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

Author

Listed:
  • Hangyu Li

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266000, China)

  • Ze Zhou

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266000, China)

  • Tao Long

    (State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China)

  • Yao Wei

    (School of Computer Science & Engineering, South China University of Technology, Guangzhou 510006, China)

  • Jianchun Xu

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266000, China)

  • Shuyang Liu

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266000, China)

  • Xiaopu Wang

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266000, China)

Abstract

The U.S. Environmental Protection Agency’s (EPA) Superfund—the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) database—has 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.

Suggested Citation

  • Hangyu Li & Ze Zhou & Tao Long & Yao Wei & Jianchun Xu & Shuyang Liu & Xiaopu Wang, 2022. "Big-Data Analysis and Machine Learning Based on Oil Pollution Remediation Cases from CERCLA Database," Energies, MDPI, vol. 15(15), pages 1-10, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5698-:d:881282
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    References listed on IDEAS

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    1. James T. Hamilton & W. Kip Viscusi, 1999. "How costly is “clean”? An analysis of the benefits and costs of Superfund site remediations," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 18(1), pages 2-27.
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