Changes in analytical methods over time pose problems for assessing long-term trends in environmental contamination by PCBs. Congener-specific analyses vary widely in the number and identity of the 209 distinct PCB chemical configurations (congeners) that are quantified, leading to inconsistencies among summed PCB concentrations (ΣPCB) reported by different studies. Here, we present a modeling approach using linear regression to compare ΣPCB concentrations derived from different congener-specific analyses measuring different co-eluting groups. The approach can be used to develop a specific conversion model between any 2 sets of congener-specific analytical data from similar samples (similar matrix and geographic origin). We demonstrate the method by developing a conversion model for an example data set that includes data from 2 different analytical methods, a low resolution method quantifying 119 congeners and a high resolution method quantifying all 209 congeners. We used the model to show that the 119-congener set captured most (93%) of the total PCB concentration (i.e., Σ209 PCB) in sediment and biological samples. ΣPCB concentrations estimated using the model closely matched measured values (mean relative percent difference = 9.6). General applications of the modeling approach include 1) generating comparable ΣPCB concentrations for samples that were analyzed for different congener sets; and 2) estimating the proportional contribution of different congener sets to ΣPCB. This approach may be especially valuable for enabling comparison of long-term remediation monitoring results even as analytical methods change over time. Integr Environ Assess Manag 2017;13:227-232. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.
Keywords: Congener-based analysis; Great Lakes Areas of Concern; Manistique River and Harbor; PCBs; Remedy effectiveness monitoring.
Published 2016. This article is a U.S. Government work and is in the public domain in the USA.