Machine learning-based prediction of ambient CO2 and CH4 concentrations with high temporal resolution in Seoul metropolitan area

Abstract

Machine learning has the potential to support the growing need for high-resolution greenhouse gas monitoring in urban and industrial environments, where deploying extensive sensor networks is often limited by cost and operational challenges. This study presents a novel approach for estimating greenhouse gas (GHG) concentrations using routinely collected air quality and meteorological data from existing monitoring stations. Focusing on the Seoul metropolitan area, we developed and evaluated three machine learning models - Random Forest, Long Short-Term Memory (LSTM), and an ensemble learning approach - to predict CO₂ and CH₄ concentrations without relying on additional GHG monitoring equipment. Among these, the ensemble learning model outperformed the individual models, consistently achieving lower error metrics, even in data-limited scenarios. Feature importance analysis identifies NO₂, CO, O₃, and temperature as key predictors of CO₂ and CH₄ level variations, highlighting the influence of combustion-related pollutants and photochemical processes. Cross-validation results confirm the model’s out-of-sample capabilities; however, local factors, such as traffic density, industrial activities, and meteorology, can affect performance. Consequently, model retraining or transfer learning may be required when applying the model to new locations with comparable emission profiles or atmospheric conditions. These findings emphasize the importance of localized context in model application while also demonstrating the broader applicability of the approach. By utilizing data already available through urban monitoring networks, this study offers a scalable and cost-effective strategy to support high-resolution GHG monitoring and inform targeted climate policies in complex urban-industrial regions.