Development of USEtox characterisation factors for dishwasher detergents using data made available under REACH

Purpose Today's chemical society use and emit an enormous number of different, potentially ecotoxic, chemicals to the environment. The vast majority of substances do not have characterisation factors describing their ecotoxicity potential. A first stage, high throughput, screening tool is needed for prioritisation of which substances need further measures. Methods USEtox characterisation factors were calculated in this work based on data generated by quantitative structure-activity relationship (QSAR) models to expand substance coverage where characterisation factors were missing. Existing QSAR models for physico-chemical data and ecotoxicity were used, and to further fill data gaps, an algae QSAR model was developed. The existing USEtox characterisation factors were used as reference to evaluate the impact from the use of QSARs to generate input data to USEtox, with focus on ecotoxicity data. An inventory of chemicals that make up the Swedish societal stock of plastic additives, and their associated predicted emissions, was used as a case study to rank chemicals according to their ecotoxicity potential. Results and discussion For the 210 chemicals in the inventory, only 41 had characterisation factors in the USEtox database. With the use of QSAR generated substance data, an additional 89 characterisation factors could be calculated, substantially improving substance coverage in the ranking. The choice of QSAR model was shown to be important for the reliability of the results, but also with the best correlated model results, the discrepancies between characterisation factors based on estimated data and experimental data were very large. Conclusions The use of QSAR estimated data as basis for calculation of characterisation factors, and the further use of those factors for ranking based on ecotoxicity potential, was assessed as a feasible way to gather substance data for large datasets. However, further research and development of the guidance on how to make use of estimated data is needed to achieve improvement of the accuracy of the results.

Section: Discussionsupporting

confidence: 81%

The potential to use QSAR to populate ecotoxicity characterisation factors for simplified LCIA and chemical prioritisation

Holmquist

Lexén

Rahmberg

et al. 2018

“…Previous studies have shown that for emissions to freshwater, it is in general the toxicity parameters (i.e. EC 50 values) that have the largest influence on freshwater ecotoxicity in USEtox (Alfonsín et al 2014;Igos et al 2014). Other parameters might be important too, depending on toxicity pathways and effects .…”

Section: Generic Cf Dependency On Input Parametersmentioning

confidence: 99%

USEtox characterisation factors for textile chemicals based on a transparent data source selection strategy

Roos

Holmquist

Jönsson³

et al. 2017

Purpose Life cycle assessments (LCAs) of textile products which do not include the use and emission of textile chemicals, such as dyes, softeners and water-repellent agents, will give non-comprehensive results for the toxicity impact potential. The purpose of this paper is twofold: (1) to provide a set of characterisation factors (CFs) for some of the most common textile chemicals and (2) to propose a data source selection strategy in order to increase transparency when calculating new CFs. Methods A set of 72 common textile-related substances was matched with the USEtox 2.01, USEtox 1.01 and the COSMEDE databases in order to investigate coverage and coherence. For the 25 chemicals that did not already have established CFs in any of these databases, new CFs were calculated. A data source selection strategy was developed and followed in order to ensure consistency and transparency, and USEtox 2.01 was used for calculations. The parameters that caused the most uncertainty were identified during the modelling and strategies for handling them were developed. Results and discussion Of the 72 textile-related substances, 48 already had calculated recommended or indicative CFs in existing databases, which showed good coherence. The main uncertainty identified during the calculation of 25 new CFs was the selection of input data regarding toxicity and degradation in water. However, for substances such as per-and polyfluoroalkyl substances (PFAS), the acid dissociation constant (pK a ) and partitioning coefficients (K ow and K OC ) also require special considerations. Other input parameters had less than one order of magnitude impact on the CF result for essentially all substances. Conclusions The paper presents a strategy for how to provide a complete set of toxicity CFs for a given list of substances. In addition, such a set of CFs for common textile-related substances is presented. The data source selection strategy provides a structured and transparent way of calculating additional CFs for textile chemicals with USEtox. Consequently, this study can help future LCA studies to provide relevant guidance towards environmentally benign chemical management in the textile industry.

“…But the USEtox score is very low, mainly because the property of environmental persistence of organic chemicals is handled differently in this method. Indeed, it has recently been reported that the inherent toxicity of the chemical, the avlogEC50 is the input parameter which contributes most to the freshwater toxicity in USEtox (Alfonsín et al 2014;Igos et al 2014).…”

Section: Comparison Of the Results From The Methodsmentioning

confidence: 99%

Three methods for strategic product toxicity assessment—the case of the cotton T-shirt

Roos

Peters

2015

Purpose The use and emission of chemicals and the intrinsic toxic properties of some of these chemicals are an important topic in the textile industry. Quantitative evaluation of toxic impacts is a life cycle assessment (LCA) approach, termed Btoxic footprint^in this article. We ask whether calculation of toxic footprints is a useful method to steer the textile industry towards more sustainable use of chemicals. Methods Three different methods by which strategic product toxicity assessment can be performed within the context of LCA are illustrated and compared using a wet treatment process for a cotton T-shirt as the basis of a case study. The methods are the USEtox model chosen for the European Product Environmental Footprint work, the Score System presented in the European Commission's Reference Document on Best Available Techniques for the Textiles Industry, and the Strategy Tool presented by Askham. The methods are compared in terms of their ease of use and whether the results give a consistent evaluation of a set of chemicals. Results and discussion New USEtox characterisation factors for textile chemicals were calculated and used for this article. The results show that the three methods do not give a consistent evaluation of the different wet treatment chemicals. Both the Score System and the Strategy Tool are very concerned with persistent contaminants such as the optical brightener in this case study, which is deemed to be less important by USEtox. The calculations also show how the results generated by the USEtox model depend on whether users apply (1) only the recommended characterisation factors or (2) these and the interim characterisation factors or (3) these and the new characterisation factors calculated for this article.Conclusions and recommendations With current policy initiatives such as the Product Environmental Footprint now being applied for textile products, toxicity assessment will by default be performed in the LCA of textiles. It is important that the results are relevant and representative as the intended users are supposed to take actions based on them. Confidence in the results is crucial for a scientific method, and therefore, this exploratory comparison exercise shows how benchmarking can be a tool to make the differences in background assumptions explicit, to better understand the differences in the results, and help create such confidence.