DOI: 10.1007/s10584-013-1029-4
Scopus记录号: 2-s2.0-84902293838
论文题名: Improving resolution of a spatial air pollution inventory with a statistical inference approach
作者: Horabik J. ; Nahorski Z.
刊名: Climatic Change
ISSN: 0165-0009
EISSN: 1573-1480
出版年: 2014
卷: 124, 期: 3 起始页码: 575
结束页码: 589
语种: 英语
Scopus关键词: Climate change
; Climate models
; Ammonia emissions
; Conditional autoregressive
; Emission inventories
; Linear regression models
; Maximum likelihood approaches
; Optimal predictor
; Spatial dependence
; Statistical inference
; Regression analysis
; ammonia
; atmospheric pollution
; data set
; emission inventory
; prediction
; resolution
; spatial analysis
英文摘要: This paper presents a novel approach to allocation of spatially correlated data, such as emission inventories, to finer spatial scales, conditional on covariate information observable in a fine grid. Spatial dependence is modelled with the conditional autoregressive structure introduced into a linear model as a random effect. The maximum likelihood approach to inference is employed, and the optimal predictors are developed to assess missing values in a fine grid. An example of ammonia emission inventory is used to illustrate the potential usefulness of the proposed technique. The results indicate that inclusion of a spatial dependence structure can compensate for less adequate covariate information. For the considered ammonia inventory, the fourfold allocation benefited greatly from incorporation of the spatial component, while for the ninefold allocation this advantage was limited, but still evident. In addition, the proposed method allows correction of the prediction bias encountered for the upper range emissions in the linear regression models. © 2014 The Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/84834
Appears in Collections: 气候减缓与适应 气候变化事实与影响
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作者单位: Polish Academy of Sciences, Systems Research Institute, ul. Newelska 6, 01-447 Warsaw, Poland
Recommended Citation:
Horabik J.,Nahorski Z.. Improving resolution of a spatial air pollution inventory with a statistical inference approach[J]. Climatic Change,2014-01-01,124(3)