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

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 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 five 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 five-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 one-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 five 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 one-year dataset. A boost in the inverse of the regularization strength to 1.8 will optimize the regularization.

Multi-target and control group selection for multi-explanatory multi-output regression problem

“…This challenge finds relevance in diverse domains, like finance (considering stocks) or more general time-series prediction [18], environment domain, e.g. multiple air pollution measurements [19] [35], energy management e.g. wind farm [20] with different (including changing environment, i.e.…”

Greedy control group selection for multi-explanatory multi-output regression problem

“…This challenge finds relevance in diverse domains, like finance (considering stocks) or more general time-series prediction [19], environment domain, e.g. multiple air pollution measurements [20] [37], energy management e.g. wind farm [21] with different (including changing environment, i.e.…”