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.

Aerosol laser ablation mass spectrometry of suspended powders from PM sources and its implications to receptor modeling.

Primary sources of particulate matter (PM) were analyzed by suspending powdered samples into an aerosol laser ablation mass spectrometer (LAMS). PM sources studied included vehicle exhaust particulates, dust from a non-ferrous smelter, cement powder, incinerator fly ash, two coal fly ash samples, and two soils. Marker peaks signified certain PM source sectors: construction particles could be distinguished by abundant Ca and Ca compounds, fuel combustion was marked by elemental carbon clusters, and nonferrous industrial particles showed inorganic As, Cu, Pb, Zn, and SOx. In addition to the distinction between particles from these different source sectors, mass spectral results also showed that for a single source, different particle types existed, and among different sources within a sector, similar spectra were present. The aerosol LAMS results show the difficulty in differentiating among separate fly ash sources as well as among different soil samples. A particle class balance receptor model that measures the amount of specific particle types rather than the amount of a chemical component is suggested as a means of source apportionment when particle spectra with overlapping source possibilities occur. The assumptions and limitations of receptor modeling aerosol LAMS data are also described. In particular, methods need to be developed to account for the contribution of secondary sources.