Spatially distributed leaching modelling (SDLM) of pesticides is a methodology to estimate the leaching potential over a large spatial extent such as national or European scale. It is described in the FOCUS groundwater report and foreseen to be used as higher tier leaching risk assessment as well as supporting groundwater monitoring studies. At the SETAC Europe 2020 meeting, the initiative was officially formalised as a SETAC working group, consisting of a triad of members from regulatory agencies, academia, and the private sector. As the SDLM team continues to work, this presentation provides an update to interested parties. The group has now developed a first version of the framework. The process-based leaching models PEARL and PELMO will form the core of the framework. The model will run for approximately 10,000 scenarios, which are unique combinations of land-cover, climate and soil data. The scenarios were created from the geodata using a k-means clustering procedure, which aims to minimise within-cluster variances. The group has selected datasets that cover the entire EU and UK. The framework will use soil data from the SoilGrids database. Because the soil organic matter content depends on land-use, a map specific for arable soils was developed. For this, machine learning algorithms were applied using the available data in the SoilGrids database. The group paid a lot of attention to the harmonisation of pedotransfer functions. For this, available pedotransfer functions for soil bulk density and soil hydraulic properties were reviewed. The framework will be tested using several test substances. Specific attention will be given to the plausibility of the predicted leaching maps. Furthermore, the 90th overall percentile leaching concentration for the nine FOCUS zones will be calculated and compared with results from the nine FOCUS scenarios that are used in Tier-1. The work so far demonstrates that the development of a framework involves expert judgements that need to be thoroughly checked and documented. A plausiblity check and a comparison of different frameworks is therefore part of this project. However, in the long run, a monitoring network consisting of mulitple field sites would be helpful to further gain confidence in the modelling tools.
Tiktak, A., Poot, A., Jene, B., Hoogeweg, G., Braakhekke, M., Wipfler, L., Ghafoor, A., Klein, J., Stemmer, M., Ritter, A., Sur, R., Spickermann, G., Heuvelink, G., Hughes, G., Marahrens, S., Reichenberger, S., Suciu, N., Morris, M., Harmonised Framework for the SETAC Spatially Distributed Leaching Modelling of Pesticides Initiative:2024 update, Abstract de <<SETAC Europe 34th Annual Meeting>>, (Siviglia, Spagna, 05-09 May 2024 ), Society of Environmental Toxicology and Chemistry Europe (SETAC Europe), Seville (Spain) 2024:<<ABSTRACTS BOOK>>, 500-500 [https://hdl.handle.net/10807/297304]
Harmonised Framework for the SETAC Spatially Distributed Leaching Modelling of Pesticides Initiative: 2024 update
Suciu, NicoletaPenultimo
Writing – Review & Editing
;
2024
Abstract
Spatially distributed leaching modelling (SDLM) of pesticides is a methodology to estimate the leaching potential over a large spatial extent such as national or European scale. It is described in the FOCUS groundwater report and foreseen to be used as higher tier leaching risk assessment as well as supporting groundwater monitoring studies. At the SETAC Europe 2020 meeting, the initiative was officially formalised as a SETAC working group, consisting of a triad of members from regulatory agencies, academia, and the private sector. As the SDLM team continues to work, this presentation provides an update to interested parties. The group has now developed a first version of the framework. The process-based leaching models PEARL and PELMO will form the core of the framework. The model will run for approximately 10,000 scenarios, which are unique combinations of land-cover, climate and soil data. The scenarios were created from the geodata using a k-means clustering procedure, which aims to minimise within-cluster variances. The group has selected datasets that cover the entire EU and UK. The framework will use soil data from the SoilGrids database. Because the soil organic matter content depends on land-use, a map specific for arable soils was developed. For this, machine learning algorithms were applied using the available data in the SoilGrids database. The group paid a lot of attention to the harmonisation of pedotransfer functions. For this, available pedotransfer functions for soil bulk density and soil hydraulic properties were reviewed. The framework will be tested using several test substances. Specific attention will be given to the plausibility of the predicted leaching maps. Furthermore, the 90th overall percentile leaching concentration for the nine FOCUS zones will be calculated and compared with results from the nine FOCUS scenarios that are used in Tier-1. The work so far demonstrates that the development of a framework involves expert judgements that need to be thoroughly checked and documented. A plausiblity check and a comparison of different frameworks is therefore part of this project. However, in the long run, a monitoring network consisting of mulitple field sites would be helpful to further gain confidence in the modelling tools.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
