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Conditional bivariate probability function for source identification

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JournalEnvironmental modelling & software
DateE-pub ahead of print - 24 May 2014
DatePublished (current) - Sep 2014
Volume59
Number of pages9
Pages (from-to)1-9
Early online date24/05/14
Original languageEnglish

Abstract

In this paper a new receptor modelling method is developed to identify and characterise emission sources. The method is an extension of the commonly used conditional probability function (CPF). The CPF approach is extended to the bivariate case to produce a conditional bivariate probability function (CBPF) plot using wind speed as a third variable plotted on the radial axis. The bivariate case provides more information on the type of sources being identified by providing important dispersion characteristic information. By considering intervals of concentration, considerably more source information can be revealed that is absent in the basic CPF or CBPF. We demonstrate the application of the approach by considering an area of high source complexity, where many new sources can be identified and characterised compared with currently used techniques. Dispersion model simulations are undertaken to verify the approach. The technique has been made available through the openair R package.

    Research areas

  • Air pollution, Air quality data, Dispersion model, Openair, Receptor model, Source identification

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