Knowledge based convolutional transformer for joint estimation of PM2.5 and O3 concentrations

• Published in: Scientific reports Vol. 15; no. 1; pp. 25340 - 16
• Main Authors: Ren, Ying, Wang, Siyuan, Xia, Bisheng, Xia, Biesheng
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14.07.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705; 704/106/35; Aerosols; Air pollution; Convtrans; Deep learning; Design; Environmental monitoring; Humanities and Social Sciences; Humidity; Joint Estimation; Knowledge; Machine learning; multidisciplinary; Neural networks; Particulate matter; PM2.5; Pollutants; Radiation; Remote sensing; Science; Science (multidisciplinary); Spatial variations; Time series; VOCs; Volatile organic compounds; Beijing China; China; Convtrans; Joint Estimation; PM; Knowledge; O

Most of the methods for predicting air pollutant concentrations are targeting at single pollutants, which is time-consuming and laborious. To solve this problem, this study proposes a Convolutional Transformer (Convtrans) model that incorporates knowledge to make a collaborative estimation of PM 2.5...