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Revealing microplastic pollution hotspots in Indonesia freshwaters: A synthesis of methodological biases, anthropogenic drivers, and predictive hotspot mapping
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A generalized additive model built on open-access spatial data explained over 92% of deviance in microplastic abundance across Indonesian freshwaters, identifying population density and human activity types as the strongest predictors, and mapping high-risk unmonitored hotspots. This predictive framework offers a cost-effective tool for prioritizing microplastic monitoring and intervention in one of the world's largest plastic-polluting nations.
Abstract Microplastic (MPs) pollution is an emerging global threat, and freshwater ecosystems are critical pathways that transport these particles to the ocean. As one of the world most populous and plastic consuming nations, Indonesia freshwater ecosystems contain some of the highest reported MPs concentrations worldwide, yet a clear understanding of their distribution and drivers remains limited. In this study, we synthesized all available literature on Indonesia freshwater MPs and identified two critical issues: (1) local MPs concentrations are notably higher than reported global averages, and (2) variations in sampling methods introduce substantial biases that hinder data comparability. To overcome these inconsistencies, we developed a predictive framework using a Generalized Additive Model (GAM) that successfully explains over 92% of the deviance in MPs abundance. The model pinpoints population density and different human activity types as the most significant predictors of pollution hotspots. Built entirely on open-access spatial data, our model enables identification of high-risk, previously unmonitored areas for targeted intervention and can be adapted to unexplored regions. Our results highlight the urgent need for methodological standardization and demonstrate how bias adjusted modelling can guide cost effective monitoring and management. An interactive prediction map is available at: [Indonesia MPs prediction web server]
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