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Brahmaputra_MP_Analysis: Reproducible statistical and machine-learning pipeline for microplastic bioaccumulation in Brahmaputra River ichthyofauna
Summary
Scientists studied fish from the Brahmaputra River (a major river in South Asia) and found that microplastic contamination in fish bodies changes with the seasons, rising and falling with rainfall and river flow patterns. Since people who eat this fish may also be eating these tiny plastic particles, the research helps estimate how much microplastic exposure comes from local diets and points to certain times of year when fish may carry higher contamination. This kind of data matters because it helps identify when the risk of microplastic exposure through food is greatest, informing better food safety guidance for communities that rely on river fish.
This repository contains the complete, reproducible Python analysis pipeline for the manuscript "Seasonal hydrology governs microplastic bioaccumulation and human dietary exposure in the ichthyofauna of the transboundary Brahmaputra River continuum." It includes cleaned input data (microplastic abundance, particle characterisation subsets, and monthly rainfall context) and code implementing non-parametric tests (Kruskal–Wallis, Dunn's post-hoc, Mann–Whitney U), a negative-binomial generalised linear model with a linear mixed-effects robustness check and station×season interaction test, PERMANOVA, exploratory Random Forest and gradient-boosting (SHAP) variable-importance analyses, tissue-partitioning and human dietary-intake estimation, and three ecotoxicological indices (Pollution Load Index, Polymer Hazard Index, Shannon diversity). Running python run_all.py regenerates every table and figure in the manuscript with a fixed random seed (42) for exact reproducibility. This work was supported by the Asia-Pacific Network for Global Change Research (APN), project CRRP2021-09MY-Rashid. v2 adds SEM surface texture quantification. pipeline (Supplimentary method Note SM1), including SDI analysis and Fig 4.