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Limitations of SHAP-based interpretations in environmental and membrane filtration applications
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Researchers critically analyze the use of SHAP values — a machine learning interpretability method — in microplastic filtration studies, arguing that SHAP's dependence on model assumptions and inability to handle correlated variables can produce misleading conclusions about which process parameters matter most in complex environmental systems.
Maliwan et al. (2025) identified key parameters in microplastic ultrafiltration using interpretable machine learning (SHAP), attributing 57.6-70.6 % feature importance to factors like transmembrane pressure. This paper critically examines their methodological approach, highlighting significant concerns regarding SHAP's application. SHAP values are inherently model-dependent and lack ground truth for validating feature importance accuracy, leading to potentially biased and erroneous conclusions; high prediction accuracy does not ensure reliable insights. SHAP's underlying assumptions, particularly feature independence, rarely hold in complex environmental systems characterized by multicollinearity, potentially misattributing variable importance. We advocate for a more robust analytical framework incorporating unsupervised machine learning (e.g., feature agglomeration) and nonlinear nonparametric statistical methods (e.g., Spearman's correlation) to provide more reliable insights into variable relationships, moving beyond model-dependent interpretations.
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XAI for Decision Support in Microplastic Pollution Management
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Researchers examined how explainable AI (XAI) techniques—including SHAP values, LIME, and generative models—can make machine learning decision-support systems for microplastic pollution management more transparent, helping environmental scientists and policymakers trust and interpret model-driven risk assessments and remediation strategies.
Decoding the transport thresholds of emerging contaminants in watersheds using explainable machine learning
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Researchers collected 517 water samples from the Huangshui River over four years and used an explainable machine learning framework with SHAP analysis to model how land use, landscape metrics, and climate variables drive the transport of microplastics, antibiotics, and heavy metals through the watershed.
Hybrid Ensemble Machine Learning Models with SHAP Explainability for Robust Prediction of Suspended Particle Attachment Efficiency in Complex Environmental Systems
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Scientists developed a new computer model that can better predict how tiny particles—including microplastics—clump together and move through the environment. The model found that salt levels in water are the main factor controlling how single particles stick together, while electrical charge differences matter most when different types of particles interact. This research could help us better understand how microplastics and other harmful particles spread through water systems and potentially affect human health.
XAI for Decision Support in Microplastic Pollution Management
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Researchers trained machine learning models on a dataset of microplastic fibers, beads, and fragments, finding that Random Forest achieved the best classification accuracy with particle size and color intensity as the most predictive features, and that integrating SHAP-based explainability reduced expert decision time by over 60% compared to opaque AI outputs.
Elucidating microplastic adsorption mechanisms in biomass composite materials through interpretable machine learning
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Researchers used interpretable machine learning to study how biomass composite materials adsorb microplastics from water. They found that initial microplastic concentration and surface electrical potential were the most important factors determining adsorption effectiveness. The study demonstrates that data-driven approaches can help design more efficient and sustainable materials for removing microplastics from contaminated water.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.