IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i6p5089-d1096180.html
   My bibliography  Save this article

Long Short-Term Memory Approach for Short-Term Air Quality Forecasting in the Bay of Algeciras (Spain)

Author

Listed:
  • María Inmaculada Rodríguez-García

    (Department of Computer Science Engineering, Algeciras School of Engineering and Technology (ASET), University of Cádiz, 11202 Algeciras, Spain)

  • María Gema Carrasco-García

    (Department of Industrial and Civil Engineering, Algeciras School of Engineering and Technology (ASET), University of Cádiz, 11202 Algeciras, Spain)

  • Javier González-Enrique

    (Department of Computer Science Engineering, Algeciras School of Engineering and Technology (ASET), University of Cádiz, 11202 Algeciras, Spain)

  • Juan Jesús Ruiz-Aguilar

    (Department of Industrial and Civil Engineering, Algeciras School of Engineering and Technology (ASET), University of Cádiz, 11202 Algeciras, Spain)

  • Ignacio J. Turias

    (Department of Computer Science Engineering, Algeciras School of Engineering and Technology (ASET), University of Cádiz, 11202 Algeciras, Spain)

Abstract

Predicting air quality is a very important task, as it is known to have a significant impact on health. The Bay of Algeciras (Spain) is a highly industrialised area with one of the largest superports in Europe. During the period 2017–2019, different data were recorded in the monitoring stations of the bay, forming a database of 131 variables (air pollutants, meteorological information, and vessel data), which were predicted in the Algeciras station using long short-term memory models. Four different approaches have been developed to make SO 2 and NO 2 forecasts 1 h and 4 h in Algeciras. The first uses the remaining 130 exogenous variables. The second uses only the time series data without exogenous variables. The third approach consists of using an autoregressive time series arrangement as input, and the fourth one is similar, using the time series together with wind and ship data. The results showed that SO 2 is better predicted with autoregressive information and NO 2 is better predicted with ships and wind autoregressive time series, indicating that NO 2 is closely related to combustion engines and can be better predicted. The interest of this study is based on the fact that it can serve as a resource for making informed decisions for authorities, companies, and citizens alike.

Suggested Citation

  • María Inmaculada Rodríguez-García & María Gema Carrasco-García & Javier González-Enrique & Juan Jesús Ruiz-Aguilar & Ignacio J. Turias, 2023. "Long Short-Term Memory Approach for Short-Term Air Quality Forecasting in the Bay of Algeciras (Spain)," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5089-:d:1096180
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/6/5089/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/6/5089/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vanessa Durán-Grados & Rubén Rodríguez-Moreno & Fátima Calderay-Cayetano & Yolanda Amado-Sánchez & Emilio Pájaro-Velázquez & Rafael A. O. Nunes & Maria C. M. Alvim-Ferraz & Sofia I. V. Sousa & Juan Mo, 2022. "The Influence of Emissions from Maritime Transport on Air Quality in the Strait of Gibraltar (Spain)," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
    2. Justyna Kujawska & Monika Kulisz & Piotr Oleszczuk & Wojciech Cel, 2022. "Machine Learning Methods to Forecast the Concentration of PM10 in Lublin, Poland," Energies, MDPI, vol. 15(17), pages 1-23, September.
    3. Axel Gedeon Mengara Mengara & Eunyoung Park & Jinho Jang & Younghwan Yoo, 2022. "Attention-Based Distributed Deep Learning Model for Air Quality Forecasting," Sustainability, MDPI, vol. 14(6), pages 1-34, March.
    4. Moreno-Gutiérrez, Juan & Calderay, Fátima & Saborido, Nieves & Boile, Maria & Rodríguez Valero, Rafael & Durán-Grados, Vanesa, 2015. "Methodologies for estimating shipping emissions and energy consumption: A comparative analysis of current methods," Energy, Elsevier, vol. 86(C), pages 603-616.
    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. Vanessa Durán-Grados & Yolanda Amado-Sánchez & Fátima Calderay-Cayetano & Rubén Rodríguez-Moreno & Emilio Pájaro-Velázquez & Antonio Ramírez-Sánchez & Sofia I. V. Sousa & Rafael A. O. Nunes & Maria C., 2020. "Calculating a Drop in Carbon Emissions in the Strait of Gibraltar (Spain) from Domestic Shipping Traffic Caused by the COVID-19 Crisis," Sustainability, MDPI, vol. 12(24), pages 1-14, December.
    2. Al-Falahi, Monaaf D.A. & Jayasinghe, Shantha D.G. & Enshaei, Hossein, 2019. "Hybrid algorithm for optimal operation of hybrid energy systems in electric ferries," Energy, Elsevier, vol. 187(C).
    3. Chi, Hongtao & Pedrielli, Giulia & Ng, Szu Hui & Kister, Thomas & Bressan, Stéphane, 2018. "A framework for real-time monitoring of energy efficiency of marine vessels," Energy, Elsevier, vol. 145(C), pages 246-260.
    4. Wang, Jinggai & Zhong, Meisu & Wang, Tianni & Ge, Ying-En, 2023. "Identifying industry-related opinions on shore power from a survey in China," Transport Policy, Elsevier, vol. 134(C), pages 65-81.
    5. Morten Simonsen & Hans Jakob Walnum & Stefan Gössling, 2018. "Model for Estimation of Fuel Consumption of Cruise Ships," Energies, MDPI, vol. 11(5), pages 1-29, April.
    6. Yan, Ran & Wang, Shuaian & Psaraftis, Harilaos N., 2021. "Data analytics for fuel consumption management in maritime transportation: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    7. Patrizia Serra & Gianfranco Fancello, 2020. "Towards the IMO’s GHG Goals: A Critical Overview of the Perspectives and Challenges of the Main Options for Decarbonizing International Shipping," Sustainability, MDPI, vol. 12(8), pages 1-32, April.

    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:15:y:2023:i:6:p:5089-:d:1096180. 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.