1 Department of Management Engineering, Technical University of Denmark2 Quantitative Sustainability Assessment, Department of Management Engineering, Technical University of Denmark3 University of Suttgart4 University of Michigan5 University of Stuttgart6 École Polytechnique Fédérale de Lausanne7 University of Michigan
Dynamic plant uptake models are suitable for assessing environmental fate and behavior of toxic chemicals in food crops. However, existing tools mostly lack in-depth analysis of system dynamics. Furthermore, no existing model is available as parameterized version that is easily applicable for use in spatially resolved frameworks for comparative assessment. In the present paper, we thus analyze the dynamics of substance masses in a multi-compartment plant–environment system by applying mathematical decomposition techniques. We thereby focus on the evolution of pesticide residues in crop components harvested for human consumption by taking wheat grains as example. Results show that grains, grain surface and soil are the compartments predominantly influencing the mass evolution of most pesticides in the plant–environment system as a function of substance degradation in plant components and overall residence time in soil. Additional influences are associated with substance molecular weight and time span between pesticide application and crop harvest. Building on these findings, we provide an accurate and yet simple linear approximation of the dynamical system to predict masses in harvested crop components relative to the total applied pesticide, defined as harvest fractions. Parameterized predictions correspond well with results from the full dynamic model, with an overall deviation of a factor 22 for harvest fractions in the relevant range between 1 and 10−10 in wheat. The in-depth analysis of model dynamics provides additional information of the evolution of pesticides in food crops, which is important for regulators and practitioners. In addition, the parametric representation of system dynamics allows for drastically reducing input data requirements and for comparing harvest fractions of a wide range of substances without using a complex dynamic model.
Environmental Modelling and Software, 2013, Vol 40, p. 316-324