Author:
Wang Xin,Li Rong,Tian Yong,Zhang Bowei,Zhao Ying,Zhang Tingting,Liu Chongxuan
Abstract
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.
Funder
National key research and development program
National Natural Science Foundation of China
Program for Guangdong Introducing Innovative and Entrepreneurial Teams
Subject
Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献