Source Apportionment of Fine Particulate Matter by Clustering Single-Particle Data: Tests of Receptor Model Accuracy

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Abstract
The source apportionment accuracy of a neural network algorithm (ART-2a) is tested on the basis of its application to synthetic single-particle data generated by a source-oriented aerosol processes trajectory model that simulates particle emission, transport, and chemical reactions in the atmosphere. ART-2a successfully groups particles from the majority of sources actually present, when given complete data on ambient particle composition at monitoring sites located near the emission sources. As particles age in the atmosphere, accumulation of gas-to-particle conversion products can act to disguise the source of the primary core of the particles. When ART-2a is applied to synthetic single-particle data that are modified to simulate the biases in aerosol time-of-flight mass spectrometry (ATOFMS) measurements, best results are obtained using the ATOFMS dual ion operating mode that simultaneously yields both positive and negative ion mass spectra. The results of this study suggest that the use of continuous single-particle measurements coupled with neural network algorithms can significantly improve the time resolution of particulate matter source apportionment.