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Lakes at Risk of Chloride Contamination.
Dugan, Hilary A; Skaff, Nicholas K; Doubek, Jonathan P; Bartlett, Sarah L; Burke, Samantha M; Krivak-Tetley, Flora E; Summers, Jamie C; Hanson, Paul C; Weathers, Kathleen C.
Afiliación
  • Dugan HA; Center for Limnology, University of Wisconsin-Madison. 680 North Park Street Madison, Wisconsin 53706, United States.
  • Skaff NK; Department of Fisheries and Wildlife, Michigan State University, 13 Natural Resources Building, East Lansing, Michigan 48824, United States.
  • Doubek JP; School of Natural Resources & Environment and Center for Freshwater Research and Education, Lake Superior State University, Sault Sainte Marie, Michigan 49783, United States.
  • Bartlett SL; NEW Water, 2231 North Quincy Street Green Bay, Wisconsin 54302, United States.
  • Burke SM; University of Guelph, School of Environmental Sciences, Guelph, Ontario N1G 2W1, Canada.
  • Krivak-Tetley FE; Aquatic Contaminants Research Division, Environment & Climate Change Canada, Burlington, Ontario L7S 1A1, Canada.
  • Summers JC; Department of Biological Sciences, Dartmouth College, 78 College Street, Hanover, New Hampshire 03768, United States.
  • Hanson PC; WSP Canada Incorporated, 2300 Yonge Street, Toronto, Ontario M4P 1E4, Canada.
  • Weathers KC; Center for Limnology, University of Wisconsin-Madison. 680 North Park Street Madison, Wisconsin 53706, United States.
Environ Sci Technol ; 54(11): 6639-6650, 2020 06 02.
Article en En | MEDLINE | ID: mdl-32353225
Lakes in the Midwest and Northeast United States are at risk of anthropogenic chloride contamination, but there is little knowledge of the prevalence and spatial distribution of freshwater salinization. Here, we use a quantile regression forest (QRF) to leverage information from 2773 lakes to predict the chloride concentration of all 49 432 lakes greater than 4 ha in a 17-state area. The QRF incorporated 22 predictor variables, which included lake morphometry characteristics, watershed land use, and distance to the nearest road and interstate. Model predictions had an r2 of 0.94 for all chloride observations, and an r2 of 0.86 for predictions of the median chloride concentration observed at each lake. The four predictors with the largest influence on lake chloride concentrations were low and medium intensity development in the watershed, crop density in the watershed, and distance to the nearest interstate. Almost 2000 lakes are predicted to have chloride concentrations above 50 mg L-1 and should be monitored. We encourage management and governing agencies to use lake-specific model predictions to assess salt contamination risk as well as to augment their monitoring strategies to more comprehensively protect freshwater ecosystems from salinization.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Lagos / Ecosistema Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies País/Región como asunto: America do norte Idioma: En Revista: Environ Sci Technol Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Lagos / Ecosistema Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies País/Región como asunto: America do norte Idioma: En Revista: Environ Sci Technol Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos