Regulatory bodies worldwide are currently developing modeling frameworks to simulate pesticide drift following applications from remotely piloted aerial application systems (RPAAS). Unfortunately, there are no currently validated mechanistic models that simulate off-target droplet movement from these systems. To respond to this modeling gap, we evaluated AGDISPpro, an established Lagrangian-based drift and deposition model following applications by fixed and rotary wing aircraft. Specifically, we evaluated two of the nine RPAAS models available in AGDISPpro, i.e., PV22 quadcopter and PV35X hexacopter models. Our detailed evaluation relied on two sets of field studies: a series of single-swath applications using medium and extremely coarse spray nozzles, and a series of four-swath applications using fine and ultra coarse spray nozzles. AGDISPpro model predictions were compared to in-swath and downwind deposition measurements. The r index of agreement ranged from 0.47 to 0.92 for medium nozzles, 0.61-0.94 for extremely coarse nozzles, and from 0.86 to 0.93 to 0.48-0.55 for fine and ultra-coarse nozzles respectively. There is uncertainty regarding how swath width and swath displacement behavior from the RPAAS affect the location, width, and magnitude of the peak deposition and deposition plume. Thus, further research is required to reduce this uncertainty. Overall, this study demonstrates that AGDISPpro is a promising tool for modeling off-target spray deposition effects from RPAAS.

The release of dispersant from an aircraft onto an oil spill is simulated using the AGDISPpro computer model, to develop a better understanding of how aircraft type, spray systems, and meteorological conditions affect the prediction of surface deposition. This model, originally developed for predicting the aerial release of pesticides for agricultural spray applications, is ideally suited to simulate the effects of aircraft type and flight condition/configuration, spray system arrangement, wind speed and direction, temperature and relative humidity (evaporation), release height, and spray application rate when spraying an oil spill. Predictions of droplet trajectories from the aircraft to the surface, drop size distributions at the release height, and deposition profiles are compared to two historical datasets for the Lockheed C-130, from field studies conducted in 1982 and 1993. This article shows that model accuracy improves from R2 = 0.411 to 0.827 with the earlier data, to R2 = 0.885 to 0.968 with the later data, most probably because of a better understanding of nozzle locations in the 1993 data. Model accuracy also appears improved when the aircraft flies in an in-wind direction, a configuration strongly recommended in the available literature.

HighlightsSummarizes AGSprayOPT model development for ground sprayer simulations.Extends previously published modeling efforts to time-varying meteorology, wind speed/direction, and vehicle motion.Enables real-time (onboard) prediction of spray deposition and drift during ground sprayer field operations.Confirms model accuracy through comparison to spray trial data.Abstract.Under a recent U.S. Department of Agriculture Small Business Innovation Research program, a sprayer-based software system for the prediction and prevention of undesirable spray drift during ground sprayer operations was developed. This software system, designated AGSprayOPT, is an extension of the modeling approach employed in the development, validation, and continued enhancement of the AGDISP/AGDISPpro family of models. While focused more specifically on ground sprayer operations, AGSprayOPT employs a more general drift and deposition prediction model with time-varying meteorology, spray path, and tractor/sprayer configuration. This article describes model development and comparisons with wind tunnel data, detailing a series of spray trials and subsequent model validation against these data. Keywords: AGDISP/AGDISPpro, AGSprayOPT, Field study, Ground sprayer, Simulation, Tractor/sprayer.

