DOI: 10.1016/j.atmosenv.2017.01.004
Scopus记录号: 2-s2.0-85008946361
论文题名: Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China: Methods and assessment
作者: Li T ; , Shen H ; , Zeng C ; , Yuan Q ; , Zhang L
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2017
卷: 152 起始页码: 477
结束页码: 489
语种: 英语
英文关键词: AOD
; Assessment
; GRNN
; PM2.5
; Point-surface fusion
; Satellite remote sensing
Scopus关键词: Atmospheric aerosols
; Backpropagation
; Linear regression
; Neural networks
; Particles (particulate matter)
; Pollution
; Remote sensing
; Satellite imagery
; Satellites
; Assessment
; Back propagation neural networks
; Fine particulate matter
; Generalized Regression Neural Network(GRNN)
; Geographically weighted regression
; GRNN
; Multiple linear regressions
; Satellite remote sensing
; Regression analysis
; accuracy assessment
; aerosol
; artificial neural network
; assessment method
; atmospheric modeling
; atmospheric pollution
; concentration (composition)
; ground-based measurement
; MODIS
; optical depth
; particulate matter
; pixel
; pollution monitoring
; regression analysis
; remote sensing
; satellite altimetry
; spatial distribution
; accuracy
; Article
; back propagation neural network
; China
; generalized regression neural network
; machine learning
; measurement
; optical depth
; particulate matter
; point surface fusion
; priority journal
; satellite imagery
; seasonal variation
; China
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: Fine particulate matter (PM2.5, particulate matters with aerodynamic diameters less than 2.5μm) is associated with adverse human health effects, and China is currently suffering from serious PM2.5pollution. To obtain spatially continuous ground-level PM2.5concentrations, several models established by the point-surface fusion of station measurements and satellite observations have been developed. However, how well do these models perform at national scale in China? Is there space to improve the estimation accuracy of PM2.5concentration? The contribution of this study is threefold. Firstly, taking advantage of the newly established national monitoring network, we develop a national-scale generalized regression neural network (GRNN) model to estimate PM2.5concentrations. Secondly, different assessment experiments are undertaken in time and space, to comprehensively evaluate and compare the performance of the widely used models. Finally, to map the yearly and seasonal mean distribution of PM2.5concentrations in China, a pixel-based merging strategy is proposed. The results indicate that the conventional models (linear regression, multiple linear regression, and semi-empirical model) do not obtain the expected results at national scale, with cross-validation R values of 0.49–0.55 and RMSEs of 30.80–31.51μg/m3, respectively. In contrast, the more advanced models (geographically weighted regression, back-propagation neural network, and GRNN) have great advantages in PM2.5estimation, with R values ranging from 0.61 to 0.82 and RMSEs from 20.93 to 28.68μg/m3, respectively. In particular, the proposed GRNN model obtains the best performance. Furthermore, the mapped PM2.5distribution retrieved from 3-km MODIS aerosol optical depth (AOD) products agrees quite well with the station measurements. The results also show that the approach used in this study has the capacity to provide reasonable information for the global monitoring of PM2.5pollution in China. © 2017 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82659
Appears in Collections: 气候变化事实与影响
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作者单位: School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei, China; The Collaborative Innovation Center for Geospatial Technology, Wuhan, Hubei, China; The Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan, Hubei, China; The State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China; School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei, China; The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China
Recommended Citation:
Li T,, Shen H,, Zeng C,et al. Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China: Methods and assessment[J]. Atmospheric Environment,2017-01-01,152