Fluoride contamination is a persistent public health concern, yet existing risk assessments often emphasize individual exposure pathways, most commonly drinking water, and may not fully capture the substantial variation in health outcomes observed across communities with similar drinking-water fluoride concentrations. Drawing on a structured database-based search of the peer-reviewed literature, this review synthesizes evidence on fluoride sources, cross-media environmental transport, multi-pathway human exposure, health outcomes, and risk assessment methodology, aiming to link these elements within an integrated evaluation framework. Dental and skeletal fluorosis remain the most consistently documented outcomes of chronic excessive exposure, supported by well-characterized dose-response relationships. Evidence for renal and hepatic effects is reviewed with attention to the limited and inconsistent human evidence base. Neurodevelopmental and endocrine effects are examined with explicit consideration of the methodological limitations that currently prevent causal conclusions. The progression of fluoride risk assessment is examined from deterministic screening models through probabilistic Monte Carlo simulation to machine learning-based spatial prediction, with attention to how these approaches may function as complementary components of a unified framework. Connecting machine learning-predicted fluoride concentrations across environmental media to multi-pathway intake models, and then applying probabilistic simulation to characterize population-level risk distributions, may provide a more comprehensive basis for identifying dominant exposure routes and informing pathway-specific interventions than single-pathway deterministic assessment alone, although the complete workflow requires empirical implementation and external validation. This integrated perspective addresses a gap in existing reviews, which have addressed groundwater occurrence, toxicological mechanisms, or remediation separately, but have not systematically connected multi-pathway exposure characterization with evolving assessment methodologies.
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Gao et al. (2026) studied this question.
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