Work

Improved seamless mapping of surface O3 concentrations using an integrated deep learning framework

Year: 2025

Type: article

Abstract: Satellite-derived ozone (O 3 ) data often contain spatial gaps due to factors such as cloud cover. To achieve seamless O 3 mapping, researchers typically either reconstructed the missing satellite input data before the O 3 inversion or reconstructed the missing O 3 data after inversion. Unlike previous step-by-step approaches, this study proposed a deep learning-based “inversion-reconstruction” integrated framework to estimate seamless surface O 3 . By inputting gapped satellite data and other auxiliary information, the framework directly yielded gap-free O 3 data. The O 3 inversion and reconstruction results were jointly optimized in the framework, ensuring high consistency in the seamless mapping of O 3 concentrations. Holdout, spatial, and temporal validations demonstrated the effectiveness of our method for mapping seamless O 3 across China in 2019, with R² values of 0.809, 0.760, and 0.733, respectively. Daily seamless mapping revealed the spatiotemporal patterns of O 3 , pollution episodes, and their potential transport routes. The satellite-inverted gapped O 3 data showed a 7.37 ± 4.18% difference from the gap-free merged O 3 data on a national daily scale.

Source: npj Climate and Atmospheric Science

DOI: 10.1038/s41612-025-01007-x

Authors: Tongwen Li, Jingan Wu, Yuan Wang, Yuenong Su

Institutions: Sun Yat-sen University, Nanjing University of Information Science and Technology

Language: English

Topic: Air Quality Monitoring and Forecasting

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $3,590