{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T05:06:48Z","timestamp":1761196008384,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2017,8,31]],"date-time":"2017-08-31T00:00:00Z","timestamp":1504137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["8174066"],"award-info":[{"award-number":["8174066"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry  of Science and Technology of China","award":["IUMKY201612"],"award-info":[{"award-number":["IUMKY201612"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Previous studies have estimated ground-level concentrations of particulate matter 2.5 (PM2.5) using satellite-derived aerosol optical depth (AOD) in conjunction with meteorological and land use variables. However, the impacts of urbanization on air pollution for predicting PM2.5 are seldom considered. Nighttime light (NTL) data, acquired with the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite, could be useful for predictions because they have been shown to be good indicators of the urbanization and human activity that can affect PM2.5 concentrations. This study investigated the potential of incorporating VIIRS NTL data in statistical models for PM2.5 concentration predictions. We developed a mixed-effects model to derive daily estimations of surface PM2.5 levels in the Beijing\u2013Tianjin\u2013Hebei region using 3 km resolution satellite AOD and VIIRS NTL data. The results showed the addition of NTL information could improve the performance of the PM2.5 prediction model. The NTL data revealed additional details for predication results in areas with low PM2.5 concentrations and greater apparent seasonal variation due to the seasonal variability of human activity. Comparison showed prediction accuracy was improved more substantially for the model using NTL directly than for the model using the vegetation-adjusted NTL urban index that included NTL. Our findings indicate that VIIRS NTL data have potential for predicting PM2.5 and that they could constitute a useful supplemental data source for estimating ground-level PM2.5 distributions.<\/jats:p>","DOI":"10.3390\/rs9090908","type":"journal-article","created":{"date-parts":[[2017,8,31]],"date-time":"2017-08-31T10:54:44Z","timestamp":1504176884000},"page":"908","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Improving Satellite-Driven PM2.5 Models with VIIRS Nighttime Light Data in the Beijing\u2013Tianjin\u2013Hebei Region, China"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5834-8104","authenticated-orcid":false,"given":"Xiya","family":"Zhang","sequence":"first","affiliation":[{"name":"Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibo","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1016\/S0140-6736(13)62693-8","article-title":"Haze, air pollution, and health in China","volume":"382","author":"Xu","year":"2013","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1001\/jama.295.10.1127","article-title":"Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases","volume":"295","author":"Dominici","year":"2006","journal-title":"JAMA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1001\/jama.287.9.1132","article-title":"Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution","volume":"287","author":"Pope","year":"2002","journal-title":"JAMA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7436","DOI":"10.1021\/es5009399","article-title":"Estimating ground-level PM2.5 in China using satellite remote sensing","volume":"48","author":"Ma","year":"2014","journal-title":"Environ. Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envres.2012.11.003","article-title":"Estimating ground-level PM2.5 concentrations in the southeastern U.S. Using geographically weighted regression","volume":"121","author":"Hu","year":"2013","journal-title":"Environ. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.rse.2013.08.032","article-title":"Estimating ground-level PM2.5 concentrations in the southeastern United States using MAIAC AOD retrievals and a two-stage model","volume":"140","author":"Hu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1289\/ehp.0901623","article-title":"Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: Development and application","volume":"118","author":"Martin","year":"2010","journal-title":"Environ. Health Perspect."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"645","DOI":"10.3155\/1047-3289.59.6.645","article-title":"Remote sensing of particulate pollution from space: Have we reached the promised land?","volume":"59","author":"Hoff","year":"2009","journal-title":"J. Air Waste Manag. Assoc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2495","DOI":"10.1016\/j.atmosenv.2004.01.039","article-title":"Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality","volume":"38","author":"Holloman","year":"2004","journal-title":"Atmos. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7991","DOI":"10.5194\/acp-11-7991-2011","article-title":"A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations","volume":"11","author":"Lee","year":"2011","journal-title":"Atmos. Chem. Phys."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"32195","DOI":"10.1029\/98JD01752","article-title":"Sensitivity of multiangle imaging to aerosol optical depth and to pure-particle size distribution and composition over ocean","volume":"103","author":"Kahn","year":"1998","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"You, W., Zang, Z., Zhang, L., Li, Y., Pan, X., and Wang, W. (2016). National-scale estimates of ground-level PM2.5 concentration in China using geographically weighted regression based on 3 km resolution MODIS AOD. Remote Sens., 8.","DOI":"10.3390\/rs8030184"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.rse.2006.05.022","article-title":"Using aerosol optical thickness to predict ground-level PM2.5 concentrations in the St. Louis area: A comparison between MISR and MODIS","volume":"107","author":"Liu","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5800","DOI":"10.1021\/es703181j","article-title":"Spatiotemporal associations between GOES aerosol optical depth retrievals and ground-level PM2.5","volume":"42","author":"Paciorek","year":"2008","journal-title":"Environ. Sci. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1289\/ehp.0800123","article-title":"Estimating regional spatial and temporal variability of PM2.5 concentrations using satellite data, meteorology, and land use information","volume":"117","author":"Liu","year":"2009","journal-title":"Environ. Health Perspect."