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<p>Understanding the spatiotemporal dynamics of riverine nitrate (NO₃⁻) is crucial for sustainable nitrogen (N) management in rapidly urbanizing basins. This study integrated a Bayesian mixing model (MixSIAR) with isotope tracers (<i>δ</i>¹⁵N/<i>δ</i>¹⁸O-NO₃⁻, <i>δ</i>D/<i>δ</i>¹⁸O-H<sub>2</sub>O), hydrochemical analyses, and partial least squares structural equation modelling (PLS-SEM) to quantify NO₃⁻ sources, transformation processes, and key drivers along the rural–urban gradient of China’s subtropical Liuxi River Basin. The results showed N concentrations rose steadily from rural to urban reaches, and were higher in tributaries than in the mainstream, which was attributed to the slower flow velocity-enhanced pollutant retention. MixSIAR indicated manure and sewage (54.4 ± 16.3%) as the primary NO₃⁻ source, followed by soil N (28.8 ± 11.6%) and chemical fertilizers (13.5 ± 7.4%), with the latter two concentrated in rural areas. Isotopic and hydrochemical data suggested minimal in-stream nitrate transformation (removal and production processes), with NO₃⁻ increases mainly driven by external inputs. PLS-SEM (GOF = 0.569) indicated that river NO₃⁻ levels were mainly governed by urbanization (31.9% explanatory power), which increased with greater urbanization. Meanwhile, the in-stream environment (22.9%) positively influenced NO₃⁻ levels through local biogeochemical regulation, while vegetation (21.9%) primarily inhibited NO₃⁻ levels through soil and water conservation and N fixation. This multidisciplinary approach provides a mechanistic framework for water-N management in heterogeneous basins, with direct implications for stage-specific pollution control strategies.</p>

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Sources, transformations and driving factors of nitrate in a typical urban-rural gradient basin: implications for riverine nitrogen management

  • 期刊论文

  • 发表日期：

    2026年2月28日

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  • Xin Wu,
  • Yi Wang,
  • Xiaoming He,
  • Yi Zeng,
  • Xingjian Yang,
  • Ying Zhao,
  • Alexander Gelfan,
  • Zifeng Luo,
  • Yongtao Li,
  • Zhen Zhang

摘要

Understanding the spatiotemporal dynamics of riverine nitrate (NO₃⁻) is crucial for sustainable nitrogen (N) management in rapidly urbanizing basins. This study integrated a Bayesian mixing model (MixSIAR) with isotope tracers (δ¹⁵N/δ¹⁸O-NO₃⁻, δD/δ¹⁸O-H2O), hydrochemical analyses, and partial least squares structural equation modelling (PLS-SEM) to quantify NO₃⁻ sources, transformation processes, and key drivers along the rural–urban gradient of China’s subtropical Liuxi River Basin. The results showed N concentrations rose steadily from rural to urban reaches, and were higher in tributaries than in the mainstream, which was attributed to the slower flow velocity-enhanced pollutant retention. MixSIAR indicated manure and sewage (54.4 ± 16.3%) as the primary NO₃⁻ source, followed by soil N (28.8 ± 11.6%) and chemical fertilizers (13.5 ± 7.4%), with the latter two concentrated in rural areas. Isotopic and hydrochemical data suggested minimal in-stream nitrate transformation (removal and production processes), with NO₃⁻ increases mainly driven by external inputs. PLS-SEM (GOF = 0.569) indicated that river NO₃⁻ levels were mainly governed by urbanization (31.9% explanatory power), which increased with greater urbanization. Meanwhile, the in-stream environment (22.9%) positively influenced NO₃⁻ levels through local biogeochemical regulation, while vegetation (21.9%) primarily inhibited NO₃⁻ levels through soil and water conservation and N fixation. This multidisciplinary approach provides a mechanistic framework for water-N management in heterogeneous basins, with direct implications for stage-specific pollution control strategies.