Application of Logistic Regression Model in the Prediction of Air Quality Level in Zibo City
Vol 7, Issue 4, 2024
VIEWS - 19 (Abstract) 23 (PDF)
Abstract
current status of air quality and controlling pollution sources. In this paper, logistic regression analysis is carried out for 366 days of air quality data from January to December 2020 in Zibo City, Shandong Province, with air quality class as the categorical variable, and six variables,
PM2.5, PM10, SO2, CO, NO2, O3, are selected as pollution indicators affecting the air quality in Zibo City, and the stepwise regression method
is utilized to establish a model and determine the weights of each pollution indicator. The established model is used to predict the samples of
Zibo City, and the predicted data and actual data are compared to test the fit of the model. The results show that the logistic regression model
fits well, and SO2 is the strongest factor affecting air quality, and the probability of air pollution is 1.035 times higher for every unit it increases and other variables remain unchanged, providing a basis for controlling the emissions of the primary pollution factors.
Keywords
Full Text:
PDFReferences
1. [1] Song Hong, Sun Yajie, Chen Dengke. Evaluation of the effect of governmental air pollution control--an empirical study from the
2. construction of “low-carbon cities” in China[J]. Management World,2019,35(06):95-108+195.
3. [2] Zhou Q, Li X, Hu J, et al. Dynamics and optimal control for a spatial heterogeneity model describing respiratory infectious diseases
4. affected by air pollution[J]. Mathematics and Computers in Simulation,2024,220276-295.
5. [3] Li Weibing, Zhang Kaixia. The impact of air pollution on firm productivity - Evidence from Chinese industrial firms[J]. Management World, 2019, 35(10): 95-112+119.DOI:10.19744/j.cnki.11-1235/f.2019.0134.
6. [4] Ghaffarpasand O, Okure D, Green P, et al. The impact of urban mobility on air pollution in Kampala, an exemplar sub-Saharan African city[J].Atmospheric Pollution Research, 2024, 15(4):102057.
7. [5] Wang Min, Huang Ying. Environmental pollution and economic growth in China[J]. Economics(Quarterly),2015,14(02):557-578.
8. [6] Fan Xingxing. Source analysis and health risk evaluation of black carbon aerosol in Zibo[D]. Tianjin University of Technology,
9. 2023.
10. [7] Fang Bin, Liu Houfeng. Characteristics of ambient air quality and relationship with meteorological conditions in Zibo[J]. Green
11. Science and Technology, 2017, (24): 26-2.
12. [8] Hao Yujiao. Research on Emission Reduction Countermeasures of Mobile Pollution Sources with Multi-source Data Fusion[D].
13. Shandong University of Technology, 2022.
14. [9] Ma Xinhua. Causes of ambient air pollution in Boshan District, Zibo City, Shandong Province and countermeasures against it[J].
15. Qinghai Environment, 2001.
16. [10] Zibo Municipal Bureau of Statistics, National Bureau of Statistics Zibo Investigation Team.2020 Zibo City National Economic
17. and Social Development Statistical Yearbook [M]. Beijing:China Statistics Press, 2020.
18. [11] OHLMACHER GC, DAVIS JC. Using multiple logistic regression and GIS technology to predict landslide hazard in northeast
19. Kansas, USA[J]. Engineering Geology, 2003, 69(3/4):331-343.
DOI: https://doi.org/10.18686/ijmss.v7i4.5520
Refbacks
- There are currently no refbacks.
This site is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.