Application of machine learning in atmospheric pollution research: A state-of-art review

Peng, Zezhi; Zhang, Bin; Wang, Diwei; Niu, Xinyi; Sun, Jian; Xu, Hongmei; Cao, Junji; Shen, Zhenxing

doi:10.1016/j.scitotenv.2023.168588

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2024

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Cited by 14 publications

References 88 publications

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Application of Data Mining and AI&ML in Aerosol Pollution and Aerosol Atmospheric Rivers

Goyal,

Rautela

2024

Aerosol Atmospheric Rivers

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Application of Data Mining and AI&ML in Aerosol Pollution and Aerosol Atmospheric Rivers

Goyal,

Rautela

2024

Aerosol Atmospheric Rivers

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The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques

Canché-Cab,

San-Pedro,

Ali

et al. 2024

Artif Intell Rev

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Atmospheric boundary layer (ABL) structure and dynamics are important aspects to consider in human health. The ABL is characterized by a high degree of spatial and temporal variability that hinders their understanding. This paper aims to provide a comprehensive overview of machine learning (ML) methodologies, encompassing deep learning and ensemble approaches, within the scope of ABL research. The goal is to highlight the challenges and opportunities of using ML in turbulence modeling and parameterization in areas such as atmospheric pollution, meteorology, and renewable energy. The review emphasizes the validation of results to ensure their reliability and applicability. ML has proven to be a valuable tool for understanding and predicting how ABL spatial and seasonal variability affects pollutant dispersion and public health. In addition, it has been demonstrated that ML can be used to estimate several variables and parameters, such as ABL height, making it a promising approach to enhance air quality management and urban planning.

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Machine learning exploring the chemical compositions characteristics and sources of PM2.5 from reduced on-road activity

Liao,

Hong,

Huang

et al. 2024

Atmospheric Pollution Research

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Application of machine learning in atmospheric pollution research: A state-of-art review

Cited by 14 publications

References 88 publications

Application of Data Mining and AI&ML in Aerosol Pollution and Aerosol Atmospheric Rivers

Application of Data Mining and AI&ML in Aerosol Pollution and Aerosol Atmospheric Rivers

The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques

Machine learning exploring the chemical compositions characteristics and sources of PM2.5 from reduced on-road activity

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