Modeling of Microplastic Contamination Using Soft Computational Methods: Advances, Challenges, and Opportunities

Johnbosco C. Egbueri; Daniel A. Ayejoto; Johnson C. Agbasi; Nchekwube D. Nweke; Leonard N. Onuba

doi:10.1007/978-3-031-64253-1_24

0

Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Sign in to save

Modeling of Microplastic Contamination Using Soft Computational Methods: Advances, Challenges, and Opportunities

Emerging contaminants and associated treatment technologies 2024 7 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.

Johnbosco C. Egbueri, Daniel A. Ayejoto, Johnson C. Agbasi, Nchekwube D. Nweke, Leonard N. Onuba

Summary

Researchers reviewed the application of soft computational methods — including machine learning and statistical modeling — to predict and map microplastic contamination, identifying advances in model accuracy alongside persistent challenges around data scarcity and environmental variability.

Microplastic (MP) pollution has become a global concern due to its impact on ecosystems, wildlife, and potentially human health. Inferential and predictive modelling of this phenomenon using soft computational methods adds a valuable dimension to its research. This...

Read via DOI

Share this paper

Post Share Share Pin Email