Beyond Steady-State: An Integrated Framework Unveils BPAP as the Highest-Risk Bisphenol in a Dynamic River System

Area of Science:

• Environmental Science
• Ecological Modeling
• Risk Assessment

Background:

• Current management of emerging contaminants relies on static concentration control.
• This approach is limited by the lack of mechanistic models linking environmental processes to risk prioritization.
• A shift towards dynamic, process-informed risk forecasting is needed.

Purpose of the Study:

• To present an integrated modeling framework for dynamic risk forecasting of emerging contaminants.
• To resolve driver collinearity, integrate multimedia risks, and attribute pollution sources.
• To demonstrate the framework's utility in identifying critical risk priorities.

Main Methods:

• Developed an Environmental Condition Index (ECI) for synergistic environmental influences.
• Extended the dual-media Toxicological Priority Index (ToxPi) for holistic risk integration.
• Utilized an enhanced Positive Matrix Factorization model (PMF-DMC) for source attribution.

Main Results:

• Applied the framework to a watershed in the Pearl River Basin.
• Identified a risk priority reversal: Bisphenol AP (BPAP) emerged as a higher priority than Bisphenol A (BPA).
• Concentration-centric assessments failed to identify BPAP as the top priority.

Conclusions:

• Dynamic, process-informed frameworks are essential for effective emerging contaminant management.
• The developed framework offers a predictive and adaptive tool for ecological risk governance.
• This methodological advance is crucial for managing contaminants in non-stationary environments.