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Connecting microbial, nutrient, physiochemical, and land use variables for the evaluation of water quality within mixed use watersheds.
Flood, Matthew T; Hernandez-Suarez, J Sebastian; Nejadhashemi, A Pouyan; Martin, Sherry L; Hyndman, David; Rose, Joan B.
Affiliation
  • Flood MT; Department of Fisheries and Wildlife, Michigan State University, East Lansing MI 48824, USA. Electronic address: floodmat@msu.edu.
  • Hernandez-Suarez JS; Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing MI 48824, USA.
  • Nejadhashemi AP; Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing MI 48824, USA.
  • Martin SL; Department of Earth and Environmental Sciences, Michigan State University, East Lansing MI.
  • Hyndman D; Department of Geosciences, School of Natural Sciences and Mathematics, University of Texas at Dallas, Richardson TX, 75080, USA.
  • Rose JB; Department of Fisheries and Wildlife, Michigan State University, East Lansing MI 48824, USA.
Water Res ; 219: 118526, 2022 Jul 01.
Article in En | MEDLINE | ID: mdl-35598465
ABSTRACT
As non-point sources of pollution begin to overtake point sources in watersheds, source identification and complicating variables such as rainfall are growing in importance. Microbial source tracking (MST) allows for identification of fecal contamination sources in watersheds; when combined with data on land use and co-occuring variables (e.g., nutrients, sediment runoff) MST can provide a basis for understanding how to effectively remediate water quality. To determine spatial and temporal trends in microbial contamination and correlations between MST and nutrients, water samples (n = 136) were collected between April 2017 and May of 2018 during eight sampling events from 17 sites in 5 mixed-use watersheds. These samples were analyzed for three MST markers (human - B. theta; bovine - CowM2; porcine - Pig2Bac) along with E. coli, nutrients (nitrogen and phosphorus species), and physiochemical paramaters. These water quality variables were then paired with data on land use, streamflow, precipitation and management practices (e.g., tile drainage, septic tank density, tillage practices) to determine if any significant relationships existed between the observed microbial contamination and these variables. The porcine marker was the only marker that was highly correlated (p value <0.05) with nitrogen and phosphorus species in multiple clustering schemes. Significant relationships were also identified between MST markers and variables that demonstrated temporal trends driven by precipitation and spatial trends driven by septic tanks and management practices (tillage and drainage) when spatial clustering was employed.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Water Microbiology / Water Quality Type of study: Prognostic_studies Limits: Animals Language: En Journal: Water Res Year: 2022 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Water Microbiology / Water Quality Type of study: Prognostic_studies Limits: Animals Language: En Journal: Water Res Year: 2022 Document type: Article