Generalized linear modelling based monitoring methods for air quality surveillance

Tahir Mahmood

doi:10.1016/j.jksus.2024.103145

Generalized linear modelling based monitoring methods for air quality surveillance

Tahir Mahmood

School of Computing, Engineering and Physical Sciences

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Abstract

Rising industrial pollution, exacerbated by climate change, underscores the need for effective environmental monitoring. Leveraging sensor advancements and Birnbaum-Saunders distribution, this study introduces a novel surveillance method for environmental data, crucial for shaping impactful industrial policies. Simulation studies demonstrate the method's performance, and a case study on nitrogen oxide levels in Italy validates its efficacy in the early detection of severe air pollution events.

Original language	English
Article number	103145
Number of pages	8
Journal	Journal of King Saud University - Science
Volume	36
Issue number	4
Early online date	28 Feb 2024
DOIs	https://doi.org/10.1016/j.jksus.2024.103145
Publication status	Published - 30 Apr 2024

Keywords

Birnbaum-Saunders Regression model
deviance residuals
environmental pollution
standardized residuals
statistical process monitoring

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.jksus.2024.103145Licence: CC BY 4.0

2024 02 26 Mahmood surveillance finalFinal published version, 1.68 MBLicence: CC BY 4.0

Cite this

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KW - deviance residuals

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KW - standardized residuals

KW - statistical process monitoring

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Generalized linear modelling based monitoring methods for air quality surveillance

Abstract

Keywords

UN SDGs

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