We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
A Decade of Machine Learning for Adsorption of Emerging Contaminants: A Systematic Review of Algorithms, Performance, and Methodological Transparency
AI summary Read the abstract
This review summarizes 202 studies from 2015 to 2025 on computer models predicting how well materials absorb contaminants from water. It found neural networks were most common, results varied widely between studies, and most pollutants studied were antibiotics and pharmaceuticals, while PFAS and microplastics were studied less often. The review also found methodological weaknesses, including small datasets and lack of testing on independent data.
Machine learning (ML) is increasingly applied to predict and optimize adsorption-based removal of emerging contaminants (ECs) from water, yet the literature remains fragmented across diverse algorithms, adsorbents, and pollutant classes, and existing reviews of the topic report neither a quantitative synthesis of performance nor a cross-study interpretability analysis. This systematic review, conducted following PRISMA 2020 guidelines, screened 2,164 records from Scopus and PubMed and analyzed 202 peer-reviewed studies published between 2015 and 2025. Among 59 unique algorithms, artificial neural networks (ANN) dominated (64.9%), though ensemble methods (particularly random forest and XGBoost) are rapidly gaining adoption. Inverse-variance weighted meta-analysis of 110 studies revealed pooled R2 values ranging from 0.998 (ANFIS) to 0.929 (CatBoost), with extremely high heterogeneity ( 98\%]]>I2>98%) precluding universal algorithm recommendations; funnel plot analysis revealed patterns consistent with publication bias. A cross-study synthesis of SHAP-based feature importance from 15 explainable ML studies identified initial concentration, BET surface area, and pH as universally important predictors, consistent with classical adsorption theory, while revealing system-specific drivers (elemental ratios for biochar, molecular weight for PFAS) that extend beyond traditional frameworks. Antibiotics, pharmaceuticals, NSAIDs and analgesics together accounted for 81.7% of target contaminants, while PFAS (9.4%) and microplastics (5.9%) remained substantially underrepresented. Critical methodological concerns persist: among studies reporting a dataset size, 40% used fewer than 50 data points, and an audit of 30 randomly selected papers read in full found that although 90% describe a validation scheme, every one is an internal partition of a single experimental dataset and none validates against independently collected data. To address these gaps, we propose a 22-item Minimum Reporting Checklist for ML in Adsorption Studies (MRCAS), a technology readiness assessment for six frontier architectures (PINNs, GNNs, transfer learning), and five evidence-based priority directions to advance the field from prediction-focused modeling toward practical decision-support tools for water treatment.
More Papers Like This
Machine Learning-Driven Prediction of Organic Compound Adsorption onto Microplastics in Freshwater
AI summary Read the abstract
Seven machine learning algorithms were trained on 173 published measurements to predict how strongly organic contaminants adsorb onto different types of microplastics in freshwater. Accurate adsorption predictions are essential for assessing environmental risk, because microplastics that strongly bind pollutants become vectors that concentrate and transport toxic chemicals through aquatic food webs.
Machine Learning to Predict the Adsorption Capacity of Microplastics
AI summary Read the abstract
Researchers developed machine learning models to predict the adsorption capacity of microplastics for chemical pollutants, providing a computational tool to better understand how microplastics act as vectors for contaminant dispersal in aquatic environments.
Predicting aqueous sorption of organic pollutants on microplastics with machine learning
AI summary Read the abstract
Researchers developed machine learning models to predict how organic pollutants bind to microplastics in water, using data from 475 published experiments. The models outperformed traditional approaches by accounting for properties of both the microplastics and the pollutants simultaneously. The study provides a more universal tool for understanding how microplastics can transport and concentrate harmful chemicals in freshwater systems.
Machine Learning to Predict the Adsorption Capacity for Microplastics
AI summary Read the abstract
Researchers developed and compared three machine learning models — random forest, support vector machine, and artificial neural network — to predict microplastic/water partition coefficients (log Kd) for chemical pollutant adsorption, addressing the limited experimental data available on microplastic adsorption capacity in aquatic environments.
Artificial Intelligence-Powered Prediction of Microplastic–Pollutant Adsorption Coefficients Enables Scalable Risk Prioritization in Aquatic Environments
AI summary Read the abstract
Researchers developed an AI-powered neural network called MPAP to predict how microplastics adsorb hazardous pollutants in aquatic environments. Trained on over 1,100 adsorption records covering 403 compounds and six microplastic types, the model integrates molecular fingerprints, microplastic features, and water chemistry parameters to enable scalable risk prioritization for microplastic-pollutant interactions.
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.