Coupling DREAM Suite and PEARL for Parameter Inference in a Bayesian Framework , 5th HYDRUS Conference in Prague Stephan Sittig

Transcription

1 Coupling DREAM Suite and PEARL for Parameter Inference in a Bayesian Framework , 5th HYDRUS Conference in Prague Stephan Sittig 1

2 Introduction Environmental fate risk assessment for plant protection products (PPP) partly conducted using numerical models Input parameters measured in lab/field studies or derived using inverse modelling ( automatic calibration ) Field trials: Regulation in EFSA and/or OECD guidances, but: no detailed description of application of inverse techniques Here, an approach for Bayesian parameter inference is presented: Usage of legacy trials enabled, i.e. cropped trials, substance applied to the soil surface and not incorporated and/or Absence of substance-free horizon 2

3 Introduction A variety of environmental processes contributes to the overall dissipation of a compound (OECD, 2016), i.e.: Microbial degradation Leaching Volatilization Phototransformation Foliar interception and dissipation Wash-off from plant surfaces Plant uptake Lateral flow Surface runoff Spray drift Numerical models, e.g. PEARL require input of DegT50: (microbial) degradation in soil only OECD (2016): Guidance Document for Conducting Pesticide Terrestrial Field Dissipation Studies. OECD Environment, Health and Safety Publications - Series on Testing and Assessment No. 232; Series on Pesticides No. 82. ENV/JM/MONO(2016)6

4 Introduction Kinetic evaluation in European Framework conducted following regulation laid down by the FOrum for Co-ordination of pesticide fate models and their Use (FOCUS) Legacy Studies: PPP were applied to soil surface or to cropped plots Surface processes might contribute to overall dissipation of PPP modelling after 10 mm precipitation after recent guideline (EFSA DegT50, EFSA (2014)) Evaluation is conducting following time-step normalisation to standard conditions in terms of temperature and soil moisture Guidance lacks information on detailed procedure to derive a true DegT50 (not a DT50), particularly when inverse modelling strategies are applied For leaching substances, inverse modelling techniques using numerical models might be the only option to make use of legacy studies EFSA (2014): EFSA Guidance Document for evaluating laboratory and field dissipation studies to obtain DegT50 values of active substances of plant protection products and transformation products of these active substances in soil. EFSA Journal 2014:12(5):3662. doi: /j.efsa

5 Introduction In contrast to FOCUS kinetics approach, here (I) all processes contributing to the overall dissipation are addressed and quantified, (II) realistic boundary conditions including meteorological data are accounted for, (III) being trapped in local minima is avoided by the global optimization process, and therefore independence of initial values is gained, (IV) a scientifically sound quantification of the uncertainties is yielded Combination of PEARL and Bayesian statistical approach is presented herein, to derive true DegT50

6 Method Markov Chain Monte Carlo simulator DREAM (Vrugt, 2016), here in the form of a plug-in for the DREAM Suite v1.02 Dynamics of the residues in profiles in field trials described with the numerical model PEARL Synthetic data produced in a forward simulation using FOCUS standard scenario Hamburg, 1 kg ha -1 to spring cereals immediately before emergence, FOCUS standard substance C Parameter values estimated: applied mass (M0), DegT50 parent, formation fraction of primary metabolite (ff), DegT50 meta Vrugt, J. A. (2016): Environmental Modeling & Software, vol. 75, pp , /j.envsoft ,

7 Method DREAM Suite Likelihood function no. 2 used, i.e. log-likelihood Uniform (i.e. non-informative) prior definition model evaluations in 8 markov chains, 1250 each Reasonable boundaries of the parameter values given 5-% random error on the measurements 7

8 Method 8

9 Results DREAM Suite Original Input parameters: DegT50 parent : 20 d; DegT50 meta : 100 d; ff: 0.71; M0: 1.0 kg ha -1 9

10 Results DREAM Suite Maximum a-posteriori values (Map), their standard deviations and coefficients of variation (COV) Parameter Map Std. COV DegT50 parent DegT50 meta Formation fraction Applied mass Correlations between the estimated parameters Parameter Formation fraction DegT50 parent DegT50 meta Applied mass Formation fraction DegT50 parent DegT50 meta Applied mass Acceptance rate: 20 30%, i.e. DREAM algorithm performed acceptable Original Input parameters: DegT50 parent : 20 d; DegT50 meta : 100 d; ff: 0.71; M0: 1.0 kg ha -1 10

11 Results Standard Evaluation Approximately, 30% of the metabolites mass either leached (below 1 m profile depth) or uptaken by the plants Standard evaluation not justified Consequently, normalised evaluation according to recent guidance underestimates the formation of the metabolite: FOCUS procedure: DT50 parent : 13.4 d; DT50 meta : 92.4 d; ff: 0.35; M0: 1.0 kg ha -1 Original Input parameters: DegT50 parent : 20 d; DegT50 meta : 100 d; ff: 0.71; M0: 1.0 kg ha -1 11

12 Conclusions Our DREAM Suite plug-in potentially enables the evaluation of results from field trials (legacy trials), in which the substance was applied to the soil surface and/or to cropped plots Substantial amount of the parents primary metabolite has disappeared by means other than microbial degradation (i.e. leaching below the target depth and/or plant uptake), but the original parameter values could be retrieved 12

13 Outlook Other likelihood functions enable insights into types of errors, i.e. measurement error or model structural error Dynamic properties of the error residuals can be derived Inverse modelling strategies can be applied more regularly in the future, since the GUI facilitates the procedure 13

14 Outlook Usage with DREAM_ZS (a modified version of DREAM) in real field trial data presented in (publication under preparation): Stephan Sittig, Zhenglei Gao, Gerald Reinken, and Gerhard Görlitz (2016): Kinetic Evaluation of Field Dissipation Studies: Applying MCMC simulator DREAM for Parameter Inference and Uncertainty Analysis. SETAC Europe 26th Annual Meeting, Nantes, France,

15 Thank you! ANY QUESTIONS? 15