Conditional bivariate probability function for source identification

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. 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/). Software availability The methods described in this work are available as part of software called openair. The openair software is freely available as an R package. Details on installing R and optional packages including openair can be found at R Core Team (2014) and http:// www.r-project.org. R will run on Microsoft Windows, linux and Apple Mac computers. No special hardware is required to run openair other than a standard desktop computer. Some large data sets or complex analyses may require a 64-bit platform. Ref: R Core Team (2014). R: A language and environment for statistical computing. RFoundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.