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

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.

SUBMITTER: Xie W

PROVIDER: S-EPMC11208539 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature

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

Xie Wenjun W Yu Qingyuan Q Fang Wen W Zhang Xiaoge X Geng Jinghua J Tang Jiayi J Jing Wenfei W Liu Miaomiao M Ma Zongwei Z Yang Jianxun J Bi Jun J

Nature communications 20240626 1

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 availabilit ...[more]

PMID: 38926394

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Project description:Per- and polyfluoroalkyl substances (PFAS) are a class of synthetic compounds widely used in industrial and consumer products. While PFAS provide product durability, these chemicals are ubiquitous, persistent, bioaccumulative, and toxic. These characteristics make the ultimate disposal of PFAS a challenge. One current disposal method is incineration; however, little research has been conducted on the safety and effectiveness of PFAS incineration. The characteristics of communities with hazardous waste incinerators that have received PFAS shipments indicate that more individuals with lower incomes and individuals with less education than the US average are at higher risk of exposure, which presents important environmental justice and health equity concerns of PFAS incineration. Situated in eastern Ohio, East Liverpool is an Appalachian community that is home to a large hazardous-waste incinerator, operated by Heritage WTI, that began accepting PFAS in 2019. Residents are concerned that the disposal lacks the research necessary to assure safety for the residents. Due to both community interest and data gaps regarding PFAS incineration, our research team conducted a pilot study to examine the distribution and concentration of PFAS in soil samples surrounding the incinerator. All 35 soil samples had measurable amounts of PFAS including perfluorobutanesulfonic acid (PFBS), perfluorooctanesulfonic acid (PFOS), perfluorooctanoic acid (PFOA), and hexafluoropropylene oxide dimer acid (HFPO-DA)/GenX. PFOS was measured in the majority of soil samples (97%) with a range of 50-8,300 ng/kg. PFOA was measured in 94% of soil samples with a range of 51 ng/kg to 1300 ng/kg. HFPO-DA/GenX was measurable in 12 soil samples with concentrations of ranging from 150 ng/kg to 1500 ng/kg. Further research on PFAS disposal will advance knowledge and action related to regulatory requirements and exposure prevention, ultimately improving individual and community protections and health equity.

Dataset Information

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

Publications

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

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