Volume 13, No. 4, August 2013, Pages 1253-1262 PDF(764 KB)
Evaluation of a Modified Receptor Model for Solving Multiple Time Resolution Equations: A Simulation Study
Ho-Tang Liao1, Cheng-Pin Kuo1, Philip K. Hopke2, Chang-Fu Wu1,3,4
1 Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University, Taipei, Taiwan
2 Center for Air Resources Engineering and Science, and Department of Chemical Engineering, Clarkson University, Potsdam, NY, USA
3 Department of Public Health, National Taiwan University, Taipei, Taiwan
4 Institute of Environmental Health, National Taiwan University, Taipei, Taiwan
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.
Source apportionment; Receptor modeling; Multilinear engine; Multiple time resolution.