A dynamic risk-based framework for BTEX bioremediation: integrating micro-exposure events and optimization strategies

Abstract

Groundwater contamination by BTEX compounds—benzene, toluene, ethylbenzene, and xylene—poses serious environmental and public health risks, particularly in hydrogeologically complex industrial areas. This study proposes a novel, dynamic framework for in situ bioremediation of BTEX-contaminated groundwater at the Shiraz oil refinery site, integrating the Micro-Exposure Event (MEE) method as a central component for high-resolution, time-sensitive health risk assessment. Unlike conventional risk models that rely on long-term average concentrations, the MEE approach evaluates individual exposure events across temporal intervals, enabling the identification of short-term contaminant spikes that may otherwise go undetected. This enhances the accuracy and responsiveness of risk estimations, particularly in heterogeneous aquifer settings. To address the site's geological complexity—characterized by variable stratigraphy, non-uniform flow directions, and multiple pollution sources—the framework couples advanced simulation tools (MODFLOW 6 and MT3D-USGS) with adaptive optimization algorithms. A Genetic Algorithm (GA) is employed for spatial optimization of well placement, while Differential Evolution (DE) is used for temporal adjustment of injection and extraction rates. Simulation results over a ten-year period demonstrate a 92 % reduction in benzene concentrations and sustained health risk levels below the U.S. EPA threshold of 1 × 10⁻⁶. Additionally, the optimized strategy achieves an 18 % reduction in total operational costs compared to baseline scenarios. By linking MEE-based risk evaluation with simulation-optimization techniques, the proposed framework offers a replicable and scalable approach for managing BTEX pollution in complex subsurface environments. It advances the state of practice by balancing environmental performance, cost-efficiency, and health protection in groundwater remediation.