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Supervised Machine Learning Approaches for Predicting Key Pollutants and for the Sustainable Enhancement of Urban Air Quality: A Systematic Review

Author

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  • Ismail Essamlali

    (Electrical Engineering and Intelligent Systems Laboratory, ENSET Mohammedia, Hassan 2nd University of Casablanca, Mail Box 159, Mohammedia 28810, Morocco)

  • Hasna Nhaila

    (Electrical Engineering and Intelligent Systems Laboratory, ENSET Mohammedia, Hassan 2nd University of Casablanca, Mail Box 159, Mohammedia 28810, Morocco)

  • Mohamed El Khaili

    (Electrical Engineering and Intelligent Systems Laboratory, ENSET Mohammedia, Hassan 2nd University of Casablanca, Mail Box 159, Mohammedia 28810, Morocco)

Abstract

Urban air pollution is a pressing global issue driven by factors such as swift urbanization, population expansion, and heightened industrial activities. To address this challenge, the integration of Machine Learning (ML) into smart cities presents a promising avenue. Our article offers comprehensive insights into recent advancements in air quality research, employing the PRISMA method as a cornerstone for the reviewing process, while simultaneously exploring the application of frequently employed ML methodologies. Focusing on supervised learning algorithms, the study meticulously analyzes air quality data, elucidating their unique benefits and challenges. These frequently employed ML techniques, including LSTM (Long Short-Term Memory), RF (Random Forest), ANN (Artificial Neural Networks), and SVR (Support Vector Regression), are instrumental in our quest for cleaner, healthier urban environments. By accurately predicting key pollutants such as particulate matter (PM), nitrogen oxides (NO x ), carbon monoxide (CO), and ozone (O 3 ), these methods offer tangible solutions for society. They enable informed decision-making for urban planners and policymakers, leading to proactive, sustainable strategies to combat urban air pollution. As a result, the well-being and health of urban populations are significantly improved. In this revised abstract, the importance of frequently employed ML methods in the context of air quality is explicitly emphasized, underlining their role in improving urban environments and enhancing the well-being of urban populations.

Suggested Citation

  • Ismail Essamlali & Hasna Nhaila & Mohamed El Khaili, 2024. "Supervised Machine Learning Approaches for Predicting Key Pollutants and for the Sustainable Enhancement of Urban Air Quality: A Systematic Review," Sustainability, MDPI, vol. 16(3), pages 1-26, January.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:976-:d:1324864
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    References listed on IDEAS

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    1. Matthew A. Cole & Robert J R Elliott & Bowen Liu, 2020. "The Impact of the Wuhan Covid-19 Lockdown on Air Pollution and Health: A Machine Learning and Augmented Synthetic Control Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 553-580, August.
    2. Bollen, Johannes & van der Zwaan, Bob & Brink, Corjan & Eerens, Hans, 2009. "Local air pollution and global climate change: A combined cost-benefit analysis," Resource and Energy Economics, Elsevier, vol. 31(3), pages 161-181, August.
    3. Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2020. "The relationship between air pollution and COVID-19-related deaths: An application to three French cities," Applied Energy, Elsevier, vol. 279(C).
    4. Cherniwchan, Jevan, 2012. "Economic growth, industrialization, and the environment," Resource and Energy Economics, Elsevier, vol. 34(4), pages 442-467.
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