Hendley P, Holmes C, Kay S, Maund S J, Travis K Z, Zhang M
Zeneca Ag Products, Western Research Center, Richmond, California 94804-4610, USA.
Environ Toxicol Chem. 2001 Mar;20(3):669-78. doi: 10.1897/1551-5028(2001)020<0669:praocp>2.0.co;2.
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.
根据美国环境保护局农药项目办公室环境归宿与效应司概述的传统程序,对在棉花种植中使用拟除虫菊酯类杀虫剂可能产生的水生生物接触浓度进行的估算似乎高得离谱。因此,利用美国密西西比州一个重要的棉花生产县的遥感数据,对农药接触评估建模情景中固有的假设进行了研究。利用图像处理技术和地理信息系统来调查该县水体的数量和大小以及它们与棉花的距离。对研究区域内约600个静态水体测量了对水生生物接触建模至关重要 的变量。还获得了关于棉花与水体的相对空间方位、区域土壤质地和坡度以及农田与水体之间物理缓冲区组成的详细性质的定量信息。结果表明,遥感和地理信息系统可有效地用于描述农业景观,并提供可验证的数据以完善保守的模型假设。例如,该区域68%的池塘在360米范围内没有棉花,92%的池塘在60米范围内没有棉花。只有2%的池塘在其周围120米范围内各个方向都有棉花。这些是对传统农药风险评估接触建模假设的重大修正,并例证了利用景观层面的风险评估来更好地描述密西西比州棉花农业景观的重要性。将具有空间特征的景观信息纳入农药水生生物接触情景中,可能比许多其他改进措施对模型输出产生更大影响。