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Supplementary Information-Anthropogenic drivers and runoff-mediated transport of microplastics in the Chongqing Section of the Jialing River: A partial least squares regression-based mechanistic study
Summary
Researchers applied partial least squares regression to 14 environmental variables measured along the Jialing River and identified population density and industrial energy consumption as the strongest drivers of microplastic transport, with runoff acting as the primary hydrological delivery mechanism to the river channel.
This dataset contains field measurements collected from the Chongqing Section of the Jialing River, China, to investigate the anthropogenic drivers and runoff‑mediated transport of microplastics (MPs). A total of 14 environmental variables were monitored alongside microplastic concentrations. These variables include geographical distance, water temperature, number of factories, water level, population, industrial energy consumption (IEC), fertilizer use, electrical conductivity (EC), chemical oxygen demand (CODcr), precipitation, discharge, total nitrogen (TN), total phosphorus (TP), and pH. Descriptive statistics (maximum, minimum, mean, and standard error of the mean) for each variable are provided in Table S1, offering a comprehensive overview of their ranges and central tendencies. Furthermore, a partial least squares regression (PLSR) model was applied to identify key drivers of MP transport. Table S2 reports the variable importance in projection (VIP) scores, loading weights, and the direction of influence (positive or negative) for each environmental factor. This dataset is valuable for understanding how human activities and hydrological processes jointly affect microplastic pollution in urban river systems. It can also serve as a benchmark for similar mechanistic studies in other watersheds.