Toxicity impacts in the environmental footprint method: calculation principles

Abstract

Purpose The EU environmental footprint (EF) is a life cycle assessment (LCA)-based method which aims at assessing the environmental impacts of products and organisations through 16 midpoint impact categories, among which three address toxicity-related impacts. This paper presents the principles underpinning the calculation of the set of characterisation factors (CFs) for the toxicity-related impact categories in the EF version 3.0: freshwater ecotoxicity (ECOTOX), human toxicity cancer (HTOX_c) and human toxicity non-cancer (HTOX_nc). Methods In order to respond to the issues that emerged during the EF pilot phase, the input data and the calculation principles of the USEtox® model were updated. In particular, (i) robustness factors (RFs) were introduced to reduce the dominance of metals and to balance the lackness of a robust fate modelling for non-organic compounds in USEtox®; (ii) high-quality data were selected from databases of EU agencies (European Chemicals Agency and European Food Safety Authority) to guarantee the transparency and the reliability of input data; and (iii) a new approach based on HC20 (hazard concentration killing 20% of the exposed population) was implemented to derive freshwater ecotoxicity effect factors (EfF). Results and discussion The new approach increased the number of characterised chemicals in the three impact categories: ECOTOX (6038 chemicals, + 140%), HTOX_c (1024 chemicals, + 70%) and HTOX_nc (3317 chemicals, + 660%). Moreover, specific derivation principles were defined for assigning CFs also to relevant groups of chemicals (e.g. polycyclic aromatic hydrocarbons), and specific strategies were implemented to better align LCA toxicity data with data used for risk assessment purposes. Conclusions The new set of CFs was calculated to ensure a broader coverage of characterised chemicals and to overcome some limitations of the USEtox® model identified during the environmental footprint pilot phase.

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