A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID-19 pandemic

• Published in: Scientific reports Vol. 13; no. 1; pp. 1015 - 17
• Main Authors: Zhao, Zixi, Wu, Jinran, Cai, Fengjing, Zhang, Shaotong, Wang, You-Gan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18.01.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/705; Air Pollutants - analysis; Air pollution; Air Pollution - analysis; Air quality; China - epidemiology; Cities; Communicable Disease Control; Coronaviruses; COVID-19; COVID-19 - epidemiology; COVID-19 - prevention & control; Deep Learning; Environmental Monitoring; Humanities and Social Sciences; Humans; multidisciplinary; Outdoor air quality; Pandemics; Pandemics - prevention & control; Particulate Matter - analysis; Science; Science (multidisciplinary); Spatial Analysis; China
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-28287-8

China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framework. In addition to historical pollutant concentrations and meteorological factors, we incorporate social and spatio-temporal influences in the framework. In particular, spatial autocorrelation (SAC), which combines temporal autocorrelation with spatial correlation, is adopted to reflect the influence of neighbouring cities and historical data. Our deep learning analysis obtained the estimates of the lockdown effects as − 25.88 in Wuhan and − 20.47 in Shanghai. The corresponding prediction errors are reduced by about 47% for Wuhan and by 67% for Shanghai, which enables much more reliable AQI forecasts for both cities.