Untangling Sediment PCB Signatures in Disparate Datasets for Source Allocation | Integral Consulting Inc.

Introduction

Polychlorinated biphenyls (PCBs) are contaminants of concern in many industrial waterways due to their predominant historical use and persistent nature in sediments and soil. In urban waterways with multiple industrial sources, expensive remediation is driven both by the overall concentration of contaminants (including PCBs) and by assigning responsibility to the parties responsible for the releases. Understand that responsibility rests, in part, on the differentiation of source signatures. Because there are 209 individual PCB compounds, the chemical nature of PCBs lends itself to forensic analysis to unravel source signatures in sediment and soil – if data handling and analysis is done carefully.

What are PCBs?

PCBs are a mixture of biphenyls containing 1 to 10 chlorine atoms (Figure 1). Each variation of this structure is called a congener, and there are a total of 209 congeners. In any environmental sample affected by PCBs, there is a “fingerprint” of individual congener concentrations that can provide information about the sources of PCBs. PCBs were historically sold and used in the United States between 1930 and 1977 under the trade name Aroclor1. In the Aroclor product line, several distinct mixtures of congeners were used and each mixture (ie Aroclor) consisted of a characteristic congener “fingerprint”. Since Aroclors have been manufactured for specific industrial uses, the observation of some Aroclors in sediment or soil may indicate likely industrial contributors to the mixture of PCBs measured in sediment or soil.

A challenge of working with PCBs: coelutions of congeners

The type of laboratory analysis performed informs the forensic methods that can be used to examine sources. Congener assays provide the most information, compared to Aroclor or other assays2. However, the chromatographic peaks of two congeners cannot always be completely separated from each other – this phenomenon is called coelution. Instead of reporting the concentrations of individual congeners, the coelutions are reported as the sum of the concentrations of the co-eluting congeners. Co-eluting congeners are often laboratory and study specific, with differences resulting from slight differences in methodologies. The variability of coelutions poses a challenge to forensic analyzes when PCB congener data from multiple studies must be compared.

A PCB Forensics Case Study: Making the Most of Coelutions

For an industrial waterway with historic PCB use that Integral reviewed, seven different PCB congener datasets were available. Over the years of sample collection, the methodologies changed slightly and multiple labs analyzed the samples, resulting in datasets with different individual and co-eluting congeners reported. In order to perform statistical evaluations and machine learning methods with the full dataset, a common list of congeners (or coelutions) is needed. One approach is to compare only exactly matched congeners and coelutions among all studies, which sacrifices several variables in the analyzes and limits understanding of the characteristics of each mixture. In the industrial waterway network example, this would limit the analysis to less than half of the congeners and ignore some congeners most associated with specific industrial sources. Instead of sacrificing data that might clarify source signatures, Integral uses R software to combine congeners and coelutions, producing a list of consistent analytes across sample sets. Figure 2 illustrates this concept in more detail; in the industrial waterway system example, this results in a list of 101 congener groups encompassing the 209 congeners.

With a consistent set of congener groups, Integral analysts are able to compare samples from all available data sets and use advanced statistical and machine learning methods such as fingerprinting, principal component analysis, unmixing (non-negative matrix factorization) and clustering. All of these forensic techniques can be used together to help elucidate sources of PCBs in contaminated sediments or soils.

The importance of careful manipulation of congener data to reveal patterns is illustrated in Figure 3. Each subplot is the PCB fingerprint composed of the relative abundance of congeners in an individual sample. The top row represents the Integral congeners and coeluting groups used to compare the sample sets (after combining as shown in Figure 2), and the bottom row represents the same samples but with only the congeners and coeluting which were constant across all sample sets. It is clear in the top row that the two samples represented by green bars (samples 1 and 2) are similar in composition and richer in less-chlorinated congeners compared to the samples represented by orange bars (samples 3 and 4). However, when looking only at perfectly matched congeners in the bottom row, we are missing key information needed to differentiate the samples, both visually and statistically.

Finding meaning in disparate data sets is difficult and can take a long time. Integral streamlined workflow by storing data in a central database with consistent structure and management, allowing analysts to pull data from multiple sources and use internal scripts to translate congeners into a space coeluting cell for forensic analysis. When a new data set with different co-elutions of congeners is added to the database, the process is easily adapted to update forensic analyzes and regenerate the numbers to be reported. In systems with active data generation, this allows Integral to efficiently deliver up-to-date information to help customers make decisions using robust analytics and the most data available.

By combining a deep understanding of environmental chemistry and advanced data science techniques, Integral is at the forefront of using environmental investigation methods in complex systems.

References

1Erickson, MD, and RG Kaley. 2011. Applications of polychlorinated biphenyls. Environmental Science and Pollution Research 18(2):135–151.

2Shields, WJ, T. Saba, PD Boehm, and J. Pietari. 2015. Congeners: A Forensic Analysis. In: Introduction to Environmental Forensics. BL Murphy and RD Morrison (eds). Third edition. p. 347–393. Academic press.

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