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Here, we proposed a night PM2.5 concentration estimation (NightPMES) model based on the random forest model. This model integrates the radiance of the Visible Infrared Imaging Radiometer Suite (VIIRS) Day\/Night Band (DNB), moon phase angle, and meteorological data. We collected 13486 samples from the Beijing Tianjin\u2013Hebei (BTH) region. The determination coefficient (R2) of the NightPMES model was 0.82, the root mean square error (RMSE) was 16.67 \u00b5g\/m3, and the mean absolute error (MAE) was 10.20 \u00b5g\/m3. The applicability analysis of the moon phase angles indicated that the amount of data available increased by 60% while the accuracy remained relatively unchanged. In the seasonal model, the meteorological factors and DNB radiance were found to be the primary factors affecting the PM2.5 concentration in different seasons. In conclusion, this study provided a method for estimating nighttime PM2.5 concentration that will improve our understanding of air pollution and associated trends in PM2.5 variation.<\/jats:p>","DOI":"10.3390\/rs15030825","type":"journal-article","created":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T03:47:33Z","timestamp":1675309653000},"page":"825","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Estimation of Ground-Level PM2.5 Concentration at Night in Beijing-Tianjin-Hebei Region with NPP\/VIIRS Day\/Night Band"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6034-9990","authenticated-orcid":false,"given":"Yu","family":"Ma","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China"},{"name":"Hebei Collaborative Innovation Center for Aerospace Remote Sensing Information Processing and Application, Langfang 065000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8762-1878","authenticated-orcid":false,"given":"Wenhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China"},{"name":"Hebei Collaborative Innovation Center for Aerospace Remote Sensing Information Processing and Application, Langfang 065000, China"}]},{"given":"Lili","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Xingfa","family":"Gu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Tao","family":"Yu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112713","DOI":"10.1016\/j.rse.2021.112713","article-title":"A Machine Learning Model to Estimate Ambient PM2.5 Concentrations in Industrialized Highveld Region of South Africa","volume":"266","author":"Zhang","year":"2021","journal-title":"Remote Sens. 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