Highlights Airblast sprayer drift potential was evaluated up to 183 m (600 ft) downwind from an orchard edge. A central leader apple orchard was sprayed at dormant and full canopy stage. Higher drift at full canopy stage was likely due to higher wind speeds and lower humidity. String and artificial foliage samplers had higher collection efficiencies than Mylar cards. Abstract . Risk assessment of orchard pesticide spraying is currently based on spray drift estimation using a worst-case scenario (dormant stage). However, most spray applications are conducted during non-dormant canopy growth stages. Such overestimation leads to restrictive operational regulations in pest management activities. Therefore, field data were generated and studied for a mechanistic model that will predict spray drift from airblast spray applications in tree fruit orchards. Spray trials were conducted at dormant and full canopy growth stages in a central leader trained apple orchard. An axial-fan airblast sprayer sprayed fluorescent tracer in the third row from the orchard’s downwind edge, with four passes being one run. A total of 20 runs, i.e., 17 spray runs and three blanks, were performed during each of the two crop growth stages. Mylar cards, artificial foliage (AF), and horizontal strings (HS) were used to quantify drifting spray deposition up to 183 m (600 ft) downwind. Within the orchard, the deposition on card samplers 3 m upwind of the sprayed row was 21.94% ±4.63% (mean ± standard deviation) of applied dose (AD) at dormant stage and 16.02% ±2.86% AD at full canopy stage. Deposition downwind and adjacent (-3 m) to the sprayed row was 17.92% ±2.70% AD and 7.15% ±1.78% AD at dormant and full canopy stages, respectively. Spray drift decreased substantially at the orchard edge to 3.18% ±1.30% AD at dormant stage and 2.30% ±1.16% AD at full canopy stage. Spray drift was very low at 183 m (600 ft) downwind of the orchard, with deposition of 0.002% ±0.003% AD at dormant stage and 0.003% ±0.004% AD at full canopy stage. Deposition data collected at common sampler locations showed that HS and AF samplers collected significantly (p < 0.05) more drifting spray than card samplers. Downwind speeds had a strong linear relationship with spray drift at both growth stages (dormant: R2= 0.80, full canopy: R2= 0.86), while the influence of temperature and humidity could not be directly observed from the collected data. Keywords: Airblast spraying, Deposit samplers, Dormant and full canopy, Drift, Modern orchard systems.

HighlightsRecent large field programs are re-examined in the context of model development.Details of plant canopy wind fields are discussed.Collection efficiency of rotorods is discussed in detail, and the theory is used to re-examine field data.The approach used in the AGDISP model to simulate canopy wind fields is discussed in detail.Abstract. Recent field studies provided data to evaluate the performance of the aerial spray deposition algorithm in AGDISP. Those studies provided data for forest canopy settings that are either outside the stated domain of AGDISP or where assumptions in the model greatly impact the model performance. The two data sets were collected with the intention of providing input to drive model upgrades, but data limitations restricted that objective. Rather, this technical note shows that collection efficiency (CE) must always be considered (the model currently adjusts for CE only if the modeled output is canopy capture). One of the previous studies showed that the model substantially overpredicted droplet flux 65 m downwind of the spray line. Consideration of CE resolves some of this overprediction, but the model physics employed in AGDISP remain a substantial simplification of the complex flows that transport droplets in the atmospheric boundary layer near and in deep, three-dimensionally varying forest canopies. Keywords: Aerial application, AGDISP, Model, Spray drift.

Abstract. AGDISP (AGricultural DISPersal) models the release of aerially applied sprays with a Lagrangian-based droplet tracking algorithm initialized by user inputs (aircraft description, spray boom nozzle locations, drop size distribution, spray material properties, release height, and meteorology). The model offers an extensive set of output plots and toolbox options (deposition, spray block, stream, and multiple application assessments) to predict the downwind behavior of released sprays and assess their potential environmental impact. The model is used in risk analysis, operational planning, post-operation analysis, and training, particularly by the USDA Forest Service (FS) and its cooperators, including the U.S. Environmental Protection Agency (USEPA), the U.S. Fish and Wildlife Service, the U.S. Department of Defense, and various other state and private entities. This article updates the further development of the model since 2003, including the implementation of a quadratic droplet evaporation model and its behavior as Reynolds number approaches zero, a more accurate time step algorithm tied to droplet settling velocity, an optical canopy model, a Gaussian model for far-field extension (downwind to 20 km), an Eulerian model for tracking volatile active spray material, and the Tier 1 ground and orchard assessments previously developed by the Spray Drift Task Force (SDTF). Keywords: Aerial application, AGDISP, Model, Spray drift.

