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How microplastics influence health risk pathways in urban water systems? integrated binary classification, machine learning and structural equation modeling approach.
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Researchers combined data from 47 studies using computer modeling to figure out how microplastics in city water systems—from sources like industrial discharge, sewage, and stormwater runoff—actually translate into health harm. They found that it's not just the presence of microplastics that matters, but the whole chain of events: how much land development and wastewater pollution occurs, how much people are exposed, and how the body responds with inflammation and cellular stress—together these factors explained nearly 90% of health impacts. This matters because it gives cities a data-backed tool to identify high-risk water sources and pollution patterns, potentially guiding better regulations before
Microplastic contamination in urban water systems poses emerging risks to human health, yet integrated frameworks linking sources, exposure pathways, and health outcomes remain limited. This study develops an integrated machine learning-Bayesian-structural equation modeling framework to quantify and predict microplastic-driven health risks across urban water systems. A systematic literature review screened 1,742 records, of which 47 studies met the predefined eligibility criteria and were included in the final analysis. Here, we integrate machine learning, Bayesian inference, and structural equation modeling (SEM) to resolve microplastic contamination dynamics and associated health impacts across urban water systems. The synthesized dataset incorporated environmental source characteristics, contamination indicators, exposure metrics, biological response proxies, and health-related outcomes to support integrated modeling and risk prediction. Random Forest clustering identified six distinct contamination-health regimes (R = 0.73), including industrial discharge-dominated, wastewater-associated, stormwater-influenced, mixed urban residential, landfill/leachate-associated, and low-contamination background conditions. Random Forest regression predicted health impairment with high accuracy (R = 0.84; RMSE = 3.77). Additionally, microplastic health risk is governed by coupled environmental pressures, exposure pathways, and biological responses rather than isolated factors. Binary classification achieved 80% accuracy for detecting regulatory exceedance risk. Bayesian correlation revealed strong coupling between microplastics and exposure, oxidative stress, and inflammation. Structural equation modeling demonstrated that land-use intensity impacts health both directly and indirectly through wastewater loading and biological stress pathways, explaining 89.9% of health variance. This study integrates systematic evidence synthesis, machine learning, Bayesian inference, within a unified predictive framework, providing a novel approach for quantifying contamination-health relationships and supporting evidence-based urban water management. These findings establish a data-driven framework for predicting microplastic risks and optimizing mitigation strategies in urban water cycles.
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How Microplastics Influence Health Risk Pathways in Urban Water Systems? Integrated Binary classification, Machine Learning and Structural Equation Modeling Approach
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Researchers used advanced computer modeling and statistical tools to trace how microplastics move through city water systems and identify what factors make them more likely to pose health risks. This matters because it helps pinpoint which sources of water pollution are riskiest, giving cities better tools to target cleanup efforts and protect drinking water before these tiny plastic particles reach our taps. While the exact health effects of microplastics are still being studied, this research helps build a clearer picture of our exposure risk.
How Microplastics Influence Health Risk Pathways in Urban Water Systems? Integrated Binary classification, Machine Learning and Structural Equation Modeling Approach
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