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The Spatial-Scale Effect of an Atmospheric Environmental Impact Assessment in Regional Strategic Environmental Assessment (R-SEA)

机译:区域战略环境评估(R-SEA)中大气环境影响评估的空间尺度效应

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When evaluating the atmospheric environment in regional strategic environment assessment (R-SEA), the variation and choice of the spatial scale have a substantial influence on the conclusions of the assessment. In this study, we used numerical simulation to investigate the spatial-scale effect. Two varying spatial extents and two varying spatial details of pollutant emission data (emission inventories in this case) were provided for numerical modeling, and output distributions of atmospheric pollutants at different air pollution levels were compared. The results show that the resolution and spatial range of data collection do indeed influence the atmospheric prediction and assessment results in R-SEA. The spatial-scale effect is more significant under the air pollution condition than under excellent and good air quality conditions. A comparison of varying spatial extents of emission inventory shows that narrowing the prediction area to a local scale is more conducive to identifying the impact of local pollution sources. A comparison of varying spatial details of emission inventory indicates that a higher resolution is favorable for identifying local high concentrations of pollutants and their locations.
机译:在区域战略环境评估(R-SEA)中评估大气环境时,空间尺度的变化和选择对评估结论有重大影响。在这项研究中,我们使用数值模拟来研究空间尺度效应。提供了两个不同的污染物排放数据的空间范围和两个不同的空间细节(在这种情况下为排放清单)进行数值模拟,并比较了不同空气污染水平下大气污染物的输出分布。结果表明,数据收集的分辨率和空间范围确实确实影响了R-SEA中的大气预测和评估结果。在空气污染条件下,空间尺度效应比在优良和良好的空气质量条件下更为显着。排放清单空间变化程度的比较表明,将预测范围缩小到局部范围更有助于识别局部污染源的影响。比较各种排放清单的空间细节表明,较高的分辨率有利于识别局部高浓度的污染物及其位置。

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