Integrated risk assessment of sediment-bound heavy metals using macroinvertebrate indicators in South Korea’s major river basins

• Heavy metal contamination and ecological risk were assessed across four major river basins. • Cd, As, and Hg were key contributors to ecological risk in urban and industrial areas. • OMI and tolerance metrics identified species-specific responses to metal pollution gradients. • Species were classified into four ecological response groups based on OMI–Tolerance traits. • The framework supports trait-based biomonitoring and watershed-specific risk management. Heavy metal contamination in river sediments poses significant ecological risks, yet traditional assessments often neglect biological responses. This study evaluates sediment-bound metal risks by integrating chemical indicators and trait-based biological metrics across South Korea’s four major river basins. We analyzed eight metals (Cr, Ni, Cu, Zn, As, Cd, Pb, and Hg) from 80 sites and calculated individual ecological risk factors using the Hakanson index. Biological responses were assessed using the Outlying Mean Index (OMI) and species-specific tolerance values, which reflect environmental marginality and ecological amplitude of benthic macroinvertebrates. Cd, As, and Hg were the primary contributors to elevated E ri values, especially in industrial and urban stream segments. Principal component analysis (PCA) and hierarchical clustering revealed distinct spatial patterns of contamination. Based on OMI and tolerance values, macroinvertebrate taxa were classified into four response groups: Reference (low OMI, low tolerance), Niche (high OMI, low tolerance), Tolerant (low OMI, high tolerance), and Niche tolerant (high OMI, high tolerance). Species such as Bothrioneurum vejdoskynum and Polypedilum decematoguttatum were consistently associated with high-risk sites, suggesting their potential as indicators of chronic metal exposure. By linking sediment contamination profiles with trait-based ecological responses, this approach enhances diagnostic capability for sediment quality assessment. The proposed framework supports bioindicator development and predictive modeling for managing metal pollution in riverine ecosystems.