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Data-driven approaches linking wastewater and source estimation hazardous waste for environmental management

Author

Listed:
  • Wenjun Xie

    (Nanjing University)

  • Qingyuan Yu

    (Nanjing University)

  • Wen Fang

    (Nanjing University)

  • Xiaoge Zhang

    (The Hong Kong Polytechnic University)

  • Jinghua Geng

    (Nanjing University)

  • Jiayi Tang

    (Nanjing University)

  • Wenfei Jing

    (Nanjing University)

  • Miaomiao Liu

    (Nanjing University)

  • Zongwei Ma

    (Nanjing University)

  • Jianxun Yang

    (Nanjing University)

  • Jun Bi

    (Nanjing University)

Abstract

Industrial enterprises are major sources of contaminants, making their regulation vital for sustainable development. Tracking contaminant generation at the firm-level is challenging due to enterprise heterogeneity and the lack of a universal estimation method. This study addresses the issue by focusing on hazardous waste (HW), which is difficult to monitor automatically. We developed a data-driven methodology to predict HW generation using wastewater big data which is grounded in the availability of this data with widespread application of automatic sensors and the logical assumption that a correlation exists between wastewater and HW generation. We created a generic framework that used representative variables from diverse sectors, exploited a data-balance algorithm to address long-tail data distribution, and incorporated causal discovery to screen features and improve computation efficiency. Our method was tested on 1024 enterprises across 10 sectors in Jiangsu, China, demonstrating high fidelity (R² = 0.87) in predicting HW generation with 4,260,593 daily wastewater data.

Suggested Citation

  • Wenjun Xie & Qingyuan Yu & Wen Fang & Xiaoge Zhang & Jinghua Geng & Jiayi Tang & Wenfei Jing & Miaomiao Liu & Zongwei Ma & Jianxun Yang & Jun Bi, 2024. "Data-driven approaches linking wastewater and source estimation hazardous waste for environmental management," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49817-6
    DOI: 10.1038/s41467-024-49817-6
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    References listed on IDEAS

    as
    1. Xianying Li & Feng Xu & Nan Xiang & Yating Wang & Yingkui Zhang, 2019. "Dynamic Optimized Cleaner Production Strategies to Improve Water Environment and Economic Development in Leather Industrial Parks: A Case Study in Xinji, China," Sustainability, MDPI, vol. 11(23), pages 1-18, December.
    2. Ahmed Ghaithan & Mohammed Khan & Awsan Mohammed & Laith Hadidi, 2021. "Impact of Industry 4.0 and Lean Manufacturing on the Sustainability Performance of Plastic and Petrochemical Organizations in Saudi Arabia," Sustainability, MDPI, vol. 13(20), pages 1-20, October.
    3. Federico Castelletti & Guido Consonni, 2020. "Discovering causal structures in Bayesian Gaussian directed acyclic graph models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1727-1745, October.
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