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Monitoring and Prediction of Particulate Matter (PM 2.5 and PM 10 ) around the Ipbeja Campus

Author

Listed:
  • Flavia Matias Oliveira Silva

    (Polytechnic Institute of Beja, 7800-000 Beja, Portugal)

  • Eduardo Carlos Alexandrina

    (Department of Mining Engineering, Federal University of Mato Grosso, Várzea Grande 78060-900, MT, Brazil)

  • Ana Cristina Pardal

    (Polytechnic Institute of Beja, 7800-000 Beja, Portugal)

  • Maria Teresa Carvalhos

    (Polytechnic Institute of Beja, 7800-000 Beja, Portugal)

  • Elaine Schornobay Lui

    (Department of Engineering, Federal Technological University of Paraná, Francisco Beltrão 85884-000, PR, Brazil)

Abstract

Nowadays, most of the world’s population lives in urban centres, where air quality levels are not strictly checked; citizens are exposed to air quality levels over the limits of the World Health Organization. The interaction between the issuing and atmospheric sources influences the air quality or level. The local climate conditions (temperature, humidity, winds, rainfall) determine a greater or less dispersion of the pollutants present in the atmosphere. In this sense, this work aimed to build a math modelling prediction to control the air quality around the campus of IPBeja, which is in the vicinity of a car traffic zone. The researchers have been analysing the data from the last months, particle matter (PM 10 and PM 2.5 ), and meteorological parameters for prediction using NARX. The results show a considerable increase in particles in occasional periods, reaching average values of 135 μg/m 3 for PM 10 and 52 μg/m 3 for PM 2.5 . Thus, the monitoring and prediction serve as a warning to perceive these changes and be able to relate them to natural phenomena or issuing sources in specific cases.

Suggested Citation

  • Flavia Matias Oliveira Silva & Eduardo Carlos Alexandrina & Ana Cristina Pardal & Maria Teresa Carvalhos & Elaine Schornobay Lui, 2022. "Monitoring and Prediction of Particulate Matter (PM 2.5 and PM 10 ) around the Ipbeja Campus," Sustainability, MDPI, vol. 14(24), pages 1-9, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16892-:d:1005523
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    References listed on IDEAS

    as
    1. Bukhari, Ayaz Hussain & Raja, Muhammad Asif Zahoor & Shoaib, Muhammad & Kiani, Adiqa Kausar, 2022. "Fractional order Lorenz based physics informed SARFIMA-NARX model to monitor and mitigate megacities air pollution," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
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