Receptor modeling for multiple time resolved species: The Baltimore supersite

Ogulei, David; Hopke, Philip K.; Zhou, Liming; Paatero, Pentti; Park, Seung Shik; Ondov, John M.

doi:10.1016/j.atmosenv.2005.03.012

Cited by 84 publications

(62 citation statements)

References 38 publications

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“…PDRM confines the solution within preset bounds determined from dispersion models, as described in Table 1b. Zhou et al 46 and Ogulei et al 47 used a multilinear engine (ME or ME2) to analyze PM 2.5 speciation data combining measurements on different time scales. This cannot be achieved by PMF without averaging measurements to the same time scale.…”

Section: Solutions To the Receptor Model Equationsmentioning

confidence: 99%

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

Watson

Chen

Chow

et al. 2008

Journal of the Air & Waste Management Association

222

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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Section: Solutions To the Receptor Model Equationsmentioning

confidence: 99%

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

Watson

Chen

Chow

et al. 2008

Journal of the Air & Waste Management Association

222

132

View full text Add to dashboard Cite

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“…This technique has also been used extensively in studies of aerosol sources (Liu et al 2005;Maykut et al 2003;Kim et al 2005;Reff et al 2007;Ogulei et al 2005;Lestari and Mauliadi 2009). The contributions of the source resulting from the PMF analyses were calculated by multiple regression of the G matrix (Paatero 1997) against the measured mass concentrations.…”

Section: Mass Balance and Source Apportionment Of Water-soluble Ions mentioning

confidence: 99%

Chemical Characteristics of Fine Particles (PM₁) from Xi'an, China

Shen

Cao

Arimoto

et al. 2010

Aerosol Science and Technology

102

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Daily mass concentrations of water-soluble inorganic (WS-i) ions, organic carbon (OC), and elemental carbon (EC) were determined for fine particulate matter (PM 1 , particles < 1.0 µm in diameter) collected at Xi'an, China. The annual mean PM 1 mass concentration was 127.3 ± 62.1 µg m -3 : WS-i ions accounted for ∼38% of the PM 1 mass; carbonaceous aerosol was ∼30%; and an unidentified fraction, probably mostly mineral dust, was ∼32%. WS-i ions and carbonaceous aerosol were the dominant species in winter and autumn, whereas the unidentified fraction had stronger influences in spring and summer. Ion balance calculations indicate that PM 1 was more acidic than PM 2.5 from the same site. PM 1 mass, sulfate and nitrate concentrations followed the order winter > spring > autumn > summer, but OC and EC levels were higher in autumn than spring. Annual mean OC and EC concentrations were 21.0 ± 12.0 µg m −3 and 5.1 ± 2.7 µg m -3 with high OC/EC ratios, presumably reflecting emissions from coal combustion and biomass burning. Secondary organic carbon, estimated from the minimum OC/EC ratios, comprised 28.9% of the OC. Positive matrix factorization (PMF) analysis indicates that secondary aerosol and combustion emissions were the major sources for PM 1 .

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“…Even in the case of "natural sources", methods for disentangling true natural elements from contaminants originating from polluted areas may be necessary. Finally, PM samples are usually monitored on a daily basis, whereas (in particular in towns) a shorter time resolution (1-2 h) would be necessary to separate the rapidly changing sources related to urban life (Ogulei et al, 2005(Ogulei et al, , 2006Park et al, 2005Park et al, , 2006Zhou et al, 2005;Mazzei et al, , 2007.…”

Section: Introductionmentioning

confidence: 99%

Characterization of particulate matter sources in an urban environment

Mazzei

D’Alessandro²,

Lucarelli³

et al. 2008

Science of The Total Environment

244

100

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Daily time series measurements of elements or compounds are widely used to apportion the contribution of specific sources of particulate matter concentration in the atmosphere. We present results obtained for the urban area of Genoa (Italy) based on several hundred of PM10, PM2.5 and PM1 daily samples collected in sites with different geo-morphological and urbanization characteristics. Elemental concentrations of Na to Pb were obtained through Energy Dispersive X-Ray Fluorescence (ED-XRF), and the contributions of specific sources of particulate matter (PM) concentration were apportioned through Positive Matrix Factorization (PMF). By sampling at different sites we were able to obtain, in each PM fraction, the average and stable values for the tracers of specific sources, in particular traffic (Cu, Zn, Pb) and heavy oil combustion (V, Ni). We could also identify and quote the contamination of anthropogenic PM in "natural" sources (sea, soil dust). Sampling at several sites in the same urban area allowed us to resolve local characteristics as well as to quote average values. IntroductionIn recent years, atmospheric aerosols have been studied extensively (Charlson et al., 1992;Harrison et al., 2001;Satheesh and Moorthy, 2005 , 2002a,b;Stieb et al., 2002;Fernandez et al., 2003).Up to now, PM concentrations have been routinely monitored. However, this level of monitoring is insufficient and a measurement of the elemental and chemical composition of PM is recommended in order to achieve a more complete picture. Indeed concentration limits have been set in Europe for some toxic elements (Pb, Ni, Cd, Hg; see the recent European Directive 2004/107/CE). Element and/or compound measurements can also help to trace specific emission patterns. Thus, the knowledge of the chemical composition of particulate matter can be used to evaluate the impacts of the various pollution sources on air quality. Several "source apportionment" strategies have been developed; receptor models (Gordon, 1988) are presently considered the most effective approach. These models usually provide three pieces of information: the number of (major) sources of particulate matter, the source profiles and the mass contribution of each source to total PM. These models single out groups of elements with correlated concentration trends, which

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Receptor modeling for multiple time resolved species: The Baltimore supersite

Cited by 84 publications

References 38 publications

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

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

Chemical Characteristics of Fine Particles (PM₁) from Xi'an, China

Characterization of particulate matter sources in an urban environment

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Receptor modeling for multiple time resolved species: The Baltimore supersite

Cited by 84 publications

References 38 publications

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

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

Chemical Characteristics of Fine Particles (PM1) from Xi'an, China

Characterization of particulate matter sources in an urban environment

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Product

Resources

About

Chemical Characteristics of Fine Particles (PM₁) from Xi'an, China