Exploring REACH as a potential data source for characterizing ecotoxicity in life cycle assessment

Müller, Nienke; Zwart, Dick de; Hauschild, Michael Zwicky; Kijko, Gaël; Fantke, Peter

doi:10.1002/etc.3542

Cited by 32 publications

(30 citation statements)

References 22 publications

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“…For the algae, it could be argued that the EC50 determined at 72 h is a chronic endpoint (algal cells divide many times in 72 h), although from a regulatory point of view they are considered acute and NOEC or EC10 are considered chronic endpoints (EC‐JRC ). The use of duration limits to separate acute from chronic is consistent with a recent attempt to use REACH data to calculate USEtox substance hazard values (Müller et al ).…”

Section: Methodssupporting

confidence: 62%

Using REACH for the EU Environmental Footprint: Building a Usable Ecotoxicity Database, Part I

Saouter

Biganzoli

Pant

et al. 2019

Integr Envir Assess & Manag

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The European Union Environmental Footprint (EU‐EF) is a harmonized method to measure and communicate the life cycle environmental performance of products and organizations. Among 16 different impact categories included in the EU‐EF, 1 focuses on the impact of substances on freshwater ecosystems and requires the use of toxicity data. This paper evaluates the use of the aquatic toxicity data submitted to the EU Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation. It presents an automated computerized approach for selecting substance ecotoxicity values, building on a set of quality and reliability criteria to extract the most relevant data points for calculating the substance specific hazard values. A selected set of criteria led to the exclusion of approximately 82% of the original REACH ecotoxicological data available as of May 2015 due to incomplete initial encoding of the data by the REACH registrant, missing information such as duration of exposure, endpoint measured, species tested, and imprecise toxicity values (i.e., reported with greater than or less than signs). From an initial set of 305 068 ecotoxicity data records available in the REACH database, the final usable database contains 54 353 toxicity records (29 421 characterized as acute and 24 941 as chronic) covering 9 taxonomic groups, with algae, crustaceans, and fish representing 93% of the data. This data set is valuable for assessing the environmental toxicity of the substance contained whether through traditional substance risk assessment, product toxicity labeling, life cycle assessment (LCA) or environmental impact assessment approaches. However, the resulting loss of approximately 82% of the data suggests that changes in procedures used to generate, report, and document the data within REACH are needed to improve data utility for the various assessment approaches. The rules used to select the data to be used are the primary focus of this article. Integr Environ Assess Manag 2019;15:783–795. © 2019 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

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Section: Methodssupporting

confidence: 62%

Using REACH for the EU Environmental Footprint: Building a Usable Ecotoxicity Database, Part I

Saouter

Biganzoli

Pant

et al. 2019

Integr Envir Assess & Manag

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“…For textile chemicals in products, as well as many other types of chemicals in products, there are no such strict regulations, which limit toxicity data availability. Müller et al (2017) explored the registration dossiers generated under the European REACH legislation as a data source for CFs. They found that REACH registration data can be used in USEtox but also highlight issues, such as differences in aim and scope between LCA and risk assessment methodology.…”

Section: Challenges and Possible Pitfallsmentioning

confidence: 99%

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

Roos

Holmquist

Jönsson³

et al. 2017

Int J Life Cycle Assess

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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.

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“…Several authors have indeed tested the usability of European data sources, e.g. Müller et al (2017), Saouter et al (2017a, b), and have concluded that the USEtox model and procedures need to be adapted to make it possible to use all available data, e.g. by allowing the use of chronic data expressed in other forms than EC 50 .…”

Section: Discussionmentioning

confidence: 99%

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

Holmquist

Lexén

Rahmberg

et al. 2018

Int J Life Cycle Assess

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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.

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Exploring REACH as a potential data source for characterizing ecotoxicity in life cycle assessment

Cited by 32 publications

References 22 publications

Using REACH for the EU Environmental Footprint: Building a Usable Ecotoxicity Database, Part I

Using REACH for the EU Environmental Footprint: Building a Usable Ecotoxicity Database, Part I

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

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

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