Chapter 9 Normalisation of Field Half-lives Ian Hardy Battelle AgriFood Ltd., Ongar, UK.

Chapter 9Normalisation of Field Half-lives

Ian HardyBattelle AgriFood Ltd.,Ongar,UK

• Overview / Basic Processes

• Availability of data for soil temperature and moisture content

• Approaches to normalisation– Average temperature and moisture content

– Time-step normalisation

– Rate constant optimisation

• Conclusions

Overview

Overview

•Why do we want to normalise field data ?

–Degradation is investigated under more realistic use conditions for the product

–Enables use in risk assessments – e.g. FOCUS groundwater models

–Large amounts of useful information are generated during the field studies which are not fully utilised in evaluations

Assessment of Study Design and Results

• A preliminary check of the field study should be made to assess the suitability for its use in normalisation procedures:– Assess the significance of dissipation processes such as

photodegradation and volatilisation. If they are unimportant, or can be properly addressed during the evaluation, then the use of the data in normalisation procedures is possible

– The soil should be well characterised at different depths

– The sampling depth and analytical method should allow for the bulk of the applied material to be evaluated

– Daily meteorological data should be available (rainfall, air temperatures etc.)

– Cropping and pesticide use history

Basic Processes

• Normalisation techniques should be consistent with the process implementation in the subsequent model used for risk assessment

• For temperature: Standard FOCUS Q10 (2.2) or Arrhenius approaches can be used

• For moisture: Walker B-factor (0.7) approach typically used

• Can normalise to any reference conditionse.g. 20oC/pF2 for EU or 25oC/75% pF2.5 for US

B

ref

act1050act50ref MC

MC*

)/10)T((TQ*DTDT ref

Data Availability

• What data should we use for normalisation ?

• Field half-lives are normalised to reference conditions reflecting the major influence factors on field dissipation – soil temperature and soil moisture

• The normalisation is conducted using daily measured or simulated values for soil temperature and moisture

• A number of algorithms are available for calculating soil temperatures from min/max air temperatures

• Soil moisture can be readily estimated using the FOCUS groundwater models

Soil Temperature Estimation

0.0

5.0

10.0

15.0

20.0

25.0

01/11/1998 09/02/1999 20/05/1999 28/08/1999 06/12/1999 15/03/2000 23/06/2000 01/10/2000 09/01/2001 19/04/2001

Date

So

il T

em

per

atu

re (

oC

)

Measured ST

Predicted ST

Approaches to Normalisation

•Three approaches considered:

–Average soil temperature and moisture content

–Time-step normalisation

–Rate constant optimisation

Average Temperature and Moisture Content

• Good approximation for short-term kinetics when the mean temperature is relatively stable

• The average soil temperature and moisture content are determined over an appropriate period and normalisation conducted as for laboratory studies

• Useful for older studies with limited measurement data and can give comparable results to the more complex methods

• Not suitable for long periods e.g. over several seasons where the conditions vary significantly

Average Temperature and Moisture Content

Measured Soil Temperatures

25.3

-5

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01-Jun-94 21-Jul-94 09-Sep-94 29-Oct-94 18-Dec-94 06-Feb-95 28-Mar-95 17-May-95 06-Jul-95

Date

Tem

per

atu

re (

oC

)

5cm

5cm average

Appropriate over this period

Not appropriate over this period

Average Temperature and Moisture Content

• Advantages– Good approximation for ‘short-term’ kinetics (i.e. over 1-

month) where there is no big variation in conditions

– Easy to calculate

– Same methodology as for laboratory studies

• Disadvantages– Not appropriate for ‘long-term’ kinetics

Time-step Normalisation

Concept:

• Daily variation in soil temperature and moisture content is accounted for using a normalised day-length (NDL) approach:– 1 day at 15oC and 80%FC is equivalent to 0.58 days at

20oC and 100%FC

– 1 day at 25oC and 90%FC is equivalent to 1.38 days at 20oC and 100%FC

• Cumulative NDL is then calculated between sampling points

• Standard kinetic tools used for evaluations

Time-step Normalisation

Raw Data - DT50=20 days

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0 5 10 15 20 25 30 35 40 45 50 55 60 65

Time (days)

% A

pp

lied

data

Temperature (oC)

Moisture content (%FC)

Moisture content = 80% Field Capacity (FC)

Temperature = 15oC

Time-step Normalisation

FOCUS Normalised

0

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20

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40

50

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100

0 5 10 15 20 25 30 35 40 45 50 55 60 65

Time (days)

% A

pp

lied

data

DT50=11.53 days

DT50=20 days

Data points ‘regressed’ along

the time axis

Time-step Normalisation

Time (days)

Normalised day length

Cumulative NDL

Residue (%)

