A Computational Framework for Design and Optimization of Risk-Based Soil and Groundwater Remediation Strategies

Wang, Xin1,2; Li, Rong2; Tian, Yong2; Zhang, Bowei2; Zhao, Ying3; Zhang, Tingting3; Liu, Chongxuan2

2022-12-01

10.3390/pr10122572

PROCESSES   Impact Factor & Quartile

2227-9717

Soil and groundwater systems have natural attenuation potential to degrade or detoxify contaminants due to biogeochemical processes. However, such potential is rarely incorporated into active remediation strategies, leading to over-remediation at many remediation sites. Here, we propose a framework for designing and searching optimal remediation strategies that fully consider the combined effects of active remediation strategies and natural attenuation potentials. The framework integrates machine-learning and process-based models for expediting the optimization process with its applicability demonstrated at a field site contaminated with arsenic (As). The process-based model was employed in the framework to simulate the evolution of As concentrations by integrating geochemical and biogeochemical processes in soil and groundwater systems under various scenarios of remedial activities. The simulation results of As concentration evolution, remedial activities, and associated remediation costs were used to train a machine learning model, random forest regression, with a goal to establish a relationship between the remediation inputs, outcomes, and associated cost. The relationship was then used to search for optimal (low cost) remedial strategies that meet remediation constraints. The strategy was successfully applied at the field site, and the framework provides an effective way to search for optimal remediation strategies at other remediation sites.

remediation strategy machine learning soil and groundwater remediation optimization contaminated site

SCI

English

Corresponding

Engineering

Engineering, Chemical

WOS:000903439000001

MDPI

Web of Science

1.Harbin Inst Technol, Sch Environm, Harbin 150090, Peoples R China
2.Southern Univ Sci & Technol, Sch Environm Sci & Engn, State Environm Protect Key Lab Integrated Surface, Shenzhen 518055, Peoples R China
3.Wisdri City Environm Protect Engn Ltd Co, Wuhan 430205, Peoples R China

Wang, Xin,Li, Rong,Tian, Yong,et al. A Computational Framework for Design and Optimization of Risk-Based Soil and Groundwater Remediation Strategies[J]. PROCESSES,2022,10(12).

Wang, Xin.,Li, Rong.,Tian, Yong.,Zhang, Bowei.,Zhao, Ying.,...&Liu, Chongxuan.(2022).A Computational Framework for Design and Optimization of Risk-Based Soil and Groundwater Remediation Strategies.PROCESSES,10(12).

Wang, Xin,et al."A Computational Framework for Design and Optimization of Risk-Based Soil and Groundwater Remediation Strategies".PROCESSES 10.12(2022).