Receptor modeling for air quality management

Hester, R. E.; Harrison, Roy M.; Hopke, Philip K.

doi:10.1039/9781847550101-00095

Cited by 121 publications

(113 citation statements)

References 5 publications

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“…PCA attempts to capture the maximum amount of variation in the data set, whereas PMF tries to solve the mass balance equation with positive constraints on the mass contributions. PMF also accounts for the uncertainty in each data point as opposed to the overall uncertainty of the fitted model (18). In summary, the PCA and PMF features are not the same.…”

Section: Receptor Modelingmentioning

confidence: 99%

Testing the metals hypothesis in Spokane, Washington.

Claiborn

Larson

Sheppard

2002

Environ Health Perspect

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A 7-year, time-series, epidemiologic study is ongoing in Spokane, Washington, to examine the associations between ambient particulate constituents or sources and health outcomes such as emergency department (ED) visits for asthma or respiratory problems. One of the hypotheses being tested is that particulate toxic metals are associated with these health outcomes. Spokane is a desirable city in which to conduct this study because of its relatively high concentrations of particulate matter, low concentrations of potentially confounding air pollutants, variability of particulate sources, and presence of several potential particulate metals sources. Daily fine-and coarse-fraction particulate samples are analyzed for metals via energy-dispersive X-ray fluorescence (EDXRF) and instrumental neutron activation analysis. Particulate sources are determined using receptor modeling, including chemical mass balancing and positive matrix factorization coupled with partial source contribution function analysis. Principal component analysis has also been used to examine the influence of sources on the daily variability of the chemical composition of particulate samples. Based upon initial analyses using the EDXRF elemental analyses, statistically significant associations were observed between ED visits for asthma and increased combustion products, air stagnation, and fine particulate Zn. Although there is a significant soil particulate component, increased crustal particulate levels were not found to be associated with ED visits for asthma. Further research will clarify whether there is an association between specific health outcomes and either coarse or fine particulate metal species. Spokane is a semiarid, western city with a medium-sized urban population of approximately 200,000, and over 400,000 who live in the Spokane Valley. The area experiences periodic violations of the air quality standards for particulate matter with a mass median aerodynamic diameter less than 10 µm (PM 10 ) and is currently classified as a nonattainment area for PM 10 . There are several particulate "seasons" that lead to greatly differing particulate chemical characteristics and concentrations (10). In the winter, the Spokane Valley is prone to inversions, and in addition to vehicular exhaust, residential wood burning contributes significantly to particulate pollution. In the fall, prescribed agricultural burning of myriad acres of bluegrass seed and wheat stubble fields takes place. In the late summer, during hot, dry periods, Spokane experiences high PM 10 because of re-entrainment of dust from unpaved roads and parking lots, and during windy periods, windblown dust storms may occur (11). In contrast to many urban areas in the Eastern United States, the sulfur dioxide (SO 2 ) concentration, the fraction of secondary PM mass (including both sulfates and nitrates), and the level of particulate acidity are relatively low in Spokane PM (12). Major point sources of particulate pollution include a municipal waste incinerator and an aluminum smelter and...

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Section: Receptor Modelingmentioning

confidence: 99%

Testing the metals hypothesis in Spokane, Washington.

Claiborn

Larson

Sheppard

2002

Environ Health Perspect

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“…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.…”

Section: Introductionmentioning

confidence: 99%

U.S. National PM_2.5Chemical Speciation Monitoring Networks—CSN and IMPROVE: Description of networks

Solomon

Crumpler

Flanagan

et al. 2014

Journal of the Air & Waste Management Association

220

173

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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 modeling for air quality management

Cited by 121 publications

References 5 publications

Testing the metals hypothesis in Spokane, Washington.

Testing the metals hypothesis in Spokane, Washington.

U.S. National PM_2.5Chemical Speciation Monitoring Networks—CSN and IMPROVE: Description of networks

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

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Receptor modeling for air quality management

Cited by 121 publications

References 5 publications

Testing the metals hypothesis in Spokane, Washington.

Testing the metals hypothesis in Spokane, Washington.

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

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

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Product

Resources

About

U.S. National PM_2.5Chemical Speciation Monitoring Networks—CSN and IMPROVE: Description of networks