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 DV50 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 ??

2
3

Tank Mix Factors

Mix DV50 (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

13

Wind 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

14

Wind (mph)

Time (24 hr)

 5 Min. Avg.

Gust

7/17/2012

Max.

Min.

30 second reporting, 5 

second sampling
90th or 50th Percentile ?

8

1 10 100

1E-3

0.01

0.1

1

10

Percent 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

10

Percent 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 90th 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).

11
12

Downwind distance (m)

1 2 5 10 20 40 120

Deposit (% 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

10

Fraction 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 kph

Fraction 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 kph

Fraction 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

0.1

Fraction 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

XR8003

Fraction 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 mph Fraction 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

0.0003

Deposition at 300' (Frac of Applied)

Wind Speed (mph)

AAFC Data '2000

AI110025

8 9 10 11 12 13

0.0000

0.0001

0.0002

0.0003

Deposition 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

0.030

Deposition 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

0.0008

Deposition 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

0.030

Deposition at 400' (Frac of Applied)

Wind Speed (mph)

AAFC Data '2000

AI11004
25

Aerosol Formation: Nozzle x Formulation

0

2

4

6

8

10

12

14

16

F 2 F 3

 XR8004

 TTI11004

 AIXR11004

% < 105um

Aerosol

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 

“90th 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