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Machine learning integrates routine monitoring, targeted PFAS and microplastic analyses, and hydrometeorological data to prioritize hotspots and guide predict-sample-verify cycles, enabling more focused interventions in stressed water systems.
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Amankwaa et al. (2026) studied this question.
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