Prioritising substances of concern for monitoring by using knowledge graphs and public datasets

Abstract

Abstract Air pollution is identified by the World Health Organisation (WHO) as the leading environmental threat to human health, with indoor exposure particularly concerning given its daily, long-term nature. Europeans spend approximately 90% of their time indoors, where poor indoor air quality (IAQ) contributes an estimated 2 million disability-adjusted life-years lost annually in the EU, with vulnerable populations especially at risk. This study explores a Knowledge Graph (KG) as a decision-support tool to prioritise Substances of Concern (SoC) for indoor dust and air. Two complementary approaches were applied: a top-down screening that filters a broad substance universe against defined criteria to identify monitoring candidates, and a bottom-up characterisation of already monitored substances against the same criteria. Integrating publicly available chemical data within the KG and applying a risk-oriented approach based on hazard classification, REACH registration tonnage and use information, we narrowed a list of over 16,000 substances (PlastChem list) down to 42. This work shows that KG can effectively connect diverse data sources and identified both known and emerging SoC not yet monitored in private settings. It highlights that non-REACH chemicals may contribute significantly to indoor dust chemistry analysis, and REACH consumer-use data alone does not capture all uses relevant to residential indoor exposure. The experiments show that expanding the number and diversity of integrated data sources is recommended to refine scoping for chemical monitoring, risk assessment and management. Key concerns among identified substances include reproductive toxicity and respiratory tract irritation, often linked to numerous consumer products. The ECHA Classification and Labelling Inventory was found to be a useful hazardous substance pool, though further refinement with additional health-related data is recommended. Finally, we discuss how integrating Large Language Models (LLMs) and KG into the framework could support partial automation of the validation of SoC, improving future monitoring and analysis efficiency.