Using information on uncertainty to improve environmental fate modeling: a case study on DDT

Area of Science:

• Environmental Chemistry
• Environmental Modeling
• Toxicology

Background:

• Dichlorodiphenyltrichloroethane (DDT) is a persistent organic pollutant with widespread environmental presence.
• Understanding DDT's environmental fate and persistence is crucial for risk assessment and management.

Purpose of the Study:

• To calculate present and future environmental concentrations of DDT using the global multimedia model CliMoChem.
• To assess the impact of uncertainties in substance properties, emissions, and environmental parameters on DDT concentration predictions.
• To update model inputs using field measurements of DDT via a Bayesian Monte Carlo approach.

Main Methods:

• Global multimedia modeling with CliMoChem.
• Monte Carlo simulations to quantify uncertainties.
• Sensitivity analysis using rank correlations.
• Bayesian Monte Carlo approach for model updating.

Main Results:

• Uncertainties in DDT concentrations are typically 1-2 orders of magnitude.
• Emission estimates and atmospheric degradation rates are the most influential inputs.
• Temperature dependencies significantly affect DDT levels in the Arctic.
• Bayesian updating reduced DDT half-life in air and uncertainty in Kow, aligning model results closer to observations.

Conclusions:

• The combination of sensitivity analysis and Bayesian updating provides valuable insights into DDT's global environmental fate.
• Model predictions are improved and uncertainties reduced through data assimilation.
• Key drivers of DDT persistence and distribution have been identified.