The Analysis of Nanjing Air Quality by Using Multiple-Linear Regression Model
DOI:
https://doi.org/10.61173/skq8dq87Keywords:
AQI, Analyse, multiple-linear regression modelAbstract
For the sake of industrial evolution, booming population, increased vehicles, and so forth, the air quality index (AQI) needs to be emphasized. This research paper takes Nanjing’s AQI as the survey background. This essay has deployed the multiple linear regression model to help analyze the basic circumstances of the AQI by using monthly data on six basic factors. The results show that the most significant factor of Nanjing’s AQI is the density of O3. What’s more, NO2 plays the role of the negative correlation and there exists a linear relationship between the variables. To improve or maintain the “generally good” circumstances of the AQI, it’s better to take measures like enlarging the vegetation, reducing the activities of incomplete incendiary, and increasing the usage of public transport... Additionally, this essay’s goal is to raise the awareness of the public and point out the mainly significant factors, that can offer evidence for the proceeding survey and measures.
References
[1] Wang Z, Huang X, Ding A. Dome effect of black carbon and its key influencing factors: a one-dimensional modelling study. Atmos. Chem. Phys., 2018, 18: 2821-2834.
[2] Guo Q H, Chen K. Analysis of ambient air quality in Nanjing. Journal of Nanjing University of Information Science and Technology (Natural Science Edition), 2022, 14(03): 294- 303.
[3] Xie M, Zhu K G, Wang T J, et al. Temporal characterization and regional contribution to O3 and NOx at an urban and a suburban site in Nanjing, China. Science of the Total Environment, 2016, 551/552: 533-545.
[4] Wei G, Yang Y, Li R, Liu Y, He B-J. Digital economy exhibits varying degrees of mitigation of air pollution in China: Total cities-economic subdivisions-urban agglomerations. iScience, 2024, 27(6): 110091.
[5] Wang W. Application study of Air Quality Health Index in Nanjing based on functional time series analysis. D, Nanjing University of Finance and Economics, 2023.
[6] Huang J, Pan X, Guo X, Li G. Health impact of China’s Air Pollution Prevention and Control Action Plan: an analysis of national air quality monitoring and mortality data. Lancet Planet. Health, 2018, 2: e313-e323.
[7] Zhang Q, Zheng Y, Tong D, Shao M, Wang S, Zhang Y, Xu X, Wang J, He H, Liu W, Ding Y, Lei Y, Li J, Wang Z, Zhang X, Wang Y, Cheng J, Liu Y, Shi Q, Yan L, Geng G, Hong C, Li M, Liu F, Zheng B, Cao J, Ding A, Gao J, Fu Q, Huo J, Liu B, Liu Z, Yang F, He K, Hao J. Drivers of improved PM2.5 air quality in China from 2013 to 2017. Proc. Natl. Acad. Sci. USA, 2019, 116: 24463-24469.
[8] Chen G, Li S, Knibbs L D, Hamm N A S, Cao W, Li T, Guo J, Ren H, Abramson M J, Guo Y. A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information. Sci. Total Environ., 2018, 636: 52-60.
[9] Wu H-W, Kumar P, Cao S-J. The role of roadside green infrastructure in improving air quality in and around elderly care centres in Nanjing, China. Atmospheric Environment, 2024, 332: 120607. ISSN 1352-2310.
[10] Huang Z, Kong S, Seo J, Yan Y, Cheng Y, Yao L, Wang Y, Zhao T, Harrison R M. Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022. Environment International, 2024, 183: 108361.
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