Dispersion of Particles Released into a Neutral Planetary Boundary Layer Using a Markov Chain–Monte Carlo Model

Abstract

Abstract The dispersion and concentration of particles (fluid elements) that are continuously released into a neutral planetary boundary layer is presented. The velocity fluctuations of the particles are generated using a Markov chain–Monte Carlo (MCMC) process at random time intervals with a one-step memory. The local mean concentration of the particles is calculated by using a fully Lagrangian method, which performs an efficient near-neighbor search and employs a smoothing kernel for eliminating the statistical noise. The predicted vertical and transversal root-mean-square of the particles’ deviation from their mean position [()1/2 and ()1/2] for an elevated continuous release source in a neutral atmosphere are compared with empirical parameters like the Pasquill–Gifford σz and σy. The numerical predictions of the particle concentration are compared with a Gaussian model and field measurement data on the ground concentration obtained during the Green Glow Program. The comparison between the numerical predictions and the field data shows that the MCMC model can successfully predict the particle dispersion and concentration in a neutral atmosphere.