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Data and code for cross-study interpretable machine-learning analysis of nanoplastic transport through saturated porous media
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This is a dataset and code file, not a new experiment. It combines results from 715 lab experiments across 42 published studies on how tiny nanoplastic particles move through sand-like materials, using computer analysis to find patterns linked to water chemistry and particle surface properties.
This dataset accompanies the manuscript "Cross-study synthesis identifies context-dependent patterns in nanoplastic transport through saturated porous media". The curated database comprises 715 breakthrough-curve-derived observations from 42 published studies, organized into 527 experimental-condition groups. The target variable is the maximum normalized effluent concentration (Max C/C₀) observed for each breakthrough curve, which is distinct from total mass recovery and the complete breakthrough curve. The study uses condition-group-separated machine-learning validation, held-out SHAP attribution, accumulated local effects (ALE), and publication-level sensitivity analyses to investigate context-dependent nanoplastic transport patterns. Particular attention is given to water chemistry, electrolyte composition, particle surface chemistry, and porous-medium characteristics. The deposited research materials are intended to support transparent data interpretation and reproducibility. The analyses are relevant to the experimental conditions represented in the database and should not be interpreted as validated predictions for entirely unseen publications.
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Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.