[Simulation of nitrate pollution in groundwater using pump-and treat optimization method]

Area of Science:

• Environmental Science
• Hydrogeology
• Computational Science

Background:

• Landfill sites pose risks of groundwater contamination, particularly from nitrates.
• Effective remediation strategies are crucial for mitigating environmental and health impacts.

Purpose of the Study:

• To optimize the pump-and-treat technology for groundwater nitrate remediation using computational algorithms.
• To determine the most cost-effective well placement, number, and pumping rates for nitrate plume management.

Main Methods:

• Application of genetic algorithms (GA) and simulated annealing (SA) for optimizing pumping well configurations.
• Modeling nitrate concentration reduction to 10 mg/L within 100 days.

Main Results:

• Both GA and SA achieved target nitrate reduction, with SA yielding a higher total mass removal rate (84.92%) compared to GA (76.89%).
• SA demonstrated a 6.8% lower system management cost than GA, indicating greater cost-efficiency.
• Optimal well placement identified along the central axis (midstream or downstream) of the nitrate plume, with higher pumping rates in the midstream.

Conclusions:

• Simulated annealing is a more efficient algorithm for optimizing groundwater remediation systems due to lower costs and higher removal rates.
• Strategic well placement and adjusted pumping rates are critical for effective nitrate plume management.
• Computational optimization significantly enhances the performance and cost-effectiveness of pump-and-treat technologies.