兰州大学机构库 >大气科学学院
High temporal and spatial resolution PM2.5 dataset acquisition and pollution assessment based on FY-4A TOAR data and deep forest model in China
Song, Zhihao1,2; Chen, B(陈斌)1,2; Zhang, Peng3; Guan, XD(管晓丹)1,2; Wang, X(王鑫)1,2; Ge, JM(葛觐铭)1,2; Hu, Xiuqing3; Zhang, Xingying3; Wang, Yixuan1,2
2022-08-15
Source PublicationAtmospheric Research
ISSN0169-8095
Volume274
AbstractDue to urbanization and industrialization, PM2.5 (particulate matter with a diameter less than 2.5 μm) pollution has become a serious environmental problem. The low spatial resolution and insufficient coverage of PM2.5 observation stations affect research on pollution causes and human health risks. With the launch of FY-4A, new generation of Chinese geostationary weather satellites, it is possible to obtain PM2.5 with high temporal and spatial resolution covering all China. In this study, FY-4A top-of-the-atmosphere reflectance data, meteorological factors, and geographic information were input into the deep forest (DF) model to obtain the hourly PM2.5 in China. The samples based 10-fold cross validation of DF with an hourly R2 of 0.83–0.88, and the root mean square error is 8.81–14.7 μg/m3, while the R2 of the 10-fold cross validation result based on sites was 0.77. The monthly (R2 = 0.98) and seasonal (R2 = 0.99) estimated results showed high consistency with the observations. Feature importance showed that the contribution of estimated features to the model varies with regions and seasons. Estimation results indicated the substantial spatiotemporal differences in PM2.5, and pollution was the highest between 09:00–10:00 and then gradually decreased. Regions with highest pollution of PM2.5 in China were mainly distributed in the Tarim Basin and Central China. The pollution assessment results in China indicated that: 1) In more than 80% of the winter days PM2.5 was higher than the World Health Organization interim target 3 (37.5 μg/m3); 2) The bimodal distribution of PM2.5 indicated that there are obvious differences in pollution between cities and suburbs; 3) In autumn and winter, the regions where population-weighted PM2.5 was higher than IT-3 were mainly in Beijing-Tianjin-Hebei, Central China, Guanzhong Plain, Sichuan Basin, and Yangtze River Delta. Our results showed that FY-4A has advantages of high resolution and coverage and thus shows great potential for estimating pollutants. © 2022 The Authors
KeywordForestry Health risks Image resolution Mean square error River pollution 10-fold cross-validation Bimodal distribution Deep forest Forest modelling FY-4a High spatial resolution High temporal resolution PM 2.5 Pollution assessment Temporal and spatial
PublisherElsevier Ltd
DOI10.1016/j.atmosres.2022.106199
Indexed ByEI
Language英语
EI Accession Number20221712012352
EI KeywordsGeostationary satellites
EI Classification Number453 Water Pollution ; 461.7 Health Care ; 655.2 Satellites ; 821.0 Woodlands and Forestry ; 922.2 Mathematical Statistics
Original Document TypeJournal article (JA)
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/481745
Collection大气科学学院
Corresponding AuthorChen, Bin
Affiliation1.Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou; 730000, China;
2.Collaborative Innovation Center for Western Ecological Safety, Lanzhou; 730000, China;
3.Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center, China Meteorological Administration, Beijing; 100081, China
First Author AffilicationCollege of Atmospheric Sciences;  Collaborative Innovation Center for Western Ecological Safety
Corresponding Author AffilicationCollege of Atmospheric Sciences;  Collaborative Innovation Center for Western Ecological Safety
Recommended Citation
GB/T 7714
Song, Zhihao,Chen, Bin,Zhang, Peng,et al. High temporal and spatial resolution PM2.5 dataset acquisition and pollution assessment based on FY-4A TOAR data and deep forest model in China[J]. Atmospheric Research,2022,274.
APA Song, Zhihao.,Chen, Bin.,Zhang, Peng.,Guan, Xiaodan.,Wang, Xin.,...&Wang, Yixuan.(2022).High temporal and spatial resolution PM2.5 dataset acquisition and pollution assessment based on FY-4A TOAR data and deep forest model in China.Atmospheric Research,274.
MLA Song, Zhihao,et al."High temporal and spatial resolution PM2.5 dataset acquisition and pollution assessment based on FY-4A TOAR data and deep forest model in China".Atmospheric Research 274(2022).
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