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Brahmaputra_MP_Analysis: Reproducible statistical and machine-learning pipeline for microplastic bioaccumulation in Brahmaputra River ichthyofauna
Summary
Scientists studied how the seasons and rainfall patterns of the Brahmaputra River affect microplastic buildup in local fish—the same fish that people in the region regularly eat. Using detailed statistical and computer-modeling tools, they estimated how much microplastic people might be consuming through their diet, which helps clarify the real-world health risks of eating fish from polluted rivers. This kind of analysis matters because it moves beyond just detecting microplastics to actually estimating human exposure levels, giving health officials better data to assess risk.
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.