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Receptor models infer contributions from particulate matter (PM) source types using multivariate measurements of particle chemical and physical properties. Receptor models complement source models that estimate concentrations from emissions inventories and transport meteorology. Enrichment factor, chemical mass balance, multiple linear regression, eigenvector. edge detection, neural network, aerosol evolution, and aerosol equilibrium models have all been used to solve particulate air quality problems, and more than 500 citations of their theory and application document these uses. While elements, ions, and carbons were often used to apportion TSP, PM10, and PM2.5 among many source types, many of these components have been reduced in source emissions such that more complex measurements of carbon fractions, specific organic compounds, single particle characteristics, and isotopic abundances now need to be measured in source and receptor samples. Compliance monitoring networks are not usually designed to obtain data for the observables, locations, and time periods that allow receptor models to be applied. Measurements from existing networks can be used to form conceptual models that allow the needed monitoring network to be optimized. The framework for using receptor models to solve air quality problems consists of: (1) formulating a conceptual model; (2) identifying potential sources; (3) characterizing source emissions; (4) obtaining and analyzing ambient PM samples for major components and source markers; (5) confirming source types with multivariate receptor models; (6) quantifying source contributions with the chemical mass balance; (7) estimating profile changes and the limiting precursor gases for secondary aerosols; and (8) reconciling receptor modeling results with source models, emissions inventories, and receptor data analyses.

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[1] Sign and magnitude of solar aerosol direct radiative forcing are largely determined by the aerosol single scattering albedo (SSA) and the albedo of the underlying scene (e.g., surface albedo). On a global scale, mineral dust aerosol has the largest mass emission rate, average column mass burden, and average optical depth of all aerosol types. Therefore, better understanding of its optical properties with a focus on SSA is of great importance. Here, we entrain ten bulk soil samples from deserts and semi-arid regions in the Arabian Peninsula, from locations representative of the Sahara and Sahel in North Africa, and from Northeast Africa and South-Central Asia. We segregate the fine particle fraction (aerodynamic diameter < 2.5 mm) and measure its iron content with an x-ray fluorescence (XRF) spectrometer and its aerosol SSA and absorption, scattering, and extinction Ångström coefficients for two wavelengths (405 nm and 870 nm) with a photoacoustic instrument with integrating reciprocal nephelometer. Results show that SSA is much lower at 405 nm than at 870 nm and that SSA at both wavelengths is dominated by and linearly correlated with the iron content of the entrained mineral dust. The second result points toward potential use of SSA remote sensing to measure aerosol iron content and vice versa, measurements of iron content with simple filter sampling followed by XRF analysis, providing an assessment of SSA for aerosol radiative forcing calculations and modeling. Average Ångström coefficients were ≈3.2 for absorption, ≈À0.4 for scattering, and ≈À0.3 for extinction and showed little correlation with iron content.Citation: Moosmüller, H., J. P. Engelbrecht, M. Skiba, G. Frey, R. K. Chakrabarty, and W. P. Arnott (2012), Single scattering albedo of fine mineral dust aerosols controlled by iron concentration,