High Spatial Resolution Nighttime PM_2.5 Datasets in the Beijing–Tianjin–Hebei Region from 2015 to 2021 Using VIIRS/DNB and Deep Learning Model

The concentration of particulate matter (PM_2.5) can be estimated using satellite data collected during the daytime. However, there are currently no long-term evening PM_2.5 datasets, and the application of low-light satellite data to analyze nighttime PM_2.5 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_2.5 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 − R² = 0.51 − 0.68). Using a high spatial resolution of 500 m, we successfully generated a PM_2.5 concentration map for the BTH region. This finer resolution enabled a detailed representation of the PM_2.5 distribution over the area. Interannual and seasonal trends in nighttime PM_2.5 concentrations were analyzed. Winter had the highest seasonal spatial PM_2.5, followed by spring and autumn, whereas summer had the lowest. The annual concentration of PM_2.5 at night steadily decreased. Finally, the estimation of nighttime PM_2.5 was applied in scenarios such as continuous day–night changes, rapid short-term changes, and single-point monitoring. A deeper understanding of PM_2.5, enabled by nightly PM_2.5, will serve as an invaluable resource for future research.