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Area of Influence (AOI) Development: Fast Generation of Receptor-Oriented Sensitivity Fields for Use in Regional Air Quality Modeling

机译:影响范围(AOI)开发:快速生成面向受体的灵敏度场,用于区域空气质量建模

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摘要

An approach is developed and tested to extend discrete, source-based sensitivity results to provide a complete set of information for source-air quality impacts, including inversion of those results to develop receptor-oriented source-impact sensitivities. First, the decoupled direct sensitivity analysis method in 3D (DDM-3D) is used to calculate a finite number of forward sensitivities from discrete points. These results are then interpolated using tessellation to provide complete fields of forward, emissions-based sensitivities, i.e., how emissions in any one grid cell within the domain impact any other cell. Receptor-oriented sensitivities are then found by inverting the set of forward sensitivities and can be used to identify the area of influence (AOI). This economically provides results similar to what would be found using an adjoint model. The present approach is computationally less intensive than adjoint modeling for a large number of receptors, and provides both source-oriented and receptor-oriented pollutant response fields that can be used for air quality management and health impact analyses. The forward sensitivity interpolation procedure, as well as the receptor-oriented sensitivities, is evaluated using data withholding.
机译:已开发并测试了一种方法,以扩展基于源的离散敏感度结果,以提供有关源空气​​质量影响的完整信息,包括将这些结果进行反演以开发面向受体的源碰撞敏感性。首先,使用3D解耦直接灵敏度分析方法(DDM-3D)从离散点计算有限数量的正向灵敏度。然后使用镶嵌细分对这些结果进行插值,以提供基于发射的前向灵敏度的完整字段,即域内任何一个网格单元中的发射如何影响其他任何单元。然后,通过反转正向灵敏度的集合来找到面向受体的灵敏度,并将其用于识别影响区域(AOI)。从经济上讲,它提供的结果与使用伴随模型所发现的结果相似。对于大量的受体,本方法的计算强度不如伴随模型,而且提供了面向源和面向受体的污染物响应域,可用于空气质量管理和健康影响分析。使用数据保留来评估前向灵敏度插值过程以及面向受体的灵敏度。

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