適用於多重時間解析度資料之空氣污染物來源解析模式之研究方法探討及其應用

Abstract

This study was conducted to evaluate the performance of an improved source apportionment model that is suitable for incorporating data with multiple time resolutions. This evaluation was achieved by using synthetic data sets that simulated environmental concentrations of volatile organic compounds (VOCs) and fine particulate matter (PM2.5) from the five following sources: petroleum refinery, vehicle exhaust, industrial coating, coal combustion, and natural gas. Hourly VOCs and speciated PM2.5 data were simulated for a one-week period. The PM2.5 data were further averaged every twelve hours to generate data sets with mixed temporal resolutions. The Multilinear Engine program was applied to resolve the source profiles and contributions. A series of sensitivity analyses was conducted to examine how uncertainties in the profile variation, measurement error, and source collinearity affected the model performance. The resolved factor profiles closely matched the input profiles, and the measurement error had a larger impact on the modeling results than the profile variation. In the most comprehensive data set that contained all three types of uncertainty, the R2 values between the input and retrieved source contributions were between 0.87 and 0.95. The estimated percentage contributions were also comparable with the input ones, demonstrating the applicability and validity of this improved model. Additionally, a field study was conducted to identify and quantify the sources of selected VOCs and PM2.5 by using a partially constrained source apportionment model suitable for multiple time resolution data. Hourly VOC, 12-h and 24-h PM2.5 speciation data were collected in three seasons in 2013. Eight factors were retrieved from the Positive Matrix Factorization models and adding source profile constraints enhanced the interpretability of source profiles. Results showed that the evaporative emission factor was the largest contributor (25%) to VOC mass concentration, while the largest contributor to PM2.5 mass concentration was soil dust/regional transport related factor (26%). Besides a petrochemical related factor, several factors (including traffic/industry related, evaporative emission, combustion, and soil dust/regional transport) were partially related with the petrochemical complex which should be considered when estimating the overall contribution from it. Furthermore, field campaigns were conducted at multiple receptor sites using a mobile monitoring platform set up to collect particle size distribution and PM2.5 speciation data. The most relevant sources of selected air pollutants to all mobile monitoring sites were identified and quantified using the improved source apportionment model. Results indicated that a mixed source was the largest contributor to PM2.5 at most sites. The difficulty in estimating accurate source contributions of a mixed source profile suggests that a further study is needed to resolve this type of problems. Different patterns of seasonal contributions among monitoring sites specified association with both spatial heterogeneity and temporal variability.