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Spot Mfa V1.4.1-g-0426
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Scientists have updated a global model that tracks where plastic waste escapes into the environment, from local towns all the way to worldwide totals. This tool helps identify pollution hotspots so communities and policymakers can target cleanup and waste management efforts more effectively. Better tracking of plastic leakage ultimately supports reducing the plastic and microplastic pollution that can affect our air, water, food, and health.
This record contains the code, input data and model outputs for version 1.4.1-G-0426 of the material flow analysis (MFA) component of the Spatio-temporal quantification of plastic pollution origins and transport (SPOT) model. https://doi.org/10.5281/zenodo.22231994 SPOT is a plastic pollution emissions (leakage / release) inventory and transport model. The MFA component of SPOT combines machine learning with probabilistic MFA to quantify municipal solid waste management flows and macroplastic emissions across geographical scales using a bottom-up approach. Version 1.4.1-G-0426 is a global application of SPOT that developed in April 2026, primarily for use in The Lancet Countdown on Health and Plastics. Estimates are provided for the year 2020 at a municipal, national, regional and global scales. Version 1.4.1-G-0426 incorporates updates from the original published version of SPOT (V1.1.0-G-1223) available from Cottom, J. W., Cook, E. & Velis, C. A. (2024). A local-to-global emissions inventory of macroplastic pollution. Nature, 633, 101–108. https://doi.org/10.1038/s41586-024-07758-6. A full list of model versions and changes is listed alongside the code in the accompanying GitHub directory: https://github.com/SPOT-Model. Additional methodological details for this version are available in the supplementary appendix of The Lancet Countdown on Health and Plastics. Outputs should be interpreted alongside their reported uncertainty and the model’s assumptions and scope.
More Papers Like This
Spot Mfa V1.4.1-g-0426
AI summary Read the abstract
Scientists have updated a global model that tracks where plastic waste escapes into the environment, from local towns all the way up to whole countries. This matters because knowing exactly where plastic pollution comes from helps target cleanup efforts and policies, ultimately reducing the plastic and microplastics that can end up in our food, water, and bodies.
ST-MAP: Spatio-Temporal Proxy Dataset for Modeling Microplastic Pollution in Global Water Bodies
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Scientists have created a new data tool that estimates microplastic pollution levels in water around the world, without needing to physically collect and test water samples everywhere, which is expensive and slow. This matters because microplastics are showing up in our drinking water, seafood, and even our bodies, so having a faster, cheaper way to track pollution hotspots could help researchers and policymakers figure out where the problem is worst and act on it sooner. Note that this paper introduces the dataset and modeling method itself, rather than new findings about health effects.
Seeing the forest for the plastic trees: a model-driven visualisation framework for material flow analysis
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Researchers created a visual mapping tool to track how plastic moves through six countries — from production to waste — even in places where data is scattered and incomplete. They found that even "data-scarce" countries actually have enough existing information to build a useful baseline picture of their plastic problem, which matters because you can't fix plastic pollution (and the microplastics that end up in our food, water, and bodies) without first knowing where it's coming from and where it's leaking into the environment. This kind of tracking system could help countries and policymakers target cleanup and prevention efforts more effectively, rather than guessing where the biggest problems lie.
A local-to-global emissions inventory of macroplastic pollution.
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This study developed a high-resolution global inventory of macroplastic pollution by distributing nationally reported waste management data down to sub-national and local scales, producing maps of plastic emission hotspots. The dataset is intended to support negotiations for a global plastics treaty by providing a data-driven baseline for identifying sources and prioritizing interventions.
Tracking Marine Litter With a Global Ocean Model: Where Does It Go? Where Does It Come From?
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Using a global ocean model, researchers simulated the movement of plastic waste released from countries around the world to track where marine litter ends up and where it comes from. The study found that ocean currents create a complex web of connections between distant countries, meaning plastic released in one region can wash up on far-away shores. The results are publicly available through an interactive website and provide a quantitative framework for understanding international responsibility for ocean plastic pollution.
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