Empirical analysis of air quality in China based on multiple linear regression analysis

Xiaoyu Qu; Yaqi Cao

doi:10.1117/12.2671882

28 March 2023 Empirical analysis of air quality in China based on multiple linear regression analysis

Xiaoyu Qu, Yaqi Cao

Proceedings Volume 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022); 1259702 (2023) https://doi.org/10.1117/12.2671882
Event: Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 2022, Nanjing, China

Abstract

In recent years, with the rapid development of human economic activities and production, the continuous improvement of science and technology and a large amount of energy consumption, at the same time, a large number of waste gas and soot substances are discharged into the atmosphere, which seriously affects the environmental quality. Based on the air quality monitoring data of 337 prefecture level and above cities in China from January 2019 to July 2021, including the concentrations of AQI (Air Quality Index), PM2.5, PM10, CO, NO2, O3 and other pollutants, this paper makes an empirical study on the influencing factors of air quality in China. Using the knowledge of econometrics and Stata software, this paper establishes a multiple linear regression model, continuously estimates, tests and improves it on the basis of the primary model, analyzes the impact of pollutants on the air quality index, and analyzes the main pollutants affecting the air quality through testing. The results show that the main air pollutants in China are NO2 and PM2.5. Based on the analysis results, this paper will put forward corresponding suggestions on improving China's air quality and protecting the ecological environment.

Citation Download Citation

Xiaoyu Qu and Yaqi Cao "Empirical analysis of air quality in China based on multiple linear regression analysis", Proc. SPIE 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 1259702 (28 March 2023); https://doi.org/10.1117/12.2671882

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