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Exploring action-law of microplastic abundance variation in river waters at coastal regions of China based on machine learning prediction
Summary
Researchers used machine learning to predict microplastic levels in rivers across seven coastal regions of China, identifying population density, urbanization, and industrial activity as the strongest predictors of contamination. The models successfully captured how microplastics accumulate and move through river systems using 19 different environmental and human factors. This approach could reduce the need for costly field sampling while helping target pollution management efforts where they are needed most.
Surface waters, particularly the river systems, constitute a vital freshwater resource for human beings and aquatic life on Earth. In economically developed and densely populated coastal regions, river water is facing severe microplastic pollution, posing a threat to public health and ecological safety. Reliable prediction of microplastic abundance (MPA) can significantly reduce the costs associated with microplastic field sampling and analysis. This study employed spatial correlation, geographical detector, principal component analysis and five mainstream machine learning models to analyze 79 datasets of MPAs in seven coastal areas of China and performed correlation, regression and attribution analyses based on 19 terrestrial influencing factors that potentially affect the MPA life cycle processes (generation, aging, and migration). The results showed that the Neural Network (NN) and the Gaussian Process Regression (GPR) models achieved the best prediction performance, with the predicted R close to 1. Principal component analysis and Shapley additive explanations concluded that meteorological factors, in particular the annual geotemperature, surface solar radiation, and annual relative humidity, had a key influence on the aging of microplastics. The second key factor in improving the MPA prediction ability was the dynamic description of microplastic migration, which was primarily governed by hydrological factors such as annual precipitation and average terrain slope. Unexpectedly, the effects of land use and level of urbanization were relatively small in describing the generation of microplastics. Only the percentage of built areas was strongly correlated with the MPA levels. Note that the MPA prediction and its contribution factors may vary across different basins. Nevertheless, the findings of this study are applicable to predicting and analyzing the distribution of microplastics in other coastal rivers, and for indicating the main contributing factors, ultimately serving as a basis for guiding microplastic pollution control strategies in different river basins.
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