多時間解析度資料於推估懸浮微粒與揮發性有機氣體來源之效用

Efficacy of Utilizing Multiple Time Resolution Data for Source Apportionment of Particulate Matter and Volatile Organic Compounds

郭承彬(Cheng-Pin Kuo)

指導教授 ： 吳章甫

國立臺灣大學/公共衛生學院/職業醫學與工業衛生研究所/碩士(2013年)

https://doi.org/10.6342/NTU.2013.02703

摘要

本研究利用不同時間解析度之懸浮微粒PM2.5（fine particle matter）與揮發性有機氣體（volatile organic compounds, VOCs）監測資料，驗證使用複合型資料於來源推估模式（Source apportionment）的可行性。本研究以台灣新北市土城測站監測資料為例，每12小時收集懸浮微粒PM2.5樣本，共收集19天，並使用測站所監測之每時揮發性有機氣體資料，最後來源推估模式共納入12種元素、6種離子與38種揮發性有機氣體資料，而本研究所使用的來源推估模式為受體模式（receptor model）之一—正矩陣因子法（Positive Matrix Factorization, PMF）。
　　以模式解析之汙染源指紋（source profile）結果，並將其貢獻量估計量（source contribution estimates, SCE）結合時間與氣象資料，推估土城測站至少有5種主要汙染源，含交通排放源一（Vehicle 1）、交通排放源二（Vehicle 2）、工業製程排放（Industry processing source）、其他地域傳播源（Transported regional source）與二次汙染源（Secondary pollution source）。在土城地區，揮發性有機氣體主要來源為交通排放源，佔36%。而懸浮微粒PM2.5主要排放源則為其他地域傳播源與二次汙染源，佔54%。
　　為了驗證使用複合型資料的效用，利用前述模式結果與僅使用揮發性有機氣體資料模式推估之結果比較。在本研究中，使用複合型資料所推估之汙染源相較於僅使用揮發性有機氣體資料推估，多解析出一個汙染源（其他地域傳播源）。使用複合型資料時，懸浮微粒PM2.5所解析出的汙染源指紋資料可提供汙染來源特性判斷。同時，高時間解析度的揮發性有機氣體資料也可協助解析懸浮微粒PM2.5汙染源。綜觀而論，即使資料有不同時間解析度，使用複合型資料對於推估懸浮微粒PM2.5與揮發性有機氣體來源是有助益的。
　　另外，使用危害性空氣汙染物（hazardous air pollutants）來源貢獻量估計結果，亦可進一步推估健康危害風險來源。汙染源因其組成不同，所造成的健康風險亦不盡相同。以本研究結果為例，工業製程排放結果雖然僅佔懸浮微粒PM2.5總貢獻量之13%，但其所造成的癌症風險卻相對較高。因此，若此一風險來源推估技術應用於暴露評估，以減少居民暴露於特定危害性空氣汙染物，其結果可提供風險控制策略擬定之參考。

關鍵字

揮發性有機汙染物 ； 懸浮微粒 ； 來源推估 ； 受體模式 ； 正矩陣因子法 ； 風險來源評估

並列摘要

This study demonstrated the effect of utilizing composition data set which included fine particle matter (PM2.5) and volatile organic compounds (VOCs) data with multiple time resolution for source apportionment. 12-hr PM2.5 composition data and hourly VOCs data were collected from Tucheng monitoring site in Taiwan for 19 days. A total of 12 elementals, 6 ions and 38 VOCs species were included in receptor modeling. The model of positive matrix factorization (PMF) was applied in this study. Based on the resolved source profiles, source contribution estimates (SCE), and meteorological data, five characterized sources were identified: Vehicle 1, Vehicle 2, Industry processing source, Transported regional source and Secondary pollution source. In Tucheng site, VOCs emission was mainly contributed by vehicle emission (36%), while the largest contributors of PM2.5 were transported regional source and secondary pollution source (54%).
Effect of using composition data set was demonstrated by comparing to results from modeling with VOCs data only. Inclusion of PM2.5 component data extracted one more source (Transported regional source) in this study and information of PM2.5 elements in the source profiles facilitated interpretation of source type and formation. Meanwhile, higher time-resolved VOCs data also assisted source apportionment of PM2.5. Overall, using composition data sets even with different time resolution is contributive to source apportionment of PM2.5 and VOCs.
Additionally, SCE of species were applied for risk apportionment of hazardous air pollutants (HAPs) in this study. Distributions of source specific risks could be different from contributions to mass concentrations. For example in this study, industry emission sources had relative low contribution (13%) to PM2.5 mass concentration but could pose considerable cancer risk. Applying this risk apportionment approach appropriately could provide the references for future risk management to design risk reduction strategy more effectively.

並列關鍵字

Volatile Organic Compounds (VOCs) ； Fine Particle Matter (PM2.5) ； Source Apportionment ； Receptor Model ； Positive Matrix Factorization (PMF) ； Risk Apportionment

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