Title: Data-driven approaches to bestow environmental management: linking wastewater data to source estimation of hazardous waste

Authors: Wenjun Xie, Qingyuan Yu, Wen Fang, Xiaoge Zhang, Jinghua Geng, Jiayi Tang, Wenfei Jing, Miaomiao Liu, Zongwei Ma, Jianxun Yang, and Jun Bi

Abstract: Industrial enterprises are prominent sources of contaminant discharge on the planet and regulating their operations is vital for sustainable development. However, accurately tracking contaminant generation at the firm-level remains an intractable global issue due to significant heterogeneities among enormous enterprises and the absence of a universally applicable estimation method. This study addressed the challenge by focusing on hazardous waste (HW), known for its severe harmful properties and difficulty in automatic monitoring, and developed a data-driven methodology that predicted HW generation utilizing wastewater big data in a uniform and lightweight manner. The idea is grounded in the availability of wastewater big data with widespread application of automatic sensors, enabling depiction of heterogeneous enterprises, and the logical assumption that a correlation exists between wastewater and HW generation. We simulated this relationship by designing a generic framework that jointly 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. To illustrate our approach, we applied it to 1024 enterprises across 10 sectors in Jiangsu, a highly industrialized province in China. Validation results demonstrated the model’s high fidelity (R2=0.87) in predicting HW generation using 4,260,593 daily wastewater data.

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