{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T20:47:21Z","timestamp":1773089241478,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T00:00:00Z","timestamp":1635292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012456","name":"National Social Science Foundation of China","doi-asserted-by":"publisher","award":["19CSH004"],"award-info":[{"award-number":["19CSH004"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]},{"name":"General Financial Grant from China Postdoctoral Science Foundation","award":["2019M662723"],"award-info":[{"award-number":["2019M662723"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In China, ground-level ozone has shown an increasing trend and has become a serious ambient pollutant. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) is urgently needed. Generalized linear models (GLMs) and Bayesian maximum entropy (BME) models are practical for predicting GOCs. However, GLMs have limited capacity to capture temporal variations and can miss some short-term and regional patterns, while the performance of BME models may degrade in cases of sparse or imperfect monitoring networks. Thus, to predict nationwide 1 km monthly average GOCs for China, we designed a novel hybrid model containing three modules. (1) A GLM was established to accurately describe the variability in GOCs in the space domain. (2) A BME model incorporating GLM residuals was employed to capture the temporal variability of GOCs in detail. (3) A combination of GLM and BME models was developed based on the specific broad range of each submodel. According to the cross-validation results, the hybrid model exhibited superior performance, with coefficient of determination (R2) values of 0.67. The predictive performance of the large-scale and high-resolution hybrid model is superior to that in previous studies. The nationwide spatiotemporal variability of the GOCs derived from the hybrid model shows that they are valuable indicators for ground-level ozone pollution control and prevention in China.<\/jats:p>","DOI":"10.3390\/rs13214324","type":"journal-article","created":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T23:24:42Z","timestamp":1635377082000},"page":"4324","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["When a Generalized Linear Model Meets Bayesian Maximum Entropy: A Novel Spatiotemporal Ground-Level Ozone Concentration Retrieval Method"],"prefix":"10.3390","volume":"13","author":[{"given":"Yingying","family":"Mei","sequence":"first","affiliation":[{"name":"School of Sociology, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiayi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deping","family":"Xiang","sequence":"additional","affiliation":[{"name":"School of Sociology, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingxiong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,27]]},"reference":[{"key":"ref_1","first-page":"533","article-title":"Monitoring the impact of ambient ozone on human health using time series analysis and air quality model approaches","volume":"27","author":"Javanmardi","year":"2018","journal-title":"Fresenius Environ. Bull."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"12177","DOI":"10.5194\/acp-17-12177-2017","article-title":"Projected global ground-level ozone impacts on vegetation under different emission and climate scenarios","volume":"17","author":"Sicard","year":"2017","journal-title":"Atmos. Chem. Phys."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1891","DOI":"10.1007\/s00024-011-0437-5","article-title":"Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis","volume":"169","author":"Chattopadhyay","year":"2012","journal-title":"Pure Appl. Geophys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4399","DOI":"10.5194\/acp-20-4399-2020","article-title":"Ozone pollution over China and India: Seasonality and sources","volume":"20","author":"Gao","year":"2020","journal-title":"Atmos. Chem. Phys."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1080\/14498596.2016.1277560","article-title":"Mapping the spatial distribution of tropospheric ozone and exploring its association with elevation and land cover over North Jordan","volume":"62","author":"Jaber","year":"2017","journal-title":"J. Spat. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1109\/TGRS.2003.822751","article-title":"Total ozone mapping by integrating databases from remote sensing instruments and empirical models","volume":"42","author":"Christakos","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/S1364-8152(01)00077-9","article-title":"Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks","volume":"17","year":"2002","journal-title":"Environ. Model. Softw."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s11270-008-9829-2","article-title":"Identification of NOx and Ozone Episodes and Estimation of Ozone by Statistical Analysis","volume":"198","author":"Castellano","year":"2009","journal-title":"Water Air Soil Pollut."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.jes.2017.08.011","article-title":"Regionalization based on spatial and seasonal variation in ground-level ozone concentrations across China","volume":"67","author":"Cheng","year":"2018","journal-title":"J. Environ. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.apr.2016.02.001","article-title":"Assessing of surface-ozone concentration in Bucharest, Romania, using OML and satellite data","volume":"7","author":"Grigoras","year":"2016","journal-title":"Atmos. Pollut. