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 d

SUBMITTER: Xie W 

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

REPOSITORIES: biostudies-literature