Improving water quality assessments through a hierarchical Bayesian analysis of variability.

Published

Journal Article

Water quality measurement error and variability, while well-documented in laboratory-scale studies, is rarely acknowledged or explicitly resolved in most model-based water body assessments, including those conducted in compliance with the United States Environmental Protection Agency (USEPA) Total Maximum Daily Load (TMDL) program. Consequently, proposed pollutant loading reductions in TMDLs and similar water quality management programs may be biased, resulting in either slower-than-expected rates of water quality restoration and designated use reinstatement or, in some cases, overly conservative management decisions. To address this problem, we present a hierarchical Bayesian approach for relating actual in situ or model-predicted pollutant concentrations to multiple sampling and analysis procedures, each with distinct sources of variability. We apply this method to recently approved TMDLs to investigate whether appropriate accounting for measurement error and variability will lead to different management decisions. We find that required pollutant loading reductions may in fact vary depending not only on how measurement variability is addressed but also on which water quality analysis procedure is used to assess standard compliance. As a general strategy, our Bayesian approach to quantifying variability may represent an alternative to the common practice of addressing all forms of uncertainty through an arbitrary margin of safety (MOS).

Full Text

Duke Authors

Cited Authors

  • Gronewold, AD; Borsuk, ME

Published Date

  • October 2010

Published In

Volume / Issue

  • 44 / 20

Start / End Page

  • 7858 - 7864

PubMed ID

  • 20853866

Pubmed Central ID

  • 20853866

Electronic International Standard Serial Number (EISSN)

  • 1520-5851

International Standard Serial Number (ISSN)

  • 0013-936X

Digital Object Identifier (DOI)

  • 10.1021/es100657p

Language

  • eng