Source Apportionment: Findings from the U.S. Supersites Program

Abstract

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

Concentration-weighted trajectory approach to identifying potential sources of speciated atmospheric mercury at an urban coastal site in Nova Scotia, Canada

“…unlikely for airflow to pass over a particular area). Known as the tailing effect, the few number of trajectory segment endpoints can lead to the false identification of source areas because the concentrations are uniformly weighted along a trajectory (Watson et al, 2008;Jeong et al, 2011). Although a minimum of 2 trajectory segment endpoints per grid cell seems like a small number, it is equivalent to two sets of 3 h Hg concentrations and trajectory residence times in the seasonal CWT calculation.…”

“…This is also known as the conditional probability function as previously described in Liu et al (2007), Watson et al (2008), and Huang et al (2010). The analysis used wind direction data measured at the Dartmouth site.…”

“…RTWC applies a similar concept; however, concentrations are equally divided along each trajectory initially and then the logarithm of the redistributed concentrations is used as a weighting factor. Some of these models have been reviewed by Watson et al (2008), which include a summary of the models' strengths and weaknesses. Model comparisons found comparable results when Concentration Fields (using logarithmic concentrations), CWT, and PSCF were applied to virtual sources and modeled concentrations (Kabashnikov et al, 2011).…”

Optical Calibration and Equivalence of a Multiwavelength Thermal/Optical Carbon Analyzer

“…Absorptions are translated to BC and BrC with mass absorption efficiencies derived from source emission measurements (i.e., part of the source profile). A more complex, and potentially accurate, source apportionment approach would look at carbon associated with each of the seven wavelengths in source samples and use these in a receptor-oriented model along with other source indicators (Watson et al, 2008).…”

U.S. National PM2.5Chemical Speciation Monitoring Networks—CSN and IMPROVE: Description of networks

“…Understanding the sources of this complex mixture of ambient PM is important because PM has been shown to have adverse effects on human health, degrade visibility, increase the acidity of lakes and streams, damage materials and crops, and impact global climate (U.S. Environmental Protection Agency [EPA], 2009a). Linking sources to ambient PM concentrations and ultimately to health effects and visibility degradation is achieved, in part, by understanding the chemical composition of atmospheric PM and how it varies temporally and spatially (e.g., Hopke, 1991;Schauer et al, 1996;Hopke, 2003;Brook et al, 2004;Watson et al, 2008;Solomon et al, 2012). However, near the end of the 20th century detailed information on particle composition was lacking for many locations around the country.…”