Inverting cascade impactor data for size-resolved characterization of fine particulate source emissions

Abstract

Abstract In this paper, the inversion processing of cascade impactor data to construe continuous size distributions within fine particulate matter ( PM 2.5 ) is examined for residential oil furnace and fireplace appliance emissions. Impactor data from tests with these emissions sources are selected for the challenges they pose to comprehending the size distributions of aerosol mass and chemical species. In specific, the oil furnace aerosol offers an opportunity to apply data inversion to study a bimodal lognormal distribution in which much of the aerosol mass is impactor-penetrating nanoparticles ( 30 nm ) . The fireplace emissions on the other hand cover the issue of a chemical size distribution, which is subject to particle loss and characterized by a single lognormal, accumulation mode peak. Computational steps relevant to the application of the data inversion are illustrated in detail. Evaluation of correlation coefficients ( ⩾ 0.992 ) indicates that the inversion model predictions fit the impactor data well. Simulations of systematic measurement error ( ± 10 % ) at each impactor stage are shown to have a negligible impact on the inversion results for test data. It is concluded that data inversion can be effective when (i) source emissions contain a portion of particles that falls outside the measurement range of cascade impactors, (ii) a mass size distribution of an individual species is determined without the knowledge of the total mass concentration for that species, or when (iii) losses in the particle charger system are significant.