How can Machine Learning inform about chemical risks in circular textiles?

Journal: Integrated environmental assessment and management
Published Date: Jul 15, 2025

Abstract

Hazardous chemicals in textiles represent a serious health issue. This is mainly due to missing data on the used chemicals and/or on their hazard, which prevents proper chemical risk assessment. Although identifying and filling these data gaps is crucial, the myriad of chemicals used for textile production and multiple data sources make it extremely difficult to manually collect and process all the data. Here, we propose a machine learning-based approach to tackle this issue. First, we identify the relevant sources and data that can be analyzed with machine learning. Then we propose knowledge graphs as a tool to organize and analyze the data. We finally provide specific examples and detail the expected outcomes of our approach.

Authors

  • Agathe Bour
    Department of Science and Environment, Roskilde University, Roskilde, Denmark.
  • Kateryna Melnyk
    RISE Research Institutes of Sweden, Humanized Autonomy Unit, Gothenburg, Sweden.
  • Agnieszka D Hunka
    RISE Research Institutes of Sweden, Sustainable Business Unit, Gothenburg, Sweden.
  • Emanuela Vanacore
    RISE Research Institutes of Sweden, Sustainable Business Unit, Gothenburg, Sweden.
  • Annemette Palmqvist
    Department of Science and Environment, Roskilde University, Roskilde, Denmark.
  • Thanh Bui
    RISE Research Institutes of Sweden, Humanized Autonomy Unit, Gothenburg, Sweden.
  • Kristian Syberg
    Department of Science and Environment, Roskilde University, Roskilde, Denmark.

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