Abstract
The concentration series of PM2.5 (particulate matter ≤ 2.5 μm) is nonlinear, nonstationary, and noisy, making it difficult to predict accurately. This paper presents a new PM2.5 concentration prediction method based on a hybrid model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and bi-directional long short-term memory (BiLSTM). The new method was applied to predict the same kind of particulate pollutant PM10 and heterogeneous gas pollutant O3, proving that the prediction method has strong generalization ability. First, CEEMDAN was used to decompose PM2.5 concentrations at different frequencies. Then, the fuzzy entropy (FE) value of each decomposed wave was calculated, and the near waves were combined by K-means clustering to generate the input sequence. Finally, the combined sequences were put into the BiLSTM model with multiple hidden layers for training. We predicted the PM2.5 concentrations of Seoul Station 116 by the hour, with values of the root mean square error (RMSE), the mean absolute error (MAE), and the symmetric mean absolute percentage error (SMAPE) as low as 2.74, 1.90, and 13.59%, respectively, and an R2 value as high as 96.34%. The “CEEMDAN-FE” decomposition-merging technology proposed in this paper can effectively reduce the instability and high volatility of the original data, overcome data noise, and significantly improve the model’s performance in predicting the real-time concentrations of PM2.5.
Highlights
With the acceleration of industrialization and urbanization, the discharge of pollutants, such as exhaust gas, wastewater, and waste, has greatly increased
PM2.5 blocks the transmission of solar radiation, causing air convection to stagnate, which is not conducive to the diffusion of air pollutants
The accurate predictions of real-time PM2.5, PM10, and O3 are of great practical significance to the governments of various countries for implementing air pollution improvement policies, protecting human health, and ensuring normal production and life activities
Summary
With the acceleration of industrialization and urbanization, the discharge of pollutants, such as exhaust gas, wastewater, and waste, has greatly increased. Air pollution has become an issue of close concern to all countries. The main causes of serious atmospheric pollution are aerosols (PM2.5, PM10, etc.) and gases (O3, etc.). When PM particles exceed the standard, the atmosphere is in a turbid state. When the O3 concentration exceeds the standard, it causes pollution, such as ash and photochemical smog [1]. PM2.5 blocks the transmission of solar radiation, causing air convection to stagnate, which is not conducive to the diffusion of air pollutants. The accurate predictions of real-time PM2.5, PM10, and O3 are of great practical significance to the governments of various countries for implementing air pollution improvement policies, protecting human health, and ensuring normal production and life activities
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