Fraunhofer IME InversePELMO - a specific software to perform inverse modelling simulations with FOCUSPELMO Peter Gallien*, Romeo Herr* and Michael Klein** * Federal Environment Agency D - 06844 Dessau, Wörlitzer Platz 1 E-mail: peter.gallien@uba.de (corresponding author) **Fraunhofer-Institute Molecular Biology and Applied Ecology D - 57392 Schmallenberg, Auf dem Aberg 1 E-mail: michael.klein@ime.fraunhofer.de Introduction In the assessment for authorisation of a plant protection product its leaching behaviour is an important factor to protect the groundwater. In this context the sorption to soil (Parameter: KOC) and the degradation (Parameter: DT50) are processes that must be taken into account. Recently, the new FOCUS groundwater group suggested a third methodology for the input parameter setting (FOCUS 2009). The idea is to analyse outdoor studies, especially lysimeters, using the inverse modelling method that allows the estimation of KOC and DT50 parameters within a single step. For this procedure an optimisation tool, the program PEST (Model Independent Parameter Estimation) has to be combined with a leaching model (here: FOCUSPELMO). The aim of inverse modelling simulations is to find those KOC and DT50 values that could describe the outdoor study best by considering all data recorded during the experiments (e.g. rainfall, temperatures, percolate, and substance fluxes). Therefore all data of a lysimeter study are used to vary the input parameters KOC and DT50 until the leaching model shows the same results as the experiment. Material and Methods Fig. 2: Inverse modelling in general The two programs FOCUSPELMO and PEST are useful to perform inverse modelling studies (see Fig.1 below). Generally, two steps have to be conducted. Firstly, the hydrology in soil must be optimised and secondly the optimisation of the pesticide fate has to be performed. The common procedure is given in Fig.2. Fig. 1: Software used to perform an inverse modelling (FOCUSPELMO; PEST; inversePELMO) Collection of available information from lysimeter studies cumulative fluxes (water, substance), soil residues at study end Optimisation of the hydrology in soil Fitting parameters: evapotranspiration, min. depth for evaporation, initial soil water Optimisation of chemicals’ fate (software: PEST) (parameters in optimisation: KOC, DT50, Freundlich 1/n) Re-assessment of KOC and DT50 Quality check based on information provided in standard FOCUSPELMO output files Results of an inverse modelling for an active substance and its metabolite X based on data from a real lysimeter study Define the model in FOCUSPELMO and import input files in the inversePELMO shell (Fig. 3) Check the initial simulation by using the pesticide input file, the scenario input file (i.e. Borstel_daily) and the climate input files (daily values are necessary). Enter the experimental data from the lysimeter study and start the optimisation. The results are given in Fig. 4. (blue points show measured percolate volumes from lysimeter study and the continuous line represents the simulated model data. 4. Enter experimental data for soil concentrations of the active substance and define fitting parameter (i.e. KOC; DT50). Start the optimisation. Note: The active substance degrades to sink during this process, only. Therefore the DT50 value to form the metabolite has to be set very high. That means no metabolite is formed. When looking for a critical metabolite of the active substance copy the project above and create a new one based on the model of the active substance and let it degrade to the metabolite X. Note: The DT50 value for the degradation of the active substance to sink must be high in this case (i.e. 1000d) but those to form the metabolite X should be realistic. Activate the radio button for percolate concentrations and enter the values from lysimeter study, respectively. Define the fitting parameter as given in point 4, select the substance which has to be considered (in this case metabolite X) and start the optimisation. The cumulative flux in µg/m2 for the metabolite X is given in Fig. 5 and the protocol containing the optimised KOC and DT50 values is partly given in Fig. 6. Fig. 3: Defined model (Pesticide input file) Fig. 4: Optimisation of the percolate flux Fig. 5: Cumulative flux in µg/m2 Fig. 6: Part of the Optimisation between lysimeter and model for the metabolite X protocol Notes / Conclusions FOCUSPELMO is a useful program for doing inverse modelling simulations. As a general rule sandy soils (FOCUS-Borstel scenario is a sandy soil) are taken into account. A comparison between FOCUSPELMO and FOCUSPEARL confirms it in the official scenarios. Field capacity and wilting point are no sensitive parameters as long as the lysimeter is not dried out. But a drying-out would be in contradiction to the German lysimeter guidence. It is not necessary to include the time dependent sorption (TDS). An optimisation by using the equilibrium state of sorption in InversePELMO is possible as shown above. Furthermore, the system would be overdone if TDS and KOC-values are taken into account in the same simulation. With regard to the FOCUS groundwater report (2009) lower tier and higher tier simulations should be done under comparable conditions. That means if a simulation at tier 1 was done without TDS it should be the same at tier 2. Otherwise the TDS-parameter found at tier 1 can be used at tier 2 but they will not optimized in that case. It is not possible to compare different KOC-values under different conditions. The dispersion length has no effect on the percolate distribution except on a tracer (mostly KBr). But if there was no tracer used, the dispersion length will not be optimised. Furthermore, a correlation exists between dispersion and sorption with regard to the pesticide concentration in the percolate. In case of a very high dispersion in the lysimeter there will be a compensation by a higher KOC-value in the inverse modelling simulation. References FOCUS (2009) Assessing Potential for Movement of Active Substances and their Metabolites to Ground Water in the EU – EC Document Reference Sanco/2009 version 1 Gabriele Holdt, Peter Gallien, Angelika Nehls, Inga Bonath, Anne Osterwald, Wolfram König*, Bernhard Gottesbüren, Bernhard Jene, Herbert Resseler, Robin Sur , B. Zillgens: Recommendations for Simulation Calculations to Predict Environmental  Concentrations of Active Substances of Plant Protection Products and Their Metabolites in Ground Water (PECgw) in the National Authorisation Procedure in Germany. Part 1 :Tier 1 and 2 (UBA-Texte); 2011 InversePELMO: Link for Downloading: http://server.ime.fraunhofer.de/download/permanent/mk/inversePELMO/