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<p>Wastewater treatment facilities are substantial sources of greenhouse gas emissions, significantly impacting global climate change, especially via methane and nitrous oxide emissions. This thorough review critically evaluates the function of sophisticated Artificial Intelligence and Machine Learning methodologies—including Artificial Neural Networks, Support Vector Machines, Extreme Gradient Boosting, Random Forest, Adaptive Neuro-Fuzzy Inference Systems, and Convolutional Neural Networks in enhancing wastewater treatment processes for efficient greenhouse gas emission reduction. Global case studies indicate significant potential for Artificial Intelligence and Machine Learning technologies to properly forecast emissions, improve energy efficiency, and optimize process control, yielding notable environmental and economic advantages. Nonetheless, despite these encouraging results, various technical, regulatory, and practical obstacles impede widespread adoption, particularly data scarcity, inconsistent data quality, infrastructural limitations, computational resource demands, and the lack of comprehensive, supportive regulatory frameworks. This analysis delineates significant research deficiencies and recommends strategies include the development of integrated hybrid Artificial Intelligence &amp; Intergovernmental Panel on Climate Change -models, the establishment of standardized global Internet of Things-enabled data repositories, and the formulation of explicit governmental incentives to expedite the integration of Artificial Intelligence and Machine Learning in wastewater treatment plants. Future advancements in innovation and international collaboration in policymaking, research, and technological implementation are essential for maximizing the promise of Artificial Intelligence and Machine Learning in reaching low-carbon and energy-neutral wastewater treatment. This study emphasizes the critical necessity for collaborative efforts among policymakers, industry stakeholders, and researchers to improve the sustainability of the global wastewater sector, aligning operational practices with international climate neutrality objectives.</p> Graphical Abstract <p></p>

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Application of artificial intelligence and machine learning with international guidelines for greenhouse gas reduction in wastewater treatment

  • 期刊论文

  • 发表日期：

    2025年8月14日

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  • A. Kothale,
  • P. Sadgir

摘要

Wastewater treatment facilities are substantial sources of greenhouse gas emissions, significantly impacting global climate change, especially via methane and nitrous oxide emissions. This thorough review critically evaluates the function of sophisticated Artificial Intelligence and Machine Learning methodologies—including Artificial Neural Networks, Support Vector Machines, Extreme Gradient Boosting, Random Forest, Adaptive Neuro-Fuzzy Inference Systems, and Convolutional Neural Networks in enhancing wastewater treatment processes for efficient greenhouse gas emission reduction. Global case studies indicate significant potential for Artificial Intelligence and Machine Learning technologies to properly forecast emissions, improve energy efficiency, and optimize process control, yielding notable environmental and economic advantages. Nonetheless, despite these encouraging results, various technical, regulatory, and practical obstacles impede widespread adoption, particularly data scarcity, inconsistent data quality, infrastructural limitations, computational resource demands, and the lack of comprehensive, supportive regulatory frameworks. This analysis delineates significant research deficiencies and recommends strategies include the development of integrated hybrid Artificial Intelligence & Intergovernmental Panel on Climate Change -models, the establishment of standardized global Internet of Things-enabled data repositories, and the formulation of explicit governmental incentives to expedite the integration of Artificial Intelligence and Machine Learning in wastewater treatment plants. Future advancements in innovation and international collaboration in policymaking, research, and technological implementation are essential for maximizing the promise of Artificial Intelligence and Machine Learning in reaching low-carbon and energy-neutral wastewater treatment. This study emphasizes the critical necessity for collaborative efforts among policymakers, industry stakeholders, and researchers to improve the sustainability of the global wastewater sector, aligning operational practices with international climate neutrality objectives.

Graphical Abstract