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

Wenjun Xie, Qingyuan Yu, Wen Fang (), Xiaoge Zhang, Jinghua Geng, Jiayi Tang, Wenfei Jing, Miaomiao Liu (), Zongwei Ma, Jianxun Yang and Jun Bi ()
Additional contact information
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

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: 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.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-49817-6 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49817-6

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-49817-6

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().