Multivariate Polynomial Time-Series Models and Importance Ratios to Qualify Fecal Coliform Sources
Publication: Journal of Environmental Engineering
Volume 136, Issue 7
Abstract
Sensitivity analysis using importance ratios was applied to multivariate polynomial regression (MPR) models to make inferences about the nature and magnitude of fecal coliform (FC) sources in a combined sewer overflow-impacted stretch of the Passaic River at Paterson, New Jersey. The predictor variables in this study were temperature, discharge, precipitation, and upstream concentrations. The MPR models are applicable only for the current conditions with respect to pollutant sources. New models should be developed in case of any change in pollutant sources, by location or magnitude. This is a limitation that MPR models have in common with any empirical modeling approach, including multilinear regression or artificial neural networks. The performance of the MPR models using was significantly better than simple linear models using the same variables. The importance ratio, a dimensionless measure of model sensitivity, was used for comparison of the effects of different variables. Because model sensitivities, and therefore importance ratios, are not constant in nonlinear models, this work examines their distributions and relates them to system behavior, for example by showing under what conditions dilution does or does not affect FC concentrations in the stream. The analysis showed that MPR models and importance ratios can be used to provide significant information to better understand pollutant sources at a site and the relative importance of various predictor variables in explaining the variability in the FC concentrations.
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Acknowledgments
We thank the NJDEP for providing us with partial financial assistance for this project. We also thank PVSC, USGS, and NOAA for providing us with data. We appreciate three anonymous reviewers comments which improved the paper and Soujanya Pillala and Santhi Jagupilla for their help in writing the paper.
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Information & Authors
Information
Published In
Journal of Environmental Engineering
Volume 136 • Issue 7 • July 2010
Pages: 657 - 665
Copyright
© 2010 ASCE.
History
Received: Jul 27, 2009
Accepted: Dec 7, 2009
Published online: Dec 18, 2009
Published in print: Jul 2010
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