Illustrating a Species Sensitivity Distribution for Nano- and Microplastic Particles Using Bayesian Hierarchical...

Area of Science:

• Environmental Science
• Ecotoxicology
• Risk Assessment

Background:

• Environmental contamination by nano- and microplastic (NMP) particles is a significant global issue.
• Species sensitivity distributions (SSDs) are crucial for determining safe environmental concentrations (e.g., predicted-no-effect concentrations).
• Previous SSD estimations for NMP have not quantitatively incorporated factors like particle size.

Purpose of the Study:

• To develop a species sensitivity distribution (SSD) for NMP particles that accounts for particle size, polymer type, and test media.
• To quantitatively assess the influence of NMP properties on effect concentrations.
• To improve the accuracy of ecological risk assessments for NMP.

Main Methods:

• Utilized chronic lowest-observed-effect concentrations (LOECs) from existing toxicity datasets.
• Employed Bayesian hierarchical modeling techniques to analyze NMP toxicity data.
• Developed a log-normal SSD incorporating particle size, polymer type, and freshwater/marine media.

Main Results:

• The SSD mean was negatively associated with particle size, indicating smaller particles had lower effect concentrations.
• Estimated hazardous concentrations for 5% of species (HC5) were lower in marine environments compared to freshwater.
• HC5 values varied by a factor of 10 based on NMP properties and environmental media.

Conclusions:

• Bayesian hierarchical modeling effectively clarifies the impact of NMP properties on ecotoxicity.
• Particle size and water type are critical factors influencing NMP risk assessment.
• This approach enhances the relevance and accuracy of ecological risk assessments for NMP contamination.