Environmental management with knowledge of uncertainty: a methylmercury case study

Area of Science:

• Environmental Science
• Toxicology
• Water Quality Management

Background:

• Health advisories in Oregon's Willamette River Basin restrict fish consumption due to methylmercury accumulation.
• These advisories signify an impairment of the beneficial use of fish consumption under the Clean Water Act, necessitating a mercury total maximum daily load (TMDL).

Purpose of the Study:

• To investigate the application of probabilistic methods in quantifying uncertainty for water column guidance values.
• To assess how this quantified uncertainty influences decision-making regarding mercury load reductions for the Willamette River Basin.
• To determine a mercury load reduction strategy that balances human health protection with stakeholder input.

Main Methods:

• Utilized probabilistic (Monte Carlo) methods to quantify uncertainty in water column guidance values for methylmercury.
• Compared mercury levels in surface water to a water column guidance value linked to beneficial use protection.
• Engaged in stakeholder consultations to select a water column guidance value (0.92 ng/L) representing a 50% probability of achieving the tissue criterion.

Main Results:

• A 26% mercury load reduction was estimated to achieve a 50% probability of meeting the human health tissue criterion.
• Probabilistic methods provided decision-makers with a clearer understanding of the probability of achieving human health protection.
• The use of uncertainty knowledge led to a lower required load reduction compared to deterministic analyses.

Conclusions:

• Explicitly addressing uncertainty through probabilistic methods enhances informed decision-making for water quality management.
• A collaborative approach involving stakeholders and probabilistic analysis can lead to more nuanced and potentially lower load reduction targets.
• Further development of management, communication, and regulatory frameworks is needed to effectively integrate probabilistic insights into environmental policy.