The dominant mechanism driving aerially released spray material toward the ground is the flow field generated by the aircraft, in the form of either aircraft vortices or downwash. In AGDISP, the initial strength of this flow field is reduced over time by a simple damping mechanism tied to atmospheric turbulence. When these flow fields enter a forest canopy, the scrubbing impact of the canopy structure further reduces their strength and influences the behavior of spray droplets released into the canopy. This study uses a simple model to approximate the canopy damping mechanism and then applies this model to a recent canopy dataset in an effort to validate the approach proposed.

This article summarizes the ability of CHARM+AGDISP to predict the drift and deposition of sprays released from rotary wing unmanned aerial vehicles (UAVs). This predictive capability results from merging algorithms for spray transport, as found in AGDISP (AGricultural DISPersal), with CHARM (Comprehensive Hierarchical Aeromechanics Rotorcraft Model). The resulting software tracks the release of spray droplets from nozzles on the UAV to deposition on the ground. To date, both AGDISP and CHARM, a code that provides a complete representation of the time-varying, unsteady flow field surrounding a helicopter during transient maneuvering flight near the ground, have been extensively validated. The CHARM+AGDISP software is applied to two UAVs to explore the flow field regimes that present challenges for effective UAV operations. The simulations undertaken indicate flight conditions that yield acceptable deposition levels and minimize drift; inversely, conditions are also identified that result in off-target drift that may be problematic.

The objective of these tests was to evaluate pesticide drift from ground applications using a standard manual pump backpack sprayer and a UTV-mounted boomless sprayer. Three deposition sampler types were deployed: Mylar cards, water-sensitive papers, and artificial foliage. This study indicates that drift of pesticide at 20 m downwind of backpack or UTV spraying was about 0.001 of the applied rate. This order of magnitude of drift was similar for both application methods. In the case of the backpack spraying, deposition decreased to about 0.01 of the applied rate just 1.5 m downwind of the swath edge. In the UTV trials, the closest off-swath measurement was 0.5 m downwind of the downwind swath edge, and deposition there ranged from about 0.5 of the applied rate down to less than 0.001. At 2.5 m downwind of the downwind swath edge, these values ranged from about 0.02 down to near 0.0001 of the applied rate. The study concludes that very small amounts of material were deposited at 20 m downwind, but drifted material was present. This conclusion is reached understanding that these trials were conservative by design, as there was almost no intervening vegetation between the release line and the collectors. The study also confirms the dependence of drift on wind speed in these applications.

This article summarizes further experimental data collected regarding the evaporation rate of isolated water droplets and strengthens previously published results regarding droplet evaporation at low relative wind speeds. The results suggest that as the Reynolds number (based on the relative velocity between the droplet and ambient air) decreases toward zero, the droplet evaporation rate falls to one-half its value. Because released spray droplets quickly attain near-background velocities, this result implies that evaporation continues for longer times than historically thought but at a lower rate. Additional measurements on traditional fluids (WHO, deionized, distilled, and tap water) provide a consistent benchmark with which to compare evaporation rates.

This article summarizes additional experimental data collected regarding the evaporation rate of water droplets stacked on several threads, positioned downwind of one another, and strengthens previously published results regarding droplet evaporation rates inside a spray cloud. Here, the effect of evaporating water droplets upwind of other droplets is examined, along with changes in tunnel speed, temperature, and relative humidity on droplets positioned on single threads. These results further quantify the wet bulb temperature depression felt by droplets within a spray cloud, along with the effect of the Sherwood number, and are used to enhance the ability of AGDISP to make accurate model predictions for droplets released from spray nozzles.

