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Updated: Jul 4, 2026

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
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Published on: November 18, 2015

Machine learning-based simulation of groundwater DIC distribution and source apportionment.

Yaxin Ji1,2, Lu Li3,4, Zheng Li5

  • 1School of Ecology and Environment, Inner Mongolia University, Hohhot, 010021, Inner Mongolia, China.

Environmental Geochemistry and Health
|July 3, 2026
PubMed
Summary
This summary is machine-generated.

Groundwater dissolved inorganic carbon (DIC) concentrations increase from recharge to discharge areas, primarily driven by carbonate dissolution and water-rock interactions. This study clarifies DIC sources and spatial patterns in arid regions.

Keywords:
Carbon cycleDissolved inorganic carbon (DIC)GroundwaterRandom forest modelStable isotopes

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Area of Science:

  • Hydrogeology
  • Geochemistry
  • Environmental Science

Background:

  • Groundwater is a critical reservoir for dissolved inorganic carbon (DIC), linking terrestrial and aquatic carbon cycles.
  • Understanding DIC dynamics, spatial distribution, and sources in groundwater is essential for carbon cycling research but remains limited.
  • Arid and semi-arid regions face unique challenges in groundwater management due to limited water resources and complex hydrogeological settings.

Purpose of the Study:

  • To investigate the spatial distribution patterns of groundwater DIC in the Bojianghaizi Basin.
  • To identify the primary sources and formation processes of DIC in the study area.
  • To elucidate the controlling factors influencing DIC migration and transformation in groundwater.

Main Methods:

  • Employed a random forest model to predict groundwater DIC concentrations based on hydrochemical and spatial data.
  • Utilized ArcGIS for spatial interpolation of DIC estimates to visualize regional patterns.
  • Applied Geodetector analysis to quantify the influence of hydrochemical variables on DIC spatial variability.
  • Interpreted DIC sources using stable isotope analysis (δ13C-DIC), pCO2 measurements, and hydrochemical characteristics.

Main Results:

  • Groundwater DIC concentrations generally increased from mountainous recharge areas towards the discharge area around Tao'ahazi Lake.
  • Mg2+ concentration exhibited the highest explanatory power for DIC spatial variability, indicating significant Mg-bearing carbonate dissolution.
  • Interactions involving Mg2+, K+, F-, and SiO2, along with clastic-rock weathering and evaporative concentration, jointly controlled DIC distribution.
  • δ13C-DIC values (-8 to -1‰, concentrated near -6‰) and elevated pCO2 suggest DIC primarily originates from soil CO2-driven carbonate dissolution.

Conclusions:

  • Water-rock interaction and mineralization evolution within the regional recharge-runoff-discharge framework are the main drivers of groundwater DIC spatial variation.
  • Local sulfate-related processes in mining areas can further influence carbon input and isotopic signatures.
  • The findings provide a scientific foundation for understanding groundwater carbon cycling and managing groundwater resources in arid/semi-arid environments.