Source apportionment of Baltimore aerosol from combined size distribution and chemical composition data

Ogulei, David; Hopke, Philip K.; Zhou, Liming; Pancras, J. Patrick; Nair, Narayanan P.; Ondov, John M.

doi:10.1016/j.atmosenv.2005.11.075

Cited by 111 publications

(59 citation statements)

References 45 publications

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“…Ogulei et al 96 demonstrated how this could be done in two ways. First, particle concentrations in various size intervals (bins) and species concentrations were pooled as PMF input variables.…”

Section: How Well Can We Identify and Quantify Source Contributions Umentioning

confidence: 99%

“…The second approach involved the PLS solution, which sought to identify common factors in time series of chemical concentrations and particle size distributions. Ogulei et al 96 identified four factors (latent variables) but the physical significance of those factors was unclear. PLS can be used to determine the degree of dependence between two variables (e.g., size, chemical composition, or meteorology) and whether they can be combined into a receptor model analysis.…”

Section: How Well Can We Identify and Quantify Source Contributions Umentioning

confidence: 99%

“…It assumes that the predictor and response variables are controlled by independent "latent variables" less in number than either the predictor or the response variables. In recent applications, 96 PM chemical composition and size distribution are used as predictor (X ) and response (Y ) variables, respectively. Equation 2 is modified to:…”

Section: Pls 128mentioning

confidence: 99%

See 2 more Smart Citations

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: How Well Can We Identify and Quantify Source Contributions Umentioning

confidence: 99%

Section: How Well Can We Identify and Quantify Source Contributions Umentioning

confidence: 99%

See 1 more Smart Citation

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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“…Secondary aerosols of particulate matter include nitrates and sulfates emitted directly from anthropogenic or natural sources, and/or formed in the atmosphere. Biomass burns, wood burning, and vegetative burning have been characterized as having high concentrations of potassium and sulphates by various source apportionment studies (Ogulei et al, 2006;Wu et al, 2007). This source also reflects regional or long-range transport.…”

Section: Source Apportionmentmentioning

confidence: 99%

“…Source 4 in the current study was observed to be composed of higher mass fractions of secondary nitrates and sulphates, namely NO 3 − , NH 4 + and SO 4 2− . In source apportionment studies conducted by Kim et al, (2007) in Ohio, Raman and Hopke (2007) in New York and Tsai and Chen (2006) secondary nitrates, and the secondary sulfates have been identified as a major source of PM 2.5 in various source apportionment studies Ogulei et al, 2006). The current PMF analysis shows that secondary aerosols contributed to about 24.33%, 22.27% and 37.25% for PM 10 , PM 2.5 and PM 0.1 mass concentrations, respectively.…”

Section: Source Apportionmentmentioning

confidence: 99%

Source Characterization and Apportionment of PM10, PM2.5 and PM0.1 by Using Positive Matrix Factorization

Gugamsetty

Han

Liu

et al. 2012

Aerosol Air Qual. Res.

209

117

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Ambient Particulate Matters (PM 10 , PM 2.5 and PM 0.1 ) were investigated at Shinjung station in New Taipei City, Taiwan. Samples were collected simultaneously using a dichotomous sampler (Andersen Model SA-241) and a MOUDI (MSP Model 110) over a 24-h period from May 2011 to November 2011 at Shinjung station. Samples were analyzed for metallic trace elements using ion coupled plasma mass spectroscopy (ICP-MS) and ionic compounds by ion chromatography (IC). The average concentrations of PM 10 , PM 2.5 and PM 0.1 were found to be 39.45 ± 11.58, 21.82 ± 7.50 and 1.42 ± 0.56 μg/m 3 , respectively. Based on the chemical information, positive matrix factorization (PMF) was used to identify PM sources. A total of five source types were identified, including soil dust, vehicle emissions, sea salt, industrial emissions and secondary aerosols, and their contributions were estimated using PMF. The crustal enrichment factors (EF) were calculated using Al as a reference for the trace metal species to identify the sources. Conditional probability functions (CPF) were computed using wind profiles and factor contributions. The results of CPF analysis were used to identify local point sources. The results suggest a competitive relationship between anthropogenic and natural source processes over the monitoring station.

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Comprehensive assessment of PM_2.5 physicochemical properties during the Southeast Asia dry season (southwest monsoon)

et al. 2016

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A comprehensive assessment of fine particulate matter (PM2.5) compositions during the Southeast Asia dry season is presented. Samples of PM2.5 were collected between 24 June and 14 September 2014 using a high‐volume sampler. Water‐soluble ions, trace species, rare earth elements, and a range of elemental carbon (EC) and organic carbon were analyzed. The characterization and source apportionment of PM2.5 were investigated. The results showed that the 24 h PM2.5 concentration ranged from 6.64 to 68.2 µg m−3. Meteorological driving factors strongly governed the diurnal concentration of aerosol, while the traffic in the morning and evening rush hours coincided with higher levels of CO and NO2. The correlation analysis for non sea‐salt K+‐EC showed that EC is potentially associated with biomass burning events, while the formation of secondary organic carbon had a moderate association with motor vehicle emissions. Positive matrix factorization (PMF) version 5.0 identified the sources of PM2.5: (i) biomass burning coupled with sea salt [I] (7%), (ii) aged sea salt and mixed industrial emissions (5%), (iii) road dust and fuel oil combustion (7%), (iv) coal‐fired combustion (25%), (v) mineral dust (8%), (vi) secondary inorganic aerosol (SIA) coupled with F− (15%), and (vii) motor vehicle emissions coupled with sea salt [II] (24%). Motor vehicle emissions, SIA, and coal‐fired power plant are the predominant sources contributing to PM2.5. The response of the potential source contribution function and Hybrid Single‐Particle Lagrangian Integrated Trajectory backward trajectory model suggest that the outline of source regions were consistent to the sources by PMF 5.0.

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Source apportionment of Baltimore aerosol from combined size distribution and chemical composition data

Cited by 111 publications

References 45 publications

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

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

Source Characterization and Apportionment of PM10, PM2.5 and PM0.1 by Using Positive Matrix Factorization

Comprehensive assessment of PM_2.5 physicochemical properties during the Southeast Asia dry season (southwest monsoon)

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Source apportionment of Baltimore aerosol from combined size distribution and chemical composition data

Cited by 111 publications

References 45 publications

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

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

Source Characterization and Apportionment of PM10, PM2.5 and PM0.1 by Using Positive Matrix Factorization

Comprehensive assessment of PM2.5 physicochemical properties during the Southeast Asia dry season (southwest monsoon)

Contact Info

Product

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Comprehensive assessment of PM_2.5 physicochemical properties during the Southeast Asia dry season (southwest monsoon)