Urban ambient air temperature estimation using hyperlocal data from smart vehicle-borne sensors. (November 2020)

• Record Type: Journal Article
• Authors: Yin, Yanzhe; Tonekaboni, Navid Hashemi; Grundstein, Andrew; Mishra, Deepak R.; Ramaswamy, Lakshmish; Dowd, John
• Is Part Of: Computers, environment and urban systems. Volume 84(2020)
• Publication Date: 2020-11
• Subjects: Human thermal comfort -- Mobile sensing -- Machine learning -- Urban heat hazards -- Smart and connected communities; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2020.101538
• Languages: English
• ISSNs: 0198-9715

Abstract

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. Highlights: Vehicle-borne temperature data show a range of 5–10 °C within the city We developed a novel machine learning model to predict ambient air temperature LST and other biophysical parameters have high predictive power in modeling Model output is consistent with the observed temperature variability