Dispersion Coefficients for Gaussian Puff Models

• Published in: Boundary-layer meteorology Vol. 139; no. 3; pp. 487 - 500
• Main Authors: Cao, Xiaoying, Roy, Gilles, Hurley, William J., Andrews, William S.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2011; Springer; Springer Nature B.V
• Subjects: Analysis; Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Convection, turbulence, diffusion. Boundary layer structure and dynamics; Correlation coefficient; Dispersion; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Exact sciences and technology; External geophysics; Field tests; Hostages; Meteorology; Neural networks; Normal distribution; Toy industry; Puff dispersion coefficients; Artificial neural network; Multi-variable regression; Gaussian puff model; models; Atmospheric condition; Non linear regression; Linear regression; neural networks; Gaussian distribution; Dispersion coefficient; Multiple linear regression; Multiple regression; Mean square error; transient phenomena; correlation coefficient; Power law; stability; Concentration distribution
• ISSN: 0006-8314; 1573-1472
• DOI: 10.1007/s10546-011-9595-3

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.