# Comparison of Multiple Spray Drift **Deposition Data Sets**

**Environmental Protection Agency EMPM Potomac Yard One**  **March 24, 2014**

**Scott H. Jackson Regulatory Technical Stewardship & Sustainability** 

# Determining Spray Buffers

 A new tool (REGDISP) has been released for the calculation of ground spray buffer distances.

 This new tool allows the user to calculate deposition from about 100 unique experimentally derived deposition datasets (AAFC, SDTF).

 One question that can be asked is it possible (or desirable) to average datasets that appear similar ?

The goal of this examination is getting the most descriptive spray buffer distance when one is necessary on the label.

1

# Averaging Field Datasets ??

 Datasets are unique

 Velocity and vectoring affect off target movement

 Tank mix certainly affects DV 50 

# and RS

 Boom height can be a factor

 Wind Speed Measurement height

 Travel speed can be a factor

 Problem with some SDTF datasets is that 2 variables were changed simultaneously thus make isolation of the driving factor undiscernibly (e.g. boom height and travel speed). 

# So can we average datasets ??

23 

# Tank Mix Factors

Mix DV 50 

## (um) Classification
H1 F1 432 VC
H1 F2 419 VC
H1 F2 + H2 360 C

**Same pressure, same operating speed**



## H=herbicide **F=formulation Wind Tunnel Data** 

**Nozzle**

**- AI XR11004** 

# Wind Vector and Velocity

– How Constant?

 Remember unlike other studies a registrant might conduct where wind is just handled as a maximum ceiling speed (e.g. 5 mph), wind in a drift field study is the study driver.

 Consistent wind velocity and vectoring are critical to a meaningful deposition datasets.

 Wind is the engine to the deposition sets and generally both low wind and high wind are required for a meaningful results.

4

# Wind Vectoring in a 6 minute Spray Window

5 N NNE

NE

ENE

E

ESE

SE

SSE S SSW SW WSW

W WNW NW NNW 

## 30 second sampling

# Wind Velocity in a 6 minute Spray Window

6 0 50 100 150 200 250 300 5 6 7 8 9 10 11 12 13Wind Speed (mph)



### Time (Seconds) Mean WInd Gusts Mean Wind Speed = 7.1 mph Mean Wind Gusts = 8.8 mph 

## 30 second **reporting, 5 second sampling** 

# Wind Speed

7 08:24 09:48 11:12 12:36 14:00 15:24 16:48 18:12 2 4 6 8 10 12 14Wind (mph)



### Time (24 hr) 5 Min. Avg. Gust 7/17/2012

Max.

Min. 

## 30 second reporting, 5 **second sampling**

# 90 th 

# or 50 th 

# Percentile ?

8 1 10 100 1E-3 0.01

0.1

1 10Percent of Applied

Down Field Distance (m) - log 90th Pct

50th Pct

Mean Nozzle - 11004

Dr. T. Wolf AAFC '2000



## AGDRIFT 1 10 100 1E-3 0.01

0.1

1 10Percent of Applied (log)

log - Down Field Distance (m) 13.5 14.2 18.7 20.7 33 Wind (kph) AI11004

# Conceptual Errors

 In the past dataset were viewed as replicates rather than unique data with individual velocities and vectors (with other variables).

 Large datasets were averaged together with disparaging conditions and a 90 th 

# percentile value was derived.

 This treatment of the data was a gross mis

- handling of data, and should not of been done in such a fashion.

 Another factor for mixing datasets is field treatment size.

 Most extreme example of averaging data “abuse” is AgDrift Ground.

9

# Field Treatment Size / Swath Size

10

# On Field Treatment Size

How big an area is necessary for treatment so that little additional contribution is gain with additional sprayer passes??

 Spray Drift Task Force 4 x 45’ = 180’

 Agriculture and Agri

- Food Canada 4 x 60’ = 236’ (metric).

 Agriculture and Agri

- Food Canada ‘2011 work indicates no additional contribution beyond 250’

– 300’ (high wind).

1112 

### Downwind distance (m) 1 2 5 10 20 40 120Deposit (% of applied)



### 0.001 0.01

0.1 1 10 1

1

1

1

1

1 1

1 2

2

2 2
2 3 3

3 3 3 5 5

5 5 5

6 6 6 6 6 7 7
7 7 7 8 8 8 8 8 9 9

9 9 9 4 4
4 4 4 

# ‘2011 AAFC Multi

**- Swath Data** 

**4**

**- 5 swaths no**  **additional contribution (‘60 swaths)** 

0 100 200 300 400 1E-4 1E-3 0.01

0.1

1

10Fraction of Applied

Distance (ft) 1 Swath 

## Swaths exhibit inverse behavior & **More swaths, more contribution AgDrift** 13 

# Comparing Datasets

 **‘1992 / ‘1993 Spray Drift Taskforce Datasets.**

 **‘2000 / ‘2004 / ‘2011 Agriculture and Agri**

**- Food Canada**  **datasets.**

 **About 100 unique deposition datasets.**

 **SDTF Data is all convectional (flat fan) nozzles.**

 **AAFC Data has venturi, air induction, and some conventional nozzle datasets.** 14 

# Analysis of Datasets

**In order to compare datasets, we used a comparison of linear models routine.** 
 Data were log/log transformed.
 A simple regression fit was done.
 Next the compare (regression) model routine was used in Origin software (Origin Pro 9.0).
 The routine is a “F

