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

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.