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Nonlinear regression models to forecast PM$_{2.5}$ concentration
[Submitted on 28 Feb 2023 (v1), last revised 14 Jun 2026 (this v · 2026-06-16 · via stat updates on arXiv.org

Statistics > Applications

arXiv:2302.14505 (stat)

[Submitted on 28 Feb 2023 (v1), last revised 14 Jun 2026 (this version, v2)]

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Abstract:Forecasting PM$_{2.5}$ concentration is important to solving air pollution problems in Wuhan. This paper proposes a PM$_{2.5}$ concentration forecast model based on nonlinear regression, including a single-value forecast model and an interval forecast model. The single-value forecast model can precisely forecast PM$_{2.5}$ concentration for the next day, with forecast bias about 6 $\mu g/m^3$ in goodness of fit analysis. The interval forecast model can efficiently forecast high-concentration and low-concentration days, which covers 60%-80% observed samples in model validation. Moreover, this paper combines the PM$_{2.5}$ concentration forecast model with NCEP Climate Forecast System Version 2 to realize its forecast application, then develops NCEP CFS2's PM$_{2.5}$ concentration forecast model to enhance forecast accuracy. The results indicate that the PM$_{2.5}$ concentration forecast model has good capacity for independent forecasting.

Submission history

From: Jinghong Zeng [view email]
[v1] Tue, 28 Feb 2023 11:49:14 UTC (2,701 KB)
[v2] Sun, 14 Jun 2026 04:10:07 UTC (2,701 KB)

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