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Bacterial Source Tracking in the Total Maximum Daily Load (TMDL) Process

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Pp. 361-366 in Total Maximum Daily Load (TMDL) Environmental Regulations: Proceedings of the March 11-13, 2002 Conference, (Fort Worth, Texas, USA)  701P0102.(doi:10.13031/2013.7581)
Authors:   James Kern, Phillip McClellan, Byron Petrauskas ,Vernon Shanholtz
Keywords:   Bacterial Source Tracking, Fecal Coliform, TMDL, Assessment, Modeling, Implementation

In the United States, pathogen contamination (e.g. fecal coliforms, bacteria, and E. coli) is the second leading cause for placing surface waters on the 1998 303(d) list of impairments, second only to sediments. With 5,281 impairments nationwide, fecal coliform contamination accounts for approximately 13% of all impairments. Bacterial Source Tracking (BST) is an essential tool for addressing all aspects of the TMDL process, as it is applied to pathogen-impaired waters. This paper presents a brief review of currently available BST methodologies. The utility of BST in all stages of the TMDL process is discussed, including; assessment of waters, TMDL development, implementation plan development, and implementation. The need to accurately model all sources of fecal coliforms is critical, as TMDL allocations will represent only those sources included in the model. Any sources omitted from the model will necessitate greater reductions among those sources that are included, in order to bring the impaired segment back into compliance. MapTech, Inc. is in the process of developing calibration techniques for models simulating fecal-coliform fate and transport by incorporating the results of bacterial source tracking (BST). This paper demonstrates how BST can be used in the TMDL process. A minimal amount of BST data can be used to verify the presence or absence of sources in a watershed, which allows for a qualitative analysis of the source inventory and modeling effort. With adequate data, the initial calibration of the model can be improved to more accurately reflect the contribution of different sources. A model calibrated in such a fashion and linked to measured data should be more widely accepted by stakeholders in a TMDL setting.

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