Evaluation of a Modified Receptor Model for Solving Multiple Time Resolution Equations: A Simulation Study

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 (PM 2.5 ) from the five following sources: petroleum refinery, vehicle exhaust, industrial coating, coal combustion, and natural gas. Hourly VOCs and speciated PM 2.5 data were simulated for a one-week period. The PM 2.5 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 R 2 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.

Source and risk apportionment of selected VOCs and PM2.5 species using partially constrained receptor models with multiple time resolution data

“…On the other hand, 5 and 8 VOC species remained in Factors 3 and 8, indicating that both factors represent a mixture of gaseous and particle source types, similar to previous findings (Chan et al, 2011;Marris et al, 2012;Seco et al, 2013;Wagstrom and Pandis, 2011). With regard to the modeling, Kuo et al (2014) and Liao et al (2013) indicated that PM chemical species enhanced the separation of sources with low VOC contributions (e.g., <5% in this study).…”

“…One limitation of the multivariate receptor models is that they are not always able to separate sources with correlated source contributions (contribution collinearity) or with similar source features (profile collinearity) (Brinkman et al, 2006;Habre et al, 2011;Liao et al, 2013). This limitation is demonstrated by Factors 2 and 5 that both represent mixed sources of traffic/industry and combustion, respectively.…”

“…Zhou et al (2004) applied a variation that incorporated aerosol composition data with various temporal resolutions. We adopted this concept and evaluated its applicability with simulated and field data consisting of VOCs and PM 2.5 species with different temporal resolutions (Kuo et al, 2014;Liao et al, 2013). Results indicated that combining VOC and PM 2.5 components enhanced the interpretation of source origins compared to modeling with VOC-only data.…”

“…Data processing and uncertainty determination are detailed in Kuo et al (2014) and Liao et al (2013). The modified receptor equation is (Zhou et al, 2004):…”

“…The number of interpretable source factors was determined by examining the maximum individual column mean (IM) and standard deviation (IS) of the scaled residual matrix (Kuo et al, 2014;Lee et al, 1999;Liao et al, 2013) and the loadings on marker species. The eight-factor solution was most reasonable, with correlations between estimated and measured concentrations exceeding 0.6 for species included in the solution.…”