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

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 (NO₂), Ozone (O₃), Sulphur Dioxide (SO₂), Fine particulate matter (PM_2.5), and Coarse particulate matter (PM₁₀). 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, NO₂, O₃, PM_2.5, PM₁₀ and SO₂ pollutants, respectively. The findings indicated that the suggested approaches are efficient and dependable for environmental supervisors in the monitoring and management of air pollution.