Review of receptor model fundamentals

Henry, Ronald C.; Lewis, Charles; Hopke, Philip K.; Williamson, H J

doi:10.1016/0004-6981(84)90375-5

Cited by 410 publications

(154 citation statements)

References 24 publications

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“…All datasets were considered good for the receptor model. According to the EPA PMF3 guide, PMF is often used for datasets with over 100 samples; moreover these datasets respect the suggestions by Henry et al (1984) (ratio between degrees of freedom and number of variables higher than 100) and by Thurston and Spengler (1985) (number of samples exceed the number of variables by at least a factor of three). Therefore we could apply PMF to each dataset separately.…”

Section: Contribution Of Ship Traffic To Metals In Pm 10mentioning

confidence: 99%

Impact of maritime traffic on polycyclic aromatic hydrocarbons, metals and particulate matter in Venice air

Gregoris

Barbaro

Morabito

et al. 2015

Environ Sci Pollut Res

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Harbours are important hubs for economic growth in both tourism and commercial activities. They are also an environmental burden being a source of atmospheric pollution often localized near cities and industrial complexes. The aim of this study is to quantify the relative contribution of maritime traffic and harbour activities to atmospheric pollutant concentration in the Venice lagoon. The impact of ship traffic was quantified on various pollutants that are not directly included in the current European legislation for shipping emission reduction: (i) gaseous and particulate PAHs; (ii) metals in PM 10 ; and (iii) PM 10 and PM 2.5 . All contributions were correlated with the tonnage of ships during the sampling periods and results were used to evaluate the impact of the European Directive 2005/33/EC on air quality in Venice comparing measurements taken before and after the application of the Directive (year 2010). The outcomes suggest that legislation on ship traffic, which focused on the issue of the emissions of sulphur oxides, could be an efficient method also to reduce the impact of shipping on primary particulate matter concentration; on the other hand, we did not observe a significant reduction in the contribution of ship traffic and harbour activities to particulate PAHs and metals.

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Section: Contribution Of Ship Traffic To Metals In Pm 10mentioning

confidence: 99%

Impact of maritime traffic on polycyclic aromatic hydrocarbons, metals and particulate matter in Venice air

Gregoris

Barbaro

Morabito

et al. 2015

Environ Sci Pollut Res

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“…[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.…”

Section: Introductionmentioning

confidence: 99%

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

Watson

Chen

Chow

et al. 2008

Journal of the Air & Waste Management Association

220

132

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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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“…Receptor models apportion the measured PM concentration to their sources. 20 The sources are identified with multivariate statistical methods that assess the correlation between the concentrations of chemical species in particles, assuming that highly correlated chemicals come from the same source. Commonly used receptor models are principal components analysis, chemical mass balance, and positive matrix factorization.…”

Section: Introductionmentioning

confidence: 99%

Sources of Personal Exposure to Fine Particles in Toronto, Ontario, Canada

Kim

Sass-Kortsāk

Purdham

et al. 2005

Journal of the Air & Waste Management Association

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Individuals are exposed to particulate matter from both indoor and outdoor sources. The aim of this study was to compare the relative contributions of three sources of personal exposure to fine particles (PM 2.5 ) by using chemical tracers. The study design incorporated repeated 24-hr personal exposure measurements of air pollution from 28 cardiac-compromised residents of Toronto, Ontario, Canada. Each study participant wore the Rupprecht & Patashnick ChemPass Personal Sampling System 1 day a week for a maximum of 10 weeks. During their individual exposure measurement days the subjects reported to have spent an average of 89% of their time indoors. Particle phase elemental carbon, sulfate, and calcium personal exposure data were used in a mixed-effects model as tracers for outdoor PM 2.5 from traffic-related combustion, regional, and local crustal materials, respectively. These three sources were found to contribute 13% Ϯ 10%, 17% Ϯ 16%, and 7% Ϯ 6% of PM 2.5 exposures. The remaining fraction of the personal PM 2.5 is hypothesized to be predominantly related to indoor sources. For comparison, central site outdoor PM 2.5 measurements for the same dates as personal measurements were used to construct a receptor model using the same three tracers. In this case, traffic-related combustion, regional, and local crustal materials were found to contribute 19% Ϯ 17%, 52% Ϯ 22%, and 10% Ϯ 7%, respectively. Our results indicate that the three outdoor PM 2.5 sources considered are statistically significant contributors to personal exposure to PM 2.5 . Our results also suggest that among the Toronto subjects, who spent a considerable amount of time indoors, exposure to outdoor PM 2.5 includes a greater relative contribution from combustion sources compared with outdoor PM 2.5 measurements where regional sources are the dominant contributor.

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Review of receptor model fundamentals

Cited by 410 publications

References 24 publications

Impact of maritime traffic on polycyclic aromatic hydrocarbons, metals and particulate matter in Venice air

Impact of maritime traffic on polycyclic aromatic hydrocarbons, metals and particulate matter in Venice air

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

Sources of Personal Exposure to Fine Particles in Toronto, Ontario, Canada

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