| Summary: | The concentration of particulate matter (PM<sub>2.5</sub>) can be estimated using satellite data collected during the daytime. However, there are currently no long-term evening PM<sub>2.5</sub> datasets, and the application of low-light satellite data to analyze nighttime PM<sub>2.5</sub> concentrations is limited. The Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB), meteorology, Digital Elevation Model, moon phase angle, and Normalized Digital Vegetation Index were used in this study to develop a Deep Neural Network model (DNN) for estimating the nighttime concentrations of PM<sub>2.5</sub> in the Beijing–Tianjin–Hebei (BTH) region from 2015 to 2021. To evaluate the model’s performance from 2015 to 2021, a ten-fold cross-validation coefficient of determination was utilized (CV − R<sup>2</sup> = 0.51 − 0.68). Using a high spatial resolution of 500 m, we successfully generated a PM<sub>2.5</sub> concentration map for the BTH region. This finer resolution enabled a detailed representation of the PM<sub>2.5</sub> distribution over the area. Interannual and seasonal trends in nighttime PM<sub>2.5</sub> concentrations were analyzed. Winter had the highest seasonal spatial PM<sub>2.5</sub>, followed by spring and autumn, whereas summer had the lowest. The annual concentration of PM<sub>2.5</sub> at night steadily decreased. Finally, the estimation of nighttime PM<sub>2.5</sub> was applied in scenarios such as continuous day–night changes, rapid short-term changes, and single-point monitoring. A deeper understanding of PM<sub>2.5</sub>, enabled by nightly PM<sub>2.5</sub>, will serve as an invaluable resource for future research.
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