Source apportionment of polychlorinated biphenyls (PCBs) using different receptor models: A case study on sediment from the Portland Harbor Superfund Site (PHSS), Oregon, USA

Feb 20, 2023

𝐄𝐱𝐜𝐢𝐭𝐢𝐧𝐠 𝐧𝐞𝐰𝐬 - 𝐨𝐮𝐫 𝐥𝐚𝐭𝐞𝐬𝐭 𝐩𝐚𝐩𝐞𝐫 𝐡𝐚𝐬 𝐣𝐮𝐬𝐭 𝐛𝐞𝐞𝐧 𝐩𝐮𝐛𝐥𝐢𝐬𝐡𝐞𝐝!🎉 

𝑯𝒂𝒗𝒆 𝒚𝒐𝒖 𝒆𝒗𝒆𝒓 𝒉𝒆𝒂𝒓𝒅 𝒐𝒇 𝒎𝒖𝒍𝒕𝒊𝒗𝒂𝒓𝒊𝒂𝒕𝒆 𝒎𝒐𝒅𝒆𝒍𝒍𝒊𝒏𝒈 𝒕𝒆𝒄𝒉𝒏𝒊𝒒𝒖𝒆𝒔 𝒖𝒔𝒆𝒅 𝒕𝒐 𝒊𝒏𝒗𝒆𝒔𝒕𝒊𝒈𝒂𝒕𝒆 𝒆𝒏𝒗𝒊𝒓𝒐𝒏𝒎𝒆𝒏𝒕𝒂𝒍 𝒊𝒔𝒔𝒖𝒆𝒔? These models are incredibly useful but can also be a source of uncertainty, especially when used for "source apportionment" studies, but different models can produce slightly different results.

Our latest research focused on the Portland Harbor Superfund Site in Oregon, where we used four different receptor models to identify the sources of polychlorinated biphenyls (PCBs) in sediment samples. We found that the models generally agreed and identified the same main sources of commercial PCB mixtures, but also showed some differences depending on the model and the number of "end members" used to represent the sources.

𝐖𝐡𝐲 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭? Well, if we don't consider these uncertainties and rely on a single model, it could impact the conclusions of scientific reports or legal cases, and ultimately affect who pays for the remediation of contaminated sites. That's why we need to carefully select a method that produces consistent results with end members that can be chemically explained.

𝐁𝐮𝐭 𝐭𝐡𝐚𝐭'𝐬 𝐧𝐨𝐭 𝐚𝐥𝐥! We also found a novel approach to use our multivariate models to identify potentially inadvertently produced PCBs that were not considered in previous studies. These PCBs accounted for a significant proportion of the total PCBs in Portland Harbor sediments, which highlights the importance of using multiple receptor models to thoroughly investigate environmental issues.

So, next time you hear about multivariate models or source apportionment studies, remember that there can be uncertainties involved and that it's crucial to use multiple models to better understand the sources of pollutants in our environment.

Please feel free to reach out to me if you have any questions or comments on the paper. Lots of cool data visualizations from this paper! 


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