Chemical and environmental factors affecting pesticide volatilization from turfgrass Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/g158bm980

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  • Volatile loss rates of pesticides from turfgrass were measured using the Backward-Time Lagrangian Stochastic Dispersion model (Flesch et al., 1995). Solar radiation, ambient temperature, surface temperature, relative humidity, wind direction, and wind speed were monitored continuously. Growth regulator was applied to the turf plot several days before pesticide application to maintain a constant grass height and aerodynamic roughness length during the experiment. No irrigation occurred following application. Pesticides were applied as mixtures to allow direct comparison of evaporative loss. Mixtures studied were chlorpyrifos + triadimefon + ethofumesate and triclopyr (acetic acid) + propiconazole + cyfluthurin. Airborne flux estimates correlated with temperature, solar radiation, wind speed, time, and vapor pressure of the active ingredient. A log vapor pressure vs. 1/Temperature (K) relationship was observed between flux and surface temperature over a single day for most pesticides. An exponential attenuation of flux was observed over a period of several days and correlated with attenuation of dislodgeable surface residues for two of the pesticides. A fugacity-based model for predicting initial evaporative loss rates from turf grass is presented. Input parameters include pesticide vapor pressure, molecular diffusion coefficient, surface temperature, wind speed profile, atmospheric stability, surface roughness, and average upwind fetch. The GC retention method (Jensen, 1966) was used to estimate pesticide vapor pressures over an environmentally relevant temperature range. The model predicts fluxes that are an order of magnitude greater than measured values. This bias may be due, in part, to deviation from the assumption of pesticide saturated vapor density at the foliar surface. In addition, sensitivity analysis suggests improved estimates of leaf surface temperature and pesticide vapor pressures have the greatest potential to improve model performance.
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