Receptor modeling application framework for particle source apportionment

Abstract

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.

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

“…These are included in the eight-step procedure recommended by Watson et al, 37 providing a framework for most Supersite source apportionment studies:…”

“…[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] This paper intends to address the following question: "How well can we identify and quantify source contributions using receptor models?" Except for historical and exemplary purposes, this paper limits its investigation to work published since 2000, with a focus on PM source apportionment in the vicinity of the Supersite cities of Atlanta, GA; Baltimore, MD; Fresno, CA; Houston, TX; Los Angeles, CA; New York, NY; Pittsburgh, PA; and St. Louis, MO.…”

Assessment of atmospheric particulate matter and heavy metals: a critical review

“…Thus, compliance monitoring networks should be designed to obtain data for the observables, locations and time periods that allowed receptor models to be applied. Also, measurements from existing networks can be used to form conceptual models that allowed the needed monitoring network to be optimized as suggested by Watson et al (2002). There is need for further work on development of applicable computer software that will allow easy simulation of these models.…”

“…Among these, receptor-oriented modeling is one of the conventional techniques and has been in broad use for source apportionment studies in the past decades (Ke et al 2008). The framework for using receptor models to solve air quality problems consists of: formulating a conceptual model; identifying potential sources; characterizing source emissions; obtaining and analyzing ambient particulate matter samples for major components and source markers; confirming source types with multivariate receptor models; quantifying source contributions with the chemical mass balance; estimating profile changes and the limiting precursor gases for secondary aerosols; and reconciling receptor modeling results with source models, emissions inventories and receptor data analyses (Watson et al 2002).…”

Investigation of Variations in Suspended Particulate Matter with Enforcement of Regulations on Diesel Vehicle Exhaust in Suburban Japan

“…As a general trend, the OC concentration was higher than the EC concentration in each size fraction, except in the < 1.1 µm fraction. Furthermore, most EC and OC was found in the < 1.1 µm fraction, which suggest that they were derived from anthropogenic sources such as combustion processes or diesel vehicle exhaust emissions (16) . Moreover, OC >7 µm also occurred in relatively high concentrations.…”