{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T22:31:52Z","timestamp":1784413912729,"version":"3.55.0"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,4,5]],"date-time":"2017-04-05T00:00:00Z","timestamp":1491350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation","award":["41401389, 41671342"],"award-info":[{"award-number":["41401389, 41671342"]}]},{"name":"Chinese Postdoctoral Science Foundation","award":["2015M570668, 2016T90732"],"award-info":[{"award-number":["2015M570668, 2016T90732"]}]},{"name":"Public Projects of Zhejiang Province","award":["2016C33021"],"award-info":[{"award-number":["2016C33021"]}]},{"name":"Zhejiang University Student Science and Technology Innovation and Xin-Miao Talented Plan Program","award":["2016R405088"],"award-info":[{"award-number":["2016R405088"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Over the past decades, regional haze episodes have frequently occurred in eastern China, especially in the Yangtze River Delta (YRD). Satellite derived Aerosol Optical Depth (AOD) has been used to retrieve the spatial coverage of PM2.5 concentrations. To improve the retrieval accuracy of the daily AOD-PM2.5 model, various auxiliary variables like meteorological or geographical factors have been adopted into the Geographically Weighted Regression (GWR) model. However, these variables are always arbitrarily selected without deep consideration of their potentially varying temporal or spatial contributions in the model performance. In this manuscript, we put forward an automatic procedure to select proper auxiliary variables from meteorological and geographical factors and obtain their optimal combinations to construct four seasonal GWR models. We employ two different schemes to comprehensively test the performance of our proposed GWR models: (1) comparison with other regular GWR models by varying the number of auxiliary variables; and (2) comparison with observed ground-level PM2.5 concentrations. The result shows that our GWR models of \u201cAOD + 3\u201d with three common meteorological variables generally perform better than all the other GWR models involved. Our models also show powerful prediction capabilities in PM2.5 concentrations with only slight overfitting. The determination coefficients R2 of our seasonal models are 0.8259 in spring, 0.7818 in summer, 0.8407 in autumn, and 0.7689 in winter. Also, the seasonal models in summer and autumn behave better than those in spring and winter. The comparison between seasonal and yearly models further validates the specific seasonal pattern of auxiliary variables of the GWR model in the YRD. We also stress the importance of key variables and propose a selection process in the AOD-PM2.5 model. Our work validates the significance of proper auxiliary variables in modelling the AOD-PM2.5 relationships and provides a good alternative in retrieving daily PM2.5 concentrations from remote sensing images in the YRD.<\/jats:p>","DOI":"10.3390\/rs9040346","type":"journal-article","created":{"date-parts":[[2017,4,5]],"date-time":"2017-04-05T10:33:01Z","timestamp":1491388381000},"page":"346","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Modelling Seasonal GWR of Daily PM2.5 with Proper Auxiliary Variables for the Yangtze River Delta"],"prefix":"10.3390","volume":"9","author":[{"given":"Man","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Geography and Spatial Information Techniques, Ningbo University, 818 Fenghua Road, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Geography and Spatial Information Techniques, Ningbo University, 818 Fenghua Road, Ningbo 315211, China"},{"name":"State Key Lab of Information Engineering on Survey, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Geography and Spatial Information Techniques, Ningbo University, 818 Fenghua Road, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dianfa","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Geography and Spatial Information Techniques, Ningbo University, 818 Fenghua Road, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1038\/nature01091","article-title":"A satellite view of aerosols in the climate system","volume":"419","author":"Kaufman","year":"2002","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.atmosenv.2013.01.017","article-title":"Assessment of human exposure level to PM10 in China","volume":"70","author":"An","year":"2013","journal-title":"Atmos. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1056\/NEJM199312093292401","article-title":"An association between air pollution and mortality in six U.S. Cities","volume":"329","author":"Dockery","year":"1993","journal-title":"N. Engl. J. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.envint.2012.10.011","article-title":"Acute health impacts of airborne particles estimated from satellite remote sensing","volume":"51","author":"Wang","year":"2013","journal-title":"Environ. Int."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1289\/ehp.1307277","article-title":"Long-term exposure to fine particulate matter: Association with nonaccidental and cardiovascular mortality in the agricultural health study cohort","volume":"122","author":"Weichenthal","year":"2014","journal-title":"Environ. Health Perspect."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1476-072X-8-27","article-title":"Spatial analysis of MODIS aerosol optical depth, PM2.5, and chronic coronary heart disease","volume":"8","author":"Hu","year":"2009","journal-title":"Int. J. Health Geogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1289\/ehp.1409111","article-title":"Low-concentration PM2.5 and mortality: Estimating acute and chronic effects in a population-based study","volume":"124","author":"Shi","year":"2016","journal-title":"Environ. Health Perspect."},{"key":"ref_8","unstructured":"Wei, Y., Zang, Z., Zhang, L., Yi, L., and Wang, W. (2016). Estimating national-scale ground-level PM25 concentration in china using geographically weighted regression based on MODIS and MISR AOD. Environ. Sci. Pollut. Res., 1\u201312."},{"key":"ref_9","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_10","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_11","doi-asserted-by":"crossref","first-page":"9665","DOI":"10.1007\/s11356-014-2996-3","article-title":"Spatiotemporal distribution and short-term trends of particulate matter concentration over China, 2006\u20132010","volume":"21","author":"Yao","year":"2014","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.scitotenv.2014.11.007","article-title":"Spatial and seasonal variations of PM2.5 mass and species during 2010 in Xi\u2019an, China","volume":"508","author":"Wang","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_13","unstructured":"Strawa, A.W., Chatfield, R.B., Legg, M.J., Scarnato, B.V., and Esswein, R. (2013, January 9\u201313). In improving retrievals of regional PM2.5 concentrations from MODIS and OMI multi-satellite observations. Proceedings of the American Geophysical Union 2013 Fall Meeting, San Francisco, CA, USA."