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

Song, Xin-Hua and Faber, Nicolaas (Klaas) M. and Hopke, Philip K. and Suess, David T. and Prather, Kimberly A. and Schauer, James J. and Cass, Glen R. (2001) Source apportionment of gasoline and diesel by multivariate calibration based on single particle mass spectral data. Analytica Chimica Acta, 446 (1-2). pp. 329-343. ISSN 0003-2670. https://resolver.caltech.edu/CaltechAUTHORS:20160628-134703721

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1016/S0003-2670(01)01270-3DOIArticle
http://www.sciencedirect.com/science/article/pii/S0003267001012703PublisherArticle
Additional Information:© 2001 Elsevier Science B.V. Received 7 November 2000; accepted 25 June 2001. Available online 30 October 2001. This work was supported by the State of California Air Resources Board through Contract 97-321, the Strategic Environmental Research and Development Program (SERDP) (FO8637-98-C-6011), and the National Renewable Energy Laboratory (NREL).
Funders:
Funding AgencyGrant Number
California Air Resources Board97-321
Strategic Environmental Research and Development Program (SERDP)FO8637-98-C-6011
National Renewable Energy Laboratory (NREL) UNSPECIFIED
Subject Keywords:Chemometrics; Aerosol source apportionment; Aerosol source characterization; Time-of-flight mass spectrometry; Artificial neural network; Partial least squares; Prediction interval estimation
Issue or Number:1-2
Record Number:CaltechAUTHORS:20160628-134703721
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20160628-134703721
Official Citation:Xin-Hua Song, Nicolaas (Klaas) M. Faber, Philip K. Hopke, David T. Suess, Kimberly A. Prather, James J. Schauer, Glen R. Cass, Source apportionment of gasoline and diesel by multivariate calibration based on single particle mass spectral data, Analytica Chimica Acta, Volume 446, Issues 1–2, 19 November 2001, Pages 329-343, ISSN 0003-2670, http://dx.doi.org/10.1016/S0003-2670(01)01270-3. (http://www.sciencedirect.com/science/article/pii/S0003267001012703)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:68717
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:29 Jun 2016 17:32
Last Modified:03 Oct 2019 10:16

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