An integrated chemical and ecological framework for deriving freshwater contaminant protection thresholds using machine learning.

Toward a Generalized Ecotox-Criticality Framework for Targeted Aquatic Ecosystem Risk Assessment

The growing number and diversity of chemical contaminants threaten aquatic ecosystem integrity, yet conventional risk assessments often overlook food-web interactions and require extensive toxicological and ecological data. We developed the Generalized Ecotox-Criticality Framework (GECF), which reframes ecosystem ris...

Machine learning for monitoring and assessment of potentially toxic elements in soils: a synthesis of spatial validation, explainability, and uncertainty

Towards predictive biomonitoring: integrating machine learning and ecological thresholds to identify potential foraminiferal molecular indicators of salmon farm impacts.

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

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, accur...

An integrated geochemical and explainable machine learning framework for mercury dynamics and risk assessment in the Musa Estuary.

Mercury (Hg) contamination in estuarine environments is of increasing concern because of its persistence, transformation into methylmercury (MeHg), and potential ecological and human health impacts. This study investigated the distribution, environmental controls, and health risks of Hg and MeHg in 41 surface sediment...

MacroSimply: An Intuitive Rule-Based Classifier to Predict Biotic Status in Supporting Ecological Restoration

We address the challenging problem of predicting the biological quality of a water body based on a set of environmental and management-related drivers. While modelling approaches for predicting water quality from hydrological and pollution-load variables are well established, analogous tools for predicting biological s...