DOI: 10.12688/openreseurope.24629.1 ISSN: 2732-5121

Modernizing the TRIAD ecological risk assessment framework.                  Part II: Omic methods for high-resolution biological risk indicators

Sama'a Djomehri, Eduardo P. Mateus, Vanessa G. Correia, Paula Guedes, Pavlos Tyrologou, Nikolaos Koukouzas, Paul Drenning, Yevheniya Volchko, Alexandra B. Ribeiro, Nazaré Couto
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The TRIAD framework is a widely used approach to assess site-specific ecological risk by integrating three Lines of Evidence (LoE): chemical, ecotoxicological, and ecological. As environmental contamination grows increasingly complex, the framework requires modernization in order to maintain ecological relevance, accuracy, and cost-efficiency. “Part I: Analytical integration for an updated TRIAD framework” (Djomehri et al., 2026) develops (1) – (4) of five proposed modernizations, strengthening transparency and analytical rigor through structured expert judgment, decision-analysis, and multivariate and machine-learning methods. However, these advances remain bound by the sensitivity of available risk indicators; unresponsive or coarse parameters limit the reliability of risk estimates and thus TRIAD’s applicability. The fifth proposal, developed here, incorporates molecular ‘omics’: sequencing-based methods, proteomics, and metabolomics that provide high-resolution biological indicators, supplying the sensitive, discriminating, and multifunctional parameters the ecological and ecotoxicological LoEs currently lack. Contamination studies employing these methods are reviewed and synthesized, evaluating their advantages, limitations, and potential for integration into TRIAD. The large datasets generated by omics are readily combined with other TRIAD LoE data through sophisticated processing software relying on multivariate analyses and machine learning (ML) algorithms capable of detecting subtle or early contaminant response missed by conventional TRIAD approaches. Omic methods identify novel risk biomarkers and potential bioremediators, and expand both environmental bioinformatics databases and reference libraries of molecular toxicity indicators. Predictive ML models trained on -omics data offer powerful tools for streamlining assessment time and cost, while improving accuracy. Keeping pace with contemporary contamination demands continual methodological renewal; the modernizations in Parts I and II deliver this renewal, securing a continuing role for TRIAD in EU soil health directives and remediation management.

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