A Data-Driven Simplified Nernst Equation for Estimating Reduction Potentials in Groundwater from pH and Temperature.

Area of Science:

• Geochemistry
• Environmental Science
• Hydrogeology

Background:

• Reduction potentials are crucial for understanding subsurface geochemistry and water resource management.
• Traditional Nernst equation calculations require extensive data and complex modeling, limiting field applications.
• There is a need for simplified methods to estimate redox potentials in data-limited settings.

Purpose of the Study:

• To develop a data-driven, simplified Nernst equation for estimating redox potentials.
• To assess the influence of pH, temperature, and redox species activity on redox potential.
• To provide a practical tool for large-scale groundwater assessments.

Main Methods:

• Integrated geochemical modeling with a global groundwater chemistry dataset.
• Developed a simplified Nernst equation using only pH and temperature.
• Validated the model's predictive accuracy across diverse groundwater environments.

Main Results:

• pH was identified as the dominant factor controlling redox potential in groundwater.
• The simplified Nernst equation demonstrated high predictive accuracy.
• The approach significantly reduced computational demands compared to traditional methods.

Conclusions:

• The simplified Nernst equation provides a rapid and scalable method for estimating redox potentials.
• This approach supports geochemical modeling, contaminant transport prediction, and groundwater quality assessments.
• It offers a practical, thermodynamically grounded framework for interpreting electron transfer in groundwater.