Download Hi-Res ImageDownload to MS-PowerPointCite This:Environ. Sci. Technol. 2019, 53, 20, 12071-12080
Insecticide Risk in US Surface Waters: Drivers and Spatiotemporal Modeling
- Jakob WolframJakob WolframiES Landau, Institute for Environmental Sciences, University of Koblenz-Landau, Fortstrasse 7, D-76829 Landau, GermanyMore by Jakob Wolfram
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- Sebastian StehleSebastian StehleiES Landau, Institute for Environmental Sciences, University of Koblenz-Landau, Fortstrasse 7, D-76829 Landau, GermanyEusserthal Ecosystem Research Station, University of Koblenz-Landau, Birkenthalstrasse 13, D-76857 Eusserthal, GermanyMore by Sebastian Stehle
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- Sascha BubSascha BubiES Landau, Institute for Environmental Sciences, University of Koblenz-Landau, Fortstrasse 7, D-76829 Landau, GermanyMore by Sascha Bub
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- Lara L. PetschickLara L. PetschickiES Landau, Institute for Environmental Sciences, University of Koblenz-Landau, Fortstrasse 7, D-76829 Landau, GermanyMore by Lara L. Petschick
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- Ralf Schulz*Ralf Schulz*E-mail: [email protected]. Phone: +49634128031327.iES Landau, Institute for Environmental Sciences, University of Koblenz-Landau, Fortstrasse 7, D-76829 Landau, GermanyMore by Ralf Schulz
Abstract
Although pesticide contamination in agricultural surface waters is a common phenomenon, large-scale studies dealing with the responsible drivers are rare. We used data from 259 publications reporting 5830 individual water or sediment concentrations of 32 insecticides and their metabolites in 644 US surface waters to determine the factors driving insecticide risks, that is, exceedance of regulatory threshold levels (RTLs). Multiple linear regressions (R2 adj. = 49.6–76.5) revealed that toxicity-normalized agricultural insecticide use (i.e. use divided by toxicity) was the most important driver. Burst rainfall erosivity and irrigation practices also had risk-promoting effects, whereas time, catchment size, and sampling interval had risk-demoting effects. A regression model (R2 adj. = 62.2, n = 1833) for small, medium, and large running waters was validated and used for risk mapping at the national scale, highlighting multiple regions, where the comparison of predicted insecticide concentrations with their RTLs indicate adverse conditions for aquatic organisms. Particularly in smaller streams, risks were most pronounced with an average RTL exceedance frequency of 27.7% in all grid cells (n = 9968). Finally, mixture toxicity was mainly (about 76.7%) explained by the most toxic compound in the mixture, causing ∼95.7% of RTL exceedances. Identifying the factors, which drive exposure for all relevant insecticide classes, and subsequently mapping these risks for surface waters of various sizes across the U.S., will support future risk management.
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This article is cited by 18 publications.
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