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.5194\/amt-6-1829-2013","article-title":"MODIS 3 km aerosol product: Algorithm and global perspective","volume":"6","author":"Remer","year":"2013","journal-title":"Atmos. Meas. Tech."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"12280","DOI":"10.1021\/acs.est.5b01413","article-title":"Daily estimation of ground-level PM2.5 concentrations over Beijing using 3 km resolution MODIS AOD","volume":"49","author":"Xie","year":"2015","journal-title":"Environ. Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"11375","DOI":"10.1002\/2014JD021920","article-title":"Improving satellite-driven PM2.5 models with Moderate Resolution Imaging Spectroradiometer fire counts in the southeastern U.S.","volume":"119","author":"Hu","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6267","DOI":"10.1016\/j.atmosenv.2011.08.066","article-title":"Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements","volume":"45","author":"Kloog","year":"2011","journal-title":"Atmos. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1289\/ehp.1409481","article-title":"Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004\u20132013","volume":"124","author":"Ma","year":"2016","journal-title":"Environ. Health Perspect."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.envpol.2014.07.022","article-title":"Impact of urbanization level on urban air quality: A case of fine particles (PM(2.5)) in Chinese cities","volume":"194","author":"Han","year":"2014","journal-title":"Environ. Pollut."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"173","DOI":"10.3390\/ijerph110100173","article-title":"Spatio-temporal variation of PM2.5 concentrations and their relationship with geographic and socioeconomic factors in China","volume":"11","author":"Lin","year":"2014","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/S0034-4257(97)00046-1","article-title":"A technique for using composite DMSP\/OLS \u2018city lights\u2019 satellite data to accurately map urban areas","volume":"61","author":"Lawrence","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2645","DOI":"10.1080\/01431160600981525","article-title":"The Nightsat mission concept","volume":"28","author":"Elvidge","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.rse.2012.04.018","article-title":"Quantitative estimation of urbanization dynamics using time series of DMSP\/OLS nighttime light data: A comparative case study from China\u2019s cities","volume":"124","author":"Ma","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3061","DOI":"10.1080\/01431160010007015","article-title":"Census from heaven: An estimate of the global human population using night-time satellite imagery","volume":"22","author":"Sutton","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/S0034-4257(98)00098-4","article-title":"Radiance calibration of DMSP\/OLS low-light imaging data of human settlements","volume":"68","author":"Elvidge","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jing, X., Shao, X., Cao, C., Fu, X., and Yan, L. (2016). Comparison between the Suomi-NPP day-night band and DSMP\/OLS for correlating socio-economic variables at the provincial level in China. Remote Sens., 8.","DOI":"10.3390\/rs8010017"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1080\/17538947.2016.1168879","article-title":"Global mapping of urban built-up areas of year 2014 by combining MODIS multispectral data with VIIRS nighttime light data","volume":"9","author":"Sharma","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4937","DOI":"10.3390\/rs70404937","article-title":"A test of the new VIIRS lights data set: Population and economic output in Africa","volume":"7","author":"Chen","year":"2015","journal-title":"Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"9296","DOI":"10.1002\/jgrd.50712","article-title":"Enhanced deep blue aerosol retrieval algorithm: The second generation","volume":"118","author":"Hsu","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.atmosenv.2016.01.002","article-title":"Spatio-temporal variation and impact factors analysis of satellite-based aerosol optical depth over China from 2002 to 2015","volume":"129","author":"He","year":"2016","journal-title":"Atmos. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0034-4257(02)00084-6","article-title":"An overview of MODIS land data processing and product status","volume":"83","author":"Justice","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.3390\/rs6021705","article-title":"Evaluating the ability of NPP-VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of China at multiple scales: A comparison with DMSP-OLS data","volume":"6","author":"Shi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mills, S., Weiss, S., and Liang, C. (2013). VIIRS day\/night band (DNB) stray light characterization and correction. Proc. SPIE, 8866.","DOI":"10.1117\/12.2023107"},{"key":"ref_36","first-page":"62","article-title":"Why VIIRS data are superior to DMSP for mapping nighttime lights","volume":"35","author":"Elvidge","year":"2013","journal-title":"Proc. Asia Pac. Adv. Netw."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4423","DOI":"10.3390\/rs5094423","article-title":"VIIRS Nightfire: Satellite pyrometry at night","volume":"5","author":"Elvidge","year":"2013","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.rse.2012.10.022","article-title":"The vegetation adjusted NTL urban index: A new approach to reduce saturation and increase variation in nighttime luminosity","volume":"129","author":"Zhang","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_39","unstructured":"Lucchesi, R. (2013). File Specification for GEOS-5 FP-IT (Forward Processing for Instrument Teams)."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1177\/0049124104268644","article-title":"Multimodel inference: Understanding AIC and BIC in model selection","volume":"33","author":"Burnham","year":"2004","journal-title":"Sociol. Methods Res."},{"key":"ref_41","first-page":"1137","article-title":"A study of cross-validation and bootstrap for accuracy estimation and model selection","volume":"Volume 2","author":"Kohavi","year":"1995","journal-title":"Proceedings of the 14th International Joint Conference on Artificial Intelligence"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/9\/908\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:43:47Z","timestamp":1760208227000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/9\/908"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,31]]},"references-count":41,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2017,9]]}},"alternative-id":["rs9090908"],"URL":"https:\/\/doi.org\/10.3390\/rs9090908","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2017,8,31]]}}}