Testing a multimedia compartmental model with monitoring data

Area of Science:

• Environmental Science
• Chemical Modeling
• Atmospheric Chemistry

Background:

• Chemical transport and transformation models are crucial for predicting environmental pollutant concentrations.
• Previous applications of such models were often qualitative and lacked rigorous validation.
• Improved emission and monitoring data in recent years enable more robust model testing.

Purpose of the Study:

• To adapt and test the SimpleBox 2.0 chemical transport model for a specific region in southern Louisiana.
• To evaluate the model's ability to predict environmental concentrations of various chemicals.
• To assess the impact of parameter sensitivity, particularly degradation half-life, on model predictions.

Main Methods:

• Geographic adaptation of the SimpleBox 2.0 model to a nine-parish area in Louisiana.
• Calibration and validation using emission and monitoring data for eight chemicals (benzene, vinyl chloride, 1,1,1-trichloroethane, atrazine, toluene, styrene, trichloroethylene, metribuzin).
• Parameter sensitivity analysis focusing on transport coefficients, temperature, and degradation half-life.

Main Results:

• Degradation half-life was identified as the most influential parameter affecting predicted concentrations.
• Model predictions for benzene, metribuzin, and trichloroethylene were within a factor of two of measured concentrations.
• Predictions for vinyl chloride, toluene, 1,1,1-trichloroethane, styrene, and atrazine showed larger discrepancies, with factors ranging from 3 to 65.

Conclusions:

• The SimpleBox 2.0 model, when adapted and calibrated with regional data, can provide valuable insights into chemical distribution.
• Model performance varies significantly depending on the chemical and its environmental partitioning (air vs. water).
• Further refinement of input parameters, especially degradation rates, is essential for improving prediction accuracy.