Atmospheric Concentrations of PCB-11 Near the Great Lakes Have Not Decreased Since 2004

Hites, Ronald A.

doi:10.1021/acs.estlett.8b00019

Cited by 22 publications

(22 citation statements)

References 16 publications

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“…Because paint is applied to surfaces in thin coats, PCB 11 likely volatilizes into the air more efficiently than heavier PCB congeners. This is also seen in [19].…”

Section: F Determining Sourcessupporting

confidence: 55%

Signal Processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples

et al. 2020

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The main contribution of this interdisciplinary work is a robust computational framework to autonomously discover and quantify previously unknown associations between well-known (target) and potentially unknown (non-target) toxic industrial air pollutants. In this work, the variability of polychlorinated biphenyl (PCB) data is evaluated using a combination of statistical, signal processing, and graph-based informatics techniques to interpret the raw instrument signal from gas chromatographymass spectrometry (GC/MS/MS) data sets. Specifically, minimum mean-squared techniques from the adaptive signal processing literature are extended to detect and separate coeluted (overlapped) peaks in the raw instrument signal. A graph-based visualization is provided which bridges two complementary approaches to quantitative pollution studies: (i) peak-cognizant target analysis (limits data analysis to few well-known compounds) and (ii) chemometric analysis (statistical large-scale data analysis) that is agnostic of specific compounds. Further, peak fitting techniques based on L2 error minimization are employed to autonomously calculate the amount of each PCB present with a normalized mean square error of-18.4851 dB. Graph-based visualization of associations between known and unknown compounds are developed through principal component analysis and both fuzzy c-means (FCM) and k-means clustering techniques are implemented and compared. The efficiency of these methods are compared using 150 air samples analyzed for individual PCBs with GC/MS/MS against traditional target-only techniques that perform analysis across only the known (target) PCBs. Parameter optimization techniques are employed to evaluate the relative contribution of PCB signals against ten potential source signals representing legacy signatures from historical manufacture of Aroclors and modern sources of PCBs produced as byproducts of pigment and polymer manufacturing. Aroclors 1232, 1254, 1016, and 1221 as well as non-Aroclor 3, 3', dichlorobiphenyl (PCB 11) were found in many of the samples as unique source signals that describe PCB mixtures in air samples collected from Chicago, IL.

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“…Because paint is applied to surfaces in thin coats, PCB 11 likely volatilizes into the air more efficiently than heavier PCB congeners. This is also seen in [19].…”

Section: F Determining Sourcessupporting

confidence: 55%

Signal Processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples

et al. 2020

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“…10 This suggests that the non-Aroclor sources of PCBs, including pigments, are significant and may be less influenced by the metropolitan size than Aroclor sources, a finding also noticed by Hites. 40…”

Section: Resultsmentioning

confidence: 99%

Emissions of Tetrachlorobiphenyls (PCBs 47, 51, and 68) from Polymer Resin on Kitchen Cabinets as a Non-Aroclor Source to Residential Air

Herkert

Jahnke

Hornbuckle

2018

Environ. Sci. Technol.

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Both Aroclor and non-Aroclor sources of airborne polychlorinated biphenyls (PCBs) were found in residential homes. We deployed passive air samplers at 16 residences and found PCB-47, PCB-51, and PCB-68 to account for up to 50% of measured indoor ΣPCBs (2700 pg m). Although PCB-47 and PCB-51 are neurotoxins present in Aroclor mixtures (<2.5 and <0.3 wt %, respectively), we found them at much higher levels than expected for any Aroclor source. PCB-68 is not present in Aroclor mixtures. Another non-Aroclor congener, PCB-11, a byproduct of pigment manufacturing, was found inside and outside of every household and was frequently the predominate congener. We conducted direct measurements of surface emissions and identified finished cabinetry to be a major source of PCB-47, PCB-51, and PCB-68. We hypothesize that these congeners are inadvertent byproducts of polymer sealant manufacturing and produced from the decomposition of 2,4-dichlorobenzoyl peroxide used as an initiator in free-radical polymerization of polyester resins. The presence of these three compounds in polymer products, such as silicone, has been widely noted, but to our knowledge they have never been shown to be a significant environmental source of PCBs.

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“…To our knowledge, this is the first time that PCB 11 concentrations are measured and reported in indoor environments. PCB 11 is an impurity that is formed during the production of yellow pigments that are contemporarily used in multiple consumer products (Hites, 2018;Hu et al, 2008;Rodenburg et al, 2009;Rodenburg et al, 2010;Shang et al, 2014). This may explain the relatively steady concentration of PCB 11 in ambient air in North America over the last decade, which is in contrast to other congeners such as dioxin-like PCBs (Hites, 2018).…”

Section: Svocs In Indoor Airmentioning

confidence: 99%

Polycyclic aromatic hydrocarbons, polychlorinated biphenyls and legacy and current pesticides in indoor environment in Australia – occurrence, sources and exposure risks

Wang

Banks

et al. 2019

Science of The Total Environment

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Atmospheric Concentrations of PCB-11 Near the Great Lakes Have Not Decreased Since 2004

Cited by 22 publications

References 16 publications

Signal Processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples

Signal Processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples

Emissions of Tetrachlorobiphenyls (PCBs 47, 51, and 68) from Polymer Resin on Kitchen Cabinets as a Non-Aroclor Source to Residential Air

Polycyclic aromatic hydrocarbons, polychlorinated biphenyls and legacy and current pesticides in indoor environment in Australia – occurrence, sources and exposure risks

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