Real-time nowcasting of NO2 products from geostationary environment monitoring spectrometer using a conditional generative adversarial network

초록

In East Asia, megacities like Seoul, Tokyo, and Shanghai frequently recording high nitrogen dioxide (NO2) concentrations due to traffic and industrial activity require urgent efforts to enhance short-term monitoring and forecasting systems. This research presents a deep-learning (DL) model for nowcasting atmospheric NO2 concentration products derived from the geostationary environment monitoring spectrometer (GEMS) on the Geo-Kompsat-2B satellite from 1-h to 3-h. The DL model utilizes pairs of GEMS NO2 products as input and output datasets. The nowcasting DL model was developed using a data-to-data (D2D) translation method incorporating conditional generative adversarial network techniques. The D2D-nowcast NO2 model was trained and tested for 1, 2, and 3-h predictions. The test results of the D2D model demonstrated excellent statistical performance, including a correlation coefficient of 0.805, a root-mean-square error of 0.162 ⨉ 1016 molecules/cm2, and a bias of 0.046 ⨉ 1016 molecules/cm2 for the 3-h prediction. Furthermore, the D2D-nowcast NO2 concentrations were validated using the Tropospheric Monitoring Instrument and Pandora NO2 measurements, demonstrating high agreement. Consequently, this study aims to support real-time operational monitoring by supplementing temporal gaps in satellite observations without relying on numerical models and provides valuable supplements for decision-making by air quality forecasters. © 2025 Turkish National Committee for Air Pollution Research and Control

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