Spatiotemporal estimation of surface NO2 concentrations in the Pearl River Delta region based on TROPOMI data and machine learning

Abstract

Nitrogen dioxide (NO2) is a major air pollutant, and its concentration data are crucial for the study of air pollution and its impact on the environment. Although satellite data provide an effective method for estimating surface concentrations on a large scale through integrated modeling, the estimation of surface NO2 concentrations is hampered by the substantial amount of missing satellite data. This restricts in-depth studies of surface NO2 pollution. This study aims to reconstruct the missing data on tropospheric NO2 vertical column density from the TROPOspheric Monitoring Instrument (TROPOMI NO2). Subsequently, the reconstructed TROPOMI NO2 data and other predictor variables were utilized to estimate the daily surface NO2 concentrations at a 1 km resolution for the Pearl River Delta (PRD) region. The TROPOMI NO2 reconstruction models and the surface NO2 estimation model were both developed using the XGBoost algorithm. Additionally, comparative experiments were conducted between the XGBoost model and other traditional machine learning models, and the performances of the XGBoost model were evaluated through 10-fold cross-validation (CV) sample-based and site-based evaluations. The results indicate that the sample-based and site-based CV R2 values were 0.873 and 0.709, respectively. The feature importance scores indicate that TROPOMI NO2 was the most significant variable contributing to the estimation model. This indicates that the reconstruction of TROPOMI NO2 data and the development of an Extreme Gradient Boosting (XGBoost) model are suitable for the spatiotemporal estimation of surface NO2 concentrations in the PRD region, effectively reflecting the spatiotemporal distribution and evolution of surface NO2 concentrations in the area.