{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:37:52Z","timestamp":1773801472524,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2016,12,22]],"date-time":"2016-12-22T00:00:00Z","timestamp":1482364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite-based PM2.5 concentration estimation is growing as a popular solution to map the PM2.5 spatial distribution due to the insufficiency of ground-based monitoring stations. However, those applications usually suffer from the simple hypothesis that the influencing factors are linearly correlated with PM2.5 concentrations, though non-linear mechanisms indeed exist in their interactions. Taking the Beijing-Tianjin-Hebei (BTH) region in China as a case, this study developed a generalized additive modeling (GAM) method for satellite-based PM2.5 concentration mapping. In this process, the linear and non-linear relationships between PM2.5 variation and associated contributing factors, such as the aerosol optical depth (AOD), industrial sources, land use type, road network, and meteorological variables, were comprehensively considered. The reliability of the GAM models was validated by comparison with typical linear land use regression (LUR) models. Results show that GAM modeling outperforms LUR modeling at both the annual and seasonal scale, with obvious higher model fitting-based adjusted R2 and lower RMSEs. This is confirmed by the cross-validation-based adjusted R2 with values of GAM-based spring, summer, autumn, winter, and annual models, which are 0.92, 0.78, 0.87, 0.85, and 0.90, respectively, while those of LUR models are 0.87, 0.71, 0.84, 0.84, and 0.85, respectively. Different to the LUR-based hypothesis of the \u201cstraight line\u201d relations, the \u201csmoothed curves\u201d from GAM-based apportionment analysis reveals that factors contributing to PM2.5 variation are unstable with the alternate linear and non-linear relations. The GAM model-based PM2.5 concentration surfaces clearly demonstrate their superiority in disclosing the heterogeneous PM2.5 concentrations to the discrete observations. It can be concluded that satellite-based PM2.5 concentration mapping could be greatly improved by GAM modeling given its simultaneous considerations of the linear and non-linear influencing mechanisms of PM2.5.<\/jats:p>","DOI":"10.3390\/rs9010001","type":"journal-article","created":{"date-parts":[[2016,12,22]],"date-time":"2016-12-22T09:48:53Z","timestamp":1482400133000},"page":"1","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":84,"title":["Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling"],"prefix":"10.3390","volume":"9","author":[{"given":"Bin","family":"Zou","sequence":"first","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"},{"name":"Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingwen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6054-7866","authenticated-orcid":false,"given":"Liang","family":"Zhai","sequence":"additional","affiliation":[{"name":"National Geographic Conditions Monitoring Research Center, Chinese Academy of Surveying and Mapping, Beijing 100830, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8603-7335","authenticated-orcid":false,"given":"Zhong","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1080\/10473289.2006.10464485","article-title":"Health effects of fine particulate air pollution: Lines that connect","volume":"56","author":"Pope","year":"2006","journal-title":"J. Air Waste Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1289\/ehp.1002255","article-title":"Particulate matter-induced health effects: Who is susceptible?","volume":"119","author":"Sacks","year":"2011","journal-title":"Environ. Health Perspect."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1289\/ehp.1104049","article-title":"Risk of nonaccidental and cardiovascular mortality in relation to long-term exposure to low concentrations of fine particulate matter: A Canadian national-level cohort study","volume":"120","author":"Crouse","year":"2012","journal-title":"Environ. Health Perspect."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1039\/b813889c","article-title":"Air pollution exposure assessment methods utilized in epidemiological studies","volume":"11","author":"Zou","year":"2009","journal-title":"J. Environ. Monit."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1016\/S0140-6736(12)61766-8","article-title":"A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990\u20132010: A systematic analysis for the Global Burden of Disease Study 2010","volume":"380","author":"Lim","year":"2013","journal-title":"Lancet"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11109","DOI":"10.1021\/es502113p","article-title":"Fifteen-year global time series of satellite-derived fine particulate matter","volume":"48","author":"Boys","year":"2014","journal-title":"Environ. Sci. Technol."},{"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":"3663","DOI":"10.1016\/j.atmosenv.2011.04.032","article-title":"A study on the potential applications of satellite data in air quality monitoring and forecasting","volume":"45","author":"Li","year":"2011","journal-title":"Atmos. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1109\/LGRS.2016.2520480","article-title":"High-resolution satellite mapping of fine particulates based on geographically weighted regression","volume":"13","author":"Zou","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2014.08.008","article-title":"A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China","volume":"154","author":"Song","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_11","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_12","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_13","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1016\/j.simpat.2010.01.005","article-title":"Performance of AERMOD at different time scales","volume":"18","author":"Zou","year":"2010","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/s10750-007-9108-z","article-title":"Habitat characteristics of crayfish (Paranephrops planifrons) in New Zealand streams using generalised additive models (GAMs)","volume":"596","author":"Jowett","year":"2008","journal-title":"Hydrobiologia"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.ecolmodel.2006.05.022","article-title":"Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions","volume":"199","author":"Leathwick","year":"2006","journal-title":"Ecol. Model."