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

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. Jiaqing Chen & Dongpeng Peng & Zhiwei Liu & Lingzhi Wu & Ming Jiang, 2024. "A Sustainable Model for Forecasting Carbon Emission Trading Prices," Sustainability, MDPI, vol. 16(19), pages 1-16, September.

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