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

Environmental Studies and Forestry

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

W. Xie, Q. Yu, et al.

This research, conducted by Wenjun Xie and colleagues, unveils a data-driven methodology for predicting hazardous waste generation using massive wastewater datasets. With impressive accuracy, the findings promise to enhance efficiency across various sectors, ultimately contributing to better environmental management.

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Playback language: English
Abstract
Tracking contaminant generation at the firm-level is challenging. This study develops a data-driven methodology to predict hazardous waste (HW) generation using wastewater big data. A generic framework uses representative variables from diverse sectors, a data-balance algorithm to address long-tail data distribution, and causal discovery to improve efficiency. The method, tested on 1024 enterprises across 10 sectors in Jiangsu, China, showed high fidelity (R² = 0.87) in predicting HW generation with 4,260,593 daily wastewater data. Sector-independent models further improved accuracy.
Publisher
Nature Communications
Published On
Jun 26, 2024
Authors
Wenjun Xie, Qingyuan Yu, Wen Fang, Xiaoge Zhang, Jinghua Geng, Jiayi Tang, Wenfei Jing, Miaomiao Liu, Zongwei Ma, Jianxun Yang, Jun Bi
Tags
hazardous waste
wastewater data
data-driven methodology
contaminant prediction
efficiency improvement
sector-independent models
big data

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