Abstract
Heavy metal (HM) contamination in arid inland river basins is intensifying with economic development, posing ongoing ecological and public health risks. Conventional monitoring is constrained by landscape heterogeneity and limited field data, hindering basin-scale spatiotemporal analysis. This study integrates multispectral Sentinel‑2A imagery, Random Forest (RF) regression, and harmonized ground observations to quantify and map As, Cd, Cu, Pb, and Zn in soils and waters of China’s Tarim River Basin (TRB), and to evaluate related health risks using the EPA model. Field data showed soil ΣHMs averaged 118.71 mg kg⁻¹ (95.15–191.32 mg kg⁻¹) and river ΣHMs 3.89 µg L⁻¹ (1.14–99.00 µg L⁻¹), with Cd and As above background levels. RF models yielded preliminary spatial patterns (R² = 0.741–0.999; RMSE = 0.12–0.38 mg kg⁻¹ for soils, 0.21–0.57 µg L⁻¹ for waters). Spatial patterns revealed As and Cd hotspots in Aksu, Cu and Pb enrichment in central and southeastern sub‑basins, and Zn predominance in the east. Pearson correlation and principal component analyses attributed Pb–Cu–Zn mainly to natural sources and riverine metals to atmospheric deposition and hydrology. Oral ingestion was the major exposure route; ~10% of soil samples exceeded the non‑carcinogenic hazard index for children, while As and Cd posed carcinogenic risks > 1 × 10⁻⁴. The geo‑accumulation index (I_geo > 2 for As) and HPI > 100 in Hotan, Kashgar, and Aksu indicated localized hazards. Recommended actions include RS‑based quarterly monitoring, targeted As/Cd control, basin‑wide HM coordination, child health screening, and nature‑based remediation to reduce HM risks by 2035. This work provides the first high-resolution, basin-wide HM assessment in the TRB, demonstrating Sentinel-2A-RF inversion as a cost-effective tool for hotspot identification, risk-informed management, and alignment with China’s dual-carbon goals, rural revitalization, and UN Sustainable Development Goals.
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Data availability
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
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Acknowledgements
This study was supported by The Third Xinjiang Scientific Expedition Program (2022xjkk010704), the Shaanxi Provincial Department of Education “Urban and Rural Spatial Hydrological Ecological Simulation and Management in Arid Area” Youth University Innovation Team. We give our deep thanks to the reviewers and editors for their valuable comments to improve this research.
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Open access funding provided by The Third Xinjiang Scientific Expedition Program (2022xjkk010704).
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Yang Zhao: Writing – original draft, Software, Methodology, Formal analysis, Data curation, Conceptualization. Yong Mu: Writing – review & editing, Supervision, Funding acquisition, Conceptualization. Pingping Luo: Writing – review & editing, Supervision, conceptualization. Jianxin Zhang: Writing – review & editing, Supervision, Conceptualization. Madhab Rijal: Writing – review & editing, Supervision, Conceptualization. Zhihui Yang: Writing – review & editing, Supervision, Conceptualization. Chengguang Lai: Writing – review & editing, Supervision, Conceptualization. Jiachao Chen: Visualization, Formal analysis, Data curation. Ahmed Elbeltagi: Visualization, Formal analysis, Data curation. Bam H.N. Razafindrabe: Visualization, Formal analysis, Data curation.
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Zhao, Y., Mu, Y., Luo, P. et al. Integrating multi-spectral remote sensing and machine learning for quantifying and mapping heavy metal contamination in the Tarim River Basin, China. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38887-9
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DOI: https://doi.org/10.1038/s41598-026-38887-9
