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Regression model and method settings for air pollution status analysis based on air quality data in Beijing (2017–2021)

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Abstract

Regression analysis is an essential tool for modeling and analyzing data, which can be utilized in various areas for predictive analysis and discovering relationships between variables. However, guidelines such as the model's features, dataset selection, and method settings for using regression models to explore air pollution status in a region are not detailed. This paper applied regression analysis based on air quality data in Beijing from 2017 to 2021, to study the characteristics of regression models, provide research guidance, and update the air pollution research data based on the dataset. This paper drew the latest conclusions: (1) PM2.5 and NO2 are positively correlated on the test set from these 5 years, yielding a correlation coefficient of 0.7036 by using linear regression. The respective coefficient of determination on small-scale test sets for 2017, 2019, and 2021 is much lower than those derived from a 5-year dataset. Single-year dataset is not befitting for linear regression analysis. (2) The polynomial regression’s coefficient of determination on the training set is higher than that of the linear regression model, which is more proper for regression analysis on a 1-year dataset. (3) PM2.5 and NO2 concentrations are strongly positively correlated with whether the air is polluted or not, and the correlation coefficient on the test set from these 5 years is 0.9697. The accuracy of logistic regression in classifying air pollution status based on these two pollutants’ concentrations reaches 0.9430. Besides, this paper proposed some appropriate parameter settings for the logistic regression method provided by Python third-party library sklearn. Specifically, L2-type regularization is better optimized for the 2017–2021 dataset. L1-type regularization works better when applying a 1-year dataset. A boost in the inverse of the regularization strength to 1.8 will optimize the regularization.

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Acknowledgements

Acknowledgment for the data support from “Weather Hindcast Website (http://tianqihoubao.com/)” and “Beijing Municipal Ecological and Environmental Monitoring Center (http://www.bjmemc.com.cn/).” We also would like to thank the reviewers and editors for their valuable comments and suggestions. And sincerely thank our corresponding author for her instruction and supervision.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Author notes

  1. Shiyun Wa and Xinai Lu have contributed equally to this work.

Authors and Affiliations

  1. College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China

    Shiyun Wa & Minjuan Wang

  2. International College Beijing, China Agricultural University, Beijing, 100083, China

    Xinai Lu

  3. Extension, University of Colorado Denver, Denver, CO, 80202, USA

    Xinai Lu

Authors
  1. Shiyun Wa
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  2. Xinai Lu
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  3. Minjuan Wang
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Contributions

All authors contributed to the study conception and design. Specifically, material preparation, data collection, and analysis were performed by SW and XL. Data visualization was accomplished by SW. The first draft of the manuscript was written by SW and XL, and all authors commented on previous versions of the manuscript. The whole study was supervised by MW and SW. All authors read and approved the final manuscript.

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Correspondence to Minjuan Wang.

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Wa, S., Lu, X. & Wang, M. Regression model and method settings for air pollution status analysis based on air quality data in Beijing (2017–2021). Int J Data Sci Anal (2023). https://doi.org/10.1007/s41060-023-00415-7

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