We define the new concept of an environmental neighborhood as the surrounding area influencing the environmental quality at a given point in a city, and propose a novel methodology to measure its spatial extent. We compute the spatial correlation of air quality and urban parameters from high spatial resolution datasets for New York City, where the urban characteristics are averaged over variable urban footprint sizes, ranging from 25m×25m to 5000m×5000m. The scale at which these correlations peak indicates the extent of the neighboring area that influences pollutant concentrations. The results indicate that the scale of these environmental neighborhoods ranges from ~ 1000m (for attributes such as road area or building footmark) down to ~ 200m (for building use or green area). Selecting this optimal neighborhood scale is thus critical for identifying the urban fabric and activity attributes that have the largest influence on air quality; smaller footprints do not contain all the pertinent urban surface information while larger footprints contain irrelevant, potentially misleading information. The quantification of this scale of influence therefore enables more effective and localized policies and interventions to improve urban environmental quality and reduce urban health disparities. More broadly, the findings indicate that, in a wide range of environmental and ecological applications where surface heterogeneity is a primary driver, the scale of analysis is not an external parameter to be chosen, but rather an internal parameter dictated by the problem physics.

中文翻译：

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城市环境街区及其空间范围

我们将环境街区的新概念定义为影响城市给定点环境质量的周边地区，并提出了一种衡量其空间范围的新方法。我们从纽约市的高空间分辨率数据集计算空气质量和城市参数的空间相关性，其中城市特征在可变的城市足迹大小上取平均值，范围从 25m×25m 到 5000m×5000m。这些相关性达到峰值的尺度表明影响污染物浓度的邻近区域的范围。结果表明，这些环境街区的规模范围从 ~ 1000m（对于道路区域或建筑物足迹等属性）到 ~ 200m（对于建筑物用途或绿地面积）。因此，选择最佳社区尺度对于识别对空气质量影响最大的城市结构和活动属性至关重要；较小的足迹不包含所有相关的城市表面信息，而较大的足迹包含不相关的、潜在的误导信息。因此，对这种影响范围进行量化可以制定更有效和本地化的政策和干预措施，以改善城市环境质量并减少城市健康差距。更广泛地说，研究结果表明，在表面异质性是主要驱动因素的广泛环境和生态应用中，分析规模不是要选择的外部参数，而是由物理问题决定的内部参数。