Spencer S. Walse

Postharvest Fumigation Research at

USDA-ARS-SJVASC

http//fresno.ars.usda.gov

Update on Postharvest Fumigation

• QPS scenario & methyl bromide

– low-emission fumigations

– alternatives

• PH3, SF

• breaking specialty crop trade barriers

– pests (insects)

– systems/processing-based

– mathematical modeling

– residues

Cl

Cl

Cl

Cl

Cl

Cl

OS

O

O

Trade Barriers

politics

$ economics

science

X

trade

barriers

Postharvest Conundrum

pest-free security

food safety no residue

Navel Orangeworm to EU

Mathematical modeling of fumigation (insect-, fumigant-, commodity-specific)

central-composite design

(matrix comparison)

center-point

axial

factorial

Factor (original units) Factor levels

- 2 - 1 0 a

1 2

x 1 : dose (mg/L)

0 24 48 72 96

x 2 : temp ( C) 5 10 15 20 25

x 3 : duration (h) 1 12 24 36 48

x 4 : pressure ( - inch. Hg)

0 7 14 21 28

a 0 = center point

axial

14 1 4 23 2 3 24 2 4 34 3 4

y = b 0 + b

1 x 1 + b

11 x 1

2 + b 2 x

2 + b 22 x

2 2 + b

3 x 3 + b

33 x 3 2 + b

4 x 4 + b

44 x 4

2 + b 12 x

1 x 2 + b

13 x 1 x

3

+ b x x + b x x + b x x + b x x

y = SF depuration y = egg survivability

y = b0 + b1x1 + b2x2 + b3x3 + b4x4

Multifactorial Modeling fumigant residues: SF-walnuts

Initial residues (30 min post fum.):

y = 1.36 + 0.87x1 – 0.51x2

mean p - value d

source sum of squares a df square b F - value c Prob > F

model 24.7 4 6.17 16.4 < 0.0001

residual 9.4 25 0.38

lack of fit 9.4 20 0.47 174.2 < 0.0001

a total for the sum of squares for the terms in the model

b estimate of variance, models sum of squares / model degrees o f freedom

c comparison of term variance (mean square) with residual variance (res. mean square)

d probability of seeing observed F value if the null hypothesis is true (no factor effect)

dose 18.0 1 17.9 47.8 < 0.0001

temp 6.14 1 6.14 16.4 0.0004

duration 0.44 1 0.44 1.2 0.2884

vacuum 0.09 1 0.09 0.24 0.6267

mean p - value d

source sum of squares a df square b F - value c Prob > F

model 24.7 4 6.17 16.4 < 0.0001

residual 9.4 25 0.38

lack of fit 9.4 20 0.47 174.2 < 0.0001

a total for the sum of squares for the terms in the model

b estimate of variance, models sum of squares / model degrees o f freedom

c comparison of term variance (mean square) with residual variance (res. mean square)

d probability of seeing observed F value if the null hypothesis is true (no factor effect)

a total for the sum of squares for the terms in the model

b estimate of variance, models sum of squares / model degrees o f freedom

c comparison of term variance (mean square) with residual variance (res. mean square)

d probability of seeing observed F value if the null hypothesis is true (no factor effect)

dose 18.0 1 17.9 47.8 < 0.0001

temp 6.14 1 6.14 16.4 0.0004

duration 0.44 1 0.44 1.2 0.2884

vacuum 0.09 1 0.09 0.24 0.6267

temp

(r2 = 0.76)

SF depuration rates:

- attempts to model failed, relative to an average rate

- ave. rate of 0.59 d-1, half-life of 1.17 d ± 20%

-125 mg/L max dose @ 60F, SF below 3ppm MRL in ~30 min

Egg biology & toxicology IMM

DFB

NOW

CM

Peach Twig Borer vs. AUS

1

2

3

4

1 2 3 4 5

10

11

12

13

14

15

16

0

post-fumigation time (day)

met

hyl b

rom

ide

resi

dues

(pp

m)

1

2

3

4

1 2 3 4 5

10

11

12

13

14

15

16

0

1

2

3

4

1 2 3 4 5

10

11

12

13

14

15

16

10

11

12

13

14

15

16

0

post-fumigation time (day)

met

hyl b

rom

ide

resi

dues

(pp

m)

Residues : MB & T104-a-1

- 0.6

- 0.5

- 0.4

- 0.3

- 0.2

- 0.1

0 0.5 1 1.5 2

time (hours)

ln (

[ MB

] t /[

MB

] o )

