The Chemical Mass Balance (CMB) Model (most recent version, EPA-CMBv8.2) is one

of several receptor models that has been applied to air quality problems over the last two decades

(U.S. EPA, 2004d and 2004e; Seigneur, 1997, Coulter, 2000). Based on an effective-variance

least squares method (EVLS), EPA has supported CMB as a regulatory planning tool through its

approval of numerous State Implementation Plans (SIPs) which have a source apportionment

component. The chemical mass balance model is probably the most directly applicable

observational approach for this purpose, since it can focus on the same day(s) considered with the

air quality model. Cautions raised previously about representativeness of the monitored data

continue to apply. CMB requires speciated profiles of potentially contributing sources and the

corresponding ambient data from analyzed samples collected at a single receptor site. CMB is

ideal for localized nonattainment problems and has proven to be a useful tool in applications

where steady-state Gaussian plume models are inappropriate, as well as for confirming or

adjusting emissions inventories.

UNMIX is named for its function, which is to "unmix" the concentrations of chemical

species measured in the ambient air to identify the contributing sources. The particular

mathematical approach used by UNMIX is based on a form of Factor Analysis, but its novelty is

that physically-meaningful constraints are imposed which are intended to remove the undesirable

ambiguity of the multiple solutions that are characteristic of ordinary Factor Analysis. For a

given selection of species, UNMIX estimates the number of sources, the source compositions, and

source contributions to each sample. Chemical profiles of the sources are not required, but

instead are generated from the ambient data.

The PMF technique is a form of factor analysis where the underlying co-variability of

many variables (e.g., sample to sample variation in PM species) is described by a smaller set of

factors (e.g., PM sources) to which the original variables are related. The structure of PMF

permits maximum use of available data and better treatment of missing and below-detection-limit

values. Also available is a document which discusses the PMF methodology: A Guide to

Positive Matrix Factorization (PDF).


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