Analysis of PM2.5 Influencing Factors Based on Various Statistical Methods—A Case Study of Beijing in 2021

DOI

10.2991/978-94-6463-034-3_20How to use a DOI?

Keywords

PM2.5 content; statistical methods; influencing factors; Beijing

Abstracts

Beijing, the capital of China, is suffering from great pollution by PM2.5. In order to give suggestions to solve this problem, several studies have been conducted to explore the internal relationship between PM2.5 and other pollutants, showing different results. This paper compared different kinds of mainstream statistical methods and gave the convincing influence factors based on the AQI index and six indicators of Beijing in 2021. Firstly, the preparation work was done by detecting the possible problems with the data itself, constructing training set and testing set. Secondly, this study generalized models with explained variable PM2.5 and explaining variables PM10, SO₂, CO, NO₂ and O₃. Then, GLS, ridge regression, LASSO regression, PCA and RF are done, which are all calculated with test MSE to show the accuracy. Finally, the conclusion is that RF is the best among those statistical methods. All methods prove that the concentration of carbon monoxide plays a decisive role in PM2.5 concentration, which means reducing automobile exhaust emission may low down the PM2.5 content.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Qing Yu
PY  - 2022
DA  - 2022/12/23
TI  - Analysis of PM2.5 Influencing Factors Based on Various Statistical Methods—A Case Study of Beijing in 2021
BT  - Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022)
PB  - Atlantis Press
SP  - 195
EP  - 204
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-034-3_20
DO  - 10.2991/978-94-6463-034-3_20
ID  - Yu2022
ER  -