Dispersion Coefficients for Gaussian Puff Models

Abstract

The Gaussian distribution is a good approximation for transient (instantaneously released) puff concentration distributions within a short period of time after release. Artificial neural network (ANN) models for puff dispersion coefficients were developed, based on observations from field experiments covering a wide range of meteorological conditions (in March, May, August and November). Their average predictions were in very good agreement with measurements, having high correlation coefficients (r > 0.99). A non-linear multi-variable regression model for dispersion coefficients was also developed, under the assumption that puff dispersion coefficients increase with time, and follow power laws. Both ANN-based and multi-regression non-linear models were able to use easily measured atmospheric parameters directly, without the necessity of predefining the Pasquill stability category. Predictions of ANN-based and multi-regression-based Gaussian puff models were compared with those of Gaussian puff models using Slade's dispersion coefficients and COMBIC, a sophisticated model based on Gaussian distributions. Predictions from our two new models showed better agreement with concentration measurements than the other Gaussian puff models, by having a much higher fraction within a factor of two of measured values, and lower normalized mean square errors.

The trail of perfumes

“…This problem is much more complicated by consideration of convection and wind effects. One can learn from atmospheric dispersion models (Gaussian plume models, Gaussian Puff models, Lagrangian/Eulerian models, Particle‐in‐Cell models) but the scale of application (meso‐scale 20–200 km) is very different from what is of interest in the study of sillage .…”

Impact of the ambient air quality due to the dispersion of non-methane organic compounds from Barka Landfill

“…org). Puff models (Holmes and Morawska 2006;Barsotti et al 2008;Cao et al 2011) are an improvement over Gaussian plume models and can be applied to non-stationary and non-homogeneous flow by representing a plume by a series of independent elements (puffs) that evolve in time as a function of temporally and spatially varying meteorological conditions (Jung et al 2003). CALPUFF modeling requires three types of input: topographical (land use data and coastline data), meteorological, and emission data.…”

BP neural network based continuous objects distribution detection in WSNs

“…The Gaussian plume model is used to simulate the process of the continuous point source diffusion. In this situation the time of leak is no less than the diffusion time, while the Gaussian puff model is used to simulate the diffusion of instantaneous leak [16]. In order to simulate the diffusion process of the continuous objects, the Gaussian puff model is adopted to model the instantaneous status of the continuous objects, and the whole process of the continuous object diffusion is an integration of the translation, superposition, and weaken process of multiple Gaussian puff models.…”