Maygan L. McGuire

Cloud and Fog Processing Enhanced Gas-to-Particle Partitioning of Trimethylamine

An aerosol time-of-flight mass spectrometer (ATOFMS) was used to detect trimethylamine (TMA) in 0.52-1.9 μm particles at urban and rural sites in Southern Ontario during the summer and winter of 2007. During the summer, TMA-containing particles were observed exclusively during high relative humidity or fog events at both the urban and rural sites. In the wintertime, greater concentrations of TMA-containing particles were linked to cloud processing of aerosol in air masses originating from over agricultural and livestock areas. A laboratory study revealed that, at high relative humidity (∼ 100%), gas phase TMA at concentrations ranging from 2 to 20,000 ppt partitions preferentially to acidic particles present in the ambient air. On the basis of the field and laboratory studies, it appears that gas phase TMA present in ambient air partitions onto pre-existing particles preferentially during periods of acidic cloud/fog processing, leading to the presence of TMA-containing particles in the 0.52-1.9 μm size range.

Receptor model based identification of PM2.5 sources in Canadian cities

Abstract Source apportionment of 24–hour integrated PM 2.5 chemical speciation data, collected at five Canadian urban sites, Windsor, Toronto, Montreal, Halifax, and Edmonton was performed using the receptor model, Positive Matrix Factorization (PMF). In order to determine the influences of local and regional sources, in–depth wind direction and back trajectory analyses were performed using the conditional probability function (CPF) and the potential source contribution function (PSCF). The highest PM 2.5 levels were observed in Windsor followed by Toronto and Montreal. Secondary sulfate and nitrate were the major factors contributing to the PM 2.5 mass, accounting for 41% – 61% in the five sites. These secondary factors were associated with trans–boundary emissions from Ohio, Pennsylvania, and New York. An elemental carbon (EC)–rich factor was identified in Windsor, Toronto, and Montreal, characterized by distinct EC and organic carbon (OC) profiles. The EC–rich factor accounted for 6% – 19% of the total PM 2.5 mass in summer and also appeared to be related to trans-boundary pollutants. The combined contributions of traffic and road dust ranged from 14% to 19%, with a portion of the nitrate factor also coming from vehicles. In Halifax, sea salt was the second strongest source, contributing 18% of the PM 2.5 . In Edmonton, strong correlation of volatile organic compounds with the major PM 2.5 factors suggested that local industrial sources were significant sources of secondary aerosol. Further, biomass burning contributed 12% of the PM 2.5 mass in Edmonton. Both local and regional sources were found to contribute at all sites. Thus, PM 2.5 can be reduced at all the sites through local controls. However given the significant contribution of trans–boundary contributions to the PM 2.5 mass, a substantial reduction of PM 2.5 in four of the cities will also require agreements to limit the production and transport of trans–boundary pollutants.

Identification of the sources and geographic origins of black carbon using factor analysis at paired rural and urban sites.

Black carbon particles, composed of forms of elemental carbon (EC), contribute significantly to regional and global warming. The origins of EC were examined in southeastern Canada as part of a source apportionment study using positive matrix factorization (PMF), performed on long-term PM2.5 chemical speciation data collected at two paired rural and urban sites. Comparisons of the urban and rural sites revealed a previously unrecognized EC-rich factor that accounted for 41-56% of the total EC in this region. This factor was characterized by the more thermally stable EC fractions that exhibit strong light absorption characteristics. While these EC fractions are often attributed to local diesel emissions, this interpretation was rejected for several reasons. The EC-rich factor was present in similar temporal patterns at both the high-traffic urban and low-traffic rural sites across this 600 km region. The geographic origins of the EC-rich factor were found to be Ohio and Western Pennsylvania regions with heavy industry and multiple coal-based electrical generating stations. The direct radiative forcing due to this EC-rich factor was roughly estimated to be +0.2 W m(-2), which represented a substantial portion of the aerosol induced warming in the region. Thus, this region was impacted by an important unidentified source of EC associated with long-range transport.

Strategies to Enhance the Interpretation of Single-Particle Ambient Aerosol Data

New instruments are beginning to reveal the chemical complexity of atmospheric aerosol particles. Exploitation of the plethora of information being made accessible through aerosol particle spectrometry and other techniques requires new strategies for data interpretation. This paper demonstrates and evaluates several analysis methods used to exploit this single-particle high-time-resolution data. In the first part of this study, Standard Reference Material (SRM) particulate matter samples were analyzed by an Aerosol Time-of-Flight Mass Spectrometer (ATOFMS) in order to evaluate the use of a modified, logarithm based, method of clustering mass spectra using the Adaptive Resonance Theory (ART-2a) algorithm. In the second part of this study, data obtained from the ATOFMS during the four seasons of 2007 were interpreted using a variety of approaches so as to elucidate the nature and sources of particles influencing the great lakes region of North America. This dataset is believed to represent the longest time-span of single-particle data ever analyzed in a study of this nature. These mass spectra were clustered into 21 different particle types using the supervised log-transformed ART-2a algorithm. Both long-term seasonal trends and high-time-resolution temporal patterns of particle type concentrations were examined. Source identification was supported by comparison with known source samples. Potential source contribution functions were used to identify source regions. This paper describes and evaluates these approaches to data interpretation using examples from the ambient air study to illustrate the methodology and highlight the findings. Furthermore, these ambient examples demonstrate how the application of these strategies enhances the interpretation of single-particle ambient aerosol data. Copyright 2012 American Association for Aerosol Research