IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i21p6019-d281545.html
   My bibliography  Save this article

Analysis and Modelling of PM 2.5 Temporal and Spatial Behaviors in European Cities

Author

Listed:
  • José Adães

    (LEPABE—Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr Roberto Frias, 4200-465 Porto, Portugal)

  • José C. M. Pires

    (LEPABE—Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr Roberto Frias, 4200-465 Porto, Portugal)

Abstract

Particulate matter with an aerodynamic diameter of less than 2.5 µm (PM 2.5 ) is associated with adverse effects on human health (e.g., fatal cardiovascular and respiratory diseases), and environmental concerns (e.g., visibility impairment and damage in ecosystems). This study aimed to evaluate temporal and spatial trends and behaviors of PM 2.5 concentrations in different European locations. Statistical threshold models using Artificial Neural Networks (ANN) defined by Genetic Algorithms (GA) were also applied for an urban centre site in Istanbul, to evaluate the influence of meteorological variables and PM 10 concentrations on PM 2.5 concentrations. Lower PM 2.5 concentrations were observed in northern Europe. The highest values were found at traffic-related sites. PM 2.5 concentrations were usually higher during the winter and tended to present strong increases during rush hours. PM 2.5 /PM 10 ratios were slightly higher at background sites and the lower values were found in northern Europe (Helsinki and Stockholm). Ratios were usually higher during cold months and during the night. The statistical model (ANN + GA) allowed evaluating the combined effect of different explanatory variables (temperature, wind speed, relative humidity, air pressure and PM 10 concentrations) on PM 2.5 concentrations, under different regimes defined by relative humidity (threshold value of 79.1%). Important information about the temporal and spatial trends and behaviors related to PM 2.5 concentrations in different European locations was developed.

Suggested Citation

  • José Adães & José C. M. Pires, 2019. "Analysis and Modelling of PM 2.5 Temporal and Spatial Behaviors in European Cities," Sustainability, MDPI, vol. 11(21), pages 1-26, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:21:p:6019-:d:281545
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/21/6019/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/21/6019/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    2. Jennifer A. Ailshire & Philippa Clarke, 2015. "Fine Particulate Matter Air Pollution and Cognitive Function Among U.S. Older Adults," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 70(2), pages 322-328.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Balkin, Sandy, 2001. "On Forecasting Exchange Rates Using Neural Networks: P.H. Franses and P.V. Homelen, 1998, Applied Financial Economics, 8, 589-596," International Journal of Forecasting, Elsevier, vol. 17(1), pages 139-140.
    2. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    3. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    4. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    5. Peng Zhu & Yuante Li & Yifan Hu & Qinyuan Liu & Dawei Cheng & Yuqi Liang, 2024. "LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU," Papers 2409.08282, arXiv.org, revised Sep 2024.
    6. Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011. "Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange," International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816, July.
    7. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    8. Leung, Philip C.M. & Lee, Eric W.M., 2013. "Estimation of electrical power consumption in subway station design by intelligent approach," Applied Energy, Elsevier, vol. 101(C), pages 634-643.
    9. Donya Rahmani & Saeed Heravi & Hossein Hassani & Mansi Ghodsi, 2016. "Forecasting time series with structural breaks with Singular Spectrum Analysis, using a general form of recurrent formula," Papers 1605.02188, arXiv.org.
    10. Wei Sun & Yujun He & Hong Chang, 2015. "Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model," Energies, MDPI, vol. 8(2), pages 1-21, January.
    11. Saman, Corina, 2011. "Scenarios of the Romanian GDP Evolution With Neural Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 129-140, December.
    12. Ghiassi, M. & Saidane, H. & Zimbra, D.K., 2005. "A dynamic artificial neural network model for forecasting time series events," International Journal of Forecasting, Elsevier, vol. 21(2), pages 341-362.
    13. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    14. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    15. Oscar Claveria & Salvador Torra, 2013. "“Forecasting Business surveys indicators: neural networks vs. time series models”," AQR Working Papers 201312, University of Barcelona, Regional Quantitative Analysis Group, revised Nov 2013.
    16. Bento, P.M.R. & Pombo, J.A.N. & Calado, M.R.A. & Mariano, S.J.P.S., 2018. "A bat optimized neural network and wavelet transform approach for short-term price forecasting," Applied Energy, Elsevier, vol. 210(C), pages 88-97.
    17. Nataša Glišović & Miloš Milenković & Nebojša Bojović & Libor Švadlenka & Zoran Avramović, 2016. "A hybrid model for forecasting the volume of passenger flows on Serbian railways," Operational Research, Springer, vol. 16(2), pages 271-285, July.
    18. Timothy Praditia & Thilo Walser & Sergey Oladyshkin & Wolfgang Nowak, 2020. "Improving Thermochemical Energy Storage Dynamics Forecast with Physics-Inspired Neural Network Architecture," Energies, MDPI, vol. 13(15), pages 1-26, July.
    19. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    20. Christian Fieberg & Daniel Metko & Thorsten Poddig & Thomas Loy, 2023. "Machine learning techniques for cross-sectional equity returns’ prediction," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 289-323, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:21:p:6019-:d:281545. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.