Estimation of Emission Strength and Air Pollutant Concentrations by Lagrangian Particle Modeling

Abstract

A Lagrangian particle model was applied to estimating emission strength and air pollutant concentrations specifically for the short-range dispersion of an air pollutant in the atmospheric boundary layer. The model performance was evaluated with experimental data. The model was then used as the platform of parametric uncertainty analysis, in which effects of uncertainties in five parameters (Monin-Obukhov length, friction velocity, roughness height, mixing height, and the universal constant of the random component) of the model on mean ground-level concentrations were examined under slightly and moderately stable conditions. The analysis was performed under a probabilistic framework using Monte Carlo simulations with Latin hypercube sampling and linear regression modeling. In addition, four studies related to the Lagrangian particle modeling was included. They are an alternative technique of formulating joint probability density functions of velocity for atmospheric turbulence based on the Koehler-Symanowski technique, analysis of local increments in a multidimensional single-particle Lagrangian particle model using the algebra of Ito integrals and the Wagner-Platen formula, analogy between the diffusion limit of Lagrangian particle models and the classical theory of turbulent diffusion, and evaluation of some proposed forms of the Lagrangian velocity autocorrelation of turbulence.