Bayesian Storm-Water Quality Model and Its Application to Water Quality Monitoring
Publication: Journal of Environmental Engineering
Volume 137, Issue 7
Abstract
A Bayesian statistical approach for determining the parameter uncertainty of a storm-water treatment model is reported. The storm-water treatment technologies included a sand filter and a subsurface gravel wetland. The two field systems were loaded and monitored in a side-by-side fashion over a two-year period. The loading to each system was storm-water runoff generated by ambient rainfall on a commuter parking lot. Contaminant transport is simulated by using a one-dimensional advection-dispersion model. The unknown parameters of the model are the contaminant deposition rate and the hydrodynamic dispersion. The following contaminants are considered in the study: total suspended solids, total petroleum hydrocarbons–diesel range hydrocarbons, and zinc. Parameter uncertainties are addressed by estimating the posterior probability distributions through a conventional Metropolis-Hastings algorithm. Results indicate that the posterior distributions are unimodal and, in some instances, exhibit some level of skewness. The Bayesian approach allowed the estimation of the 10th, 25th, 50th, 75th, and 95th percentiles of the posterior probability distributions. The prediction capabilities of the model were explored by performing a Monte Carlo simulation using the calculated posterior distributions and two rainfall-runoff events not considered during the calibration phase. The objective is to estimate effluent concentrations from the treatment systems under different scenarios of flow and contaminant loads. In general, estimated effluent concentrations and the total estimated mass fell within the defined uncertainty limits.
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Acknowledgments
The UNH Stormwater Center is housed within the Environmental Research Group (ERG) at the University of New Hampshire (UNH) in Durham, New Hampshire. Funding for the program is provided by the Cooperative Institute for Coastal and Estuarine Environmental Technology (CICEET)UNSPECIFIED and the National Oceanic and Atmospheric Administration (NOAA)NOAA, and this support is sincerely acknowledged.
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Information & Authors
Information
Published In
Journal of Environmental Engineering
Volume 137 • Issue 7 • July 2011
Pages: 541 - 550
Copyright
© 2011 American Society of Civil Engineers.
History
Received: Dec 9, 2009
Accepted: Jan 12, 2011
Published online: Jan 14, 2011
Published in print: Jul 1, 2011
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