0 0.00 0.00 100.001 0.58 0.58 96.592 0.58 1.15 93.303 0.58 1.73 90.134 0.58 2.31 87.065 0.58 2.88 84.096 0.58 3.46 81.237 0.58 4.04 78.468 0.58 4.61 75.799 0.58 5.19 73.20

10 0.58 5.77 70.7111 0.58 6.34 68.3012 0.58 6.92 65.9813 0.58 7.50 63.7314 0.58 8.07 61.5615 0.58 8.65 59.4616 0.58 9.23 57.43

Time (days)

Cumulative NDL

Residue (%)

0 0.00 100.001 0.58 96.593 1.73 90.135 2.88 84.097 4.04 78.4614 8.07 61.5621 12.11 48.3042 24.22 23.33

Plot cumulative NDL vs residue

Time-step Normalisation

FOCUS Normalised

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0 5 10 15 20 25 30 35 40 45 50 55 60 65

Time (days)

% A

pp

lied

data

DT50=20 days

Timestep

DT50=11.53 days

DT50 = 11.53 days

r2 = 1.000

Time-step Normalisation

• Real example:

• 2 year field dissipation study conducted in Northern Europe

• Winter application

Time-step Normalisation

Soil Temperature and Moisture Content

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0 100 200 300 400 500 600 700 800 900

Days after application

So

il te

mp

erat

ure

(o

C)

or

Mo

istu

re (

%w

/w)

tmp

mc

Time-step Normalisation

0 80 160 240 320 400 480 560 640 720 800

Time [days]

0.00

0.10

0.20

0.30

0.40

Res

idu

e (m

g/k

g)

Time-step Normalisation

Site 1

Sampling time (days) Timestep(days)

0 0.0

61 25.0

184 59.4

274 105.5

327 145.2

428 193.0

544 229.8

604 256.4

671 289.5

726 321.9

Time NDL Timestep(days) (days) (days)

0 0.00 0.01 0.45 0.52 0.48 0.93 0.61 1.44 0.70 2.05 0.50 2.76 0.58 3.27 0.46 3.88 0.63 4.29 0.54 4.910 0.49 5.411 0.42 5.912 0.50 6.313 0.57 6.814 0.71 7.415 0.57 8.116 0.45 8.717 0.49 9.118 0.41 9.619 0.45 10.0

Time-step NormalisationTIMESTEP

0 50 100 150 200 250 300 350

t (days)

0.0

0.1

0.2

0.3

0.4

Res

idu

e (m

g/k

g) Normalised DT50 =

102 days

Min χ2 =6.0

Significant at >99%

Residual Plot

-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

0.025

0 50 100 150 200 250 300 350

Time

Res

idu

al (

mg

/kg

)l

Time-step Normalisation

• Advantages– Easy to calculate daily factors from available data

– No restriction on time periods or parameter variation (i.e. whole year / season can be modelled)

– Applies the correction to the whole dataset at once

– Standard kinetic modelling schemes and tools can be used for the subsequent analysis of the data

• Disadvantages

– Same correction factors (Q10, B) applied to whole dataset – although multiple regressions can be made

Rate Constant Optimisation

• Uses the same assumptions and input data as the timestep approach

• The reference rate constant is adjusted on a daily basis for soil temperature and moisture content and fitted to the measured data

Rate Constant Optimisation

k (T, W)kref

T

BTref Ea

Temperature [°C] Vol. water content (ml ml-1)

Mref

T = Temperature + 273

dM/dt = - k(T, W) C

Mcalc

Mobs

Comparison of calculatedwith observed concentrations

Adjustment of kref untilgood fit is achieved

B

ref

TTR

TTEa

ref M

Mek)W,T(k ref

ref

Rate Constant Optimisation

kT12

Daily variation in K(T,W)

0 100 200 300 400 500 600 700 800

0.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

Rate constant optimisation

0 80 160 240 320 400 480 560 640 720 800

Time [days]

0.00

0.10

0.20

0.30

0.40

Res

idue

(m

g/kg

) Normalised DT50 = 99 days

Min χ2 =5.8

Significant at >99%

Residual Plot

-0.025

-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

0.025

0 100 200 300 400 500 600 700 800

Time

Res

idu

al (

mg

/kg

)l

Rate Constant Optimisation

• Advantages– No restriction on time periods or parameter variation

(i.e. whole year / season can be modelled)

– Good ‘visualisation’ of the effects of soil temperature and moisture content on the dataset and kinetics

– Individual Q10 and B factors can be applied

• Disadvantages– Requires higher level model to implement (e.g.

ModelMaker)

– Sometimes difficult to optimise complicated metabolite schemes

Conclusions

• A number of approaches can be used to robustly derive normalised degradation rates from field studies for use in risk assessments

• The methodology can be used to evaluate data from different seasons and application timings and to understand the processes important for degradation