Benthic injury dose-response models for polychlorinated biphenyl-contaminated sediment using equilibrium partitioning

Area of Science:

• Environmental Toxicology
• Ecotoxicology
• Environmental Chemistry

Background:

• Polychlorinated biphenyls (PCBs) are persistent organic pollutants found in sediments, posing risks to benthic ecosystems.
• Existing methods often use single protective values, lacking a nuanced understanding of PCB dose-response relationships in sediment.
• Assessing PCB toxicity in benthic invertebrates requires robust models linking sediment concentrations to biological effects.

Purpose of the Study:

• To develop a sediment polychlorinated biphenyl (PCB) dose-response model based on benthic invertebrate effects.
• To utilize an equilibrium partitioning (EqP) approach to predict PCB sediment effect concentrations.
• To examine a common dose-response gradient of effects for PCBs, moving beyond single protective values.

Main Methods:

• Reviewed chronic aquatic toxicity literature to identify aqueous PCB concentrations and associated benthic invertebrate effects.
• Normalized aquatic toxicity data to a common metric, 'percent injury'.
• Calculated organic carbon-normalized sediment PCB concentrations using EqP theory and the EPIWEB 4.1 water-organic carbon partition coefficient (K_OC).
• Constructed a nonlinear dose-response numerical model (Y = 100/[1 + 10^{([logEC50-logX] × [Hill slope])}]) relating sediment PCB concentrations to biological effects.

Main Results:

• Developed a dose-response model predicting percent injury in benthic biota for various Aroclor-specific sediment PCB concentrations.
• Generated 'look-up' tables and equations for practical application.
• Example prediction: Aroclor 1254 concentrations of 1, 2, 4, 8, and 16 mg/kg (at 1% organic carbon) correspond to 23.3%, 46.0%, 70.6%, 87.1%, and 95% mean benthic injury, respectively.

Conclusions:

• The developed PCB sediment dose-response model provides a valuable tool for screening environmental risks to benthic organisms.
• The model allows for the prediction of biological effects across a gradient of PCB concentrations.
• Users are encouraged to determine site-specific K_OC values for more precise risk assessments, utilizing the provided equations and tables.