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A Review of Air Quality Modeling

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Abstract

Air quality models (AQMs) are useful for studying various types of air pollutions and provide the possibility to reveal the contributors of air pollutants. Existing AQMs have been used in many scenarios having a variety of goals, e.g., focusing on some study areas and specific spatial units. Previous AQM reviews typically cover one of the forming elements of AQMs. In this review, we identify the role and relevance of every component for building AQMs, including (1) the existing techniques for building AQMs, (2) how the availability of the various types of datasets affects the performance, and (3) common validation methods. We present recommendations for building an AQM depending on the goal and the available datasets, pointing out their limitations and potentials. Based on more than 40 works on air quality, we concluded that the main utilized methods in air pollution estimation are land-use regression (LUR), machine learning, and hybrid methods. In addition, when incorporating LUR methods with traffic variables, it gives promising results; however, when using kriging or inverse distance weighting techniques, the monitoring stations measurements of air pollution data are enough to have good results. We aim to provide a short manual for people who want to build an AQM given the constraints at hands such as the availability of datasets and technical/computing resources.

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Acknowledgements

This work was supported by the scholarship of Excellence from the National Center for Scientific and Technical Research (CNRST) of Morocco and Université du Littoral Côte d’Opale of Dunkerque, France. We would like to thank Atmo Hauts-de-France for providing us the measurements used in the study case.

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Authors and Affiliations

  1. LRIT-CNRST, URAC 29, Rabat IT Center, Faculty of Sciences, Mohammed V University in Rabat, BP 1014, Rabat, Morocco

    Khaoula Karroum

  2. Spatial Sciences Institute, University of Southern California, Los Angeles, CA, USA

    Yijun Lin & Yao-Yi Chiang

  3. Laboratory of Telecommunications, Networks and Service Systems, National Institute of Posts and Telecommunications, Allal El Fassi Avenue, Rabat, Morocco

    Yann Ben Maissa

  4. Laboratoire de Physico-Chimie de l’Atmosphère, Université du Littoral Côte d’Opale, 59140, Dunkirk, France

    Khaoula Karroum, Anton Sokolov & Hervé Delbarre

  5. Higher School of Technology, BP 227, Sale, Morocco

    Mohamed El Haziti

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  1. Khaoula Karroum
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Karroum, K., Lin, Y., Chiang, YY. et al. A Review of Air Quality Modeling. MAPAN 35, 287–300 (2020). https://doi.org/10.1007/s12647-020-00371-8

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