 200和FDMS差異(以下表示為FDMS-PQ 200)與PM2.5濃度變化有不錯的線性相關;崙背站則是與大氣溫度有弱相關。進一步探討發現新莊和前鎮站(FDMS-PQ 200)和FDMS的 Reference MC有中等程度相關性,崙背站則無相關性。考慮PQ 200和FDMS不同的溫、濕度操作條件,發現崙背站PQ 200和FDMS 的Base MC差異主要來自兩種方法量測的PM2.5含水量不同,前鎮站則是受PQ 200採樣微粒留存的半揮發性離子濃度有關。
    R&P 2300和不同採樣配置的Super SASS量測PM2.5質量濃度並無顯著差異。裝設前置denuders可避免酸、鹼性氣體干擾後續沉積微粒,因此,沒有裝設denuder的Super SASS對半揮發性水溶性離子如:NH4+和NO3-的第一張鐵氟龍濾紙和第二張Nylon濾紙量測值都是最高。Nylon濾紙可吸附微粒揮發氣體,因此,SASS 2N第一張Nylon濾紙量測值Cl-最高,第二張Nylon濾紙量測的揮發Cl-則是最低;第一張Nylon濾紙量測值NO3-雖不是最高,但第二張Nylon濾紙量測的揮發NO3-則是最低。在碳成分量測方面,第一張濾紙量測的PM2.5 OC 以Super SASS 最高,R&P 2300次之,URG 3000N最低,EC則無顯著差異。這是受濾紙表面速度(URG 3000N> R&P 2300>Super SASS)影響,因為高濾紙表面速度可降低大氣中揮發性有機物的吸附。值得注意的是,Super SASS使用靜置現場空白估計石英濾紙吸附VOCs,這會低估吸附OC,高估微粒揮發OC,導致修正PM2.5 OC值偏高。
    新莊站前兩季PM2.5成分以SO42-為主,後兩季則是修正OC濃度最高;崙背站第一、二和四季成分以SO42-為最高,第三季是NO3-濃度最高;前鎮站第一、二和四季主要成分是SO42-,第三季以修正OC濃度最高。使用PMF (Positive Matrix Factorization)推估並以CPF (Conditional Probability Function) 輔助驗證新莊、崙背和前鎮站污染來源,新莊站以secondary sulfate和gasoline emissions貢獻PM2.5最大,崙背站以secondary nitrate and sea salt和biomass burning貢獻PM2.5最大;前鎮站則主要為secondary nitrate和secondary sulfate。PMF模式模擬要求數據要有100筆以上,比較新莊站103和40筆數據PMF解析結果,發現使用較少數據雖然仍可解析,但在分離PM2.5化學成分到不同污染來源剖面上會有所受限,無法明確辨識污染源類別間的差異。
    綜合彙整結果,PM2.5自動和手動儀器方法質量濃度差異受微粒半揮發物質揮發和含水量影響, FDMS Reference MC在冷季會高估微粒半揮發性物質的揮發,這在溫、濕度變化大的台灣,將會導致FDMS高估PM2.5質量濃度。沒有裝設denuder的採樣器會收集到較高濃度半揮發性水溶性離子,PMF解析結果顯示secondary sulfate、secondary nitrate和gasoline排放對台灣PM2.5濃度有顯著貢獻,降低前驅污染來源排放有助於改善PM2.5空氣品質。;Fine suspended particles (PM2.5) mass and chemical components can be used to assess air quality, human health risks, and control effectiveness of pollution sources. Owing to measurement deviation between manual and automated methods in measuring PM2.5 mass concentration, this study used BGI PQ 200 (hereinafter referred to as PQ 200) and Thermo R & P 1405-F FDMS (hereinafter referred to as FDMS ) to measure PM2.5 mass concentration and used the R & P 2300 to measure PM2.5 chemical composition at three air quality monitoring stations (Xinzhuang, Lunbei, and Cianjhen) of Taiwan Environmental Protection Administration from December 17, 2012 to August 19, 2013. To further investigate the effects of sampling artifact corrections in different PM2.5 chemical speciation samplers, this study added Met One Super SASS (hereinafter referred to as Super SASS) and URG 3000N to measure PM2.5 components from May 20 to June 30, 2013. For source apportionment of PM2.5, this study adopted Positive Matrix Factorization (PMF) and validated the results by using Conditional Probability Function (CPF) coupling with high source contributions and the associated wind directions.
