Model development to predict environmental concentrations of chemical substances in marine sediment when the substance is applied via feed in marine aquaculture: Task 1.3. Formalise and implement the pilot model

Abstract The European Food Safety Authority (EFSA) assesses the safety of feed additives used in aquaculture. One aspect of this risk assessment is their environmental exposure, which requires calculating the predicted environmental concentration (PEC) which is then compared against established thresholds or no‐effect concentrations. This calculation can be complex: fish metabolism, ocean characteristics, sediment processes, additive physio‐chemical properties, and more all influence the PEC. A conceptual model for the transport and transformation of feed additives in the marine environment was previously developed in Task 1.2 of this project, and the implementation of that conceptual model into executable code is described herein. The implemented model consists of four components: a cage model, an additive model, a physics model, and finally a PEC assessment. The cage model, implemented in Python, predicts the input of additive into the water column for a given feed input. The additive model represents the feed additive transformations (settling, degradation, resuspension) within the water column and sediment and is implemented in the Framework for Aquatic Biogeochemical Models (FABM). The physics model is a pipeline for the General Estuarine Transport Model in Python (pyGETM), which automatically retrieves and downscales regional ocean models to give the conditions (ocean currents, temperature, bathymetry, etc.) at high resolution for a particular site. The physics model then runs coupled to the additive model to give a spatially varying prediction of accumulation of additive in the sediment. The implementation of the model in FABM, which is coupled to a wide range of different forcing (hydrodynamic) models, can easily be used by expert users on domains other than the pyGETM pipeline provided here. The model is available on the EFSA GitHub repository.