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Air Quality Monitoring Using Deterministic and Statistical Methods

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Big Data and Smart Digital Environment (ICBDSDE 2018)

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Abstract

Air quality has been a serious concern amongst policy-makers and the public due to its impacts on humans and environment, it can be one of the reasons for civilization diseases. According to The Health Effects Institute (HEI) [1], over 95% of the world’s population is breathing polluted air, which contributed to the death of 6.1 million people across the world in 2016. Therefore, air pollution has become one of the major causes of death worldwide, ranked number four behind smoking, blood pressure and diet. Thus, an early warning system based on accurate forecasting tools must be implemented to avoid the adverse effects of exposure to major air pollutants. Consequently, it is necessary to obtain reliable analytical information on air quality. in this paper we provide a general overview of the main methods and approaches used in air quality monitoring including statistical and machine learning based methods.

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Authors and Affiliations

  1. Computer Systems Engineering Laboratory (LISI), Faculty of Science Semlalia, Cadi Ayyad University, Marrakesh, Morocco

    Noussair Lazrak, Jihad Zahir & Hajar Mousannif

Authors
  1. Noussair Lazrak
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  2. Jihad Zahir
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  3. Hajar Mousannif
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Correspondence to Noussair Lazrak .

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Editors and Affiliations

  1. Faculty of Sciences and Techniques, Department of Computer Science, Moulay Ismail University, Errachidia, Morocco

    Yousef Farhaoui

  2. Department of Computer Science, Hassan II University, Casablanca, Morocco

    Laila Moussaid

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Lazrak, N., Zahir, J., Mousannif, H. (2019). Air Quality Monitoring Using Deterministic and Statistical Methods. In: Farhaoui, Y., Moussaid, L. (eds) Big Data and Smart Digital Environment. ICBDSDE 2018. Studies in Big Data, vol 53. Springer, Cham. https://doi.org/10.1007/978-3-030-12048-1_39

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