{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T15:48:06Z","timestamp":1780933686096,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2017,8,13]],"date-time":"2017-08-13T00:00:00Z","timestamp":1502582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The research work was supported by the National Geographical Conditions Monitoring Project","award":["B1701"],"award-info":[{"award-number":["B1701"]}]},{"name":"Basic Research Funding in CASM","award":["grant number 7771508"],"award-info":[{"award-number":["grant number 7771508"]}]},{"name":"the Program for the 2016 Young Academic and Technological Leaders of NASG, funded by Key Laboratory of Geo-informatics of NASG","award":["Q1702"],"award-info":[{"award-number":["Q1702"]}]},{"name":"the Open Fund from the Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation","award":["2016NGCM ZD03"],"award-info":[{"award-number":["2016NGCM ZD03"]}]},{"name":"the Fundamental Research Funds for the Central Universities of Central South University","award":["2016zzts089"],"award-info":[{"award-number":["2016zzts089"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>As an extension of the traditional Land Use Regression (LUR) modelling, the generalized additive model (GAM) was developed in recent years to explore the non-linear relationships between PM2.5 concentrations and the factors impacting it. However, these studies did not consider the loss of information regarding predictor variables. To address this challenge, a generalized additive model combining principal component analysis (PCA\u2013GAM) was proposed to estimate PM2.5 concentrations in this study. The reliability of PCA\u2013GAM for estimating PM2.5 concentrations was tested in the Beijing-Tianjin-Hebei (BTH) region over a one-year period as a case study. The results showed that PCA\u2013GAM outperforms traditional LUR modelling with relatively higher adjusted R2 (0.94) and lower RMSE (4.08 \u00b5g\/m3). The CV-adjusted R2 (0.92) is high and close to the model-adjusted R2, proving the robustness of the PCA\u2013GAM model. The PCA\u2013GAM model enhances PM2.5 estimate accuracy by improving the usage of the effective predictor variables. Therefore, it can be concluded that PCA\u2013GAM is a promising method for air pollution mapping and could be useful for decision makers taking a series of measures to combat air pollution.<\/jats:p>","DOI":"10.3390\/ijgi6080248","type":"journal-article","created":{"date-parts":[[2017,8,14]],"date-time":"2017-08-14T10:23:12Z","timestamp":1502706192000},"page":"248","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A Generalized Additive Model Combining Principal Component Analysis for PM2.5 Concentration Estimation"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3767-5821","authenticated-orcid":false,"given":"Shuang","family":"Li","sequence":"first","affiliation":[{"name":"College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"},{"name":"National Geographic Conditions Monitoring Research Center, Chinese Academy of Surveying and Mapping, Beijing 100830, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Hunan 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiyong","family":"Sang","sequence":"additional","affiliation":[{"name":"National Geographic Conditions Monitoring Research Center, Chinese Academy of Surveying and Mapping, Beijing 100830, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Hunan 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hu, L., Liu, J., and He, Z. (2016). Self-Adaptive Revised Land Use Regression Models for Estimating PM2.5 Concentrations in Beijing, China. Sustainability, 8.","DOI":"10.3390\/su8080786"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/10962247.2012.697445","article-title":"A reanalysis of fine particulate matter air pollution versus life expectancy in the United States","volume":"62","author":"Krstic","year":"2012","journal-title":"J. Air Waste Manag. Assoc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"034005","DOI":"10.1088\/1748-9326\/8\/3\/034005","article-title":"Global premature mortality due to anthropogenic outdoor air pollution and the contribution of past climate change","volume":"8","author":"Silva","year":"2013","journal-title":"Environ. Res. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1111\/j.1600-0668.2010.00691.x","article-title":"The analysis of PM2.5 and associated elements and their indoor\/outdoor pollution status in an urban area","volume":"21","author":"Lim","year":"2011","journal-title":"Indoor Air"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/1476-069X-12-43","article-title":"Long-term air pollution exposure and cardio- respiratory mortality: A review","volume":"12","author":"Hoek","year":"2013","journal-title":"Environ. Health"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"28","DOI":"10.2174\/1381612822666151109111712","article-title":"Air pollution exposure and blood pressure: An updated review of the literature","volume":"22","author":"Giorginia","year":"2016","journal-title":"Curr. Pharm. Des."},{"key":"ref_7","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":"J. Am. Med. Assoc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.envres.2014.10.035","article-title":"Associations between prenatal traffic-related air pollution exposure and birth weight: Modification by sex and maternal pre-pregnancy body mass index","volume":"137","author":"Lakshmanan","year":"2015","journal-title":"Environ. Res."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ross, Z., Ito, K., Johnson, S., Yee, M., Pezeshki, G., Clougherty, J.E., Savitz, D., and Matte, T. (2013). Spatial and temporal estimation of air pollutants in New York City: Exposure assignment for use in a birth outcomes study. Environ. Health, 12.","DOI":"10.1186\/1476-069X-12-51"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1080\/136588197242158","article-title":"Mapping urban air pollution using GIS: A regression-based approach","volume":"11","author":"Briggs","year":"1997","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_12","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 land use regression model","volume":"208","author":"Meng","year":"2015","journal-title":"Environ. Pollut."},{"key":"ref_13","first-page":"1088","article-title":"LUR-based simulation of the spatial distribution of PM2.5 of Wuhan","volume":"40","author":"Jiao","year":"2015","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhai, L., Zou, B., Fang, X., Luo, Y., Wan, N., and Li, S. (2017). Land Use Regression Modeling of PM2.5 Concentrations at Optimized Spatial Scales. Atmosphere, 8.","DOI":"10.3390\/atmos8010001"},{"key":"ref_15","first-page":"50","article-title":"Comparison of different spatial interpolation methods for PM2.5","volume":"41","author":"Li","year":"2016","journal-title":"Sci. Surv. Mapp."