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eISSN: 2083-5906
ISSN: 1230-1485
ISSN: 1230-1485
ORIGINAL RESEARCH
Physicochemically Motivated Decomposition
for Long-Sequence Urban O3 Forecasting:
A Cross-Method Comparison
1
Department of Environmental Science and Engineering, Sichuan University, Chengdu, 610065, China
2
College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, 610065, China
Submission date: 2026-01-30
Final revision date: 2026-03-18
Acceptance date: 2026-04-07
Online publication date: 2026-10-28
Corresponding author
Xinyi Liu
Department of Environmental Science and Engineering, Sichuan University, Chengdu, 610065, China
Department of Environmental Science and Engineering, Sichuan University, Chengdu, 610065, China
KEYWORDS
TOPICS
ABSTRACT
Surface ozone (O3) forms via nonlinear chemistry and is highly non-stationary, complicating 168-h
forecasts. Many models omit key physical drivers, mix time scales, and degrade quickly with lead time.
We propose a decomposition-ensemble framework that fuses surface observations with NWP predictors
(e.g., solar radiation, PBL height). To prevent data leakage, decomposition (STL, GAM, CEEMDAN,
VMD) was performed exclusively on the training and validation data to isolate the annual, diurnal, and
residual components. We then train LSTM and Transformer models on each component using both direct
and incremental (ΔY) learning. Across Beijing, Shanghai, Guangzhou, and Chengdu, decomposition
lowers the error bound and curbs horizon-wise error growth. Among the tested strategies, VMD (K = 2)
and period-based merging (for CEEMDAN) robustly isolate the 24-h diurnal mode, limit cross-scale
leakage, and provide the best balance of separability, interpretability, and forecast skill. The results
further reveal pronounced heterogeneity in optimal model architectures across temporal scales
and city-specific regimes, indicating that a single universal model is insufficient to jointly optimize
short-term accuracy and long-term stability. Overall, this study provides a scenario-adaptive forecasting
paradigm that offers practical guidance for O3 pollution early warning and decision support.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
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