In Pursuit of Local Solutions for Climate Resilience: Sensing Microspatial Inequities in Heat and Air Pollution within Urban Neighborhoods in Boston, MA
In Pursuit of Local Solutions for Climate Resilience: Sensing Microspatial Inequities in Heat and Air Pollution within Urban Neighborhoods in Boston, MA
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DOI：
10.3390/su15042984
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发表时间：
2023-02
期刊：
影响因子：
3.9
通讯作者：
D. O’Brien;A. V. Mueller
中科院分区：
文献类型：
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作者：
D. O’Brien;A. V. Mueller
环境危害因地制宜，甚至从一条街到另一条街，导致微空间不平等，因此有必要针对特定的超局部条件采取应对气候变化的解决方案。这项研究使用遥感数据来估计两种与社区健康特别相关的环境危害：陆地表面温度(LST；来自陆地卫星)和空气污染(AP；通过手机记录的机动车辆数量)。这些数据结合马萨诸塞州波士顿的土地使用记录进行分析，以测试(1)每种危险集中在社区内特定街道的程度，(2)驱动危险变化的基础设施元素，以及(3)危险在空间上重叠的程度。尽管这些数据依赖于代理人，但它们提供了初步证据。同一社区不同街道之间的LST和AP存在很大差异(分别为40%和70%-80%)。前者是由树冠、不透水表面和反照率驱动的。后者与主要街道和高层建筑的分区有关。在人口普查区域内，LST和AP之间的相关性为中等(r=0.4)，而在人口普查区域内的街道上，相关性较弱(r=0.16)。综合结果不仅证实了这两种危害在微观空间上的不平等，而且它们的重合度也有限，表明一些街道同时遭受两种危害，有些既不是两种危害，而另一些只是一种危害。需要更精确的、时间动态的数据来跟踪环境危害(例如，来自环境传感器网络的危害)以及将其转化为基于社区的解决方案的战略。
Environmental hazards vary locally and even street to street resulting in microspatial inequities, necessitating climate resilience solutions that respond to specific hyperlocal conditions. This study uses remote sensing data to estimate two environmental hazards that are particularly relevant to community health: land surface temperature (LST; from LandSat) and air pollution (AP; from motor vehicle volume via cell phone records). These data are analyzed in conjunction with land use records in Boston, MA to test (1) the extent to which each hazard concentrates on specific streets within neighborhoods, (2) the infrastructural elements that drive variation in the hazards, and (3) how strongly hazards overlap in space. Though these data rely on proxies, they provide preliminary evidence. Substantial variations in LST and AP existed between streets in the same neighborhood (40% and 70–80% of variance, respectively). The former were driven by canopy, impervious surfaces, and albedo. The latter were associated with main streets and zoning with tall buildings. The correlation between LST and AP was moderate across census tracts (r = 0.4) but modest across streets within census tracts (r = 0.16). The combination of results confirms not only the presence of microspatial inequities for both hazards but also their limited coincidence, indicating that some streets suffer from both hazards, some from neither, and others from only one. There is a need for more precise, temporally-dynamic data tracking environmental hazards (e.g., from environmental sensor networks) and strategies for translating them into community-based solutions.