4.7 Article

Environmental fate model for ultra-low-volume insecticide applications used for adult mosquito management

期刊

SCIENCE OF THE TOTAL ENVIRONMENT
卷 438, 期 -, 页码 72-79

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.scitotenv.2012.07.059

关键词

Mosquito management; Aerosol physics; Pesticide fate; Environmental fate; Pyrethroid

资金

  1. USDA Western Regional IPM grant program
  2. Montana State University Institute on Ecosystems National Science Foundation Final Year Ph.D. Fellowship
  3. U.S. Armed Forces Pest Management Board's Deployed War Fighter Protection Research Program
  4. Montana Agricultural Experiment Station, Montana State University, Bozeman, Montana, USA

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One of the more effective ways of managing high densities of adult mosquitoes that vector human and animal pathogens is ultra-low-volume (ULV) aerosol applications of insecticides. The U.S. Environmental Protection Agency uses models that are not validated for ULV insecticide applications and exposure assumptions to perform their human and ecological risk assessments. Currently, there is no validated model that can accurately predict deposition of insecticides applied using ULV technology for adult mosquito management. In addition, little is known about the deposition and drift of small droplets like those used under conditions encountered during ULV applications. The objective of this study was to perform field studies to measure environmental concentrations of insecticides and to develop a validated model to predict the deposition of ULV insecticides. The final regression model was selected by minimizing the Bayesian Information Criterion and its prediction performance was evaluated using k-fold cross validation. Density of the formulation and the density and CMD interaction coefficients were the largest in the model. The results showed that as density of the formulation decreases, deposition increases. The interaction of density and CMD showed that higher density formulations and larger droplets resulted in greater deposition. These results are supported by the aerosol physics literature. A k-fold cross validation demonstrated that the mean square error of the selected regression model is not biased, and the mean square error and mean square prediction error indicated good predictive ability. (C) 2012 Elsevier B.V. All rights reserved.

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