Uncertainty in the Assessment of Atmospheric Concentrations of Toxic Contaminants from an Accidental Release

Abstract

Atmospheric dispersion models are widely used to assess the risk of human exposure to toxic air contaminants resulting from an accidental release. Decisions on protective actions during emergencies are often based on dose calculations using the dispersion models. In view of the importance of such decisions, it is essential to understand and quantify the uncertainty associated with modelled concentrations. Two kinds of uncertainty, 'reducible' and 'inherent', are emphasised. The reducible uncertainty can be minimised through more accurate and more representative measurements and better model formulations. The inherent uncertainty arising from unmeasured or unresolvable details of the atmospheric flow leads to random fluctuations of concentrations from individual calculations about their ensemble average. When these fluctuations are large, the mean concentration alone is inadequate to predict the range and probability of the concentration levels. It is necessary to present the uncertainty in atmospheric dispersion models in terms of an appropriate confidence interval on the model predictions. Such methods facilitate improved decision making based on model uncertainty and risk assessment. In this paper, the various uncertainties in the assessment of atmospheric concentrations are discussed and some aspects of model evaluation are illustrated with examples using Gaussian puff/plume models and tracer data.