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Prediction of Microplastic Emissions in River Basins Based on Mathematical Models

Applied and Computational Engineering 2025
Q. Qian, Jinsong Huang, Xikai Wang, Hongxiang Yan

Summary

Researchers developed a mathematical model integrating multivariate linear regression and stepwise regression to predict microplastic emissions in the Guangzhou section of the Pearl River Basin, using historical abundance data to forecast 2025 annual average concentrations in this urban river system.

Study Type Environmental

Microplastics have become a globally concerning persistent pollutant in aquatic environments, with rivers acting as critical pathways for their transport from land to oceans. This study developed a mathematical model to predict microplastic emissions in the Guangzhou section of the Pearl River Basina typical urban river area in the Pearl River Delta. The model integrated multivariate linear regression and stepwise regression. The annual average microplastic abundance (item/L) served as the dependent variable. The updated model was used to predict the 2025 annual average microplastic abundance in the study area, yielding a value of 2730.64 item/L. This study provides a cost-effective, reliable tool for predicting riverine microplastic emissions and offers scientific support for microplastic pollution prevention and control in the Pearl River Basin and similar urban river systems.

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