Application of a Super-Resolution Algorithm to Improve the Spatial Resolution of Air Pollutant Concentrations in the Seoul Area

Air pollution not only poses harmful effects on human health but also causes various diseases, leading to severe issues such as increased premature mortality. To accurately assess the health impacts and exposure levels of air pollution, high-resolution spatiotemporal concentration data is essential.

In previous studies, Hybrid Modeling combining CMAQ and CALPUFF was applied to estimate air pollutant concentrations at a spatial resolution of 100m. However, the Hybrid Model has limitations in that each modeling process must be conducted independently, requiring significant time and computational resources.

This study aims to improve computational efficiency and simplify the modeling process by applying a Super-Resolution Convolutional Neural Network  (SRCNN) algorithm. SRCNN uses low-resolution (9km) CMAQ data as input to produce spatial distributions similar to those generated by the Hybrid Model at a high resolution of 100m. The target pollutant is PM_2.5 and NO₂ in Seoul, with a training period from 2015 to 2021 and a test period in 2022. 

Model evaluation results show that SRCNN outperformed CMAQ in terms of PSNR, SSIM, and Spatial RMSE metrics. This demonstrates the potential of  SRCNN to efficiently generate high-resolution air pollution concentration data, contributing to more precise exposure assessments and health impact analyses.

  

Acknowledgement

This research was supported by the Korea National Institute of Health (KNIH) research project (Project No.2024-ER0606-00) and Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute (KEITI) funded by the Ministry of Environment (MOE)