Signal Processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples

Ryan A McCarthy; Ananya Sen Gupta; Bernice Kubicek; Andrew M Awad; Andres Martinez; Rachel F Marek; Keri C Hornbuckle

doi:10.1109/ACCESS.2020.3013108

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Signal Processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples

Journal article

Open access

Peer reviewed

Signal Processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples

Ryan A McCarthy, Ananya Sen Gupta, Bernice Kubicek, Andrew M Awad, Andres Martinez, Rachel F Marek and Keri C Hornbuckle

IEEE access, Vol.8, pp.147738-147755

2020

DOI: 10.1109/ACCESS.2020.3013108

PMCID: PMC7742762

PMID: 33335823

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Abstract

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 chromatography-mass 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.

Signal Processing

PCBs

Instruments

Identifying sources

Signal processing algorithms

interpreting GC/MS/MS

Compounds

Chemicals

Principal component analysis

Load modeling

ISRP Project 4 2020-2025

Analytical Core

Details

Title: Subtitle: Signal Processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples
Creators: Ryan A McCarthy - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA
Ananya Sen Gupta - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA
Bernice Kubicek - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA
Andrew M Awad - Department of Civil and Environmental Engineering, The University of Iowa, Iowa City, IA, USA
Andres Martinez - Department of Civil and Environmental Engineering, The University of Iowa, Iowa City, IA, USA
Rachel F Marek - Department of Civil and Environmental Engineering, The University of Iowa, Iowa City, IA, USA
Keri C Hornbuckle - Department of Civil and Environmental Engineering, The University of Iowa, Iowa City, IA, USA
Resource Type: Journal article
Publication Details: IEEE access, Vol.8, pp.147738-147755
DOI: 10.1109/ACCESS.2020.3013108
PMID: 33335823
PMCID: PMC7742762
NLM abbreviation: IEEE Access
ISSN: 2169-3536
eISSN: 2169-3536
Publisher: IEEE
Grant note: 1808463 / National Science Foundation (10.13039/100000001) N000141912609 / Office of Naval Research (ONR) (10.13039/100000006) NIEHS P42 ES 013661 / University of Iowa Center for Health Effects of Environmental Contamination (CHEEC), The University of Iowa through the Iowa Superfund Research Program (10.13039/100008893)
Language: English
Date published: 2020
Academic Unit: Electrical and Computer Engineering; Civil and Environmental Engineering; Occupational and Environmental Health; IIHR--Hydroscience and Engineering; Iowa Superfund Research Program
Record Identifier: 9983997982802771

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