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This article in JEQ

  1. Vol. 31 No. 3, p. 724-729
     
    Received: May 11, 2001
    Published: May, 2002


    * Corresponding author(s): dah13@cornell.edu
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doi:10.2134/jeq2002.7240

Modeling Pesticide Volatilization from Turf

  1. Douglas A. Haith *a,
  2. Po-Ching Leea,
  3. J. Marshall Clarkb,
  4. Gerald R. Royb,
  5. Margaret J. Imbodena and
  6. Rebecca R. Waldena
  1. a Biological and Environ. Eng., Riley-Robb Hall, Cornell Univ., Ithaca, NY 14853
    b Dep. of Entomology, Fernald Hall, Univ. of Massachusetts, Amherst, MA 01003-2410

Abstract

Pesticide volatilization models are typically based on equilibrium partitioning of the chemical into solid, liquid, and gaseous phases in the soil environment. In turf systems direct vaporization from vegetation surfaces is a more likely source, and it is difficult to apply equilibrium methods to plant material due to the uncertainties of solid–liquid–gas partitioning. An alternative approach is to assume that pesticide volatilization is governed by the same processes that affect water evaporation. A model was developed in which evapotranspiration values, as determined by the Penman equation, were adjusted to chemical vaporization using ratios of water and chemical saturated vapor pressures and latent heats of vaporization. The model also assumes first-order degradation of pesticide on turf vegetation over time. The model was tested by comparisons of predictions with measurements of volatilization for eight pesticides measured during 3 to 7 d in 11 field experiments. Measured volatilization fluxes ranged from 0.1 to 22% of applied chemical. Pesticides were divided into two groups based on saturated vapor pressures and organic C partition coefficients. One pesticide was selected from each group to calibrate the model's volatilization constant for the group, and the remaining pesticides were used for model testing. Testing results indicated that the model provides relatively conservative estimates of pesticide volatilization. Predicted mean losses exceeded observations by 20%, and the model explained 67% of the observed variation in volatilization fluxes. The model was most accurate for those chemicals that exhibited the largest volatilization losses.

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Copyright © 2002. American Society of Agronomy, Crop Science Society of America, Soil Science SocietyPublished in J. Environ. Qual.31:724–729.