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Papers
2 resultsShowing papers from Jiangyin Traffic Planning Survey & Design Institute (China)
ClearDecoding the transport thresholds of emerging contaminants in watersheds using explainable machine learning
Researchers collected 517 water samples from the Huangshui River over four years and used an explainable machine learning framework with SHAP analysis to model how land use, landscape metrics, and climate variables drive the transport of microplastics, antibiotics, and heavy metals through the watershed.
Microplastics migration mechanisms in high-erosion watersheds under climate warming
Scientists built a machine-learning model using 15 years of sediment data from three different-use watersheds on China's Qinghai-Tibet Plateau — grassland, cropland, and urban — to track how microplastics migrate and where they end up under changing climate conditions. The model achieved very high accuracy in tracing plastic sources and pathways, and found that wind direction and surface runoff are key drivers of transport, with cropland as a major source. The approach offers a practical tool for managing microplastic pollution in remote, high-altitude watersheds where warming is accelerating erosion.