Reliable and Representative Estimation of Extrapolation Model Application in Deriving Water Quality Criteria for...

Area of Science:

• Environmental toxicology
• Ecotoxicology
• Risk assessment

Background:

• Species sensitivity distributions (SSDs) are vital for assessing antibiotic ecological risks.
• Interspecies correlation estimation (ICE) and acute-to-chronic ratios (ACRs) supplement limited toxicity data for emerging contaminants.
• These methods predict chronic toxicity from acute data and extrapolate toxicity between species.

Purpose of the Study:

• To evaluate the impact of ICE and ACRs on SSD reliability.
• To analyze different scenarios for data extrapolation.
• To determine the best-fitting distribution model for SSDs.

Main Methods:

• Analysis of species sensitivity distributions (SSDs) using logistic, normal, and Weibull models.
• Application of interspecies correlation estimation (ICE) and acute-to-chronic ratios (ACRs) for data extrapolation.
• Monte Carlo simulation and sensitivity testing to assess uncertainty and bias.

Main Results:

• The logistic model demonstrated the best fit for SSDs.
• Extrapolated SSDs showed high reliability (82.9% R² > 0.9).
• Combining ICE and ACRs maximized R² by 10% and reduced uncertainty, with bias near 1.

Conclusions:

• Extrapolation methods, particularly combined ICE and ACRs, enhance the reliability and stability of SSDs for ecological risk assessment.
• The logistic model is recommended for SSD construction.
• Understanding bias related to toxicity endpoint values is crucial for accurate risk assessment.