Health-risk-based groundwater remediation system optimization through clusterwise linear regression.

Area of Science:

• Environmental Science
• Water Resource Management
• Risk Assessment

Background:

• Groundwater contamination poses significant environmental and human health risks.
• Effective groundwater management requires integrating environmental quality and health considerations.
• Existing models often face computational challenges in complex remediation scenarios.

Purpose of the Study:

• To develop a novel health-risk-based groundwater management (HRGM) model.
• To incorporate environmental quality and human health risks into a unified framework.
• To propose an efficient proxy-based optimization approach for solving the HRGM model.

Main Methods:

• Developed a health-risk-based groundwater management (HRGM) model.
• Employed a proxy-based optimization approach using clusterwise linear regression.
• Created rapid-response proxy modules to simulate remediation policy impacts on human health risks.
• Significantly reduced computational costs by replacing complex simulation modules.

Main Results:

• Identified inverse relationship between remediation duration and total pumping rate.
• Demonstrated that stringent risk standards necessitate higher total pumping rates.
• Showed significant reduction in benzene-related human health risks when treated as model constraints.
• Post-optimization simulations confirmed carcinogenic risk reduction to meet regulated standards.

Conclusions:

• The HRGM model provides valuable insights for decision-makers on remediation policies.
• Findings assist in designing robust groundwater remediation systems by balancing duration, risk levels, and pumping rates.
• The proxy-based optimization significantly enhances computational efficiency for complex environmental management problems.