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Receptor modeling is a method for determining the sources of air pollution based on air monitoring data.
Receptor-based models utilize chemical measurements at an individual monitoring site (the receptor) to calculate the relative contributions from major sources to the pollution at that site. Receptor-based modeling is also referred to as source apportionment. These models can be applied to investigating the sources of individual air pollution “episodes” or, as with the emission inventory, to create effective control strategies. Receptor-based models are most commonly used to investigate the sources of particulate air pollution, using speciated chemical data of the sampled particulate matter. However, more advanced techniques that incorporate wind trajectory data can be applied to the gaseous pollutants.
The main inputs for these models are individual chemical measurements at a receptor. These are usually obtained by collecting particulate matter on a filter and analyzing the filter for the elements and organic carbon. The Chemical Mass Balance (CMB) model, which is currently endorsed by EPA and available for downloading, requires the additional inputs of the error associated with each chemical measurement and the source emission profiles. A source profile is the chemical composition of the emissions, with each chemical species expressed as a mass fraction of the total (for example, resuspended dust might contain 20% aluminum, 20% calcium, 50% silicon, and 10% elemental carbon). Some more advanced models, such as Positive Matrix Factorization (PMF), do not require source profiles as an input, as this is part of the solution.
The main output from these models is an estimate of the contributions from each source to the air pollution at that site. From a management perspective, the results from these models are important for scientifically justifying priorities and observing trends.