Source apportionment of particulate matter and selected volatile organic compounds with multiple time resolution data

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• 作者：Cheng-Pin Kuo ; Ho-Tang Liao ; Charles C.-K. Chou ; Chang-Fu Wu
• 关键词：Volatile organic compounds (VOCs) ; Fine particulate matter (PM2.5) ; Source apportionment ; Receptor model ; Positive Matrix Factorization (PMF) ; Multilinear Engine (ME-2)
• 刊名：Science of the Total Environment
• 出版年：15 February, 2014
• 年：2014
• 卷：472
• 期：Complete
• 页码：880-887
• 全文大小：1762 K

文摘

Fine particulate matter (PM_2.5) and volatile organic compounds (VOCs) co-exist in ambient air and contribute to adverse health effects in human populations. This study was conducted to demonstrate the feasibility of utilizing a composite data set which included PM_2.5 and VOC data with multiple time resolutions for source apportionment. Hourly VOC and 12-h PM_2.5 speciation data were combined into an improved source apportionment model to quantify different pollutant source contributions to PM_2.5 and VOC mixtures. Five factors were retrieved, including vehicle 1, vehicle 2, industrial processing, transported regional, and secondary pollution sources. The largest contributors were vehicular emissions for VOCs (62%) and PM_2.5 (35%). Nonetheless, transported regional and secondary pollution sources accounted for a noteworthy portion of PM_2.5 (27% and 25%, respectively) relative to VOCs (8% and 5%, respectively). Additional sensitivity analyses showed that excluding the PM_2.5 data or reducing the associated temporal resolution (12-h to 24-h) retrieved fewer source factors and increased the errors of source contribution estimates.