{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T21:23:41Z","timestamp":1766697821413,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,11]],"date-time":"2018-12-11T00:00:00Z","timestamp":1544486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Key Research and Development Program of China","award":["2018YFC040174"],"award-info":[{"award-number":["2018YFC040174"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Exposure to fine particulate matter (PM2.5) is associated with adverse health impacts on the population. Satellite observations and machine learning algorithms have been applied to improve the accuracy of the prediction of PM2.5 concentrations. In this study, we developed a PM2.5 retrieval approach using machine-learning methods, based on aerosol products from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA Earth Observation System (EOS) Terra and Aqua polar-orbiting satellites, near-ground meteorological variables from the NASA Goddard Earth Observing System (GEOS), and ground-based PM2.5 observation data. Four models, which are orthogonal regression (OR), regression tree (Rpart), random forests (RF), and support vector machine (SVM), were tested and compared in the Beijing\u2013Tianjin\u2013Hebei (BTH) region of China in 2015. Aerosol products derived from the Terra and Aqua satellite sensors were also compared. The 10-repeat 5-fold cross-validation (10 \u00d7 5 CV) method was subsequently used to evaluate the performance of the different aerosol products and the four models. The results show that the performance of the Aqua dataset was better than that of the Terra dataset, and that the RF algorithm has the best predictive performance (Terra: R = 0.77, RMSE = 43.51 \u03bcg\/m3; Aqua: R = 0.85, RMSE = 33.90 \u03bcg\/m3). This study shows promise for predicting the spatiotemporal distribution of PM2.5 using the RF model and Aqua aerosol product with the assistance of PM2.5 site data.<\/jats:p>","DOI":"10.3390\/rs10122006","type":"journal-article","created":{"date-parts":[[2018,12,12]],"date-time":"2018-12-12T03:27:49Z","timestamp":1544585269000},"page":"2006","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Retrieval of Daily PM2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing\u2013Tianjin\u2013Hebei Region, China"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2059-6070","authenticated-orcid":false,"given":"Lijuan","family":"Li","sequence":"first","affiliation":[{"name":"The Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Beijing 100049, China"}]},{"given":"Baozhang","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Beijing 100049, China"},{"name":"The State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}]},{"given":"Yanhu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hebei Xingtai Environmental Monitoring Center, No. 998 Park East Street, Qiaoxi District, Xingtai 054000, China"}]},{"given":"Youzheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Yancheng Environmental Monitoring Center Station, No. 7 Wengang North Road, Tinghu District, Yancheng 224000, China"}]},{"given":"Yue","family":"Xian","sequence":"additional","affiliation":[{"name":"Yancheng Environmental Monitoring Center Station, No. 7 Wengang North Road, Tinghu District, Yancheng 224000, China"}]},{"given":"Guang","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Beijing 100049, China"},{"name":"The State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China"}]},{"given":"Huifang","family":"Zhang","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China"}]},{"given":"Lifeng","family":"Guo","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, No. 19A, Yuquan Road, Beijing 100049, China"},{"name":"The State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1289\/ehp.1408973","article-title":"Long-term PM2.5 Exposure and Neurological Hospital Admissions in the Northeastern United States","volume":"124","author":"Kioumourtzoglou","year":"2016","journal-title":"Environ. 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