Identification of high impact factors of air quality on a national scale using big data and machine learning techniques. (20th January 2020)

• Record Type: Journal Article
• Authors: Ma, Jun; Ding, Yuexiong; Cheng, Jack C.P.; Jiang, Feifeng; Tan, Yi; Gan, Vincent J.L.; Wan, Zhiwei
• Is Part Of: Journal of cleaner production. Volume 244(2020)
• Publication Date: 2020-01-20
• Subjects: Air quality index -- Big data -- GIS -- National scale -- Variable importance -- XGBoost; 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.2019.118955
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: To effectively control and prevent air pollution, it is necessary to study the influential factors of air quality. A number of previous studies have explored the relationships between air pollution and related factors. However, the methods currently used either cannot well address the multicollinearity problem or fail to explain the importance of the influential factors. Moreover, most of the existing literature limited their studied area in a city or a small region and studied factors in one aspect. There is a lack of studies that analyze the influential factors from the perspective of a country or take into consideration multiple variables. To fill the research gap, this paper proposes a multivariate analysis in the national scale to investigate the most important factors of air quality. In order to study as much influential factors as possible, 171 features ranging from environmental, demographical, economic, meteorological, and energy, were collected and analyzed. To tackle such a "big data" problem, a non-linear machine learning algorithm namely Extreme Gradient Boosting (XGBoost) is utilized to model the relationship and measure the variable importance. Geographical Information System (GIS) is employed to preprocess the diversified variables and visualize the results. Performance of XGBoost is compared with other models and its parameters are tuned using Bayesian Optimization. Experimental results of a case study in the U.S. show that our methodology framework can effectively uncover the important factors of air quality. Six kinds of factors are found to have the largest impact on air quality. Practical suggestions are also proposed from the six aspects to control and prevent air pollution. Highlights: This study analyzed the impact factors on AQI of different counties in the U.S. 171 variables were considered to study the most influential ones. XGBoost outperforms other commonly seen algorithms in modeling. Six kinds of factors are found to be most influential on AQI on a national scale.