Development and validation of satellite-derived surface NO₂ estimates using machine learning versus traditional approaches in North America

Abstract. Nitrogen dioxide (NO₂) is one of the key pollutants with profound implications for air quality, and human health, and is needed to establish the air quality health index (AQHI). Currently, over 600 surface air monitoring stations are distributed across Canada and the United States measuring NO₂, but many areas remain unmonitored leading to incomplete information for health risk assessments. This study leverages Tropospheric Monitoring Instrument (TROPOMI) satellite observations and machine learning models to derive high-resolution surface NO₂ concentrations, provides enhanced spatial coverage and accuracy, revealing urban-rural NO₂ gradients across North America. Existing traditional methods rely on scaling with modeled profiles to obtain NO₂ surface concentrations from satellite observations. Here, we compare this traditional method to a machine learning approach that utilizes NO₂ observations from TROPOMI, together with meteorological parameters, land cover type, topography, and emission inventories. Our results show that the machine learning (using random forest) yields less bias between the surface monitoring measurements and the "satellite-derived" surface concentrations, significantly improved the correlation coefficient (R²~0.77–0.91) compared to the traditional method (R²~0.39–0.57) and yields to significantly less bias.