Receptor modeling of toronto PM2.5 characterized by aerosol laser ablation mass spectrometry.

Urban Toronto fine particulate matter (PM2.5) was physically and chemically characterized by online aerosol laser ablation mass spectrometry (LAMS) between January 2002 and February 2003. The mass spectra from the analysis of individual aerosol particles were classified according to chemical composition by a neural network approach called adaptive resonance theory (ART-2a). Temporal trends of the hourly analysis rate of over 120 different particles types were constructed and subjected to positive matrix factorization (PMF). This receptor modeling technique enabled the identification of nine distinct emission sources responsible for these particle types: biogenic, mixed crustal, organic nitrate, construction dust, Toronto soil/road salt, secondary salt, wood burning, intercontinental dust, and an unknown source of aluminum fluoride dust. Episodic events occurred with the wood burning, intercontinental dust, and unknown dust sources. This is the first paper reporting the application of PMF to single-particle spectral data.

On-line analysis of urban particulate matter focusing on elevated wintertime aerosol concentrations.

A 10-day winter sampling campaign was conducted in downtown Toronto for particulate matter (PM) air pollution in the fine (<2.5 microm) size range. An aerosol laser ablation mass spectrometer (LAMS), a tapered-element oscillating microbalance (TEOM), and an aerodynamic particle sizer (APS) were operated in parallel to characterize the PM on-line. In this study, the LAMS observed differences in the chemical composition between three separate episodes with higher PM2.5 mass and APS counts. LAMS results showed that in one instance of elevated PM, organic amines were present in the particulates. Temporal analyses of this episode revealed chemical transformations as the amines, characterized by m/z peaks 58(C3HeN)+, 86(C5H2N)+, and nitrates, increased in number concentration while Ca and hydrocarbon particle classes concurrently decreased. On another day, sulfates were found to have increased significantly. The third event was only 4 h in duration and exhibited an increase in the number of submicron-sized K/hydrocarbons and sulfate-containing particles. In this last event, the hydrocarbons and a K to Fe ratio enrichment indicated there was likely a contribution from a combustion source. This work offers some of the first insights into single particle size and chemistry in a cold winter climate.

Chemically-assigned classification of aerosol mass spectra.

An Algorithm for Discriminant Analysis of Mass Spectra--ADAMS--was created that classified aerosol mass spectra into dominant chemically-assigned classes, and grouped rare cases in an outlier class. ADAMS was trained with ambient particulate matter (PM) mass spectra, and then validated through classification tests on known spectra with random noise added, various standard chemicals, and salt-spiked polystyrene latex microspheres. The classification results showed that ADAMS gave a reasonable chemical description of the particle populations. In contrast to adaptive resonance theory (ART-2a) classification, ADAMS could be trained to be advantageously sensitive or insensitive to selected chemical markers. Application of ADAMS to Toronto ambient PM and diesel PM (NIST 2975) demonstrated that these samples could be well described, with a low proportion of the cases falling into the outlier class. Such an algorithm may find application for source-receptor modeling of aerosol mass spectra.