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How Digestive Processes Can Affect the Bioavailability of PCBs Associated with Microplastics: A Modeling Study Supported by Empirical Data.

Nur Hazimah Mohamed NorZhiyue NiuMarie HennebelleAlbert A Koelmans
Published in: Environmental science & technology (2023)
The transfer kinetics of plastic-associated chemicals during intestinal digestive processes is unknown. Here, we assessed whether digestive processes affect chemical exchange kinetics on microplastics, using an in vitro gut fluid digestive model mimicking the human upper intestinal tract. Chemical exchange kinetics of microplastics were measured for 10 polychlorinated biphenyls (PCBs) as proxies for the broad class of hydrophobic organic chemicals. Following earlier studies, olive oil was used as a proxy for digestible food, under high and low digestive enzyme activities. The micelle-water and oil-water partition coefficients of the 10 PCBs were also determined to evaluate the relative contribution of each gut component to sorb PCBs. A new biphasic and reversible chemical exchange model, which included the digestion process, fitted well to the empirical data. We demonstrate that the digestive processes that break down contaminated food can lead to a substantial increase in chemical concentration in microplastics by a factor of 10-20, thereby reducing the overall chemical bioavailability in the gastrointestinal tract when compared to a scenario without microplastics. Higher enzyme activities result in more chemicals being released by the digested food, thereby resulting in higher chemical concentrations in the microplastics. While the model-calibrated kinetic parameters are specific to the studied scenario, we argue that the mechanism of the reduced bioavailability of chemicals and the modeling tool developed have generic relevance. These digestive processes should be considered when assessing the risks of microplastics to humans and also biomagnification in aquatic food webs.
Keyphrases
  • human health
  • risk assessment
  • climate change
  • heavy metals
  • endothelial cells
  • drinking water
  • machine learning
  • aqueous solution
  • artificial intelligence