Collaborative Mitigation and Reduction of Volatile Organic Compounds in Industrial Parks: A Machine Learning Approach in China

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This is a Quantitative Study study.

Yunxiang Yan

doi:10.52152/eim7129

October 2, 2025DYNAOpen Access

Collaborative Mitigation and Reduction of Volatile Organic Compounds in Industrial Parks: A Machine Learning Approach in China

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Key Points

Machine learning techniques can significantly enhance VOC emission forecasting and control in industrial parks.
Results show that over 20% VOC reductions can be achieved within a year in pilot programs using collaborative governance.
The study highlights the importance of multi-stakeholder cooperation for effective monitoring and compliance.
Advanced visualizations aid in identifying emission hotspots and optimizing pollution control strategies.

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Implication

This study demonstrates collaborative governance improving VOC emission control in industrial parks, suggesting better outcomes through machine learning techniques.

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Cite This Study

Yunxiang Yan (2025) studied this question.

synapsesocial.com/papers/68e25382d6d66a53c2474a5c https://doi.org/10.52152/eim7129

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Collaborative Mitigation and Reduction of Volatile Organic Compounds in Industrial Parks: A Machine Learning Approach in China

Key Points

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Implication

Discussion

Cite This Study

Collaborative Mitigation and Reduction of Volatile Organic Compounds in Industrial Parks: A Machine Learning Approach in China

Key Points

No abstract yet

Ask about this paper

Implication

Discussion

Cite This Study

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