- test”.
 Similar wind conditions were chosen for comparison. **Statistical comparison of datasets**

# AAFC ‘2000 vs ‘2011

15 1 10 100 1E-5 1E-4 1E-3 0.01

0.1

Wind 16.5 & 16.6 kphFraction of Applied (log)

Distance (m) '2000 '2011 110025

year Fraction of Applied @ 120 m Wind ( kph ) 2000 0.00033 15.9 2011 0.00001 15.6 

## Both X and Y are linearized

# AAFC ‘2011 vs ‘2000

16 1 10 100 1E-5 1E-4 1E-3 0.01

Wind 11.8 & 12.8 kphFraction of Applied (log)

Distance (m) 2000 2011 110025

year Fraction of Applied @ 120 m Wind ( kph )
2000 0.00002 11.8
2011 0.00002 12.8

# AAFC ‘2000 vs ‘2004

17 1 10 100 1E-4 1E-3 0.01

0.1Fraction of Applied (log)

Distance (m) '2000 '2004 XR8003

Wind 16.8 & 17.1 kph year Fraction of Applied @ 120 m Wind ( kph )
2000 0.00039 17.1
2004 0.00016 16.8

# AAFC ‘2000 vs ‘2004

18 1 10 100 1E-4 1E-3 0.01

0.1

Wind 21.3 & 22.5 kph ' 2000 (a) ' 2000 (b) ' 2004 XR8003Fraction of Applied (log)

Distance (m) year Fraction of Applied @ 120 m Wind ( kph ) 2000 0.00025 21.3 2000 0.00015 21.3 2004 0.00053 22.5

# SDTF 1992 vs 1993

19 1 10 100 1E-4 1E-3 0.01

Wind 11.0 & 10.9 mphFraction of Applied (log)

Distance (m) 1992 1993 8004

Year Fraction of Applied @ 120 m Wind (mph) 1992 0.00015 11 1993 0.00004 10.9

# Measuring Wind

- Height

20 

## Boom Height

2 m 3 m Measuring Wind

- Height Ley et al. 2009

**V10/Vh = 1/(0.233 + 0.656*log10(h+4.75))** 2 to 3 m correction

# Wind Speed or Droplet Size?

21 

## Our Goal is managing off

**- target movement** 

# AI 110025

– Deposition and Wind

22 7 8 9 10 11 12 13 14 15 16 17 18 19 20 0.0000 0.0001 0.0002 0.0003Deposition at 300' (Frac of Applied)

Wind Speed (mph) AAFC Data '2000

AI110025

8 9 10 11 12 13 0.0000 0.0001 0.0002 0.0003Deposition at 300' (Frac of Applied)

Wind Speed (mph) AAFC Data '2011

AI110025

# AAFC and BASF AI 11004

23 8 9 10 11 12 13 14 15 16 17 18 19 20 0.000 0.005 0.010 0.015 0.020 0.025 0.030Deposition at 400' (Frac of Applied)

Wind Speed (mph) AAFC Data '2000

AI11004

4 5 6 7 8 9 10 11 0.0000 0.0002 0.0004 0.0006 0.0008Deposition at 393' (Frac of Applied)

Wind Speed (mph) BASF '2012

AI11004

# AI 11004

– Deposition and Wind

24 8 9 10 11 12 13 14 15 16 17 18 19 20 0.000 0.005 0.010 0.015 0.020 0.025 0.030Deposition at 400' (Frac of Applied)



### Wind Speed (mph) AAFC Data '2000

AI1100425 

# Aerosol Formation: Nozzle x Formulation

0 2 4 6 8 10 12 14 16

F 3 F 2 XR8004 TTI11004 AIXR11004% < 105umAerosol

F 1

F=Formulation

# Summary

 With about 100 deposition datasets provided in REGDISP, selection of similar datasets can be confusing. Perhaps the averaging of similar data could be an acceptable simplification to the end user working to generate a label recommendation.
 When considering labeling for off

- target concerns, proper nozzle size is more critical than wind restrictions.
 Or to restate this, too small a nozzle size in low wind can still generate unacceptable off

- target chemical movement.

26

# Summary (More)

 Using proper statistical procedures it is possible to combine depositional datasets.
 However, methods of the past where data were indiscriminately lumped (AgDrift, others) such as to derive a “90 th 

# percentile” is conceptually, mathematically and procedurally incorrect.
 Finally the goal of predicting buffer distances is to derive predictive, protective, and reliable distances that can be used for labeling (when required).

27**02.06.2010 INTERN**  **28**