Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network

doi:10.1007/s11783-023-1621-4

Front. Environ. Sci. Eng.

2023, Vol. 17

Issue (2) : 21 https://doi.org/10.1007/s11783-023-1621-4

RESEARCH ARTICLE

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network

Yuanxin Zhang¹, Fei Li¹(

), Chaoqiong Ni², Song Gao¹(

), Shuwei Zhang¹, Jin Xue¹, Zhukai Ning¹, Chuanming Wei¹, Fang Fang², Yongyou Nie³, Zheng Jiao¹(

)

¹. School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
². Shanghai Jinshan Environmental Monitoring Station, Shanghai 201500, China
³. School of Economics, Shanghai University, Shanghai 200237, China

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Abstract

● Used a double-stage attention mechanism model to predict ozone.

● The model can autonomously select the appropriate time series for forecasting.

● The model outperforms other machine learning models and WRF-CMAQ.

● We used the model to analyze the driving factors of VOCs that cause ozone pollution.

Ozone is becoming a significant air pollutant in some regions, and VOCs are essential for ozone prediction as necessary ozone precursors. In this study, we proposed a recurrent neural network based on a double-stage attention mechanism model to predict ozone, selected an appropriate time series for prediction through the input attention and temporal attention mechanisms, and analyzed the cause of ozone generation according to the contribution of feature parameters. The experimental data show that our model had an RMSE of 7.71 μg/m³ and a mean absolute error of 5.97 μg/m³ for 1-h predictions. The DA-RNN model predicted ozone closer to observations than the other models. Based on the importance of the characteristics, we found that the ozone pollution in the Jinshan Industrial Zone mainly comes from the emissions of petrochemical enterprises, and the good generalization performance of the model is proved through testing multiple stations. Our experimental results demonstrate the validity and promising application of the DA-RNN model in predicting atmospheric pollutants and investigating their causes.

Keywords Ozone prediction Deep learning Time series Attention Volatile organic compounds

Corresponding Author(s): Fei Li,Song Gao,Zheng Jiao

About author: Tongcan Cui and Yizhe Hou contributed equally to this work.

Issue Date: 16 September 2022

Cite this article:

Yuanxin Zhang,Fei Li,Chaoqiong Ni, et al. Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network[J]. Front. Environ. Sci. Eng., 2023, 17(2): 21.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-023-1621-4
https://academic.hep.com.cn/fese/EN/Y2023/V17/I2/21

Fig.1 Scatter plot of ozone concentration versus other variables.

Fig.2 Flow chart of the DA-RNN model.

Fig.3 Pearson correlation heat map.

Fig.4 Importance weight of different features on ozone concentration. (a) Importance weight of all features on ozone concentration, (b) importance weight of the four types of species in VOCs on ozone concentration.

Tab.1 Forecast performance changes with forecast duration

Fig.5 Observed vs. predicted values of ozone concentration.

Fig.6 Comparison of predicted and observed values of hourly ozone concentration in Jinshan Station in July, August, November, and December 2020.

Fig.7 Observed vs. predicted values of ozone concentrations at different stations. (a) Jinshan Station; (b) Jinshan Industrial District Station; (c) Jinshan Boundary Station.

Tab.2 Predictive performance of other machine learning models and the DA-RNN model

Fig.8 Comparison of the WRF-CMAQ and DA-RNN models.

Tab.3 Predictive performance of the WRF-CMAQ and DA-RNN models

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