What is usually considered to be the main factor in the uncertainty between model-predicted and observed COTA concentrations?

The main factor in the uncertainty between model-predicted and observed concentrations of chemicals in a given atmospheric dispersion model, such as for COTA (Chemical of Toxicological Interest), is typically linked to the input parameters and assumptions used in the modeling process. Key variables such as emission rates, meteorological conditions, and terrain features play a significant role in determining the accuracy of the predictions.

When models are built, they often rely on simplified representations of real-world processes. These simplifications can lead to discrepancies because atmospheric conditions can be highly variable and are influenced by numerous factors, including wind patterns, temperature, humidity, and topography, all of which may not be perfectly replicated or predicted. Additionally, the quality of the input data, calibration of the model, and the inherent variability of the system being modeled contribute to the uncertainties observed when comparing model predictions to real-world observations.

In conclusion, model-predicted and observed concentration discrepancies arise mainly from uncertainties in input parameters related to the complex and dynamic nature of atmospheric dispersion. This understanding assists in refining models and improving their predictive capabilities for future assessments.