Climate & Environmentarticle2026-08-08

Pollution assessment and source analysis of heavy metals and nutrients in sediments of Liujiaxia reservoir, upper Yellow River, China

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Abstract

Abstract To investigate the pollution characteristics, ecological risks, and sources of nutrients and heavy metals in the sediments of the Liujiaxia Reservoir, 21 surface sediment samples were collected in August 2025. Concentrations of total nitrogen (TN), total phosphorus (TP), and eight heavy metals were determined, and pollution levels were evaluated using sediment quality guidelines (SQGs), the improved geo-accumulation index, and the potential ecological risk index. Pearson correlation analysis, principal component analysis (PCA), and the APCS-MLR model were further employed for source apportionment. The average concentrations of TN and TP were 566.19 mg/kg and 599.71 mg/kg, respectively. The mean concentrations of heavy metals (mg/kg) followed the order: Zn (78.71) > Cr (49.05) > Ni (29.67) > Cu (26.24) > Pb (24.52) > As (11.43) > Cd (0.11) > Hg (0.05). Nutrients and most heavy metals were mainly enriched in tributary estuaries and deep-water zones. Correlation analysis showed significant positive correlations among Cu, Ni, Zn, Cr, and As, indicating similar migration behaviors and common sources, whereas TN and TP exhibited weak correlations with heavy metals, suggesting distinct enrichment pathways. Overall, nutrient pollution was slight and heavy metals posed low ecological risk, although localized Hg enrichment and elevated Ni concentrations were observed at several sites. PCA extracted two principal components explaining 70.36% of the total variance. APCS-MLR results indicated that mixed natural and agricultural sources, industrial and traffic-related sources, and unidentified sources contributed 43.8%, 30.5%, and 25.7% of heavy metal inputs, respectively. Overall, sediment pollution in the Liujiaxia Reservoir remained at a relatively low level and was mainly controlled by natural weathering and agricultural non-point source inputs.

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View paper (DOI)Open access versionOpenAlexScientific ReportsPublished 2026-08-08

Authors: Beibei Wang, Di Li, Long Yan, Yaqin Hu, Zifeng Hong, Long Shi, Pengxin Cao

Institutions: China Institute of Water Resources and Hydropower Research, Anhui University of Science and Technology, China Three Gorges University