Particulate Dispersion Model Evaluation: A New Approach Using Receptor Models

Core, John E.; Cooper, J.A.; Hanrahan, Patrick L.

doi:10.1080/00022470.1982.10465524

Cited by 18 publications

(7 citation statements)

References 3 publications

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“…Reconciliation of results from these independent approaches has been successful in identifying non-inventoried sources, improving emission estimates, and persuading stakeholders that emission reduction plans will actually work. [151][152][153] The Rule sets a goal of attaining natural visibility conditions rather than a standard that must be attained by force of law and sanctions. The Rule states that "…all that is 'enforceable' is the set of control measures which the State has adopted to meet that goal."…”

Section: Guidance Formentioning

confidence: 99%

Visibility: Science and Regulation

Watson

2002

Journal of the Air & Waste Management Association

936

548

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The 1999 Regional Haze Rule provides a context for this review of visibility, the science that describes it, and the use of that science in regulatory guidance. The scientific basis for the 1999 regulation is adequate. The deciview metric that tracks progress is an imperfect but objective measure of what people see near the prevailing visual range. The definition of natural visibility conditions is adequate for current planning, but it will need to be refined as visibility improves. Emissions from other countries will set achievable levels above those produced by natural sources. Some natural events, notably dust storms and wildfires, are episodic and cannot be represented by annual average background values or emission estimates. Sulfur dioxide (SO2) emission reductions correspond with lower sulfate (SO4(2-)) concentrations and visibility improvements in the regions where these have occurred. Non-road emissions have been growing more rapidly than emissions from other sources, which have remained stable or decreased since 1970. Simpler models representing transport, limiting precursor pollutants, and gas-to-particle equilibrium should be used to understand where and when emission reductions will be effective, rather than large complex models that have insufficient input and validation measurements. Examples of model-based source attribution show large differences among estimates from various modeling systems and with ambient measurements.

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Section: Guidance Formentioning

confidence: 99%

Visibility: Science and Regulation

Watson

2002

Journal of the Air & Waste Management Association

936

548

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“…These equations are solved for the source contributions when ambient concentrations and source profiles are supplied as input. Several different solution methods have been applied, but the effective variance least squares estimation method is most commonly used because it incorporates precision estimates for all of the input data into the solution and propagates these errors to the model outputs Britt and Luecke (1973), Friedlander (1973aFriedlander ( , 1981, Watson (1979), Belsley et al (1980), Cooper and Watson (1980), Gordon et al (1981), Core et al (1982), deCesar and , Gerlach et al (1982), Anderson et al (1984), Dzubay et al (1984), Stevens and Pace (1984), Daisey (1985), Gordon and Olmez (1986), Holzman et al (1986), US EPA (1987), Vong et al (1988), Larson and Vong (1989), Wang and Hopke (1989), Matamala and Nininger (1990), Kim et al (1992a), Song and Hopke (1996a,b), Gleser (1997), Hopke and Song (1997), Gordon (1980Gordon ( , 1988, Watson et al (1981Watson et al ( , 1984Watson et al ( , 1990bWatson et al ( , 1991Watson et al ( , 1997bWatson et al ( , 1998c, Henry (1982Henry ( , 1992Henry ( , 1997a, Currie et al (1984Currie et al ( , 1994, deCesar et al…”

Section: Enrichment Factorsmentioning

confidence: 99%

Receptor modeling application framework for particle source apportionment

Watson¹,

Zhu²,

Chow³

et al. 2002

Chemosphere

269

144

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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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“…The most straightforward method of "combining" source and receptor models is to apply each independently to the same situation as did Core et al 15 for cross-validation purposes. The model results can be compared for the purpose of diagnosing and correcting inconsistencies.…”

Section: Methods Of Approachmentioning

confidence: 99%