A simple and fast method to downscale chemistry transport model output fields from the regional to the urban/district scale

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Environmental Modelling & Software Pub Date : 2023-06-01 DOI:10.1016/j.envsoft.2023.105692
Bertrand Bessagnet , Enrico Pisoni , Alexander de Meij , Laurent Létinois , Philippe Thunis
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引用次数: 1

Abstract

For policy applications, the need to improve the resolution of environmental variables is crucial. Air pollution assessment indeed requires the use of air pollutant concentration fields at a high resolution, to better evaluate the exposure of citizens. In this paper, we propose a fast proxy-based downscaling strategy, to downscale air quality modelling results using the fraction of the pollutant concentration influenced by precursor emissions in a given cell. The approach combines in an additive way (i) a classically interpolated background pollutant fraction, with (ii) a proxy-based concentration derived from the emissions. The proxy-based pollutant fraction is spread over the high resolution mesh into the surrounding cells with a Gaussian approach to account for diffusion effects. The evaluation of our approach against observations shows its relevance to create reliable air pollution concentration fields at a higher resolution, starting from a coarse resolution modelling results.

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一种将化学输运模型输出场从区域尺度缩小到城市/地区尺度的简单快速方法
对于政策应用来说,改进环境变量的解析是至关重要的。空气污染评估确实需要使用高分辨率的空气污染物浓度场,以更好地评估公民的暴露情况。在本文中,我们提出了一种基于代理的快速缩减策略,利用给定单元中受前体排放影响的污染物浓度的比例来缩减空气质量建模结果。该方法以一种相加的方式将(i)经典插值的背景污染物分数与(ii)从排放中得出的基于代理的浓度相结合。基于代理的污染物分数通过高分辨率网格以高斯方法扩散到周围的单元中,以考虑扩散效应。对我们的方法与观测结果的评估表明,它与从粗分辨率建模结果开始,以更高分辨率创建可靠的空气污染浓度场的相关性。
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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