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1016\/j.envsoft.2005.04.004","article-title":"Modelled surface ozone over southern Africa during the Cross Border Air Pollution Impact Assessment Project","volume":"21","author":"Zunckel","year":"2006","journal-title":"Environ. Model. Softw."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7127","DOI":"10.1016\/j.atmosenv.2007.04.061","article-title":"The effects of meteorology on ozone in urban areas and their use in assessing ozone trends","volume":"41","author":"Camalier","year":"2007","journal-title":"Atmos. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"13216","DOI":"10.1021\/acs.est.7b03130","article-title":"Quantifying O3 Impacts in Urban Areas Due to Wildfires Using a Generalized Additive Model","volume":"51","author":"Gong","year":"2017","journal-title":"Environ. Sci. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.atmosenv.2013.09.014","article-title":"Prediction of 8 h-average ozone concentration using a supervised hidden Markov model combined with generalized linear models","volume":"81","author":"Sun","year":"2013","journal-title":"Atmos. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1289\/ehp.1306566","article-title":"Spatiotemporal Modeling of Ozone Levels in Quebec (Canada): A Comparison of Kriging, Land-Use Regression (LUR), and Combined Bayesian Maximum Entropy\u2013LUR Approaches","volume":"122","author":"Brand","year":"2014","journal-title":"Environ. Health Perspect."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2471","DOI":"10.1016\/j.atmosenv.2009.01.049","article-title":"Spatiotemporal modelling of ozone distribution in the State of California","volume":"43","author":"Bogaert","year":"2009","journal-title":"Atmos. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105827","DOI":"10.1016\/j.envint.2020.105827","article-title":"Comparison of Machine Learning and Land Use Regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous United States","volume":"142","author":"Ren","year":"2020","journal-title":"Environ. Int."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.scitotenv.2015.01.106","article-title":"Analysis of surface ozone using a recurrent neural network","volume":"514","author":"Biancofiore","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105823","DOI":"10.1016\/j.envint.2020.105823","article-title":"Spatiotemporal distributions of surface ozone levels in China from 2005 to 2017: A machine learning approach","volume":"142","author":"Liu","year":"2020","journal-title":"Environ. Int."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0048-9697(03)00335-8","article-title":"Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens","volume":"313","author":"Chaloulakou","year":"2003","journal-title":"Sci. Total Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/S0143-6228(98)00039-3","article-title":"An application of artificial neural networks to the prediction of surface ozone concentrations in the United Kingdom","volume":"19","author":"Spellman","year":"1999","journal-title":"Appl. Geogr."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"951","DOI":"10.4209\/aaqr.2016.08.0374","article-title":"Spatial Variation of Ground Level Ozone Concentrations and its Health Impacts in an Urban Area in India","volume":"17","author":"Gorai","year":"2017","journal-title":"Aerosol Air Qual. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s11869-008-0023-x","article-title":"Ground-level ozone forecasting using data-driven methods","volume":"1","author":"Solaiman","year":"2008","journal-title":"Air Qual. Atmos. Health"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e2102705118","DOI":"10.1073\/pnas.2102705118","article-title":"From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model","volume":"118","author":"Yang","year":"2021","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1007\/s11869-018-0585-1","article-title":"A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction","volume":"11","author":"Pak","year":"2018","journal-title":"Air Qual. Atmos. Health"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1021\/acs.estlett.8b00366","article-title":"Severe Surface Ozone Pollution in China: A Global Perspective","volume":"5","author":"Lu","year":"2018","journal-title":"Environ. Sci. Technol. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.envpol.2006.05.006","article-title":"Ground-level ozone in China: Distribution and effects on crop yields","volume":"147","author":"Wang","year":"2007","journal-title":"Environ. Pollut."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.ecoinf.2011.03.003","article-title":"Predicting tropospheric ozone concentrations in different temporal scales by using multilayer perceptron models","volume":"6","author":"Ozbay","year":"2011","journal-title":"Ecol. Inform."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zheng, Y., Li, T., Wei, L., and Guan, Q. (2018). Temporal and Spatial Variation in, and Population Exposure to, Summertime Ground-Level Ozone in Beijing. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15040628"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.envpol.2017.10.029","article-title":"Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment","volume":"233","author":"Zhan","year":"2018","journal-title":"Environ. Pollut."