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.atmosenv.2012.02.015","article-title":"Spatiotemporal modeling with temporal-invariant variogram subgroups to estimate fine particulate matter PM2.5 concentrations","volume":"54","author":"Chen","year":"2012","journal-title":"Atmos. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.atmosenv.2013.01.038","article-title":"Estimating spatiotemporal variability of ambient air pollutant concentrations with a hierarchical model","volume":"71","author":"Li","year":"2013","journal-title":"Atmos. Environ."},{"key":"ref_18","unstructured":"Ma, Z.W., Hu, X.F., Sayer, A.M., Levy, R., Zhang, Q., Xue, Y.G., Tong, S.L., Bi, J., Huang, L., and Liu, Y. (2015). Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004\u20132013. Environ. Health Perspect."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"7069","DOI":"10.1021\/es3022705","article-title":"China needs a tighter PM2.5 limit and a change in priorities","volume":"46","author":"Chang","year":"2012","journal-title":"Environ. Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1016\/j.scitotenv.2015.01.005","article-title":"Health impacts and economic losses assessment of the 2013 severe haze event in Beijing area","volume":"511","author":"Gao","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3443","DOI":"10.1016\/j.atmosenv.2004.02.052","article-title":"The characteristics of carbonaceous species and their sources in PM2.5 in Beijing","volume":"38","author":"Dan","year":"2004","journal-title":"Atmos. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3967","DOI":"10.1016\/j.atmosenv.2005.03.036","article-title":"Seasonal trends in PM2.5 source contributions in Beijing, China","volume":"39","author":"Zheng","year":"2005","journal-title":"Atmos. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2125","DOI":"10.5194\/acp-14-2125-2014","article-title":"Column aerosol optical properties and aerosol radiative forcing during a serious haze-fog month over North China Plain in 2013 based on ground-based sunphotometer measurements","volume":"14","author":"Che","year":"2014","journal-title":"Atmos. Chem. Phys."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"17373","DOI":"10.1073\/pnas.1419604111","article-title":"Elucidating severe urban haze formation in China","volume":"111","author":"Guo","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.envres.2015.01.003","article-title":"A land use regression model for estimating the NO2 concentration in shanghai, China","volume":"137","author":"Meng","year":"2015","journal-title":"Environ. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2016.08.027","article-title":"Satellite-based ground PM2.5 estimation using timely structure adaptive modeling","volume":"186","author":"Fang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1175\/JAS3385.1","article-title":"The MODIS aerosol algorithm, products, and validation","volume":"62","author":"Remer","year":"2005","journal-title":"J. Atmos. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Levy, R.C., Remer, L.A., Mattoo, S., Vermote, E.F., and Kaufman, Y.J. (2007). Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance. J. Geophys. Res. Atmos., 112.","DOI":"10.1029\/2006JD007811"},{"key":"ref_30","unstructured":"Levy, R.C., Remer, L.A., Tanre\u0301, D., Mattoo, S., and Kaufman, Y.J. Algorithm for Remote Sensing of Tropospheric Aerosol over Dark Targets from MODIS: Collections 005 and 051: Revision 2; 2009, Available online: http:\/\/modisatmos.gsfc.nasa.gov\/_docs\/ATBD_MOD04_C005_rev2.pdf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"10399","DOI":"10.5194\/acp-10-10399-2010","article-title":"Global evaluation of the Collection 5 MODIS dark-target aerosol products over land","volume":"10","author":"Levy","year":"2010","journal-title":"Atmos. Chem. Phys."},{"key":"ref_32","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_33","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_34","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.rse.2009.09.011","article-title":"A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements","volume":"114","author":"Tian","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"491","DOI":"10.5094\/APR.2014.058","article-title":"Sulfur dioxide exposure and environmental justice: A multi-scale and source-specific perspective","volume":"5","author":"Zou","year":"2014","journal-title":"Atmos. Pollut. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"8698","DOI":"10.1038\/srep08698","article-title":"Performance comparison of LUR and OK in PM2.5 concentration mapping: A multidimensional perspective","volume":"5","author":"Zou","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_37","first-page":"297","article-title":"Generalized additive models","volume":"1","author":"Hastie","year":"1986","journal-title":"Stat. Sci."},{"key":"ref_38","unstructured":"Duchon, J. (1977). Constructive Theory of Functions of Several Variables, Springer."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1111\/1467-9868.00374","article-title":"Thin plate regression splines","volume":"65","author":"Wood","year":"2003","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_40","first-page":"20","article-title":"MGCV: GAMs and generalized ridge regression for R","volume":"1","author":"Wood","year":"2001","journal-title":"R News"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chambers, J.M., and Hastie, T.J. (1991). Statistical Models in S, CRC Press, Inc.","DOI":"10.1007\/978-3-642-50096-1_48"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/TPAMI.2009.187","article-title":"Sensitivity analysis of k-fold cross validation in prediction error estimation","volume":"32","author":"Rodriguez","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","unstructured":"Chinese National Ambient Air Quality Standards, GB 3095-2012, Available online: http:\/\/kjs.mep.gov.cn\/hjbhbz\/bzwb\/dqhjbh\/dqhjzlbz\/201203\/t20120302_224165.htm."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.envpol.2015.09.042","article-title":"Estimating ground-level PM10 in a Chinese city by combining satellite data, meteorological information and a land use regression model","volume":"208","author":"Meng","year":"2016","journal-title":"Environ. Pollut."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/1\/1\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:29:06Z","timestamp":1760210946000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/1\/1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12,22]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2017,1]]}},"alternative-id":["rs9010001"],"URL":"https:\/\/doi.org\/10.3390\/rs9010001","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,12,22]]}}}