Abstract High-quality temperature data at a finer spatio-temporal scale is critical for analyzing the risk of heat exposure and hazards in urban environments. The variability of urban landscapes makes cities a challenging environment for quantifying heat exposure. Most of the existing heat hazard studies have inherent limitations on two fronts; first, the spatio-temporal granularities are too coarse, and second, the inability to track the ambient air temperature (AAT) instead of land surface temperature (LST). Overcoming these limitations requires developing models for mapping the variability in heat exposure in urban environments. We investigated an integrated approach for mapping urban heat hazards by harnessing a diverse set of high-resolution measurements, including both ground-based and satellite-based temperature data. We mounted vehicle-borne mobile sensors on city buses to collect high-frequency temperature data throughout 2018 and 2019. Our research also incorporated key biophysical parameters and Landsat 8 LST data into Random Forest regression modeling to map the hyperlocal variability of heat hazard over areas not covered by the buses. The vehicle-borne temperature sensor data showed large temperature differences within the city, with the largest variations of up to 10 °C and morning-afternoon diurnal changes at a magnitude around 20 °C. Random Forest modeling on noontime (11:30 am – 12:30 pm) data to predict AAT produced accurate results with a mean absolute error of 0.29 °C and successfully showcased the enhanced granularity in urban heat hazard mapping. These maps revealed well-defined hyperlocal variabilities in AAT, which were not evident with other research approaches. Urban core and dense residential areas revealed larger than 5 °C AAT differences from their nearby green spaces. The sensing framework developed in this study can be easily implemented in other urban areas, and findings from this study will be beneficial in understanding the heat vulnerabilities of individual communities. It can be used by the local government to devise targeted hazard mitigation efforts such as increasing green space, developing better heat-safety policies, and exposure warning for workers.

中文翻译：

---

使用来自智能车载传感器的超本地数据估计城市环境空气温度

摘要 更精细时空尺度的高质量温度数据对于分析城市环境中的热暴露和危害风险至关重要。城市景观的多变性使城市成为量化热暴露的挑战性环境。大多数现有的热危害研究在两个方面都有固有的局限性；首先，时空粒度太粗，其次，无法跟踪环境气温（AAT）而不是地表温度（LST）。克服这些限制需要开发模型来绘制城市环境中热暴露的可变性。我们研究了一种通过利用各种高分辨率测量值（包括基于地面和基于卫星的温度数据）来绘制城市热危害图的综合方法。我们在城市公交车上安装了车载移动传感器，以收集 2018 年和 2019 年的高频温度数据。我们的研究还将关键生物物理参数和 Landsat 8 LST 数据纳入随机森林回归模型，以绘制非高温地区热危害的超局部变异性。被巴士覆盖。车载温度传感器数据显示，城市内温差较大，最大变化可达10°C，早午昼夜变化幅度在20°C左右。基于中午（上午 11:30 – 下午 12:30）数据的随机森林建模以预测 AAT 产生了平均绝对误差为 0.29 °C 的准确结果，并成功展示了城市热危害绘图中增强的粒度。这些图揭示了 AAT 中定义明确的超局部变异性，这在其他研究方法中并不明显。城市核心区和密集住宅区与附近绿地的 AAT 差异大于 5 °C。本研究中开发的传感框架可以很容易地在其他城市地区实施，本研究的结果将有助于了解各个社区的热脆弱性。当地政府可以使用它来制定有针对性的减灾措施，例如增加绿地、制定更好的热安全政策和工人暴露警告。这项研究的结果将有助于了解各个社区的热脆弱性。当地政府可以使用它来制定有针对性的减灾措施，例如增加绿地、制定更好的热安全政策和工人暴露警告。这项研究的结果将有助于了解各个社区的热脆弱性。当地政府可以使用它来制定有针对性的减灾措施，例如增加绿地、制定更好的热安全政策和工人暴露警告。