UC Davis

Previous work successfully implemented a novel system that uses a single particle mass spectrometer to conditionally sample size-segregated, source-oriented particles from the ambient atmosphere in real-time. The underlying hypothesis is that the composition of individual particles is a metric of particle source and thus sampling particles based on composition should be synonymous with sampling based on source. System operation relies on real-time pattern recognition to control the actuation of different ChemVol samplers, where each ChemVol is associated with a unique composition signature. In the current work, a synthesis of data collected during these studies is used in retrospect to reconcile the actual source combinations contributing to the particles collected by each ChemVol. Source attribution is based on correlations between ChemVol sampling periods and coincident wind direction and temporal emissions patterns, coupled to knowledge of single particle composition and surrounding sources. Residential and commercial cooking, vehicular emissions, residential heating and highly processed regional background PM were identified as the major sources. Results show that real-time patterns in single particle mixing state correctly identified specific sources and that these sources were successfully separated into different ChemVols for both summer and winter seasons.