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

Holmquist, Hanna ; Lexén, Jenny ; Rahmberg, Magnus ; Sahlin, Ullrika LU ; Palm, Julia Grönholdt and Rydberg, Tomas (2018) In International Journal of Life Cycle Assessment 23(11). p.2208-2216
Abstract

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... (More)

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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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Characterisation factors, Plastic additives, Prioritisation, QSAR, USEtox
in
International Journal of Life Cycle Assessment
volume
23
issue
11
pages
2208 - 2216
publisher
Ecomed Publishers
external identifiers
  • scopus:85045266302
ISSN
0948-3349
DOI
10.1007/s11367-018-1452-x
language
English
LU publication?
yes
id
4ead6ff4-69e0-41a4-ae84-05f2d7225483
date added to LUP
2018-04-26 16:00:44
date last changed
2021-10-06 05:31:53
@article{4ead6ff4-69e0-41a4-ae84-05f2d7225483,
  abstract     = {<p>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.</p>},
  author       = {Holmquist, Hanna and Lexén, Jenny and Rahmberg, Magnus and Sahlin, Ullrika and Palm, Julia Grönholdt and Rydberg, Tomas},
  issn         = {0948-3349},
  language     = {eng},
  number       = {11},
  pages        = {2208--2216},
  publisher    = {Ecomed Publishers},
  series       = {International Journal of Life Cycle Assessment},
  title        = {The potential to use QSAR to populate ecotoxicity characterisation factors for simplified LCIA and chemical prioritisation},
  url          = {http://dx.doi.org/10.1007/s11367-018-1452-x},
  doi          = {10.1007/s11367-018-1452-x},
  volume       = {23},
  year         = {2018},
}