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

María Inmaculada Rodríguez-García (), María Gema Carrasco-García, Javier González-Enrique, Juan Jesús Ruiz-Aguilar and Ignacio J. Turias
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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

Sustainability, 2023, vol. 15, issue 6, 1-20

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.

Keywords: air pollution forecasting; LSTM models; deep learning; maritime traffic; ANNs; nitrogen oxides; sulphur dioxide (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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