Big-Data Analysis and Machine Learning Based on Oil Pollution Remediation Cases from CERCLA Database

Big-Data Analysis and Machine Learning Based on Oil Pollution Remediation Cases from CERCLA Database

Machine learning

Big data

Remediation

Soil pollution

Soils

Decision trees

Learning algorithms

Environmental Protection Agency

Database systems

Soil conservation

The U.S. Environmental Protection Agencys (EPA) Superfundthe Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) databasehas collected and built an open-source database based on nearly 2000 US soil remediation cases since 1980, providing detailed information and references for researchers worldwide to carry out remediation work. However, the cases were relatively independent to each other, so the whole database lacks systematicness and instructiveness to some extent. In this study, the basic features of all 144 soil remediation projects in four major oil-producing states (California, Texas, Oklahoma and Alaska) were extracted from the CERCLA database and the correlations among the pollutant species, pollutant site characteristics and selection of remediation methods were analyzed using traditional and machine learning techniques. The Decision Tree Classifier was selected as the machine learning model. The results showed that the growth of new contaminated sites has slowed down in recent years

physical remediation was the most commonly used method, and the probability of its application is more than 80%. The presence of benzene, toluene, ethylbenzene and xylene (BTEX) substances and the geographical location of the site were the two most influential factors in the choice of remediation method for a specific site

the maximum weights of these two features reaches 0.304 and 0.288. 2022 by the authors.

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Li, Hangyu

Zhou, Ze

Long, Tao

Wei, Yao

Xu, Jianchun

Liu, Shuyang

Wang, Xiaopu

Energies

2022

journalArticle

19961073

10.3390/en15155698