Source apportionment of fine particulate matter by clustering single-particle data: tests of receptor model accuracy.

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.

Quantification of ATOFMS data by multivariate methods.

Aerosol time-of-flight mass spectrometry (ATOFMS) is capable of measuring the sizes and chemical compositions of individual polydisperse aerosol particles in real time. A qualitative estimate of the particle composition is acquired in the form of a mass spectrum that must be subsequently interpreted in order to draw conclusions regarding atmospheric relevance. The actual problem involves developing a calibration that allows the mass spectral data to be transformed into estimates of the composition of the atmospheric aerosol. A properly calibrated ATOFMS system should be able to quantitatively determine atmospheric concentrations of various species. Ideally, it would be able to accomplish this more rapidly, accurately, with higher size and time resolution, and at a far lower marginal cost than the manual sampling methods that are currently employed. Attempts have already been made at using ATOFMS and similar techniques to extract the bulk chemical species concentration present in an ensemble of particles. This study represents the use of a multivariate calibration method, two-dimensional partial least-squares analysis, for calibrating single-particle mass spectral data. The method presented here is far less labor-intensive than the univariate methods attempted to date and allows for less observer bias. Because of the labor savings, this is also the most comprehensive calibration performed to date, resulting in the quantification of 44 different chemical species.