},{"key":"ref_14","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_15","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 PM 2.5 estimation over the Pearl River Delta region in China","volume":"154","author":"Song","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"184","DOI":"10.3390\/rs8030184","article-title":"National-scale estimates of ground-level PM2.5 concentration in China using geographically weighted regression based on 3 km resolution MODIS AOD","volume":"8","author":"You","year":"2016","journal-title":"Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"180","DOI":"10.3390\/ijerph13020180","article-title":"Comparison of four ground-level PM2.5 estimation models using parasol aerosol optical depth data from China","volume":"13","author":"Guo","year":"2016","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_18","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_19","first-page":"2285","article-title":"Mapping annual mean ground-level PM2.5 concentrations using multiangle imaging spectroradiometer aerosol optical thickness over the contiguous united states","volume":"109","author":"Liu","year":"2004","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.rse.2014.09.015","article-title":"Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM 2.5","volume":"156","author":"Lin","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.rse.2015.02.005","article-title":"Remote sensing of atmospheric fine particulate matter (PM2.5) mass concentration near the ground from satellite observation","volume":"160","author":"Zhang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_23","unstructured":"Ma, Z. (2015). Study on Spatiotemporal Distribution of PM2.5 in China Using Satellite Remote Sensing. [Ph.D. Thesis, Nanjing University]."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.rse.2015.11.019","article-title":"Remote sensing of atmospheric particulate mass of dry PM2.5 near the ground: Method validation using ground-based measurements","volume":"173","author":"Li","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_25","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 (PM 2.5) 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_26","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.rse.2015.07.020","article-title":"Estimating ground-level PM10 concentration in northwestern China using geographically weighted regression based on satellite AOD combined with Calipso and MODIS fire count","volume":"168","author":"You","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envres.2012.11.003","article-title":"Estimating ground-level PM(2.5) concentrations in the southeastern U.S. Using geographically weighted regression","volume":"121","author":"HuHu","year":"2012","journal-title":"Environ. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.atmosenv.2016.03.040","article-title":"Satellite-derived high resolution PM concentrations in Yangtze river delta region of China using improved linear mixed effects model","volume":"133","author":"Ma","year":"2016","journal-title":"Atmos. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bai, Y., Wu, L., Qin, K., Zhang, Y., Shen, Y., and Zhou, Y. (2016). A geographically and temporally weighted regression model for ground-level PM2.5 estimation from satellite-derived 500 m resolution AOD. Remote Sens., 8.","DOI":"10.3390\/rs8030262"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6267","DOI":"10.1016\/j.atmosenv.2011.08.066","article-title":"Assessing temporally and spatially resolved PM 2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements","volume":"45","author":"Kloog","year":"2011","journal-title":"Atmos. Environ."},{"key":"ref_31","unstructured":"Hu, X. (2015, January 14\u201318). Estimation of PM2.5 concentrations in the conterminous U.S. using MODIS data and a three-stage model. Proceedings of the American Geophysical Union 2015 Fall Meeting, San Francisco, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10482","DOI":"10.1021\/acs.est.5b02076","article-title":"High-resolution satellite-derived PM2.5 from optimal estimation and geographically weighted regression over North America","volume":"49","author":"Martin","year":"2015","journal-title":"Environ. Sci. Technol."},{"key":"ref_33","unstructured":"Jiang, M., and Sun, W. (2016, January 10\u201318). Investigating meteorological and geographical effect in remote sensing retrieval of PM2.5 concentration in Yangtze River Delta. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium, Beijing, China."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2016.08.027","article-title":"Satellite-based ground PM 2.5 estimation using timely structure adaptive modeling","volume":"186","author":"Fang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_35","first-page":"694","article-title":"Assessment of the Trend of Heavy PM2.5 Pollution Days and Economic Loss of Health Effects during 2001\u20132013","volume":"51","author":"Mu","year":"2015","journal-title":"Acta Sci. Nat. Univ. Pekin."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"4573","DOI":"10.5194\/acp-14-4573-2014","article-title":"Impact of biomass burning on haze pollution in the Yangtze River Delta, China: A case study of summer in 2011","volume":"14","author":"Cheng","year":"2014","journal-title":"Atmos. Chem. Phys."},{"key":"ref_38","first-page":"3119","article-title":"Esitmation of PM2.5 concentration over the Yangtze Delta using remote sensing: analysis of spatial and temporal variations","volume":"36","author":"Xu","year":"2015","journal-title":"Environ. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1068\/a301905","article-title":"Geographically weighted regression: A natural evolution of the expansion method for spatial data analysis","volume":"30","author":"Fotheringham","year":"1998","journal-title":"Environ. Plan. A"},{"key":"ref_40","first-page":"87","article-title":"Comparison of values of Pearson\u2019s and Spearman's correlation coefficients on the same sets of data","volume":"30","author":"Hauke","year":"2015","journal-title":"Quaest. Geogr."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1038\/sj.bdj.4812743","article-title":"Problems of correlations between explanatory variables in multiple regression analyses in the dental literature","volume":"199","author":"Tu","year":"2005","journal-title":"Br. Dent. J."},{"key":"ref_42","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":"2013","journal-title":"Int. J. Environ. Res. Public Health"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/4\/346\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:32:02Z","timestamp":1760207522000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/4\/346"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,5]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2017,4]]}},"alternative-id":["rs9040346"],"URL":"https:\/\/doi.org\/10.3390\/rs9040346","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,4,5]]}}}