DOI: 10.1016/j.atmosenv.2014.12.057
Scopus记录号: 2-s2.0-84920062743
论文题名: Quantification of non-linearities as a function of time averaging in regional air quality modeling applications
作者: Thunis P ; , Clappier A ; , Pisoni E ; , Degraeuwe B
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 103 起始页码: 263
结束页码: 275
语种: 英语
英文关键词: Air quality modeling
; Integrated assessment modeling
; Non-linearity
; Photo-chemistry
; Surrogate models
; Time averaging
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: Air quality models which are nowadays used for a wide range of scopes (i.e. assessment, forecast, planning) see their intrinsic complexity progressively increasing as better knowledge of the atmospheric chemistry processes is gained. As a result of this increased complexity potential non-linearities are implicitly and/or explicitly incorporated in the system. These non-linearities represent a key and challenging aspect of air quality modeling, especially to assess the robustness of the model responses. In this work the importance of non-linear effects in air quality modeling is quantified, especially as a function of time averaging. A methodology is proposed to decompose the concentration change resulting from an emission reduction over a given domain into its linear and non-linear contributions for each precursor as well as in the contribution resulting from the interactions among precursors. Simulations with the LOTOS-EUROS model have been performed by TNO over three regional geographical areas in Europe for this analysis. In all three regions the non-linear effects for PM10 and PM2.5 are shown to be relatively minor for yearly and monthly averages whereas they become significant for daily average values. For Ozone non-linearities become important already for monthly averages in some regions. An approach which explicitly deals with monthly variations seems therefore more appropriate for O3. In general non-linearities are more important at locations where concentrations are the lowest, i.e. at urban locations for O3 and at rural locations for PM10 and PM2.5. Finally the impact of spatial resolution (tested by comparing coarse and fine resolution simulations) on the degree of non-linearity has been shown to be minor as well. The conclusions developed here are model dependent and runs should be repeated with the particular model of interest but the proposed methodology allows with a limited number of runs to identify where efforts should be focused in order to include the relevant terms into a simplified surrogate model for integrated assessment purposes. © 2014 The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82007
Appears in Collections: 气候变化事实与影响
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作者单位: European Commission, JRC, Institute for Environment and Sustainability, Air and Climate Unit, Via E. Fermi 2749, Ispra, VA, Italy; Université de Strasbourg, Laboratoire Image Ville Environnement, 3, rue de l'Argonne, Strasbourg, France
Recommended Citation:
Thunis P,, Clappier A,, Pisoni E,et al. Quantification of non-linearities as a function of time averaging in regional air quality modeling applications[J]. Atmospheric Environment,2015-01-01,103