A Technical Review of MULV-Disp, a Recent Mosquito Ultra-Low Volume Pesticide Spray Dispersion Model.

The authors of a recently published paper summarized the development of a regression model for ground-based ultra-low volume applications, suggesting that their model was sufficiently verified that it could be used extensively for mosquito control. These authors claimed that their statistical model was superior in its predictive capability to the extensively developed and Environmental Protection Agency-validated AGDISP mechanistic model. In this technical review, the assumptions, reduction and interpretation of data, and conclusions reached with regard to their model are discussed, and explicit misstatements and incorrect mathematical relationships are pointed out. Two published versions of the model regression equation give substantially different results without explanation. Petri dish collection was used for very small droplets, with no mention of collection efficiency. Meteorological data were misused based on manufacturer's specification of instrument accuracy. We strongly disagree with many of the model results and show that the model misrepresents the actual behavior of aerosol sprays applied in the manner tested.

The agricultural dispersal-valley drift spray drift modeling system compared with pesticide drift data.

The coupling of the valley drift (VALDRIFT) atmospheric dispersion/deposition model with the agricultural dispersal (AGDISP) aircraft wake model generates a modeling system for predicting the off-target drift of pesticides sprayed in a mountain valley. The approach uses the AGDISP near-field spray model to estimate the mass fraction of pesticide remaining airborne after initial application, then the VALDRIFT complex terrain model to estimate the drift of pesticide from the target area. The modeling system inputs include detailed spray information, a measure (or estimate) of winds in the valley, and the valley topographic characteristics; the results are pesticide concentrations throughout the valley atmosphere and pesticide deposition to the valley surface. The AGDISP and VALDRIFT models are operated independently, with the results from AGDISP being used as input to VALDRIFT through user-created data files. The modeling system was evaluated using pesticide drift data from spray trials conducted in the Mill Creek Canyon of Utah's Wasatch Mountains, USA, during the late spring of 1993. The predicted deposition compared within a factor of three of the observations (70% of the time) at all sampling locations extending several kilometers down-valley from the spray treatment block. The overall average ratio of predicted-to-observed deposition was 0.9.

AgDRIFT: a model for estimating near-field spray drift from aerial applications.

The aerial spray prediction model AgDRIFT embodies the computational engine found in the near-wake Lagrangian model AGricultural DISPersal (AGDISP) but with several important features added that improve the speed and accuracy of its predictions. This article summarizes those changes, describes the overall analytical approach to the model, and details model implementation, application, limits, and computational utilities.

Evaluation of the AgDISP aerial spray algorithms in the AgDRIFT model.

A systematic evaluation of the AgDISP algorithms, which simulate off-site drift and deposition of aerially applied pesticides, contained in the AgDRIFT model was performed by comparing model simulations to field-trial data collected by the Spray Drift Task Force. Field-trial data used for model evaluation included 161 separate trials of typical agriculture aerial applications under a wide range of application and meteorological conditions. Input for model simulations included information on the aircraft and spray equipment, spray material, meteorology, and site geometry. The model input datasets were generated independently of the field deposition results, i.e., model inputs were in no way altered or selected to improve the fit of model output to field results. AgDRIFT shows a response similar to that of the field observations for many application variables (e.g., droplet size, application height, wind speed). However, AgDRIFT is sensitive to evaporative effects, and modeled deposition in the far-field responds to wet bulb depression whereas the field observations did not. The model tended to overpredict deposition rates relative to the field data for far-field distances, particularly under evaporative conditions. AgDRIFT was in good agreement with field results for estimating near-field buffer zones needed to manage human, crop, livestock, and ecological exposure.