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Decoding the transport thresholds of emerging contaminants in watersheds using explainable machine learning
Summary
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.
Understanding watershed emerging contaminants (ECs) transport is vital for pollution control but challenging due to complex land-climate interactions and limited models. This study collected 517 seasonal water samples from the Huangshui River (2020-2024) and quantified microplastics (MPs), antibiotics, heavy metals, and water quality indicators. A novel machine learning (ML-SHAP) framework was developed to model ECs transport (train R² = 0.94, test R² = 0.65), integrating multiscale land use (200, 500, 1000, 2000 m riparian buffers), landscape metrics (Patch Density (PD), Largest Patch Index (LPI), Contiguity Index Mean (CONTIG-MN)), and 11 climate variables. Overall, the water quality and heavy metals complied with Class III and Class I standards (GB3838-2002), respectively. However, MPs (1831 items/L) and antibiotics (55.33 ng/L) posed significant threats to regional water security. MPs transport was enhanced in fragmented urban land (PD > 1 in 2000-m buffer) and highly connected cropland (LPI > 50 in 500-m buffer), whereas antibiotic transport intensified in cropland with low landscape connectivity (LPI < 50 in 1000-m buffer). Notably, forest (cover > 45 % in 1000-m buffer) and grassland (CONTIG-MN > 0.5 in 500-m buffer) effectively mitigated ECs transport. Therefore, enhancing riparian forest and grassland connectivity while reducing urban fragmentation within a 2000 m buffer could substantially mitigate the transport of ECs. MPs transport increased under heavy rainfall (>6 mm) and low wind speeds (<1.2 m/s), while antibiotic concentrations rose under strong winds (>2 m/s), low rainfall (<2 mm) and weak solar radiation (<1.7 × 10⁷ J/m²). Climate warming under SSP585 increased MPs by 10.90 items/L and antibiotics by 0.007 ng/L per decade. Low-emission SSP245 with 40 % riparian reforestation reduced pollutants. These findings provide new mechanistic insights into ECs transport and offer a novel model for watershed ECs management.
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