Source apportionment of gasoline and diesel by multivariate calibration based on single particle mass spectral data

Abstract

The mass apportionment of gasoline and diesel particles in ambient aerosol samples is a difficult problem because both sources exhibit very similar chemical composition. However, individual particle analysis could provide additional information and help achieve source apportionment with good accuracy. Aerosol time-of-flight mass spectrometry (ATOFMS) has proven to be a powerful technique capable of simultaneously determining both the size and chemical composition of single particles in real time. Thus, samples of gasoline and diesel particles were analyzed by ATOFMS for their single particle information. In addition to the aerodynamic diameter from which the individual particle mass can be estimated, positive and negative mass spectra were obtained for each particle. A novel data analysis approach based on the combination of an adaptive resonance theory-based neural network (ART-2a), and a multivariate calibration method, partial least squares (PLS), has been developed to apportion the mass contributions of gasoline and diesel sources to mixture samples. The ART-2a neural network was used first to classify the particle-by-particle mass spectral data. The source profile for each source (gasoline/diesel) was obtained in terms of the mass fractions of the classified particle types. Next, PLS was applied to build a model relating the mass fractions of different particle classes and the mass contributions of the two sources to mixture samples. Artificial mixture samples obtained by randomly mixing some particles from the two source samples have been used to examine the feasibility of the proposed method. Satisfactory predictions for the mass contributions of gasoline and diesel exhaust to the mixture samples have been obtained. A recently proposed formula for prediction error variance is successfully modified to quantify the uncertainty in the PLS predictions. This study exemplifies the potential promise of multivariate calibration as applied to the aerosol source apportionment problem.