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Retrieval of Daily PM2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing-Tianjin-Hebei Region, China

文献类型：期刊论文

作者Li, Lijuan2,3; Chen, Baozhang1,3,4; Zhang, Yanhu5; Zhao, Youzheng6; Xian, Yue6; Xu, Guang3,4; Zhang, Huifang4; Guo, Lifeng3,4
刊名REMOTE SENSING
出版日期2018-12-01
卷号10期号:12页码:17
关键词daily PM2 5concentrations remote sensing MODIS AOD machine learning algorithm spatial and temporal distribution
ISSN号2072-4292
DOI10.3390/rs10122006
通讯作者Chen, Baozhang(baozhang.chen@igsnrr.ac.cn)
英文摘要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-Tianjin-Hebei (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 x 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 g/m(3); Aqua: R = 0.85, RMSE = 33.90 g/m(3)). 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.
WOS关键词AEROSOL OPTICAL DEPTH ; GROUND-LEVEL PM2.5 ; SPATIOTEMPORAL VARIABILITY ; LINEAR-REGRESSION ; UNITED-STATES ; MODIS ; PRODUCTS ; MISR ; CLASSIFICATION ; ALGORITHMS
资助项目National Key R&D Program of China[2018YFA0606001] ; National Key R&D Program of China[2017YFA0604301] ; National Key R&D Program of China[2017YFA0604302] ; National Key R&D Program of China[2017YFC0503904] ; National Key R&D Program of China[088RA901YA] ; State Key Laboratory of Resources and Environment Information System[41771114] ; National Natural Science Foundation of China
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000455637600148
出版者MDPI
资助机构National Key R&D Program of China ; State Key Laboratory of Resources and Environment Information System ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/50453]  
专题中国科学院地理科学与资源研究所
通讯作者Chen, Baozhang
作者单位1.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, 11A Datun Rd, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, 11A Datun Rd, Beijing 100101, Peoples R China
5.Hebei Xingtai Environm Monitoring Ctr, 998 Pk East St, Qiaoxi Dist 054000, Xingtai, Peoples R China
6.Yancheng Environm Monitoring Ctr Stn, 7 Wengang North Rd, Tinghu Dist 224000, Yancheng, Peoples R China
推荐引用方式
GB/T 7714		 Li, Lijuan,Chen, Baozhang,Zhang, Yanhu,et al. Retrieval of Daily PM2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing-Tianjin-Hebei Region, China[J]. REMOTE SENSING,2018,10(12):17.
APA Li, Lijuan.,Chen, Baozhang.,Zhang, Yanhu.,Zhao, Youzheng.,Xian, Yue.,...&Guo, Lifeng.(2018).Retrieval of Daily PM2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing-Tianjin-Hebei Region, China.REMOTE SENSING,10(12),17.
MLA Li, Lijuan,et al."Retrieval of Daily PM2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing-Tianjin-Hebei Region, China".REMOTE SENSING 10.12(2018):17.

入库方式： OAI收割

来源：地理科学与资源研究所

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