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A novel PM2.5 concentrations probability density prediction model combines the least absolute shrinkage and selection operator with quantile regression

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Abstract

PM2.5 has a significant negative impact on human health and atmospheric quality, and accurate prediction of its concentration is necessary. When using common point prediction models for PM2.5 concentration prediction, the influence of various uncertainties on PM2.5 concentrations makes the prediction results suffer from poor accuracy. To address this issue, this paper proposes the quantile regression neural network (QRNN) model based on the least absolute shrinkage and selection operator (LASSO), combined with kernel density estimation (KDE) for probabilistic density prediction of PM2.5 concentrations. The model uses LASSO regression to select the influential factors, and then the quartiles of daily PM2.5 concentrations calculated by the QRNN model are imported into the KDE model to obtain the probability density predictions of PM2.5 concentrations. In the paper, empirical analyses are carried out with the cities of Beijing and Jinan in China as well as six other datasets, and the prediction performance of the model is assessed by using evaluation criteria in both point prediction and interval prediction. The simulation reveals that the predictive performance of the LASSO-QRNN-KDE model is well, and the model is not only effective in filtering high-dimensional data, but also has a higher accuracy compared to common research models. In addition, the model is able to describe the uncertainty of PM2.5 concentration fluctuations and carry more information on the variation of PM2.5 concentrations, which can provide a novel and excellent PM2.5 concentration prediction tool for relevant policy makers.

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Data availability

The datasets analysed during the current study are available in the National Meteorological Data Center of China repository, http://data.cma.cn/.

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Authors and Affiliations

  1. Department of Economic Management, North China Electric Power University, No. 689, Huadian Road, Baoding City, Hebei Province, China

    Shaomei Yang & Haoyue Wu

Authors
  1. Shaomei Yang
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  2. Haoyue Wu
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Contributions

Shaomei Yang: methodology, resources, and writing — review and editing. Haoyue Wu: conceptualization, software, validation, investigation, data curation, and writing — original draft.

Corresponding author

Correspondence to Haoyue Wu.

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The authors declare no competing interests.

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Responsible editor: Ilhan Ozturk

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Appendices

Appendix 1

Tables 8, 9, 10

Table 8 Statistical description of data sets
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Table 9 The results of the correlation analysis (Beijing)
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Table 10 The results of the correlation analysis (Jinan)
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Appendix 2

Tables 11, 12

Table 11 LASSO regression results of Beijing data
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Table 12 LASSO regression results of Jinan data
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Appendix 3

Tables 13, 14

Table 13 Prediction results for three situations of Beijing data
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Table 14 Prediction results for three situations of Jinan data
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Appendix 4

Tables 15, 16, 17, 18

Table 15 Predicted values under various models of Beijing data (\(\mu g/{m}^{3}\))
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Table 16 Relative errors under various models of Beijing data (%)
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Table 17 Predicted values under various models of Jinan data (\(\mu g/{m}^{3}\))
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Table 18 Relative errors under various models of Jinan data (%)
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Yang, S., Wu, H. A novel PM2.5 concentrations probability density prediction model combines the least absolute shrinkage and selection operator with quantile regression. Environ Sci Pollut Res 29, 78265–78291 (2022). https://doi.org/10.1007/s11356-022-21318-3

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  • DOI: https://doi.org/10.1007/s11356-022-21318-3

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