Cross-Regional Deep Learning for Air Quality Forecasting: A Comparative Study of CO, NO 2, O 3, PM 2.5, and PM 10

Adam Booth (), Philip James, Stephen McGough and Ellis Solaiman
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Adam Booth: School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Philip James: School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Stephen McGough: School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Ellis Solaiman: School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK

Forecasting, 2025, vol. 7, issue 4, 1-25

Abstract: Accurately forecasting air quality could lead to the development of dynamic, data-driven policy-making and improved early warning detection systems. Deep learning has demonstrated the potential to produce highly accurate forecasting models, but it is noted that much literature focuses on narrow datasets and typically considers one geographic area. In this research, three diverse air quality datasets are utilised to evaluate four deep learning algorithms, which are feedforward neural networks, Long Short-Term Memory (LSTM) recurrent neural networks, DeepAR and Temporal Fusion Transformers (TFTs). The study uses these modules to forecast CO, NO 2 , O 3 , and particulate matter 2.5 and 10 (PM 2.5 , PM 10 ) individually, producing a 24 h forecast for a given sensor and pollutant. Each model is optimised using a hyperparameter and a feature selection process, evaluating the utility of exogenous data such as meteorological data, including wind speed and temperature, along with the inclusion of other pollutants. The findings show that the TFT and DeepAR algorithms achieve superior performance over their simpler counterparts, though they may prove challenging in practical applications. It is noted that while some covariates such as CO are important covariates for predicting NO 2 across all three datasets, other parameters such as context length proved inconsistent across the three areas, suggesting that parameters such as context length are location and pollutant specific.

Keywords: air quality forecasting; deep learning; urban air quality; air quality monitoring; smart cities (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2025
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