globalchange  > 气候变化事实与影响
DOI: 10.1016/j.atmosenv.2015.04.039
Scopus记录号: 2-s2.0-84928326965
论文题名:
Methods for reducing biases and errors in regional photochemical model outputs for use in emission reduction and exposure assessments
作者: Porter P; S; , Rao S; T; , Hogrefe C; , Gego E; , Mathur R
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 112
起始页码: 178
结束页码: 188
语种: 英语
英文关键词: Air quality modeling ; Attainment demonstration ; Bias adjustment ; Emission control strategy assessment ; Exposure assessment ; Model evaluation
Scopus关键词: Air pollution ; Air quality ; Air quality standards ; Distribution functions ; Emission control ; Errors ; Ozone ; Pollution ; Quality assurance ; Air quality modeling ; Bias adjustment ; Community multi-scale air qualities ; Cumulative distribution function ; Exposure assessment ; Model evaluation ; National ambient air quality standards ; Regional photochemical modeling ; Quality control ; accuracy assessment ; air quality ; ambient air ; concentration (composition) ; emission control ; error analysis ; model test ; ozone ; photochemistry ; pollution exposure ; pollution monitoring ; spatiotemporal analysis ; standard (regulation) ; United States
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: In the United States, regional-scale photochemical models are being used to design emission control strategies needed to meet the relevant National Ambient Air Quality Standards (NAAQS) within the framework of the attainment demonstration process. Previous studies have shown that the current generation of regional photochemical models can have large biases and errors in simulating absolute levels of pollutant concentrations. Studies have also revealed that regional air quality models were not always accurately reproducing even the relative changes in ozone air quality stemming from changes in emissions. This paper introduces four approaches to adjust for model bias and errors in order to provide greater confidence for their use in estimating future concentrations as well as using modeled pollutant concentrations in exposure assessments. The four methods considered here are a mean and variance (MV) adjustment, temporal component decomposition (TC) adjustment of modeled concentrations, and two variants of cumulative distribution function (CDF) mapping. These methods were compared against each other as well as against unadjusted model concentrations and a version of the relative response approach based on unadjusted model predictions. The analysis uses ozone concentrations simulated by the Community Multiscale Air Quality (CMAQ) model for the northeastern United States domain for the years 1996-2005. Ensuring that base case conditions are adequately represented through the combined use of observations and model simulations is shown to result in improved estimates of future air quality under changing emissions and meteorological conditions. © 2015 Elsevier Ltd.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81723
Appears in Collections:气候变化事实与影响

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作者单位: Porter-Gego, Idaho Falls, ID, United States; North Carolina State University, Raleigh, NC, United States; US EPA National Exposure Research Laboratory, Research Triangle Park, NC, United States

Recommended Citation:
Porter P,S,, Rao S,et al. Methods for reducing biases and errors in regional photochemical model outputs for use in emission reduction and exposure assessments[J]. Atmospheric Environment,2015-01-01,112
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