A data-driven air quality assessment method based on unsupervised machine learning and median statistical analysis: The case of China. (15th December 2021)

• Record Type: Journal Article
• Authors: Wang, Xiaoxia; Wang, Luqi; Liu, Yuanyuan; Hu, Sangen; Liu, Xuezhen; Dong, Zhongzhen
• Is Part Of: Journal of cleaner production. Volume 328(2021)
• Publication Date: 2021-12-15
• Subjects: Air pollution -- Public health -- Air quality index -- Unsupervised classification -- Louvain community detection -- Machine learning; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2021.129531
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Because the equation for calculating the air quality index (AQI) only considers the most serious pollutants, it remains controversial, and a comprehensive AQI has been a focus of subsequent studies. The present research transformed 22, 504, 440 pollution characteristics per year into datasets of a Louvain community detection clustering analysis. Unsupervised machine learning was applied to classify 367 cities across China into seven categories. The seven representative cities were selected from seven categories, and the augmented Dickey-Fuller test was used to detect their stationarity. A situation-based composite AQI calculation method was proposed. To verify the method, we clarified the difference between it and the AQI. We also compared its relative error with the total AQI. The relative error values in the seven representative cities were 8.9%, 12.16%, 12.91%, 8.75%, 13.42%, 11.41%, and 11.27%. These values were smaller than the total AQI. The median values of I A Q I C O, I A Q I N O 2, I A Q I S O 2, I A Q I P M 2.5, a n d I A Q I P M 10 remained relatively stable. Only the changes in I A Q I O 3 were dramatic. High T h o u r values are generally encountered in more polluted cities. When there is at most one chief pollutant (CP), then T h o u r = 1 . When there are several CPs, T h o u r > 1 . The findings of this research provide the public with an intuitive understanding of air pollution and guidance on effective assessments of outdoor air quality. Graphical abstract: Image 1 Highlights: A situation-based CAQI method was proposed based on the number of I A Q I P values greater than 50. The relative error of CAQI relied on air quality and the coefficients of chief pollutants (CPs) and sub-CPs. A high T h o u r value was usually encountered in polluted cities with several CPs. The 24-h median values of the individual air quality index remained relatively stable. The situation-based CAQI may be used to identify the amount of pollutants.