Machine learning and AI approaches for predicting microplastics levels in water systems resulting from anthropogenic activities

Life in the “Plastisphere”: Microbial communities on plastic marine debris

10.1021/es401288x · doi-reference

A bibliometric analysis of microplastic pollution in aquatic environments from 2013 to 2023

10.1186/s42834-025-00257-x · doi-reference

Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance

10.3354/cr030079 · doi-reference

Effects of particle properties on the settling and rise velocities of microplastics in freshwater under laboratory conditions

10.1021/acs.est.8b06794 · doi-reference

Microplastics in freshwater ecosystems: What we know and what we need to know

10.1186/s12302-014-0012-7 · doi-reference

Cross-validation failure: Small sample sizes lead to large error bars

10.1016/j.neuroimage.2017.06.061 · doi-reference

A global inventory of small floating plastic debris

10.1088/1748-9326/10/12/124006 · doi-reference

The physical oceanography of the transport of floating marine debris

10.1088/1748-9326/ab6d7d · doi-reference

Efficient prediction of microplastic counts from mass measurements

10.1021/acsestwater.1c00316 · doi-reference

The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets

10.1371/journal.pone.0118432 · doi-reference

Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure

10.1111/ecog.02881 · doi-reference

Deep learning and process understanding for data-driven Earth system science

10.1038/s41586-019-0912-1 · doi-reference

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

10.1016/j.jcp.2018.10.045 · doi-reference

Reference database design for the automated analysis of microplastic samples based on Fourier transform infrared (FTIR) spectroscopy

10.1007/s00216-018-1156-x · doi-reference

Comparison of pyrolysis gas chromatography/mass spectrometry and hyperspectral FTIR imaging spectroscopy for the analysis of microplastics

10.1007/s00216-020-02979-w · doi-reference

Comparison of historical (1980s) and contemporary (2023–2024) microplastic contamination of arc clams (Anadara spp.) from tidal flats in Suva, Fiji

10.1016/j.marpolbul.2025.118401 · doi-reference

Automated μFTIR imaging demonstrates taxon-specific and selective uptake of microplastic by freshwater invertebrates

10.1021/acs.est.1c03119 · doi-reference

Are agricultural soils dumps for microplastics of urban origin?

10.1021/acs.est.6b04140 · doi-reference

Plastics and the environment

10.1146/annurev-environ-112522-072642 · doi-reference

Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation

10.1016/j.envsoft.2017.12.001 · doi-reference

Predicting microplastic masses in river networks with high spatial resolution at country level

10.1038/s44221-023-00090-9 · doi-reference

More than 1000 rivers account for 80% of global riverine plastic emissions into the ocean

10.1126/sciadv.aaz5803 · doi-reference

Preventing microplastic pollution in surface waters: Legal frameworks and strategic actions

10.1002/tqem.70116 · doi-reference

Interactions between microplastics and phytoplankton aggregates: Impact on their respective fates

10.1016/j.marchem.2015.04.003 · doi-reference

Microplastics in freshwater systems: A review on occurrence, environmental effects, and methods for microplastics detection

10.1016/j.watres.2017.12.056 · doi-reference

River plastic emissions to the world's oceans

10.1038/ncomms15611 · doi-reference

Intercomparison study on commonly used methods to determine microplastics in wastewater and sludge samples

10.1007/s11356-019-04584-6 · doi-reference

Sinking rates of microplastics and potential implications of their alteration by physical, biological, and chemical factors

10.1016/j.marpolbul.2016.05.064 · doi-reference

Ups and downs in the ocean: Effects of biofouling on vertical transport of microplastics

10.1021/acs.est.6b04702 · doi-reference

Solving the nonalignment of methods and approaches used in microplastic research to consistently characterize risk

10.1021/acs.est.0c02982 · doi-reference

Understanding the transportation of plastic pollution to different environmental compartments and human

10.1002/ldr.70351 · doi-reference

Microplastic predictive modelling with the integration of artificial neural networks and hidden Markov models (ANN-HMM)

10.1007/s40201-024-00920-2 · doi-reference

Ingestion of microplastics by freshwater Tubifex worms

10.1021/acs.est.7b03567 · doi-reference

Fate and occurrence of micro(nano)plastics in soils: Knowledge gaps and possible risks

10.1016/j.coesh.2017.10.006 · doi-reference

Global plastic footprint: Unveiling property trends, environmental fate, and emerging threats of microplastic and nanoplastics pollution across ecosystems

10.1007/s40974-025-00383-7 · doi-reference

Plastic litter is a part of the carbon cycle in an urban river: Microplastic and macroplastic accumulate with organic matter in floating debris rafts

10.1002/wer.11116 · doi-reference

Anthropogenic litter in urban freshwater ecosystems: Distribution and microbial interactions

10.1371/journal.pone.0098485 · doi-reference

Microplastics in the marine environment: A review of the methods used for identification and quantification

10.1021/es2031505 · doi-reference

Are we speaking the same language? Recommendations for a definition and categorization framework for plastic debris

10.1021/acs.est.8b05297 · doi-reference

Patterns and trends in marine microplastics density distributions using a long-term, global, field database

10.1016/j.marpolbul.2025.117528 · doi-reference