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<metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/"><dc:title>Early warning systems for chemical risks in Europe</dc:title><dc:creator>Karakoltzidis,	Achilleas	(Avtor)
	</dc:creator><dc:creator>Alygizakis,	Nikiforos	(Avtor)
	</dc:creator><dc:creator>Andersson,	Patrik L.	(Avtor)
	</dc:creator><dc:creator>Kosjek,	Tina	(Avtor)
	</dc:creator><dc:creator>Ahrens,	Lutz	(Avtor)
	</dc:creator><dc:subject>real-time data</dc:subject><dc:subject>early warning system</dc:subject><dc:subject>hazard prioritization</dc:subject><dc:subject>chemical risks</dc:subject><dc:description>Chemical pollution can affect ecosystems and human health, highlighting the need for approaches that identify, evaluate, and prioritize emerging chemical risks before they become established public-health issues requiring regulatory actions. This review and perspective article examines methodological components required for Early Warning Systems (EWSs) for chemical risks, with particular attention to the European policy context. It examines methodological components for chemical EWSs rather than proposing a fully implemented system. It considers how state-of-the-art and emerging methods can support signal generation, signal strengthening, prioritization, uncertainty assessment, communication, and follow-up. The reviewed components include matrices and sampling strategies; chemical monitoring, suspect screening, and non-target screening; effect-based methods, New Approach Methodologies, and effect-directed analysis; exposure and hazard modelling; QSAR, read-across, AI-supported and data-mining tools; expert evaluation; and governance processes that link scientific signals to proportionate follow-up. The conceptual workflow is used as an example of how these components may be organized into a signal-handling process, while EU-level developments provide the broader policy and governance context. The actionable value of a chemical EWS does not depend on any single method, but on structured integration of complementary evidence streams. An effective EWS should combine sensitive weak-signal detection with transparent prioritization, explicit uncertainty assessment, FAIR and interoperable data infrastructures, and clearly assigned responsibilities for communication and follow-up.</dc:description><dc:publisher>Springer Nature</dc:publisher><dc:date>2026</dc:date><dc:date>2026-06-15 11:28:08</dc:date><dc:type>Neznano</dc:type><dc:identifier>30087</dc:identifier><dc:identifier>UDK: 502.1</dc:identifier><dc:identifier>ISSN pri članku: 2190-4715</dc:identifier><dc:identifier>DOI: 10.1186/s12302-026-01427-3</dc:identifier><dc:identifier>COBISS_ID: 281489155</dc:identifier><dc:source>Nemčija</dc:source><dc:language>sl</dc:language></metadata>
