PestLCI 2.0 sensitivity to soil variations for the evaluation of pesticide distribution in Life Cycle Assessment studies.

Pesticides are commonly applied in conventional agricultural systems, but they can lead to serious environmental contamination. The calculation of on-field pesticide emissions in Life Cycle Assessment (LCA) studies is challenging, because of the difficulty in the calculation of the fate of pesticides and, therefore, several literature approaches based on different dispersion models have been developed. PestLCI 2.0 model can provide simultaneous assessment of the emission fractions of a pesticide to air, surface water and groundwater based on many parameters. The goal of this study is to exploit the extent of PestLCI 2.0 sensitivity to soil variations, with the ultimate goal of increasing the robustness of the modelling of pesticide emissions in LCA studies. The model was applied to maize cultivation in an experimental farm in Northern Italy, considering three tests, which evaluated the distribution of pesticides among environmental compartments obtained considering different soil types. Results show that small variations in soil characteristics lead to great variation of PestLCI 2.0, with a significance that depends on the type of environmental compartment. The compartment most affected by soil variations was groundwater, whereas surface waters were dominated by meteorological conditions, pesticides' physical and chemical properties and wind drift, which are independent from soil characteristics. Therefore, the use of specific soil data in PestLCI 2.0 results in the availability of a comprehensive set of emission data in the different compartments, which represents a relevant input for the inventory phase of LCA studies and can increase their robustness. Nevertheless, PestLCI 2.0 requires a great effort for the data collection and a specific expertise in soil science for interpreting the results. Moreover, characterization factors for pesticide groundwater emissions should be developed, in order to exploit these detailed results in the impact assessment phase, Finally, the study provides further insights into future improvement of PestLCI 2.0.