Probabilistic risk assessment of cotton pyrethroids: III. A spatial analysis of the Mississippi, USA, cotton landscape
Identifieur interne : 000D67 ( Istex/Curation ); précédent : 000D66; suivant : 000D68Probabilistic risk assessment of cotton pyrethroids: III. A spatial analysis of the Mississippi, USA, cotton landscape
Auteurs : Paul Hendley [États-Unis] ; Chris Holmes [États-Unis] ; Steve Kay [États-Unis] ; Stephen J. Maund [Royaume-Uni] ; Kim Z. Travis [Royaume-Uni] ; Minghua Zhang [États-Unis]Source :
- Environmental Toxicology and Chemistry [ 0730-7268 ] ; 2001-03.
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- topic : Télédétection.
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Abstract
Estimates of potential aquatic exposure concentrations arising from the use of pyrethroid insecticides on cotton produced using conventional procedures outlined by the U.S. Environmental Protection Agency's Office of Pesticide Programs Environmental Fate and Effects Division seem unrealistically high. Accordingly, the assumptions inherent in the pesticide exposure assessment modeling scenarios were examined using remote sensing of a significant Mississippi, USA, cotton‐producing county. Image processing techniques and a geographic information system were used to investigate the number and size of the water bodies in the county and their proximity to cotton. Variables critical to aquatic exposure modeling were measured for approximately 600 static water bodies in the study area. Quantitative information on the relative spatial orientation of cotton and water, regional soil texture and slope, and the detailed nature of the composition of physical buffers between agricultural fields and water bodies was also obtained. Results showed that remote sensing and geographic information systems can be used cost effectively to characterize the agricultural landscape and provide verifiable data to refine conservative model assumptions. For example, 68% of all ponds in the region have no cotton within 360 m and 92% of the ponds have no cotton within 60 m. Only 2% of ponds have cotton present in all directions around the ponds and within 120 m. These are significant modifications to conventional pesticide risk assessment exposure modeling assumptions and exemplify the importance of using landscape‐level risk assessments to better describe the Mississippi cotton agricultural landscape. Incorporating spatially characterized landscape information into pesticide aquatic exposure scenarios is likely to have greater impact on the model output than many other refinements.
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DOI: 10.1002/etc.5620200328
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Accordingly, the assumptions inherent in the pesticide exposure assessment modeling scenarios were examined using remote sensing of a significant Mississippi, USA, cotton‐producing county. Image processing techniques and a geographic information system were used to investigate the number and size of the water bodies in the county and their proximity to cotton. Variables critical to aquatic exposure modeling were measured for approximately 600 static water bodies in the study area. Quantitative information on the relative spatial orientation of cotton and water, regional soil texture and slope, and the detailed nature of the composition of physical buffers between agricultural fields and water bodies was also obtained. Results showed that remote sensing and geographic information systems can be used cost effectively to characterize the agricultural landscape and provide verifiable data to refine conservative model assumptions. For example, 68% of all ponds in the region have no cotton within 360 m and 92% of the ponds have no cotton within 60 m. Only 2% of ponds have cotton present in all directions around the ponds and within 120 m. These are significant modifications to conventional pesticide risk assessment exposure modeling assumptions and exemplify the importance of using landscape‐level risk assessments to better describe the Mississippi cotton agricultural landscape. Incorporating spatially characterized landscape information into pesticide aquatic exposure scenarios is likely to have greater impact on the model output than many other refinements.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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