Review of Weather-Affected Urban Air Pollution Forecast Models

Review of Weather-Affected Urban Air Pollution Forecast Models

Ankit Didwania, Vibha Patel
ISBN13: 9781668439814|ISBN10: 1668439816|EISBN13: 9781668439838
DOI: 10.4018/978-1-6684-3981-4.ch015
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MLA

Didwania, Ankit, and Vibha Patel. "Review of Weather-Affected Urban Air Pollution Forecast Models." Artificial Intelligence of Things for Weather Forecasting and Climatic Behavioral Analysis, edited by Rajeev Kumar Gupta, et al., IGI Global, 2022, pp. 234-246. https://doi.org/10.4018/978-1-6684-3981-4.ch015

APA

Didwania, A. & Patel, V. (2022). Review of Weather-Affected Urban Air Pollution Forecast Models. In R. Gupta, A. Jain, J. Wang, V. Singh, & S. Bharti (Eds.), Artificial Intelligence of Things for Weather Forecasting and Climatic Behavioral Analysis (pp. 234-246). IGI Global. https://doi.org/10.4018/978-1-6684-3981-4.ch015

Chicago

Didwania, Ankit, and Vibha Patel. "Review of Weather-Affected Urban Air Pollution Forecast Models." In Artificial Intelligence of Things for Weather Forecasting and Climatic Behavioral Analysis, edited by Rajeev Kumar Gupta, et al., 234-246. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-3981-4.ch015

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Abstract

Weather affects air quality globally since different aspects of the weather like humidity, temperature, wind speed, and direction essentially affect the movement, creation, and concentration of various major air pollutants like surface ozone, PM 2.5, methane, carbon dioxide, etc. Air pollution is caused when an excessive amount of harmful substances like gases, particles, etc. are poured into our atmosphere which can severely affect the health of any living organisms. In this chapter, the most relevant weather affected urban air quality prediction papers are studied along with recent IoT systems developed for air pollution, and the authors observed that modern artificial intelligence algorithms are better than traditional statistical models. However, artificial intelligence-based algorithms cannot be directly compared effectively due to the hybrid nature of data sources used. Also, a need is identified to develop a powerful end-to-end model based on artificial intelligence algorithms and IoT systems.

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