- 7

- 6

- 5

- 4

- 3

- 2

- 1

0 1 2 3 4

time (days)

ln (

[ MB

] t /[M

B] o

)

“ plums sorb slower ”

“ plums desorb slower ”

t 1/2 = 0.93

t 1/2 = 0.47

t 1/2 = 0.45

- 0.6

- 0.5

- 0.4

- 0.3

- 0.2

- 0.1

0 0.5 1 1.5 2

- 0.6

- 0.5

- 0.4

- 0.3

- 0.2

- 0.1

0 0.5 1 1.5 2

time (hours)

ln (

[ MB

] t /[

MB

] o )

- 7

- 6

- 5

- 4

- 3

- 2

- 1

0 1 2 3 4

- 7

- 6

- 5

- 4

- 3

- 2

- 1

0 1 2 3 4

time (days)

ln (

[ MB

] t /[M

B] o

)

“ plums sorb slower ”

“ plums desorb slower ”

t 1/2 = 0.93

t 1/2 = 0.47

t 1/2 = 0.45

MB sorption

Systems-based approaches: future?

CUMULATIVE JOINT PROBABILITIES

SWD

=

New Pest: back to MB

(preserve MB)

Development of low-emission chamber fumigations

0

20

40

60

80

10

0

12

0

14

0

16

0

18

0

Enclosure

0.1

1

10

100

1000

10000

100000

Me

Br

Co

nc

en

tra

tio

n, p

pm

Time, Minutes

Enclosure

Stack

Note log scale

Fumigation period

Begin Aeration

16,000 ppm

~5 ppm from

fumigation bay

~1 ppm from

adsorber exhaust

Switch to bypass

at 500 ppm

Terminate aeration

at 5 ppm

0

20

40

60

80

10

0

12

0

14

0

16

0

18

0

Enclosure

0.1

1

10

100

1000

10000

100000

Me

Br

Co

nc

en

tra

tio

n, p

pm

Time, Minutes

Enclosure

Stack

Note log scale

Fumigation period

Begin Aeration

16,000 ppm

~5 ppm from

fumigation bay

~1 ppm from

adsorber exhaust

Switch to bypass

at 500 ppm

Terminate aeration

at 5 ppm

Collaborators: Joe Pignatello (Yale)

William Mitch (Yale)

David Zilberman (UC Berkeley)

Gary Knapp (consultant)

Jim Leesch (ARS retired)

Scott Wood (APHIS-TQAU)

Development of low-emission chamber fumigations

trade

barriers

Postharvest Conundrum

pest-free security

food safety no residue

Residue remediation

fenhexamid 1-methylcyclohexanecarboxamide

CO2

+

• Soak tanks (HOCl, O3)

– oxidant-induced color changes

– other cosmetic damage

– shallow pitting on fruit surfaces

– natural organic matter competition

– inadequate activity of O3 against surface-

sorbed residues (low water sol.)

– disinfection byproducts (DBPs!)

History: wet approaches

Hakan Karaca, Joe Smilanick

1000 ppm O3

REMEDIATION OF FUNGICIDE RESIDUES

ON FRESH PRODUCE USING

GASEOUS OZONE

Environmental Science & Technology, 2011, in press

Residue remediation w/ O3: structurally selective

~2 ~2 64 38 40 % degradation

Ozone fumigation

Will my product look worse?

Situation dependent

Will worse products form?

more polar = less toxic (ozone)

• Endocrine disruptors

– endosulfan and sulfate

• POPs

– paraoxon and parathion

Ozone fumigation

oxidation

paraoxon parathion

O N O 2 P

O C 2 H 5

O C 2 H 5

S O N O 2 P

O C 2 H 5

O C 2 H 5

O

oxidation

paraoxon parathion

O N O 2 P

O C 2 H 5

O C 2 H 5

S O N O 2 P

O C 2 H 5

O C 2 H 5

O

Case studies: endocrine disruptor (ES) and POP

C l

C l

C l

C l

C l

C l O

O S

O

C l

C l

C l

C l

C l

C l O

O S

O

O

oxidation C l

C l

C l

C l

C l

C l O

O S

O

C l

C l

C l

C l

C l

C l O

O S

O

O

oxidation

endosulfan endosulfan sulfate

t=0

t=30 min

Next steps

• Develop structurally indiscriminate techs.

– Free radical processes (●OH, ●OOR)

• Obtain funding/agreements

• Tailor to commercial needs

THANK YOU!