Chapter 9 Normalisation of Field Half-lives

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Chapter 9Normalisation of Field Half-lives

• Ian HardyBattelle AgriFood Ltd.,Ongar,UK

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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

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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

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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

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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

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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

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Soil Temperature Estimation
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Approaches to Normalisation

• Three approaches considered
• Average soil temperature and moisture content
• Time-step normalisation
• Rate constant optimisation

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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

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Average Temperature and Moisture Content
Appropriate over this period
Not appropriate over this period
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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

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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 80FC is equivalent to 0.58
  days at 20oC and 100FC
• 1 day at 25oC and 90FC is equivalent to 1.38
  days at 20oC and 100FC
• Cumulative NDL is then calculated between
  sampling points
• Standard kinetic tools used for evaluations

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Time-step Normalisation
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Time-step Normalisation
Data points regressed along the time axis
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Time-step Normalisation
Plot cumulative NDL vs residue
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Time-step Normalisation
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Time-step Normalisation

• Real example
• 2 year field dissipation study conducted in
  Northern Europe
• Winter application

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Time-step Normalisation
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Time-step Normalisation
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Time-step Normalisation
Site 1 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
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Time-step Normalisation
Normalised DT50 102 days Min ?2
6.0 Significant at gt99
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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

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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

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Rate Constant Optimisation
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Rate Constant Optimisation
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Rate constant optimisation
Normalised DT50 99 days Min ?2
5.8 Significant at gt99
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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

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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