Abstract
This paper introduces the use of hierarchical Bayesian modeling as a method to estimate how kinetic properties of environmental contaminants vary across experimental factors. The hierarchical modeling framework utilizes a multilevel statistical structure, in which kinetic rate constants represent contaminant degradation behavior for individual, lower-level experiments, but also exhibit a higher-level structure across different experimental conditions. The main benefit of this modeling approach is the ability to pool information between experiments in order to reduce the influence of outliers and produce more robust predictions of degradation rate constants in an out-of-sample context. The Bayesian estimation method is also very flexible, with the ability to relax distributional assumptions on parameters, account for heteroscedasticity in model residuals, include prior information or expert knowledge when available, and propagate all uncertainties into model predictions, all with relative ease. The benefits of the hierarchical Bayesian approach are demonstrated in a case study examining the pH dependence of hydrolysis rates of haloacetamides (HAMs). A comparison is provided against a simpler least-squares method to highlight the differences and benefits of the proposed method.
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Information & Authors
Information
Published In
Journal of Environmental Engineering
Volume 141 • Issue 12 • December 2015
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Oct 9, 2014
Accepted: May 29, 2015
Published online: Jul 17, 2015
Published in print: Dec 1, 2015
Discussion open until: Dec 17, 2015
ASCE Technical Topics:
- Analysis (by type)
- Bayesian analysis
- Case studies
- Chemical degradation
- Chemical processes
- Chemistry
- Continuum mechanics
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Environmental engineering
- Kinetics
- Least squares method
- Methodology (by type)
- Models (by type)
- Pollutants
- Regression analysis
- Research methods (by type)
- Solid mechanics
- Statistical analysis (by type)
- Structural behavior
- Structural engineering
- Structural models
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