Analysis of air quality pollution in Chengdu utilizing logistic regression for multi-class classification based on the distance metric between classes

Tingting Li, Wang Lv, Jiao Zhou, Shijing Zeng

Article ID: 1976
Vol 2, Issue 1, 2021
DOI: https://doi.org/10.54517/ps.v2i1.1976
Received: 01 July 2021; Accepted: 25 July 2021; Available online: 09 August 2021; Issue release: 31 December 2021

VIEWS - 3523 (Abstract)

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Abstract

The health of individuals is intimately linked to air quality, making it crucial to investigate the pollutants that influence it. A sequential Logistic multi-classification approach, which utilizes the distance between classes, was applied to analyze the air quality data of Chengdu spanning from May 2019 to April 2020. By leveraging the inter-class distance metric, the multi-class classification challenge was converted into a series of binary classification tasks. Using a sequential strategy, binary Logistic regression was then employed for each task. The accuracy rate post-stepwise regression was utilized to assess the impact of various pollutants on air quality. The findings indicate that PM2.5, PM10, NO2, and O3 are the four primary pollutants with the most significant collective influence on Chengdu's air quality. Consequently, the government should enhance the joint monitoring of these pollutants and develop targeted policies to mitigate their levels.


Keywords

air pollutants; distance between classes; sequential; logistic multiple classification


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