Receiver Model

Receiver models are mathematical or statistical procedures for identifying and quantifying sources of air pollutants from a receiving point. Unlike the atmospheric dispersion models, the receiver models do not use pollutant emission data, meteorological data or chemical transformation mechanisms to estimate the contribution of the sources to the concentrations in the receivers.
Instead, receptor models use the chemical and physical characteristics of the gases and particles measured at the source emitter and receiver, both to identify the presence of each contaminant and to quantify the contributions of the sources at the concentrations found at the receptors. These models are therefore a natural complement to other air quality models to identify sources that contribute to air pollution.

US-EPA has developed the Chemical Mass Balance (CMB) and UNMIX models, as well as the Positive Matrix Factor (PMF) method for use in air quality management. The CMB distributes the concentrations at the receivers to chemically distinct source typologies, depending on the source profile database, while UNMIX and PMF internally generate source profiles from the environmental data.


Mass Chemical Balance (CMB) - uses source profiles and speciated ambient data to quantify source contributions. Contributions are quantified from chemically distinct source-types rather than from individual emitters. Sources with similiar chemical and physical properties cannot be distinguished from each other by CMB.

UNMIX - “unmixes” the concentrations of chemical species measured in the ambient air to identify the contributing sources. Chemical profiles of the sources are not required, but instead are generated internally from the ambient data by UNMIX, using a mathematical formulation based on a form of factor analysis. For a given selection of species, UNMIX estimates the number of sources, the source compositions, and source contributions to each sampleple.

Positive Matrix Factoring (PMF) - is a form of factor analysis where the underlying co-variability of many variables is described by a smaller set of factors 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.

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