Dispersion Coefficients for Gaussian Puff Models |
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| Authors: | Xiaoying Cao Gilles Roy William J Hurley William S Andrews |
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| Institution: | (1) Centre for Atmospheric Sciences, Indian Institute of Technology, New Delhi, 110016, India |
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| 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. |
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