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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-180
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-180
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model evaluation paper 05 Aug 2019

Submitted as: model evaluation paper | 05 Aug 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

PM2.5 / PM10 Ratio Prediction Based on a Long Short-term Memory Neural Network in Wuhan, China

Xueling Wu, Ying Wang, Siyuan He, and Zhongfang Wu Xueling Wu et al.
  • Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China

Abstract. Air pollution is a serious and urgent problem in China, and it has a great impact on the lives of residents and urban development. The particulate matter (PM) value is usually used to indicate the degree of air pollution. In addition to PM2.5 and PM10, the use of the PM2.5 / PM10 ratio as an indicator and assessor of air pollution has also become more widespread. This ratio reflects the air pollution conditions and pollution sources. In this paper, a better composite prediction system was proposed that aimed at improving the accuracy and spatio-temporal applicability of PM2.5 / PM10. First, the aerosol optical depth (AOD) in 2017 in Wuhan was obtained based on Moderate Resolution Imaging Spectroradiometer images, with a 1 km spatial resolution, by using the Dense Dark Vegetation method. Second, the AOD was corrected by calculating the planetary boundary layer height and relative humidity. Third, the coefficient of determination of the optimal subset selection was used to select the factor with the highest correlation with PM2.5 / PM10 from meteorological factors and gaseous pollutants. Then, PM2.5 / PM10 predictions based on time, space, and random patterns were obtained by using 9 factors (the corrected AOD, meteorological data and gaseous pollutant data) with the long short-term memory (LSTM) neural network method, which is a dynamic model that remembers historical information and applies it to the current output. Finally, the LSTM model prediction results were compared and analysed with the results of other intelligent models. The results showed that the LSTM model had significant advantages in the average, maximum and minimum accuracies and the stability of PM2.5 / PM10 prediction.

Xueling Wu et al.
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Status: open (until 30 Sep 2019)
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Xueling Wu et al.
Data sets

PM2.5/PM10 Ratio Prediction Based on a Long Short-term Memory Neural Network in Wuhan, China Xueling Wu, Ying Wang, Siyuan He, Zhongfang Wu https://doi.org/10.17632/zk9k53zw3z.2

Model code and software

PM2.5/PM10 Ratio Prediction Based on a Long Short-term Memory Neural Network in Wuhan, China Xueling Wu, Ying Wang, Siyuan He, Zhongfang Wu https://doi.org/10.17632/zk9k53zw3z.1

Xueling Wu et al.
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Latest update: 17 Aug 2019
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Short summary
This paper presents a composite prediction system designed to improve the accuracy and applicability of PM2.5 / PM10 predictions. Based on remote sensing images, the aerosol optical thickness was obtained and corrected. Then, we selected PM2.5 / PM10 related factors from meteorological factors and air pollutants and compared the effects of several intelligent models in different prediction patterns. The results showed that the LSTM model had significant advantages in accuracy and stability.
This paper presents a composite prediction system designed to improve the accuracy and...
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