Receptor models are used to identify and quantify source contributions to particulate matter and volatile organic compounds based on measurements of many chemical components at receptor sites. These components are selected based on their consistent appearance in some source types and their absence in others. UNMIX, positive matrix factorization (PMF), and effective variance are different solutions to the chemical mass balance (CMB) receptor model equations and are implemented on available software. In their more general form, the CMB equations allow spatial, temporal, transport, and particle size profiles to be combined with chemical source profiles for improved source resolution. Although UNMIX and PMF do not use source profiles explicitly as input data, they still require measured profiles to justify their derived source factors. The U.S. Supersites Program provided advanced datasets to apply these CMB solutions in different urban areas. Still lacking are better characterization of source emissions, new methods to estimate profile changes between source and receptor, and systematic sensitivity tests of deviations from receptor model assumptions. INTRODUCTIONReceptor-oriented source apportionment models infer source contributions and atmospheric processes from air quality measurements. Receptor models complement, rather than replace, source-oriented dispersion and chemical transformation models that begin with source emission rates to estimate ambient concentrations. 1,2 Source and receptor models are mathematical representations of reality, requiring simplifying assumptions that create uncertainty. Applying both types of models to the same situation allows them to be improved when their results diverge and lends confidence to their results when they agree. 3

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PM 2.5 , or fine particulate matter, is a category of air pollutant consisting of particles with effective aerodynamic diameter equal to or less than 2.5 μm. These particles have been linked to human health impacts as well as regional haze, visibility, and climate change issues. Due to cost and space restrictions, the U.S. Environmental Protection Agency monitoring network remains spatially sparse. To increase the spatial resolution of monitoring, previous studies have used satellite data to estimate ground‐level PM concentrations, despite these estimates being associated with moderate to large uncertainties when relating a column measure of aerosol (aerosol optical depth) with surface measurements. To this end, we discuss a low‐cost air quality monitor (LCAQM) network deployed in California. In this study, we present an application of LCAQM and satellite data for quantifying the impact of wildfires in California during October 2017. The impacts of fires on PM 2.5 concentration at varying temporal (hourly, daily, and weekly) and spatial (local to regional) scales have been evaluated. Comparison between low‐cost air quality sensors and reference‐grade air quality instruments shows expected performance with moderate to high uncertainties. The LCAQM measurements, in the absence of federal equivalent method data, were also found to be very useful in developing statistical models to convert aerosol optical depth into PM 2.5 with performance of satellite‐derived PM 2.5 , similar to that obtained using the federal equivalent method data. This paper also highlights challenges associated with both LCAQM and satellite‐based PM 2.5 measurements, which require further investigation and research.

Recent improvements in integrated and continuous PM 2.5 mass and chemical measurements from the Supersite program and related studies in the past decade are summarized. Analytical capabilities of the measurement methods, including accuracy, precision, interferences, minimum detectable levels, comparability, and data completeness are documented. Upstream denuders followed by filter packs in integrated samplers allow an estimation of sampling artifacts. Efforts are needed to: (1) address positive and negative artifacts for organic carbon (OC), and (2) develop carbon standards to better separate organic versus elemental carbon (EC) under different temperature settings and analysis atmospheres. Advances in thermal desorption followed by gas chromatography/ mass spectrometry (GC/MS) provide organic speciation of approximately 130 nonpolar compounds (e.g., n-alkanes, alkenes, hopanes, steranes, and polycyclic aromatic hydrocarbons [PAHs]) using small portions of filters from existing integrated samples. Speciation of water-soluble OC (WSOC) using ion chromatography (IC)-based instruments can replace labor-intensive solvent extraction for many compounds used as source markers. Thermal gasbased continuous nitrate and sulfate measurements underestimate filter ions by 10 -50% and require calibration against on-site filter-based measurements. IC-based instruments provide multiple ions and report comparable (Ϯ10%) results to filter-based measurements. Maintaining a greater than 80% data capture rate in continuous instruments is labor intensive and requires experienced operators. Several instruments quantify black carbon (BC) by optical or photoacoustic methods, or EC by thermal methods. A few instruments provide real-time OC, EC, and organic speciation. BC and EC concentrations from continuous instruments are highly correlated but the concentrations differ by a factor of two or more. Site-and season-specific mass absorption efficiencies are needed to convert light absorption to BC. Particle mass spectrometers, although semiquantitative, provide much information on particle size and composition related to formation, growth, and characteristics over short averaging times. Efforts are made to quantify mass by collocating with other particle sizing instruments. Common parameters should be identified and consistent approaches are needed to establish comparability among measurements.