A prediction model for air NO<sub>2</sub> concentration in Shanghai based on CEEMDAN and Informer

YIN Zuocheng; CHAI Tian

doi:10.13205/j.hjgc.202512022

Volume 43 Issue 12

Dec. 2025

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2025 > 43(12): 197-212

YIN Zuocheng, CHAI Tian. A prediction model for air NO2 concentration in Shanghai based on CEEMDAN and Informer[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(12): 197-212. doi: 10.13205/j.hjgc.202512022

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YIN Zuocheng, CHAI Tian. A prediction model for air NO₂ concentration in Shanghai based on CEEMDAN and Informer[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(12): 197-212. doi: 10.13205/j.hjgc.202512022

YIN Zuocheng, CHAI Tian. A prediction model for air NO2 concentration in Shanghai based on CEEMDAN and Informer[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(12): 197-212. doi: 10.13205/j.hjgc.202512022

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A prediction model for air NO₂ concentration in Shanghai based on CEEMDAN and Informer

doi: 10.13205/j.hjgc.202512022

YIN Zuocheng,
CHAI Tian^,

School of Environmental Science and Engineering, Xiamen University of Technology, Xiamen 361024, China

Received Date: 2024-09-05
Accepted Date: 2024-11-02
Rev Recd Date: 2024-10-14

Available Online: 2026-01-09

Abstract

Abstract

Nitrogen dioxide （NO₂） is a significant pollutant in the troposphere， posing substantial threats to the environment and human health. Accurately predicting NO₂ concentrations is crucial for air pollution management， policy formulation， and public health protection. This study focused on Shanghai as the research area and proposed a novel hybrid model integrating complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN） and the Informer deep learning model， to predict the daily average concentration of NO₂. First， CEEMDAN was employed to decompose the original NO₂ time series， and a grey wolf optimization （GWO） algorithm combined with minimum sample entropy （SE） was utilized as a fitness function to optimize the key parameters of CEEMDAN. This approach effectively reduced random fluctuations in the data and extracted intrinsic mode functions （IMFs）. Subsequently， the Pearson correlation coefficient and Spearman rank correlation coefficient were used to analyze the association strength among various pollutants， allowing for the selection of optimal features for each IMF and the construction of a high-coupling feature matrix. These features were then input into an optimized Informer model based on the Transformer architecture for encoding and modeling. By employing mechanisms such as Multi-head ProbSparse self-attention and attention distillation， the model's prediction accuracy and efficiency got enhanced. Finally， the predicted results of each IMF were summed to reconstruct the final predicted value. Experimental results indicated that the proposed model achieved an average absolute error （MAE） of 5.027 and a root mean square error （RMSE） of 6.818， demonstrating a significant improvement in prediction accuracy， compared to the original Informer model and other benchmark models including LSTM， GRU， Transformer， and SVR. The innovations in data processing， feature engineering， and model architecture presented in this study offer a more precise method for NO₂ concentration prediction， with broad application prospects that can provide robust support for regional air quality forecasting， early warning systems， and pollution control strategies.
- NO₂ concentration prediction,
- deep learning,
- Informer,
- CEEMDAN,
- feature selection

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References(48)

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