Bayesian Chemical Mass Balance Method for Surface Water Contaminant Source Apportionment
Publication: Journal of Environmental Engineering
Volume 139, Issue 2
Abstract
A Bayesian chemical mass balance (CMB) source apportionment method is developed using the Markov Chain Monte Carlo (MCMC) approach. Compared with deterministic approaches, the Bayesian method is capable of accounting for the measurement errors and the impact of variability of the source elemental compositions resulting from the heterogeneities and estimate the uncertainties associated with the estimated source contributions. The method estimates the joint probability densities and consequently, the credible intervals and correlation matrices of source contributions of various sources into a receiving water using observed elemental profiles of samples from both potential sources and the receiving surface waters. The model is applied to samples collected from possible sources and runoff and stream flow from two stream crossing sites along Highway 89 in the Lake Tahoe Basin. The contributing sources of total dissolved nitrogen, total dissolved phosphorus concentrations, and microparticles () from traffic and non-traffic-related sources have been evaluated using the method. The results showed that the model is capable of predicting the source contributions for the dissolved and particulate samples. During both rain events and snowmelt, deicing salt is a major source of dissolved solids whereas vegetation was to be the major source of dissolved nitrogen and phosphorus. Soil was found to be the primary source of microparticles and particulate phosphorus. The method does not definitively show contribution from traffic-related sources; however, in some cases it cannot conclusively rule out a significant contribution from them. The observed credible intervals for dissolved constituents were narrower compared with microparticle samples.
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Acknowledgments
This research study was partially funded by the Division of Environmental Analysis, California Department of Transportation, through Research Technical Agreement 43A0247. The views expressed by the authors are their own and the California Department of Transportation does not endorse the viewpoint of a publication or guarantee its technical correctness. The authors appreciate the assistance of Chris Alaimo, Dane Anderson, Tatanya Renee Klass, and Hyunmin Hwang for their assistance on sample collection and sample processing. We are also thankful to Drs. Adina Paytan and Peter Green, who supervised nutrient and elemental chemical analysis that was performed at UCSC and UCD, respectively.
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Information & Authors
Information
Published In
Journal of Environmental Engineering
Volume 139 • Issue 2 • February 2013
Pages: 250 - 260
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Oct 27, 2011
Accepted: Sep 6, 2012
Published online: Sep 8, 2012
Published in print: Feb 1, 2013
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