Tracking diurnal variation of NO2 at high spatial resolution in China using a time-constrained machine learning model

Abstract

The spatially continuous dynamic monitoring of near-surface NO₂ concentrations on sub-daily scales would serve to enhance awareness of the current state of air pollution, which is crucial to improving regional air quality. Satellites, like OMI and TROPOMI, are capable of observing atmospheric NO₂ column concentrations on a global scale. However, the fixed transit times of the satellites and severe data deficiencies restricted their applicability for revealing patterns of change in NO₂ on sub-daily scales. This study proposes a time-constrained XGBoost model (T-XGB) to convert multi-source information to daily cumulative near-surface NO₂ concentrations. Furthermore, a temporally conservative downscaling framework is developed to facilitate seamless monitoring of near-surface NO₂ at the 0.03°/3-hour scale in China. Evaluated with in-situ NO₂ measurements, the results have demonstrated the robust and excellent performance of the T-XGB (R²: 0.920–0.948; MAE: 2.89–3.67 µg/m³/h), as well as the accuracy of the temporally conserved downscaling technique (R² > 0.973). The 3-hour near-surface NO₂ was consistent with the TROPOMI observations at the corresponding moments and it exhibited a detailed gradient variation signature. In China, near-surface NO₂ exhibited a single-peak diurnal variation, with an initial increase followed by a subsequent decrease. The maximum concentration was observed between 8p.m. and 11p.m. in local time. The assessment of NO₂ pollution exposure can yield disparate results when evaluated at varying time scales. Sub-daily monitoring of NO₂ provides a more detailed and nuanced understanding of the pollutant, making it a more applicable and flexible tool for use in subsequent studies.