This article analyzes flow and deposition measurements downwind of a section of a full-scale ground sprayer boom tested in a wind tunnel. The measurements include the velocity and turbulence levels generated by the presence (and absence) of the nozzle spray, in addition to nozzle spray deposition patterns. It is envisioned that a description of the turbulent structure of the tractor wake (obtained previously on a subscale tractor) and the nozzle spray, along with flow velocities in the three principal directions, can be used to provide an initial database for velocities and turbulence levels in the vicinity of a typical full-scale ground boom sprayer. The goal of this effort is to use this parameterized wake model to supplement local atmospheric and surface effects around and behind a tractor and spray boom, to better predict the behavior of spray material released from nozzles on a spray boom during actual ground sprayer operation.

This article calculates the evaporation rate of water droplets stacked on several threads, positioned downwind of one another. The measurement approach was used previously to determine the evaporation rate of isolated suspended droplets, on a single thread, for development of the Spray Drift Task Force (SDTF) droplet evaporation data base. Here, the effect of evaporating droplets upwind of other droplets is examined, recovering the effective evaporation rate of individual water droplets surrounded by other water droplets in a spray cloud. The results quantify the modified wet bulb temperature depression felt by the droplets within the cloud and are used to compare AGDISP model predictions with SDTF aerial deposition data.

Additional wind tunnel measurements of wind direction effects on the wake of a subscale tractor, tank, and spray boom model (representing a typical ground sprayer and henceforth referred to as a "spray rig") are presented to demonstrate the behavior of the dominant air motions responding to the presence of a spray rig. The approach measures the velocity and turbulence levels (in three directions) with the wind blowing from five angles (from directly toward the front of the tractor to directly toward the back of the tractor in 45 degrees increments). The results quantify the effect on wind direction and turbulence level by the presence of the spray rig model. The goal of this effort is to combine and analyze these measurements to describe the full-scale wake of a specific tractor/tank/spray boom combination. In this way, the wake model will augment local atmospheric and surface effects to better predict the behavior of material released from nozzles on a spray boom during actual ground sprayer operation.

1. Our review of Schleier et al. (2014), referred hereafter as S3, focused on a discussion of their equation for predicting deposition, yet in S4 these authors state that we should “NOT [their capitalization] use the equation and coefficients in this paper to estimate deposition.” Further, they warn that using their equation would be “an improper use and would give incorrect predictions.” In effect, the authors appear to present an equation that no one should use. 2. The authors spend nearly one-half of their comments on what they define as our “fundamental misunderstanding and misuse” of their equation, first presented in their earlier paper, Schleier et al. (2012a), referred hereafter as S1, and in S3. They even state in S4 that their equation was inserted into S1 and S3 “to emphasize the most significant factors influencing deposition,” and that we misused the equation because we “treated each parameter as an independent variable that does not change other variables.” In point of fact, their MULV-Disp model, presented in Schleier and Peterson (2012b), referred hereafter as S2, is a simple linear regression model, based on the linear combination of 10 parameters: downwind distance D, application rate AR, flow rate FR, density DEN, count median diameter CMD, volume median diameter VMD, wind speed WS, temperature T, relative humidity RH, and stability category SC. The model contains three types of terms: a constant, 10 terms each involving a constant multiplying one of the 10 parameters, and 15 terms each involving a constant multiplying the product of two of the 10 parameters. Their statistical analysis determined the 26 constants giving the best fit to their field data. Apparently it was incorrect for us to determine the effect of one of the parameters by starting with default values (as suggested in S2) for all of the parameters, then increasing the value of one of the parameters by 10%. For example, it was somehow incorrect for us to increase DEN by 10% in its linear term and three product terms with CMD, WS, and SC. To verify our approach, we set aside the computer code we wrote based on their equation and instead used the MULV-Disp spreadsheet program previously provided to us. We then increased each of the parameters by 10% and compared spreadsheet results with our previous computer model results. With the exception of application rate, all of the results we discussed in Teske et al. (2015) were duplicated by their model. Inexplicably, the application rate behaved exactly as it should, increasing deposition by 10% when AR was increased by 10%. It therefore became apparent to us that the MULV-Disp program always uses a fixed application rate, specifically the default value of 7.846 g/ha, then scales the predicted deposition by the ratio of AR entered by the user divided by the default rate. This correction is not disclosed in S1, S2, S3, or S4 and suggests that application rate should not be treated as one of the parameters in the linear regression equation, but subsumed into the constant to give a revised value of 212.758181. 3. S4 suggests that the use of a 10% increase from a default value violates the limits set in the program, as we are then “inappropriately abstracting the model.” However, the sample calculation presented in Figure 1 of S3 (shown here in Table 1) uses parameter test values that in some cases far exceed the 10% limit we assumed. 4. S4 misinterprets our use of Mickle (2005), which we used only to show the typical ground deposition profile expected from a nozzle releasing spray material at 45u upward from the back of a pickup truck. To suggest that the confidence intervals around their average data (two orders of magnitude on either side, from S1) encompass the expected deposition profile (zero deposition at the nozzle increasing to a peak value downwind, and decreasing thereafter) seems a stretch, since their average 1 Continuum Dynamics, Inc., 34 Lexington Avenue, Ewing, NJ 08618. 2 USDA Forest Service, 180 Canfield Street, Morgantown, WV 26505. 3 Bonds Consulting Group, 3900 Wasp Street, Panama City Beach, FL 32408. Journal of the American Mosquito Control Association, 32(1):66–67, 2016 Copyright E 2016 by The American Mosquito Control Association, Inc.

