Assessment of environmental and socioeconomic drivers of urban stormwater microplastics using machine learning.

ABSTRACT: Microplastics (MPs) are ubiquitous environmental contaminants with urban landscapes as major source areas of MPs and stormwater runoff as an important transport pathway to receiving aquatic environments. To better delineate the drivers of urban stormwater MP loads, we created a global dataset of stormwater MP concentrations extracted from 107 stormwater catchments (SWCs). Using this dataset, we trained and tested three optimized gradient boosting Machine Learning (ML) models. Twenty hydrometeorological and socioeconomic variables, as well as the MP size definitions considered in the individual SWCs, were included as potential predictors of the observed MP concentrations. CatBoost emerged as the best-performing ML model. Shapley additive explanations revealed that features related to hydrom

SUBMITTER: Reshadi MAM 

PROVIDER: S-EPMC11845695 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature