1993
DOI: 10.1006/eesa.1993.1011
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Application of Pattern Recognition Techniques to Evaluate Polychlorinated Dibenzo-p-dioxin and Dibenzofuran Distributions in Surficial Sediments from the Lower Passaic River and Newark Bay

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Cited by 37 publications
(41 citation statements)
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“…The homologue and congener profiles of PCDD/Fs often are characteristic of pollutant sources and can be used to identify them [1,[17][18][19][20]. In the Xijiang River, the most obvious features are the high contribution of OCDD to the total PCDD/F concentration and the relatively low levels of PCDFs.…”
Section: Possible Sources Of Pcdd/fs In the Xijiang Rivermentioning
confidence: 99%
“…The homologue and congener profiles of PCDD/Fs often are characteristic of pollutant sources and can be used to identify them [1,[17][18][19][20]. In the Xijiang River, the most obvious features are the high contribution of OCDD to the total PCDD/F concentration and the relatively low levels of PCDFs.…”
Section: Possible Sources Of Pcdd/fs In the Xijiang Rivermentioning
confidence: 99%
“…In the context of evaluating congener patterns of dioxin-like compounds, cluster analysis has been used to both group variables (congeners) [8,9] and observations (samples) [10][11][12][13][14][15]. Wenning et al [10] used both principal components analysis (PCA) and cluster analysis to identify five distinct sources in 19 samples collected from Newark Bay, New Jersey, USA. No source identifications were made; however, they concluded that the congener patterns in Newark Bay samples were different from those collected from a nearby former 2,4,5-trichlorophenoxyacetic acid manufacturing plant.…”
Section: Introductionmentioning
confidence: 99%
“…The application of chemometrics to identify and distinguish chemical``fingerprint'' patterns has been an important tool to describe the similarities and differences among environmental samples. It provides a classification method for ascertaining the identity of samples and determining the most significant chemical constituents or physical attributes among different samples (Dunn et al, 1984;Stalling et al, 1985;Schwartz and Stalling, 1991;Wenning et al, 1993 ).…”
Section: Introductionmentioning
confidence: 99%