18.5.3 Receptor Models (Multi-variate Source Apportionment)

Receptor models (also referred to as multi-variate source apportionment models) are

mathematical or statistical procedures for identifying and quantifying the sources of air pollutants

at a receptor location (Henry, 1991; Hopke, 1991, 1999, 2001; Lewis, 2003; Coulter, 2000;

Willis, 2000; Watson, 2002; Maykut, 2003; Mukerjee, 2004). Receptor models are generally

used to examine primary pollutants such as the primary components of PM2.5. They are not

typically used to examine ozone or secondary PM2.5 species. Unlike photochemical and

dispersion air quality models, receptor models do not use pollutant emissions, meteorological data

and chemical transformation mechanisms to estimate the contribution of sources to receptor

concentrations. Instead, receptor models use the chemical and physical characteristics of gases

and particles measured at source and receptor to both identify the presence of and to quantify

source contributions to receptor concentrations. Receptor models can identify possible air quality

management strategy solutions especially when used in conjunction with examination of the local

emission inventory (Brook, 2004). These models are therefore a natural complement to other air

quality models and can be used as part of State Implementation Plans (SIPs) for identifying

sources contributing to air quality problems.

Receptor models provide scientific support for current ambient air quality standards and

for implementation of those standards by identifying and quantifying contributions of various

source types. As mentioned in sections 18.2 and 18.3, the richness of the ambient air quality data

sets has been increasing, due to more species being measured, species being stratified by particle

size, shorter durations of sampling, and measurements not only at the surface but also aloft. To

take advantage of these richer data sets the receptor models have become more complex. To

ensure that receptor modeling tools, both simple and complex, are available for use in the

development and implementation of air quality standards, the United States Environmental

Protection Agency's Office of Research and Development (ORD) has and continues to develop a

suite of receptor models (multivariate statistical techniques) that are freely distributed to the air

quality management community. The EPA has developed the Chemical Mass Balance (CMB)

and UNMIX models as well as the Positive Matrix Factorization (PMF) method for use in air

quality management. CMB fully apportions receptor concentrations to chemically distinct

source-types depending upon the source profile database, while UNMIX and PMF internally

generate source profiles from the ambient data. Details of CMB, UNMIX and PMF are described



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