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May 28, 2026Environmental Science & Technology

Unveiling Hydrogen Fluoride Emission Mechanisms in Municipal Solid Waste Incineration Using a Machine Learning Approach

Xingyu Feng Longshun Liu Jinshan Li Mingshun Ye Ondrej Masek Shaban Gouda Ibrahim Mohamed ElSayed Ali Kenlin Chang Xu Wang Qing Huang

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Key Points

The aim is to improve understanding of hydrogen fluoride emissions from municipal solid waste incineration.
Utilized a machine learning framework to analyze emission data.
Focused on municipal solid waste incineration (MSWI) systems to identify emission mechanisms.
Provided data-driven strategies for sustainable operation.
Identified key mechanisms contributing to hydrogen fluoride emissions.
Demonstrated potential for enhanced pollution mitigation strategies.
Highlighted the importance of machine learning in operational efficiency.

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Implication

Study unveils emission mechanisms in waste incineration, suggesting improved pollution strategies.

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Cite This Study

Feng et al. (2026) studied this question.

synapsesocial.com/papers/6a1a81e00307b785094339b3 https://doi.org/10.1021/acs.est.6c00686

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