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Source area identification with observation from limited monitor sites for air pollution episodes in industrial parks

机译:通过有限的监控站点观察工业园区中的空气污染事件,从而识别源区域

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摘要

Air pollution episodes of unknown origins are often detected by online equipment for air quality monitoring in industrial parks in China. The number of monitors available to provide observation data, as well as the source information, is often very limited. In such case, the identification of a potential source area is more practical than the precise back-calculation of the real source. The potential source area which can be deduced from the observation data from limited monitors was concerned in this paper. In order to do the source area identification, two inverse methods, a direct method and a statistical sampling method, were applied with a Gaussian puff model as the forward modeling method. The characteristic of the potential source area was illustrated by case studies. Both synthetic and real cases were presented. The distribution of the source locations and its variation with the other unknown source parameters were mainly focused in the case study. As a screening method, source area identification can be applied not only when the number of effective monitors is limited but also when an ideal number of monitors are available as long as the source information is almost uncertain. (C) 2015 Elsevier Ltd. All rights reserved.
机译:在中国工业园区,通常通过在线设备检测未知来源的空气污染事件,以进行空气质量监测。可用于提供观测数据以及源信息的监视器数量通常非常有限。在这种情况下,潜在源区域的识别比实际源的精确反向计算更为实用。本文关注的是可以从有限的监测器的观测数据中推断出的潜在源区。为了进行源区识别,应用了高斯泡芙模型作为正向建模方法,采用了直接方法和统计采样方法两种逆方法。案例研究说明了潜在源区的特征。提出了综合案例和实际案例。案例研究主要关注源位置的分布及其随其他未知源参数的变化。作为一种筛选方法,不仅可以在有效监视器的数量受到限制的情况下,而且在源信息几乎不确定的情况下可以使用理想监视器的数量时,也可以应用源区域识别。 (C)2015 Elsevier Ltd.保留所有权利。

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