Bilinear estimation of pollution source profiles and amounts by using multivariate receptor models Academic Article

abstract

• Multivariate receptor models aim to identify the pollution sources based on multivariate air pollution data. This article is concerned with estimation of the source profiles (pollution recipes) and their contributions (amounts of pollution). The estimation procedures are based on constrained nonlinear least squares methods with the constraints given by nonnegativity and identifiability conditions of the model parameters. We investigate several identifiability conditions that are appropriate in the context of receptor models, and also present new sets of identifiability conditions, which are often reasonable in practice when the other traditional identifiability conditions fail. The resulting estimators are consistent under appropriate identifiability conditions, and standard errors for the estimators are also provided. Simulation and application to real air pollution data illustrate the results. Copyright 2002 John Wiley & Sons, Ltd.

authors

• Park, Eun

published proceedings

• ENVIRONMETRICS

author list (cited authors)

• Park, E. S., Spiegelman, C. H., & Henry, R. C.

citation count

• 38

complete list of authors

• Park, ES||Spiegelman, CH||Henry, RC

publication date

• November 2002

publisher

• Wiley  Publisher

published in

• Environmetrics  Journal

keywords

• Bootstrap
• Consistency
• Constrained Nonlinear Least Squares
• Model Identifiability
• Multivariate Receptor Model

Digital Object Identifier (DOI)

• 10.1002/env.557

start page

• 775

end page

• 798

volume

• 13

issue

• 7

URL

• http://dx.doi.org/10.1002/env.557