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.

A subscale tractor and spray boom model was placed in a wind tunnel to determine the dominant air motions around and downwind of a “typical” ground sprayer. Velocity and turbulence levels (in three directions), generated by the presence of the subscale model, were measured and are presented and interpreted herein. The goal of this effort is to combine these measurements into a database that describes the full-scale wake of a tractor and spray boom combination, and then use the wake model to augment local atmospheric and surface effects to better predict the behavior of material released from nozzles on a spray boom during actual ground sprayer operation.

Over the last several years, USDA Forest Service modeling efforts have been directed toward refinement of canopy droplet capture in AGDISP. Approaches included exploring the behavior of the droplet trajectory through the canopy and the LAI characterization of the cross-sectional canopy structure, in addition to studying the effects of droplet bounce and shatter off plant surfaces. The dominant mechanism driving droplets into the canopy is the flow field generated by the aircraft vortices. While AGDISP damps these vortices by atmospheric turbulence, the model does not include the effect caused by scrubbing these vortices through the canopy. While early field work suggested that vortex scrubbing results in little change to the vortex circulation strength, it appears likely that this mechanism could slow droplet motion and enhance canopy capture. This paper examines the influence of canopy drag on vortical strength, based upon the use of an accepted atmospheric CFD code. It is expected that this work will improve the decay characteristics of aircraft vortices near and within a canopy, and help improve the modeling effort directed toward droplet deposition predictions.

Modeling of long range pesticide drift is an ongoing issue that has been brought to a head lately due to the use of long range drift in an effort to protect endangered species. AGDISP is a well-known tool to predict drift and is often used to make predictions at distances that are beyond its modeling domain. The USDA Forest Service is currently developing approaches to extend model predictions of long range drift. In these approaches AGDISP is used as a source model and interfaced with established US Environmental Protection Agency accepted long range air pollution models. This paper addresses the initial steps in interfacing AGDISP with SCIPUFF, a Lagrangian puff deposition model, and its application to complex terrain.

Schemes for classifying agricultural sprays according to droplet size and drift potential are described. These schemes have particular benefit for the comparison of spray data generated using different particle measurement techniques. Labels for agricultural chemical products can refer to the classification schemes for specification of the droplet size spectrum needed for effective application and drift minimization. As presented at: ILASS–Americas, 11th Annual Conference on Liquid Atomization and Spray Systems, Sacramento, CA, May 1998 *Corresponding author