Uncertainty analysis in air dispersion modeling
The Taylor series approach for uncertainty analyses is advanced as an efficient method of producing a probabilistic output from air dispersion models. A probabilistic estimate helps in making better-informed decisions when compared to results of deterministic models. In this work, the Industrial Source Complex Short Term (ISCST) model is used as an analytical model to predict pollutant transport from a point source. First- and second-order Taylor series approximations are used to calculate the uncertainty in ground level concentrations of ISCST calculations. The results of the combined ISCST and uncertainty calculations are then validated with traditional Monte Carlo (MC) simulations. The Taylor series uncertainty estimates are a function of the variance in input parameters (wind speed and temperature) and the model sensitivities to input parameters. While the input variance is spatially invariant, sensitivity is spatially variable; hence the uncertainty in modeled output varies spatially. A comparison with the MC approach shows that uncertainty estimated by first-order Taylor series is found to be appropriate for ambient temperature, while second-order Taylor series is observed to be more accurate for wind speed. Since the Taylor series approach is simple and time-efficient compared to the MC method, it provides an attractive alternative.
uncertainty analysis dispersion models taylor series monte carlo sensitivity analyses