A new statistical approach for establishing high-resolution emission inventory of primary gaseous air pollutants
A new statistical approach for establishing high-resolution emission inventory of primary gaseous air pollutants
复制标题
建立高分辨率空气主要气态污染物排放清单的新统计方法
DOI：
10.1016/j.atmosenv.2014.05.047
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发表时间：
2014-09
影响因子：
5
通讯作者：
Wei Wei
中科院分区：
文献类型：
--
作者：
Ying Zhou;Shuiyuan Cheng,;Dongsheng Chen;Jianlei Lang;Beibei Zhao;Wei Wei
本文针对主要气态大气污染物SO2、NOx、vocs和CO，是《大气环境》系列论文中第三篇利用回归方法建立新的排放估算模型的论文。以中国北方邯郸地区为例，提出了一组工业和非工业部门的回归模型。回归模型对工业部门的主要数据要求是煤炭消耗量、石油消耗量、气体燃料消耗量和工业年产值。非工业部门排放估算的数据要求为人口、常住人口户数、机动车保有量、建筑工地面积、林地面积、果园面积。并将该模型应用于中国北方唐山地区。结果表明，所建立的回归模型具有较为满意的性能。SO2、NOx、vocs和CO在区域水平上的建模误差分别为- 16.5%、- 10.6%、- 11.8%和- 22.6%。县级模型误差分别为39.9%、33.9%、46.3%和46.9%。这些模型也被应用到中国北方的其他地区。结果表明，与以往的排放清单研究相比，新模型能够以较低的误差编制排放清单。所开发模型的优点是只使用公开的统计信息编制高精度和高分辨率的排放清单，而不需要进行传统的“自下而上”排放清单编制方法所必需的详细数据调查。
This paper, which aims at the primary gaseous air pollutants (i.e., SO2, NOx, VOCSand 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, VOCSand 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.
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