We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Prediction of the joint toxicity of microplastics and organic pollutants on algae based on machine learning
Summary
Researchers used machine learning models to predict the combined toxicity of microplastics and organic pollutants on algae, achieving high accuracy with gradient-boosted decision tree models. They found that microplastic concentration, particle size, and the hydrophobicity of organic pollutants were the most important factors influencing toxic effects. The study provides a computational framework that could help assess environmental risks from microplastic-pollutant mixtures more efficiently than traditional laboratory testing.
Complex joint toxicity driven by microplastic (MP)-organic pollutant mixtures in aquatic ecosystems remains poorly captured by conventional models. Herein, a dataset covering 10 types of MPs and 6 organic pollutants was compiled from literature to investigate joint toxicity mechanisms. Employing 5-fold cross-validation, six machine learning models were developed to predict the algal growth inhibition ratio based on MP properties, physicochemical characteristics of organic pollutants, and experimental conditions. The best predictive performance was achieved using the CatBoost model (test-set AUC: 0.935 ± 0.021; F1: 0.832), which outperformed benchmarks. SHAP analysis revealed the significance of experimental conditions (exposure time and concentration), physicochemical properties (hydrophobicity and molecular substructure fingerprints), and their interaction terms (LogP × Time × Conc) on joint toxicity prediction, supporting the cumulative effect principle and quantitative structure-activity relationships. The phenolic groups (SHAP = 0.43) and polycyclic aromatics (FP_58) were identified as key toxic molecular substructures. Notably, a Synergy-Antagonism Index integrating structural similarity, mechanistic pathways, and SHAP weights was proposed to distinguish the joint effects. Results indicated that phenolic hydroxyls, polycyclic aromatics, and amides contributed to significant synergistic effects, whereas hydrophobic aliphatic chains (FP_47, interaction = -0.359) often drove antagonism. Overall, this work offers robust prediction for MP-organic pollutant joint toxicity and provides new insights into high-throughput risk assessment of co-exposures in aquatic environments.
Sign in to start a discussion.
More Papers Like This
Data-driven machine learning modeling reveals the impact of micro/nanoplastics on microalgae and their key underlying mechanisms
Researchers used machine learning to predict how micro- and nanoplastics affect freshwater algae, training models on a decade of published experimental data. The best-performing model identified plastic concentration, exposure time, and particle size as the most important factors determining toxicity. The study offers a data-driven framework that could reduce the need for time-consuming laboratory experiments when assessing microplastic risks to aquatic organisms.
Machine learning-based prediction and model interpretability analysis for algal growth affected by microplastics
Researchers used machine learning models to predict how microplastics affect algal growth and found that exposure time, microplastic concentration, and particle size are the most important factors. Smaller microplastics and longer exposure periods had the greatest negative effects on algae, particularly when particles were smaller than the algal cells. The study provides a data-driven approach for assessing the ecological risks of microplastic pollution in aquatic environments.
Unraveling the ecotoxicity of micro(nano)plastics loaded with environmental pollutants using ensemble machine learning.
Researchers developed an ensemble machine learning algorithm to predict the ecotoxicity of micro(nano)plastics loaded with environmental pollutants, addressing a key knowledge gap where most studies examine plastic particles alone. The model revealed that co-pollutant loading substantially amplifies toxicity and that particle characteristics govern outcomes.
Predicting aqueous sorption of organic pollutants on microplastics with machine learning
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-Accelerated Prediction of Water Quality Criteria for Microplastics
Researchers developed a machine learning framework to predict microplastic toxicity in aquatic organisms and derive water quality criteria for five common polymer types. The random forest model outperformed other algorithms, with particle size, density, and aquatic species group accounting for 72% of prediction variability. The study found that polystyrene and PET exhibited the greatest toxicity, and that microplastics were generally more toxic in freshwater than saltwater environments.