Transcript 1.7 Normalization of Field Half-Lives.ppt

Chapter 9
Normalisation of Field Half-lives
Ian Hardy
Battelle AgriFood Ltd.,
Ongar,
UK
Overview
• 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
• 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 conditions
e.g. 20oC/pF2 for EU or 25oC/75% pF2.5 for US
DT50ref
((T Tref )/10)  MC act
 DT50act * Q10
* 
 MC ref



B
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
25.0
Measured ST
Predicted ST
Soil Temperature (oC)
20.0
15.0
10.0
5.0
0.0
01/11/1998
09/02/1999
20/05/1999
28/08/1999
06/12/1999
15/03/2000
Date
23/06/2000
01/10/2000
09/01/2001
19/04/2001
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
35
Appropriate over this period
30
25.3
Temperature (oC)
25
5cm
5cm average
20
15
Not appropriate over this period
10
5
0
01-Jun-94
21-Jul-94
09-Sep-94
29-Oct-94
18-Dec-94
-5
Date
06-Feb-95
28-Mar-95
17-May-95
06-Jul-95
Average Temperature and Moisture
Content
• Advantages
– Good approximation for ‘short-term’ kinetics (i.e. over 1month) 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
100
90
Moisture content = 80% Field Capacity (FC)
80
70
data
Temperature (oC)
% Applied
60
Moisture content (%FC)
50
40
30
Temperature = 15oC
20
10
0
0
5
10
15
20
25
30
35
Time (days)
40
45
50
55
60
65
Time-step Normalisation
FOCUS Normalised
100
90
Data points
‘regressed’ along
the time axis
80
70
% Applied
60
data
DT50=11.53 days
50
DT50=20 days
40
30
20
10
0
0
5
10
15
20
25
30
35
Time (days)
40
45
50
55
60
65
Time-step Normalisation
Time
(days)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Normalised Cumulative
day length
NDL
0.00
0.00
0.58
0.58
0.58
1.15
0.58
1.73
0.58
2.31
0.58
2.88
0.58
3.46
0.58
4.04
0.58
4.61
0.58
5.19
0.58
5.77
0.58
6.34
0.58
6.92
0.58
7.50
0.58
8.07
0.58
8.65
0.58
9.23
Residue
(%)
100.00
96.59
93.30
90.13
87.06
84.09
81.23
78.46
75.79
73.20
70.71
68.30
65.98
63.73
61.56
59.46
57.43
Time
(days)
0
1
3
5
7
14
21
42
Cumulative
NDL
0.00
0.58
1.73
2.88
4.04
8.07
12.11
24.22
Residue
(%)
100.00
96.59
90.13
84.09
78.46
61.56
48.30
23.33
Plot cumulative NDL vs residue
Time-step Normalisation
FOCUS Normalised
100
90
80
70
data
% Applied
60
DT50=20 days
Timestep
50
DT50=11.53 days
40
30
DT50 = 11.53 days
r2 = 1.000
20
10
0
0
5
10
15
20
25
30
35
Time (days)
40
45
50
55
60
65
Time-step Normalisation
• Real example:
• 2 year field dissipation study conducted in Northern
Europe
• Winter application
Time-step Normalisation
Soil Temperature and Moisture Content
30.0
Soil temperature (oC) or Moisture (%w/w)
25.0
20.0
15.0
10.0
5.0
0.0
0
100
200
300
400
500
-5.0
600
700
800
tmp
mc
-10.0
Days after application
900
Time-step Normalisation
0.40
Residue (mg/kg)
0.30
0.20
0.10
0.00
0
80
160
240
320
400
Time [days]
480
560
640
720
800
Time-step Normalisation
Time
(days)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
NDL
(days)
0.00
0.45
0.48
0.61
0.70
0.50
0.58
0.46
0.63
0.54
0.49
0.42
0.50
0.57
0.71
0.57
0.45
0.49
0.41
0.45
Timestep
(days)
0.0
0.5
0.9
1.4
2.0
2.7
3.2
3.8
4.2
4.9
5.4
5.9
6.3
6.8
7.4
8.1
8.7
9.1
9.6
10.0
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-step Normalisation
TIMESTEP
0.4
Residual Plot
0.025
0.02
0.015
Residual (mg/kg)l
0.01
0.3
0.005
0
0
50
100
150
200
250
300
350
-0.005
Normalised DT50 =
102 days
Residue (mg/kg)
-0.01
-0.015
-0.02
Time
0.2
Min χ2 =6.0
Significant at >99%
0.1
0.0
0
50
100
150
200
t (days)
250
300
350
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
Mobs
Comparison of calculated
with observed concentrations
Adjustment of kref until
good fit is achieved
Mcalc
dM/dt = - k(T, W) C
Tref
k(T, W )  k ref e
k (T, W)
kref
Ea
B
Ea T  T ref 
R T Tref
Mref
T
T = Temperature + 273
Temperature [°C]
Vol. water content (ml ml-1)
 M

 Mref



B
Rate Constant Optimisation
Daily variation in K(T,W)
0.009
0.008
0.007
0.006
0.005
0.004
0.003
0.002
0.001
0.000
0
100
200
300
400
500
600
700
800
Rate constant optimisation
0.40
Residual Plot
0.025
0.02
0.015
Residual (mg/kg)l
0.01
0.30
0.005
0
0
100
200
300
400
500
600
700
800
-0.005
-0.01
-0.015
Residue (mg/kg)
-0.02
Normalised DT50 =
99 days
-0.025
Time
0.20
Min χ2 =5.8
Significant at >99%
0.10
0.00
0
80
160
240
320
400
Time [days]
480
560
640
720
800
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