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Temporal Analysis and Prediction of Ambient Air Quality Using Remote Sensing, Deep Learning, and Geospatial Technologies

Temporal Analysis and Prediction of Ambient Air Quality Using Remote Sensing, Deep Learning, and Geospatial Technologies

Aymen Bashir, Abdullah Mughal, Rafia Mumtaz, Muhammad Ali Tahir
Copyright: © 2022 |Pages: 35
ISBN13: 9781799892014|ISBN10: 1799892018|ISBN13 Softcover: 9781799892021|EISBN13: 9781799892038
DOI: 10.4018/978-1-7998-9201-4.ch002
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MLA

Bashir, Aymen, et al. "Temporal Analysis and Prediction of Ambient Air Quality Using Remote Sensing, Deep Learning, and Geospatial Technologies." Empowering Sustainable Industrial 4.0 Systems With Machine Intelligence, edited by Muneer Ahmad and Noor Zaman, IGI Global, 2022, pp. 25-59. https://doi.org/10.4018/978-1-7998-9201-4.ch002

APA

Bashir, A., Mughal, A., Mumtaz, R., & Tahir, M. A. (2022). Temporal Analysis and Prediction of Ambient Air Quality Using Remote Sensing, Deep Learning, and Geospatial Technologies. In M. Ahmad & N. Zaman (Eds.), Empowering Sustainable Industrial 4.0 Systems With Machine Intelligence (pp. 25-59). IGI Global. https://doi.org/10.4018/978-1-7998-9201-4.ch002

Chicago

Bashir, Aymen, et al. "Temporal Analysis and Prediction of Ambient Air Quality Using Remote Sensing, Deep Learning, and Geospatial Technologies." In Empowering Sustainable Industrial 4.0 Systems With Machine Intelligence, edited by Muneer Ahmad and Noor Zaman, 25-59. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-9201-4.ch002

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Abstract

As of today, increased air pollution has disrupted the air quality levels, deeming the air unsafe to breathe. Traditional systems are hefty, costly, sparsely distributed, and do not provide ubiquitous coverage. The interpolation used to supplement low spatial coverage induces uncertainty especially for pollutants whose concentrations vary significantly over small distances. This chapter proposes a solution that uses satellite images and machine/deep learning models to timely forecast air quality. For this study, Lahore is chosen as a study area. Sentinel 5-Precursor is used to gather data for Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), and Carbon Monoxide (CO) for years 2018-2021. The data is processed for several AI models, where convolutional neural networks (CNN) performed the best with mean squared error (MSE) 0.0003 for the pollutants. The air quality index (AQI) is calculated and is shown on web portal for data visualization. The trend of air quality during COVID-19 lockdowns is studied as well, which showed reduced levels of NO2 in regions where proper lockdown is observed.

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