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The estimation of hourly PM2.5 concentrations across China based on a Spatial and Temporal Weighted Continuous Deep Neural Network (STWC-DNN)

文献类型：期刊论文

作者Wang, Zhen1,6; Li, Ruiyuan2; Chen, Ziyue2; Yao, Qi2; Gao, Bingbo3; Xu, Miaoqing2; Yang, Lin4; Li, Manchun4; Zhou, Chenghu4,5
刊名ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
出版日期2022-08-01
卷号190页码:38-55
关键词PM2.5 estimation AOD Himawari-8 Deep neural network Automatic spatiotemporal weight function Continuous spatial distribution
ISSN号0924-2716
DOI10.1016/j.isprsjprs.2022.05.011
英文摘要The continuous distributions of PM2.5 concentrations and predictor variables in the surrounding regions influence the PM2.5 concentrations in the prediction positions notably, yet few machine learning models quantified the spatially continuous interactions between PM2.5 concentrations and predictor variations, which limits the prediction accuracy. To fill this gap, a Spatial and Temporal Weighted Continuous Deep Neural Network (STWC-DNN) was proposed. For STWC-DNN, three sub-networks, Single Pixel Network (SPN), Multiple Station Network (MSN), and Continuous Region Network (CRN) were designed to analyze the influence of predictor variables at the prediction position, the influence of PM2.5 concentrations from surrounding stations, and the influence of continuous raster predictor variables from surrounding pixels respectively. STWC-DNN was experimented using hourly Himawari AOD data and the outputs were compared with a series of advanced models. STWC-DNN achieved higher accuracy than existing models and the sample-based, time-based, and station-based 10-fold cross-validation (CV) R-2 were 0.92, 0.90, and 0.79, respectively. The principle of establishing STWC-DNN sheds useful lights on the effective use of raster predictor variables and automatic spatiotemporal weight function to better estimate PM2.5 and other airborne pollutants based on multiple data sources. The codes of STWC-DNN are now available at https://github.com/wangzh2022/STWC-DNN.
WOS关键词GROUND-LEVEL PM2.5 ; AEROSOL OPTICAL-THICKNESS ; PARTICULATE MATTER ; TERM EXPOSURE ; SATELLITE ; HIMAWARI-8 ; AOD ; MORTALITY ; POLLUTION ; ELEMENTS
资助项目National Natural Science Foundation of China[42171399] ; National Natural Science Foundation of China[41901414] ; Beijing Munic-ipal Natural Science Foundation, China[8202031]
WOS研究方向Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000812357400002
出版者ELSEVIER
资助机构National Natural Science Foundation of China ; Beijing Munic-ipal Natural Science Foundation, China
源URL[http://ir.igsnrr.ac.cn/handle/311030/179243]  
专题资源与环境信息系统国家重点实验室_外文论文
作者单位1.China Univ Geosci, Sch Land Sci & Technol, Beijing 100083, Peoples R China
2.Beijing Normal Univ, Coll Global & Earth Syst Sci, State Key Lab Earth Surface Proc & Resource Ecol, 19 Xinjiekou St, Beijing 100875, Peoples R China
3.China Agr Univ, Coll Land Sci & Technol, Tsinghua East Rd, Beijing 100083, Peoples R China
4.Nanjing Univ, Sch Geog & Ocean Sci, Nanjing 210023, Peoples R China
5.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
6.Shanxi Prov Key Lab Resources Environm & Disaster, Jinzhong 030600, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Zhen,Li, Ruiyuan,Chen, Ziyue,et al. The estimation of hourly PM2.5 concentrations across China based on a Spatial and Temporal Weighted Continuous Deep Neural Network (STWC-DNN)[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2022,190:38-55.
APA Wang, Zhen.,Li, Ruiyuan.,Chen, Ziyue.,Yao, Qi.,Gao, Bingbo.,...&Zhou, Chenghu.(2022).The estimation of hourly PM2.5 concentrations across China based on a Spatial and Temporal Weighted Continuous Deep Neural Network (STWC-DNN).ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,190,38-55.
MLA Wang, Zhen,et al."The estimation of hourly PM2.5 concentrations across China based on a Spatial and Temporal Weighted Continuous Deep Neural Network (STWC-DNN)".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 190(2022):38-55.

入库方式： OAI收割

来源：地理科学与资源研究所

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