<p>Understanding the complex influences of anthropogenic disturbance and lithological background on hydrogeochemical characteristics is crucial for effective water resource management. However, their effects on riverine solute sources remain underexplored in urbanized areas, such as the East Tiaoxi (ETX) River in East China. This study investigated the spatial variation of 23 hydrogeochemical parameters (including physicochemical parameters, major and trace ions, and stable isotopes) in ETX River water. The results revealed significant anthropogenic disturbances, which were examined through an integrated approach based on multivariate statistical analysis and inverse mixing modeling. Through correlation and clustering analyses, six key hydrogeochemical parameters, K<sup>+</sup>, Na<sup>+</sup>, Ca<sup>2+</sup>, NO<sub>3</sub><sup>−</sup>, δ<sup>2</sup>H, and pH, were selected for factor analysis. Two factors emerged: FAC1 (51.39%), reflecting water-rock interactions and hydrological processes, and FAC2 (36.38%), reflecting urban and agricultural effluents. The inverse mixing model indicated that carbonates (40.57%), silicates (27.63%), urban inputs (20.86%), agricultural inputs (9.52%), and atmospheric precipitation (1.42%) contribute to riverine cations. These methods collectively confirm that rock weathering dominates solute sources, followed by significant anthropogenic inputs and the influence of Taihu Lake’s backwater. Additionally, spatial cumulative effects on solute sources were significant at the sub-basin scale (<i>r</i> = 0.886, <i>p</i> = 0.019). The percentages of impervious surfaces, carbonate rocks, and silicate rocks control the contributions from urban areas, carbonate weathering, and silicate weathering to cations, respectively. The findings highlight the cumulative effects of urban inputs and rock weathering on river solute sources, providing a basis for sustainable water resource management in urbanized regions.</p>

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Combination of multivariate statistical analysis and inverse mixing modeling to unravel the solute sources and hydrological processes in a typical small urbanized watershed

  • Kunhua Yang,
  • Qian Zhang,
  • Bin Liang,
  • Qiang Lin,
  • Weijiao Wang

摘要

Understanding the complex influences of anthropogenic disturbance and lithological background on hydrogeochemical characteristics is crucial for effective water resource management. However, their effects on riverine solute sources remain underexplored in urbanized areas, such as the East Tiaoxi (ETX) River in East China. This study investigated the spatial variation of 23 hydrogeochemical parameters (including physicochemical parameters, major and trace ions, and stable isotopes) in ETX River water. The results revealed significant anthropogenic disturbances, which were examined through an integrated approach based on multivariate statistical analysis and inverse mixing modeling. Through correlation and clustering analyses, six key hydrogeochemical parameters, K+, Na+, Ca2+, NO3, δ2H, and pH, were selected for factor analysis. Two factors emerged: FAC1 (51.39%), reflecting water-rock interactions and hydrological processes, and FAC2 (36.38%), reflecting urban and agricultural effluents. The inverse mixing model indicated that carbonates (40.57%), silicates (27.63%), urban inputs (20.86%), agricultural inputs (9.52%), and atmospheric precipitation (1.42%) contribute to riverine cations. These methods collectively confirm that rock weathering dominates solute sources, followed by significant anthropogenic inputs and the influence of Taihu Lake’s backwater. Additionally, spatial cumulative effects on solute sources were significant at the sub-basin scale (r = 0.886, p = 0.019). The percentages of impervious surfaces, carbonate rocks, and silicate rocks control the contributions from urban areas, carbonate weathering, and silicate weathering to cations, respectively. The findings highlight the cumulative effects of urban inputs and rock weathering on river solute sources, providing a basis for sustainable water resource management in urbanized regions.