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.envsoft.2006.08.007","article-title":"Combining principal component regression and artificial neural networks for more accurate predictions of ground-level ozone","volume":"23","author":"Bakheit","year":"2008","journal-title":"Environ. Model. Softw."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1016\/j.atmosenv.2006.09.010","article-title":"Interannual variation in meteorologically adjusted ozone levels in the eastern United States: A comparison of two approaches","volume":"41","author":"Zheng","year":"2007","journal-title":"Atmos. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.envint.2018.03.047","article-title":"Spatiotemporal modeling of PM 2.5 concentrations at the national scale combining land use regression and Bayesian maximum entropy in China","volume":"116","author":"Chen","year":"2018","journal-title":"Environ. Int."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.atmosenv.2017.11.014","article-title":"Ground-level ozone pollution and its health impacts in China","volume":"173","author":"Liu","year":"2018","journal-title":"Atmos. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.apr.2020.09.020","article-title":"Daily spatiotemporal prediction of surface ozone at the national level in China: An improvement of CAMS ozone product","volume":"12","author":"Mo","year":"2021","journal-title":"Atmos. Pollut. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.rse.2017.04.008","article-title":"A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data","volume":"195","author":"Duan","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5419","DOI":"10.1175\/JCLI-D-16-0758.1","article-title":"The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)","volume":"30","author":"Gelaro","year":"2017","journal-title":"J. Clim."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s00477-007-0135-0","article-title":"Interactive spatiotemporal modelling of health systems: The SEKS\u2013GUI framework","volume":"21","author":"Yu","year":"2007","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"13431","DOI":"10.1021\/acs.est.5b03614","article-title":"Spatiotemporal Characterization of Ambient PM2.5 Concentrations in Shandong Province (China)","volume":"49","author":"Yang","year":"2015","journal-title":"Environ. Sci. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.1175\/1520-0477(1997)078<2153:SATSIA>2.0.CO;2","article-title":"Space and time scales in ambient ozone data","volume":"78","author":"Rao","year":"1997","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.1289\/ehp.1205006","article-title":"Comparison of Geostatistical Interpolation and Remote Sensing Techniques for Estimating Long-Term Exposure to Ambient PM(2.5) Concentrations across the Continental United States","volume":"120","author":"Lee","year":"2012","journal-title":"Environ. Health Perspect."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3511","DOI":"10.5194\/acp-11-3511-2011","article-title":"Seasonal and spatial variability of surface ozone over China: Contributions from background and domestic pollution","volume":"11","author":"Wang","year":"2011","journal-title":"Atmos. Chem. Phys."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.scitotenv.2019.06.460","article-title":"Geographical distribution of ozone seasonality over China","volume":"689","author":"Yin","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1289\/ehp.0901220","article-title":"An Estimate of the Global Burden of Anthropogenic Ozone and Fine Particulate Matter on Premature Human Mortality Using Atmospheric Modeling","volume":"118","author":"Anenberg","year":"2010","journal-title":"Environ. Health Perspect."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1097\/01.ede.0000165820.08301.b3","article-title":"Ozone exposure and mortality: An empiric Bayes metaregression analysis","volume":"16","author":"Levy","year":"2005","journal-title":"Epidemiology"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.atmosenv.2016.08.076","article-title":"Source apportionment of surface ozone in the Yangtze River Delta, China in the summer of 2013","volume":"144","author":"Li","year":"2016","journal-title":"Atmos. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2574","DOI":"10.1021\/acs.est.6b03634","article-title":"Spatial Distribution of Ozone Formation in China Derived from Emissions of Speciated Volatile Organic Compounds","volume":"51","author":"Wu","year":"2017","journal-title":"Environ. Sci. Technol."},{"key":"ref_48","first-page":"1","article-title":"Spatiotemporal distribution of ground-level ozone in China at a city level","volume":"10","author":"Yang","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.atmosenv.2018.09.024","article-title":"Impacts of O-3 on premature mortality and crop yield loss across China","volume":"194","author":"Lin","year":"2018","journal-title":"Atmos. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4324\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:21:33Z","timestamp":1760167293000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4324"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,27]]},"references-count":49,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13214324"],"URL":"https:\/\/doi.org\/10.3390\/rs13214324","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,27]]}}}