Relationships between fine particulate species, gaseous pollutants and meteorological parameters in detroit

Wolff, George T.; Korsog, Patricia E.; Kelly, Nelson A.; Ferman, Martin A.

doi:10.1016/0004-6981(85)90264-1

Cited by 43 publications

(17 citation statements)

References 16 publications

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“…Furthermore, the correlation coefficient between S0 4 f = and HUP, which has been used as an indicator of heterogeneous S04f = formation, 22 is only 0.24. The role of photochemistry is an expected result, and it is consistent with results from other locations in the eastern U.S. 36 and indicates that a significant portion of the S0 4 f = variability depends upon the residence time of the air masses over the coal combustion areas of the Midwest. The much lower correlation between S04f = and NE(t), the residence time over the NE, where fuel oil combustion dominates over coal combustion, is consistent with the low Vf -S04f = correlation and the high Sef -S04f = correlation coefficient.…”

Section: Sources Of Hazesupporting

confidence: 86%

Measurements of Sulfur Oxides, Nitrogen Oxides, Haze and Fine Particles at a Rural Site on the Atlantic Coast

Wolff

Kelly

Ferman

et al. 1986

Journal of the Air Pollution Control Association

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Section: Sources Of Hazesupporting

confidence: 86%

Measurements of Sulfur Oxides, Nitrogen Oxides, Haze and Fine Particles at a Rural Site on the Atlantic Coast

Wolff

Kelly

Ferman

et al. 1986

Journal of the Air Pollution Control Association

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“…Principal component analysis (PCA) (Hopke, 1985) uses measurements of pollutant concentrations at a sampling site to identify significantly correlated variables. This method extracts components that explain the majority of the variance in the data matrix, which are then qualitatively interpreted as possible sources (Hopke et al, 1976;Hopke, 1985;Wolff et al, 1985). PCA is often useful for providing information regarding source characteristics in terms of the elements that are associated with a given source type.…”

Section: Principal Component Analysismentioning

confidence: 99%

Multi-element Analysis and Characterization of Atmospheric Particulate Pollution in Dhaka

Begum

Biswas

Hopke

et al. 2006

Aerosol Air Qual. Res.

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PIXE (Proton Induced X-Ray Emission) and PIGE (Proton Induced Gamma Ray Emission) have been used to measure the concentration of over 20 elements present in coarse and fine atmospheric particle samples. These samples were collected at a semi-residential site at Dhaka, Bangladesh from July 2002 to June 2003. The multi-elemental capability of PIXE and PIGE enabled the use of these elemental-concentration data to define fingerprints of various coarse and fine particle sources in the atmosphere using factor analysis along with linear regression. Biplots between a major component of a given source identified by the factor analysis with another signature element were prepared. Regression analyses could then be performed on a subset of the plot to quantify the relationship between these constituents in this fingerprint. The identified fingerprints included anthropogenic sources, such as motor vehicles, Zn source, two-stroke engine, and fugitive Pb; as well as natural sources, such as sea spray and soil dust. In this study, the chemical compositions of elements of these fingerprints were compared with the results obtained from PMF modeling that had been obtained in a previous study.

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“…2 PCA extracts the principal components, explaining the majority of variance of the data matrix that are then qualitatively interpreted as possible sources. Although PCA has been applied as a tool for source identification in some air quality studies, 3,4 it suffers from several drawbacks in general. The factors of PCA are rarely physically explainable without further transformation (rotation), and no fully satisfactory rotation techniques have yet been found.…”

Section: Introductionmentioning

confidence: 99%

Identification of Sources of Phoenix Aerosol by Positive Matrix Factorization

Ramadan

Song

Hopke

2000

Journal of the Air & Waste Management Association

222

147

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Chemical composition data for fine and coarse particles collected in Phoenix, AZ, were analyzed using positive matrix factorization (PMF). The objective was to identify the possible aerosol sources at the sampling site. PMF uses estimates of the error in the data to provide optimum data point scaling and permits a better treatment of missing and below-detection-limit values. It also applies nonnegativity constraints to the factors. Two sets of fine particle samples were collected by different samplers. Each of the resulting fine particle data sets was analyzed separately. For each fine particle data set, eight factors were obtained, identified as (1) biomass burning characterized by high concentrations of organic carbon (OC), elemental carbon (EC), and K; (2) wood burning with high concentrations of Na, K, OC, and EC; (3) motor vehicles with high concentrations of OC and EC; (4) nonferrous smelting process characterized by Cu, Zn, As, and Pb; (5) heavyduty diesel characterized by high EC, OC, and Mn; (6) sea-salt factor dominated by Na and Cl; (7) soil with high values for Al, Si, Ca, Ti, and Fe; and (8) secondary aerosol with SO 4 -2 and OC that may represent coal-fired power plant emissions.For the coarse particle samples, a five-factor model gave source profiles that are attributed to be (1) sea salt, (2) soil, (3) Fe source/motor vehicle, (4) construction (high Ca), and (5) coal-fired power plant. Regression of the PM mass against the factor scores was performed to estimate the mass contributions of the resolved sources. The major sources for the fine particles were motor vehicles, vegetation burning factors (biomass and wood burning), and coal-fired power plants. These sources contributed most of the fine aerosol mass by emitting carbonaceous IMPLICATIONS A new form of factor analysis, positive matrix factorization, has been applied to PM 10 and two separate sets of PM 2.5 aerosol composition data derived from samples collected in Phoenix, AZ. The results for the two sets of samples were quite comparable, and the results overall show a good resolution of the major sources of atmospheric PM.

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Relationships between fine particulate species, gaseous pollutants and meteorological parameters in detroit

Cited by 43 publications

References 16 publications

Measurements of Sulfur Oxides, Nitrogen Oxides, Haze and Fine Particles at a Rural Site on the Atlantic Coast

Measurements of Sulfur Oxides, Nitrogen Oxides, Haze and Fine Particles at a Rural Site on the Atlantic Coast

Multi-element Analysis and Characterization of Atmospheric Particulate Pollution in Dhaka

Identification of Sources of Phoenix Aerosol by Positive Matrix Factorization

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