Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2017GL075710 |
Estimating Ground-Level PM2.5 by Fusing Satellite and Station Observations: A Geo-Intelligent Deep Learning Approach | |
Li, Tongwen1; Shen, Huanfeng1,2,3; Yuan, Qiangqiang2,4; Zhang, Xuechen1; Zhang, Liangpei2,5 | |
2017-12-16 | |
发表期刊 | GEOPHYSICAL RESEARCH LETTERS |
ISSN | 0094-8276 |
EISSN | 1944-8007 |
出版年 | 2017 |
卷号 | 44期号:23 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
英文摘要 | Fusing satellite observations and station measurements to estimate ground-level PM2.5 is promising for monitoring PM2.5 pollution. A geo-intelligent approach, which incorporates geographical correlation into an intelligent deep learning architecture, is developed to estimate PM2.5. Specifically, it considers geographical distance and spatiotemporally correlated PM2.5 in a deep belief network (denoted as Geoi-DBN). Geoi-DBN can capture the essential features associated with PM2.5 from latent factors. It was trained and tested with data from China in 2015. The results show that Geoi-DBN performs significantly better than the traditional neural network. The out-of-sample cross-validation R-2 increases from 0.42 to 0.88, and RMSE decreases from 29.96 to 13.03 mu g/m(3). On the basis of the derived PM(2.)5 distribution, it is predicted that over 80% of the Chinese population live in areas with an annual mean PM2.5 of greater than 35 mu g/m(3). This study provides a new perspective for air pollution monitoring in large geographic regions. |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000419102400035 |
WOS关键词 | AEROSOL OPTICAL DEPTH ; REMOTE-SENSING DATA ; MULTIANGLE IMAGING SPECTRORADIOMETER ; FINE PARTICULATE MATTER ; EXPOSURE ASSESSMENT ; AIR-POLLUTION ; UNITED-STATES ; CHINA ; LAND ; GENERATION |
WOS类目 | Geosciences, Multidisciplinary |
WOS研究方向 | Geology |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/27086 |
专题 | 气候变化 |
作者单位 | 1.Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Hubei, Peoples R China; 2.Collaborat Innovat Ctr Geospatial Technol, Wuhan, Hubei, Peoples R China; 3.Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan, Hubei, Peoples R China; 4.Wuhan Univ, Sch Geodesy & Geomat, Wuhan, Hubei, Peoples R China; 5.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Hubei, Peoples R China |
推荐引用方式 GB/T 7714 | Li, Tongwen,Shen, Huanfeng,Yuan, Qiangqiang,et al. Estimating Ground-Level PM2.5 by Fusing Satellite and Station Observations: A Geo-Intelligent Deep Learning Approach[J]. GEOPHYSICAL RESEARCH LETTERS,2017,44(23). |
APA | Li, Tongwen,Shen, Huanfeng,Yuan, Qiangqiang,Zhang, Xuechen,&Zhang, Liangpei.(2017).Estimating Ground-Level PM2.5 by Fusing Satellite and Station Observations: A Geo-Intelligent Deep Learning Approach.GEOPHYSICAL RESEARCH LETTERS,44(23). |
MLA | Li, Tongwen,et al."Estimating Ground-Level PM2.5 by Fusing Satellite and Station Observations: A Geo-Intelligent Deep Learning Approach".GEOPHYSICAL RESEARCH LETTERS 44.23(2017). |
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