DOI: 10.1016/j.atmosenv.2014.05.047
Scopus记录号: 2-s2.0-84901508918
论文题名: A new statistical approach for establishing high-resolution emission inventory of primary gaseous air pollutants
作者: Zhou Y ; , Cheng S ; , Chen D ; , Lang J ; , Zhao B ; , Wei W
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2014
卷: 94 起始页码: 392
结束页码: 401
语种: 英语
英文关键词: County level resolution
; Emission inventory
; Primary gaseous air pollutants
; Regression model
Scopus关键词: Air pollution
; Errors
; Industry
; Mathematical models
; Population statistics
; Regression analysis
; Sulfur dioxide
; Air pollutants
; Atmospheric environment
; County level
; Emission estimation model
; Emission inventories
; High-resolution emission
; Regression model
; Statistical information
; Industrial emissions
; nitrogen oxide
; sulfur dioxide
; volatile organic compound
; accuracy assessment
; atmospheric pollution
; emission inventory
; error analysis
; estimation method
; numerical model
; regression analysis
; statistical analysis
; accuracy
; air pollutant
; article
; building material
; case study
; energy consumption
; environmental protection
; household
; industrial production
; mathematical model
; Monte Carlo method
; motor vehicle
; orchard
; population
; priority journal
; regression analysis
; simulation
; China
; Handan
; Hebei
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: This paper, which aims at the primary gaseous air pollutants (i.e., SO2, NOx, VOCS and CO), is the third paper in the series papers published in Atmospheric Environment to develop new emission estimation models by the regression method. A group of regression models for various industrial and non-industrial sectors were proposed based on an emission investigation case study of Handan region in northern China. The main data requirements of the regression models for industrial sectors were coal consumption, oil consumption, gaseous fuel consumption and annual industrial output. The data requirements for non-industrial sector emission estimations were the population, the number of resident population households, the vehicle population, the area of construction sites, the forestland area, and the orchard area. The models were then applied to Tangshan region in northern China. The results showed that the developed regression models had relatively satisfactory performance. The modeling errors at the regional level for SO2, NOx, VOCS and CO were-16.5%,-10.6%,-11.8% and-22.6%, respectively. The corresponding modeling errors at the county level were 39.9%, 33.9%, 46.3% and 46.9%, respectively. The models were also applied to other regions in northern China. The results revealed that the new models could develop emission inventories with generally lower error than found in previous emission inventory studies. The developed models had the advantages of only using publicly available statistical information for developing high-accuracy and high-resolution emission inventory, without requiring detailed data investigation which is necessary by conventional "bottom-up" emission inventory development approach. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81125
Appears in Collections: 气候变化事实与影响
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作者单位: Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China
Recommended Citation:
Zhou Y,, Cheng S,, Chen D,et al. A new statistical approach for establishing high-resolution emission inventory of primary gaseous air pollutants[J]. Atmospheric Environment,2014-01-01,94