Deep learning framework for hourly air pollutants forecasting using encoding cyclical features across multiple monitoring sites in Beijing.

Journal: Scientific reports
Published Date: Jul 1, 2025

Abstract

Environmental managers and citizens alike are concerned with air quality. Early warning systems for air pollution are essential to prevent health issues and implement effective prevention strategies. This paper proposes a comprehensive, reliable system with air quality prediction and assessment modules for China's air pollution. In this study, six air pollutants were observed, including Carbon Monoxide (CO), Nitrogen Dioxide (NO), Ozone (O), Sulphur Dioxide (SO), Fine particulate matter (PM), and Coarse particulate matter (PM). The current dataset includes hourly air pollutants data from 10 national air-quality monitoring sites, such as Aotizhongxin, Changping, Dongsi, Guanyuan, Huairou, Nongzhanguan, Shunyi, Tiantan, Wanliu, and Wanshouxigong. The dataset was recorded hourly from 01/03/2013 to 28/02/2017. Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) were developed with both unencoded and encoded features to address the forecasting challenge of multivariate time series, specifically in predicting air pollution concentrations. The results showed that, the top accuracy was as follows: 93.8% at the Wanshouxigong station using CNN-Encoded, 91.9% at the Nongzhanguan station using (DNN-Encoded and CNN-Encoded), 93.4% at Aotizhongxin station using DNN-Encoded, 96.2% at Nongzhanguan station using DNN-Encoded, 94% at Dongsi station using CNN-Unencoded, and 92.4% at Aotizhongxin station using (CNN-Unencoded and DNN-Encoded) in forecasting CO, NO, O, PM, PM and SO pollutants, respectively. The findings indicated that the suggested approaches are efficient and dependable for environmental supervisors in the monitoring and management of air pollution.

Authors

  • Abdel Salam Alsabagh
    Department of Mechanical Engineering, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan.
  • Omer A Alawi
    Department of Thermofluids, School of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM, Skudai, Johor Bahru, Malaysia.
  • Haslinda Mohamed Kamar
    Department of Thermofluids, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor Bahru, Malaysia.
  • Ahmed Adil Nafea
    Department of Artificial Intelligence, College of Computer Science and IT, University of Anbar, Ramadi, Iraq.
  • Mohammed M Al-Ani
    Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia.
  • Hussein A Mohammed
    Mechanical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, 31261, Saudi Arabia.
  • S N Kazi
    Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
  • Atheer Y Oudah
    Department of Computer Sciences, College of Education for Pure Science, University of Thi-Qar, Thi-Qar, Iraq.
  • Zaher Mundher Yaseen
    Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.

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