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Spatio-Temporal Pattern Estimation of PM2.5 in Beijing-Tianjin-Hebei Region Based on MODIS AOD and Meteorological Data Using the Back Propagation Neural Network
Ni, Xiliang1; Cao, Chunxiang1; Zhou, Yuke2; Cui, Xianghui3; Singh, Ramesh P.4
2018-03-01
Source PublicationATMOSPHERE
ISSN2073-4433
Volume9Issue:3Pages:14
Corresponding AuthorCao, Chunxiang(caocx@radi.ac.cn)
AbstractWith the economic growth and increasing urbanization in the last three decades, the air quality over China has continuously degraded, which poses a great threat to human health. The concentration of fine particulate matter (PM2.5) directly affects the mortality of people living in the polluted areas where air quality is poor. The Beijing-Tianjin-Hebei (BTH) region, one of the well organized urban regions in northern China, has suffered with poor air quality and atmospheric pollution due to recent growth of the industrial sector and vehicle emissions. In the present study, we used the back propagation neural network model approach to estimate the spatial distribution of PM2.5 concentration in the BTH region for the period January 2014-December 2016, combining the satellite-derived aerosol optical depth (S-DAOD) and meteorological data. The results were validated using the ground PM2.5 data. The general method including all PM2.5 training data and 10-fold cross-method have been used for validation for PM2.5 estimation (R-2 = 0.68, RMSE = 20.99 for general validation; R-2 = 0.54, RMSE = 24.13 for cross-method validation). The study provides a new approach to monitoring the distribution of PM2.5 concentration. The results discussed in the present paper will be of great help to government agencies in developing and implementing environmental conservation policy.
Keywordaerosol optical depth PM2.5 MODIS air pollution artificial neural network Beijing-Tianjin-Hebei (BTH) region back propagation neural network
DOI10.3390/atmos9030105
WOS KeywordGROUND-LEVEL PM2.5 ; REMOTE-SENSING DATA ; PARTICULATE AIR-POLLUTION ; CONTINENTAL CHINA ; QUALITY ; MATTER ; MODEL
Indexed BySCI
Language英语
Funding ProjectYouth Science Fund Project[41701408] ; National Natural Science Foundation[41601478] ; National Key R&D Program of China[2017YFD0600903] ; National Key R&D Program of China[2016YFC0500103]
Funding OrganizationYouth Science Fund Project ; National Natural Science Foundation ; National Key R&D Program of China
WOS Research AreaMeteorology & Atmospheric Sciences
WOS SubjectMeteorology & Atmospheric Sciences
WOS IDWOS:000428305800027
PublisherMDPI
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54813
Collection中国科学院地理科学与资源研究所
Corresponding AuthorCao, Chunxiang
Affiliation1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Inst Geog & Nat Resources Res, Ecol Observing Network & Modeling Lab, Beijing 100101, Peoples R China
3.Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Peoples R China
4.Chapman Univ, Schmid Coll Sci & Technol, Sch Life & Environm Sci, Orange, CA 92866 USA
Recommended Citation
GB/T 7714
Ni, Xiliang,Cao, Chunxiang,Zhou, Yuke,et al. Spatio-Temporal Pattern Estimation of PM2.5 in Beijing-Tianjin-Hebei Region Based on MODIS AOD and Meteorological Data Using the Back Propagation Neural Network[J]. ATMOSPHERE,2018,9(3):14.
APA Ni, Xiliang,Cao, Chunxiang,Zhou, Yuke,Cui, Xianghui,&Singh, Ramesh P..(2018).Spatio-Temporal Pattern Estimation of PM2.5 in Beijing-Tianjin-Hebei Region Based on MODIS AOD and Meteorological Data Using the Back Propagation Neural Network.ATMOSPHERE,9(3),14.
MLA Ni, Xiliang,et al."Spatio-Temporal Pattern Estimation of PM2.5 in Beijing-Tianjin-Hebei Region Based on MODIS AOD and Meteorological Data Using the Back Propagation Neural Network".ATMOSPHERE 9.3(2018):14.
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