    The results showed that the measured PM2.5 concentration difference between PQ 200 and FDMS (hereinafter referred to as FDMS-PQ 200) and PM2.5 concentration correlated well at both the Xinzhuang and Cianjhen stations. In contrast, (FDMS-PQ 200) only correlated weakly with atmospheric temperature at the Lunbei station. Further investigation showed that (FDMS-PQ 200) correlated moderately well with FDMS Reference MC at both the Xinzhuang and Cianjhen stations but not for the Lunbei station. Considering different temperatures and humic conditions operated in PQ 200 and FDMS, the difference between PQ 200 and FDMS Base MC was considered related to water content deviation of PM2.5 between the two methods at the Lunbei station. In contrast, the difference between PQ 200 and FDMS Base MC was accounted for by the retained semi-volatile ions of PM2.5 in PQ 200 at the Cianjhen station.
    Using R&P 2300 and Super SASS with different sampling configurations to collect PM2.5 mass concentration showed no significant difference. The installation of preceding denuders can avoid from the interference of acidic and basic gases on the following deposited particles. Therefore, the concentrations of semi-volatile species of water-soluble ions such as NH4+ and NO3- on the first Teflon filter and the second Nylon filter of the Super SASS without preceding denuders were the highest among different configurations. Nylon filter can adsorb volatilized gases from deposited particles. The measured Cl- was thus the highest from the first Nylon filter and the lowest from the second Nylon filter in SASS 2N accordingly. Similarly, the measured volatilized NO3- from the second Nylon filter of SASS 2N was the lowest although the volatilized NO3- from the first Nylon filter was not the highest. For the part of carbonaceous content, the measured PM2.5 organic carbon (OC) from the first filter was the highest for Super SASS followed by R&P 2300 and URG 3000N, while EC showed no difference. This was influenced by the filter face velocity (URG 3000N> R&P 2300>Super SASS) as high face velocity will reduce the adsorption of volatile organic compounds (VOCs) from the atmosphere. It is noted that Super SASS estimates the adsorbed VOCs using passive field blank, which will underestimate positive artefacts of filter, overestimate volatilized OC, and lead to overestimation for PM2.5 OC correction.
    The dominant PM2.5 component was SO42- in the first two seasons followed by the corrected OC in the last two seasons at the Xinzhuang station. SO42- was dominated in the first, second, and fourth seasons except that NO3- was the highest in the third season at the Lunbei station. Similarly, the most significant species was SO42- in the first, second, and fourth seasons, while the corrected OC was dominant in the third season at the Cianjhen monitoring station. Source contributions were conducted by using PMF with the aid of CPF to validate the results at the Xinzhuang, Lunbei, and Cianjhen stations. The most significant source types of PM2.5 were secondary sulfate and gasoline emissions at the Xinzhuang station. Secondary nitrate mixed with sea salt and biomass burning were the two most important source types at the Lunbei station. For the Cianjhen station, the results indicated that secondary nitrate and secondary sulfate were the two greatest contribution source types. PMF modeling required a data set of more than 100 data points, a smaller data set was found workable but was limited for clearer identification in distinguishing PM2.5 chemical species from different source profiles based on the comparison between 40 and 103 data points for the Xinzhuang station.
    In summary, the difference in measuring PM2.5 mass concentrations between manual collection and automated methods is affected by the volatilization loss of semi-volatile particulate matter and moisture content of the collected particles. The FDMS tends to overestimate PM2.5 semi-volatile concentrations in cold season. This will lead to overestimate PM2.5 mass concentration when using FDMS in a place with great variations in the atmospheric temperature and relative humidity such as Taiwan. The speciation sampler without the installation of preceding denuders will collect greater concentrations of semi-volatile species of water-soluble ions. PMF modeling results show that secondary sulfate, secondary nitrate, and gasoline emissions significantly contribute to PM2.5 concentrations in Taiwan. Reducing precursor source contributions of these source types will improve PM2.5 air quality.
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