},{"key":"ref_16","first-page":"663","article-title":"The Comparision of Partial Least Squares Regression, Principal Component Regression and Ridge Regression with Multiple Line Regression for Predicting PM10 Concentration Level Based on Meteorological Parameters","volume":"13","author":"Esra","year":"2015","journal-title":"J. Data Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1021\/es403089q","article-title":"Western European land use regression incorporating satellite and ground-based measurements of NO2 and PM10","volume":"47","author":"Vienneau","year":"2013","journal-title":"Environ. Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.atmosenv.2013.02.037","article-title":"Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe\u2014The ESCAPE project","volume":"72","author":"Beelen","year":"2013","journal-title":"Atmos. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4977","DOI":"10.1016\/j.atmosenv.2011.05.073","article-title":"Spatial-temporal Variations of Regional Ambient Sulfur Dioxide Concentration and Source Contribution Analysis","volume":"45","author":"Zou","year":"2011","journal-title":"Atmos. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0269-7491(01)00308-6","article-title":"Predictive mapping of air pollution involving sparse spatial observations","volume":"119","author":"Diem","year":"2002","journal-title":"Environ. Pollut."},{"key":"ref_21","first-page":"297","article-title":"Generalized Additive Models","volume":"1","author":"Hastie","year":"1986","journal-title":"Stat. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zou, B., Chen, J., Zhai, L., Fang, X., and Zheng, Z. (2017). Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling. Remote Sens., 9.","DOI":"10.3390\/rs9010001"},{"key":"ref_23","first-page":"123","article-title":"Regional PM2.5 Concentration Effect Factors Identification and Correlation Analysis Based on GAM","volume":"38","author":"Jiao","year":"2015","journal-title":"Environ. Sci. Technol."},{"key":"ref_24","first-page":"22","article-title":"Interactive Effects of the Influencing Factors on the Changes of PM2.5 Concentration Based on GAM Model","volume":"38","author":"He","year":"2017","journal-title":"Environ. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1016\/j.atmosenv.2013.05.017","article-title":"Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA)","volume":"77","author":"Yahaya","year":"2013","journal-title":"Atmos. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1016\/j.envsoft.2004.09.001","article-title":"Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations","volume":"20","author":"Bakheit","year":"2005","journal-title":"Environ. Model. Softw."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1023\/A:1005190429095","article-title":"Evaluation of the Distribution of Mercury in Lakes in Nova Scotia and Newfoundland","volume":"117","author":"Vaidya","year":"2000","journal-title":"Water Air Soil Pollut."},{"key":"ref_28","first-page":"15","article-title":"Robust Principal Component Analysis and Geographically Weighted Regression Urbanization in the Twin Cities Metropolitan Area of Minnesota","volume":"20","author":"Debarchana","year":"2008","journal-title":"J. Urban Reg. Inf. Syst. Assoc."},{"key":"ref_29","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 Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zou, B., Xu, S., Sternberg, T., and Fang, X. (2016). Effect of Land Use and Cover Change on Air Quality in Urban Sprawl. Sustainability, 8.","DOI":"10.3390\/su8070677"},{"key":"ref_31","first-page":"58","article-title":"Multiple regression analysis on PM2.5 impact factors based on geographic conditions monitoring data","volume":"40","author":"An","year":"2015","journal-title":"Sci. Surv. Mapp."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/14786440109462720","article-title":"On lines and planes of closest fit to systems of points is space","volume":"62","author":"Pearson","year":"1901","journal-title":"Philos. Mag. Ser."},{"key":"ref_34","unstructured":"Kabacoff, R.I. (2011). R in Action: Data Analysis and Graphics with R, Manning Publications Co.. [2nd ed.]."},{"key":"ref_35","first-page":"138","article-title":"Subset selection in multiple linear regression: An improved Tabu search","volume":"40","author":"Bae","year":"2016","journal-title":"J. Korean Soc. Mar. Eng."},{"key":"ref_36","first-page":"459","article-title":"The Optimum Climate Forecasting Model Based on All Possible Rrgressions","volume":"15","author":"Shi","year":"1992","journal-title":"J. Nanjing Inst. Meteorol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Draper, N.R., and Smith, H. (1998). Applied Regression Analysis, John Wiley & Sons. [3th ed.].","DOI":"10.1002\/9781118625590"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"211","DOI":"10.3354\/meps08918","article-title":"Long-term changes in zooplankton size distribution in the Peruvian Humboldt Current System: Conditions favouring sardine or anchovy","volume":"422","author":"Swartzman","year":"2011","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_39","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_40","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_41","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/TPAMI.2009.187","article-title":"Sensitivity analysis of kappa-fold cross validation in prediction error estimation","volume":"32","author":"Rodriguez","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","first-page":"1051","article-title":"Stochastic Simulation of Land-Cover Change Using Geostatistics and Generalized Additive Models","volume":"68","author":"Brown","year":"2002","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1304","DOI":"10.1109\/TNNLS.2012.2199516","article-title":"Study on the impact of partition-induced dataset shift on k-fold cross-validation","volume":"23","author":"Saez","year":"2012","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_44","first-page":"685","article-title":"Application of land use regression to simulate ambient air PM10 and NO2 concentration in Tianjin City","volume":"29","author":"Chen","year":"2009","journal-title":"China Environ. Sci."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/8\/248\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:42:14Z","timestamp":1760208134000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/8\/248"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,13]]},"references-count":44,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2017,8]]}},"alternative-id":["ijgi6080248"],"URL":"https:\/\/doi.org\/10.3390\/ijgi6080248","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,8,13]]}}}