大氣細懸浮微粒之化學組成、來源解析及健康風險評估

Chemical composition, source identification and risk assessment for airborne particulate matters.

曾文昶(Wen-Chang Tseng)

指導教授 ： 陳詩潔 ； 張士昱

中山醫學大學/健康管理學院/公共衛生學系碩士班/碩士(2018年)

摘要

近年來，細懸浮微粒(PM2.5)相關的議題逐漸受到重視，先前的研究提到PM2.5的危害性與其化學組成有關，並且會受到天氣及排放來源的影響而變化，而台灣中部地區PM2.5的貢獻來源複雜，卻鮮少研究探討不同污染源對人體健康的影響。本研究於2016年冬季進行PM2.5的採樣，分析其中水溶性離子、碳成分及金屬元素，探討台灣中部地區，包括沙鹿、中山醫、竹山等地點，PM2.5的化學組成在不同天氣形態下的變化，並利用正因子矩陣法(PMF)探討不同地點的污染來源，進一步區分情境進行以污染源及物種觀點的人體健康風險評估。研究結果顯示，中山醫站NO3-及交通相關之金屬元素(Cu、Zn、Pb、V)，非傳輸型相對傳輸型天氣下具有較高濃度，顯示受到交通來源影響較大；PMF解析出沙鹿站的主要污染源為二次硝酸鹽及生質燃燒(32.8%)、交通來源(32.2%)、二次硫酸鹽及固定源(22.7%)，中山醫為交通來源及生質燃燒(41.4%)、二次硫酸鹽、固定源及施工揚塵(24.7%)、土壤及道路揚塵(19.4%)，竹山站為二次氣膠(46.4%)、海鹽及煤炭燃燒(26.1%)、土壤及道路揚塵(20.4%)。進一步計算非致癌風險(HQ)，中山醫站貢獻最高的污染源為土壤及道路揚塵，竹山站同為土壤及道路揚塵；兩地貢獻最高之物種均為Mn，富集因子(EF)判斷結果顯示其為自然來源，推測兩地均有一定背景濃度；致癌風險(CR)，中山醫站貢獻最高的污染源為二次硫酸鹽、固定源及施工揚塵；兩地貢獻最高之物種皆為Cr，然而兩地皆非工業區，顯示其可能由潛在污染源傳輸而來。本文透過計算不同污染源的致癌及非致癌風險，可以釐清對人體健康影響較大之污染源，進一步進行管制和改善，未來納入不同季節及更多有害物種的數據，可以更加瞭解不同污染源對人體健康的潛在危害。

關鍵字

PM2.5、化學組成、污染源解析、正因子矩陣法(PMF)、風險評估

並列摘要

Recently, the issues related to fine particulate matter (PM2.5) are gradually being emphasized. Previous studies mentioned that the hazard of PM2.5 is related to its chemical composition, which affected by weather type and emission sources. The source of PM2.5 in central Taiwan is complicated and rare studies have examined the impact of different sources on human health.
This thesis collected PM2.5 sample in the winter of 2016. Analyze the water-soluble ions, carbon components and metal elements. To investigate the chemical composition of PM2.5 in different weather type in central Taiwan, including Shalu, Chung Shan Medical University (CSMU) and Zhushan. Positive matrix factorization (PMF) was adopt to identify the source of PM2.5 in different locations. Afterwards, conducted source-based and species-based human health risk assessments.
Results indicated that NO3- and traffic-related metal elements (Cu, Zn, Pb, V) have higher concentrations in non-transmission than in transmission period at CSMU, expressed as being more affected by traffic sources. Positive matrix factorization receptor modeling identified secondary nitrate and biomass burning (32.8%), traffic source (32.2%), secondary sulfate and industrial sources (22.7%) as major source in Shalu. Traffic and biomass burning (41.4%), secondary sulfate, industrial sources and construction dust (24.7%), soil and road dust (19.4%) was identified at CSMU. Secondary aerosol (46.4%), sea salt and coal combustion (26.1%), soil and road dust (20.4%) was identified in Zhushan.
Further, to calculate the non-carcinogenic risk (HQ), the highest contributor to the source is soil and road dust both at CSMU and Zhushan. The species with the highest contribution from both area were Mn, and the enrichment factor (EF) results showed that they were natural sources, indicating that both sites had a certain background concentration. For carcinogenic risk (CR), the most contributing sources was secondary sulfate, industrial sources and construction dust. And the highest contribution from both CSMU and Zhushan is Cr, but neither is an industrial area, indicating that it may be transmitted from potential sources.
In this study, by calculating the carcinogenic and non-carcinogenic risks of different sources, that have a greater impact on human health can be clarified, further controlled and improved. In the future, more data on different seasons and more harmful species can be included to better interpret the potential health posed by various sources.

並列關鍵字

PM2.5, Chemical composition, Source apportionment, Positive matrix factorization (PMF), Risk assessment.

參考文獻

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