Title:
High Density Lagrangian Sampling for Pathogen Source Identification

dc.contributor.author Rosenquist, Shawn E.
dc.contributor.author Moak, Jason
dc.contributor.author Flite, Oscar P.
dc.contributor.corporatename Southeastern Natural Sciences Academy en_US
dc.date.accessioned 2013-07-22T13:45:22Z
dc.date.available 2013-07-22T13:45:22Z
dc.date.issued 2013-04
dc.description Proceedings of the 2013 Georgia Water Resources Conference, April 10-11, 2013, Athens, Georgia. en_US
dc.description.abstract In compliance to the Clean Water Act, each US state compiles a list of water bodies not meeting regulatory criteria. The most common impairment in US water bodies is elevated pathogens measured by fecal indicator bacteria (FIB). Reasons for this prevalence probably include the true magnitude of pathogen contamination, monitoring bias from human health concern, inaccuracy of FIB monitoring compared to other parameters, and difficulty estimating background condition. In practice, identification and citation of impairment is extensive, while development of plans that identify the source with certainty and implement high probability remediation lags behind. The difficulty in confidently identifying sources of impairment is an impediment to the protection of water bodies and increases the cost of remediation due to the need for casting a wider net of lower probability solutions. With a high proportion of resources directed to pathogen contamination, it is important to confidently identify sources. Increased confidence will improve efficacy of remediation and ability to secure funding. To achieve these objectives, we designed a study method to investigate Rocky Creek, a pathogen impaired stream in Augusta, GA. This method applied a Lagrangian FIB sampling approach to reduce confounding variability and a high sampling density to identify high contribution watershed areas. We then layered typical pathogen sources (e.g. septic, pet waste, sewer, wildlife) and alternative sources (e.g. sediment, instream growth) along with their GIS data over the FIB data. In this way, we were able to target remediation efforts on the convergence of sources and regions and thereby decrease the scale of remediation efforts. en_US
dc.description.sponsorship Sponsored by: Georgia Environmental Protection Division; U.S. Department of Agriculture, Natural Resources Conservation Service; Georgia Institute of Technology, Georgia Water Resources Institute; The University of Georgia, Water Resources Faculty. en_US
dc.description.statementofresponsibility This book was published by Warnell School of Forestry and Natural Resources, The University of Georgia, Athens, Georgia 30602-2152. The views and statements advanced in this publication are solely those of the authors and do not represent official views or policies of The University of Georgia, the Georgia Water Research Institute as authorized by the Water Research Institutes Authorization Act of 1990 (P.L. 101-307) or the other conference sponsors. en_US
dc.embargo.terms null en_US
dc.identifier.uri http://hdl.handle.net/1853/48508
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.relation.ispartofseries GWRI2013. Ecological: Function, Restoration, Monitoring en_US
dc.subject Water resources management en_US
dc.subject Clean Water Act en_US
dc.subject Water quality en_US
dc.subject Pathogen source identification en_US
dc.subject Water contamination en_US
dc.subject Rocky Creek en_US
dc.title High Density Lagrangian Sampling for Pathogen Source Identification en_US
dc.type Text
dc.type.genre Proceedings
dspace.entity.type Publication
local.contributor.corporatename Georgia Water Resources Institute
local.contributor.corporatename School of Civil and Environmental Engineering
local.contributor.corporatename College of Engineering
local.relation.ispartofseries Georgia Water Resources Conference
relation.isOrgUnitOfPublication 8873b408-9aff-48cc-ae3c-a3d1daf89a98
relation.isOrgUnitOfPublication 88639fad-d3ae-4867-9e7a-7c9e6d2ecc7c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
relation.isSeriesOfPublication e0bfffc9-c85a-4095-b626-c25ee130a2f3
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