In response to the urban heat island challenge, various mitigation measures have been explored, with water spray systems emerging as a cost-effective and efficient solution for urban outdoor cooling. However, the influential factors of a water spray system on cooling efficiency have not been fully understood, thus hindering the application of the water spray system. This study delves into the following two questions: (1) what is the cooling performance of a water mist spray in a hot and humid urban climate? (2) What are the effects of different influencing factors? To answer these two questions, the computational fluid dynamics (CFD) simulations are used to modelthe cooling process of water mist spray inside an ideal two-dimensional street canyon with an aspect ratio of 1. A sound validation for the water spray cooling was conducted prior to the following CFD simulations. Results show that for given values of the water flow rate (i.e., 9.0 L/min) and the spray nozzle height (i.e., 3 m), a maximum temperature reduction of about 4.6 °C can be achieved at pedestrian height. Raising the installation height is more effective in maintaining the cooling zone proportion than decreasing the water flow rate. The clockwise recirculation inside the street canyon disappears with the upward airflow weakened when the spray nozzle is installed in the middle of the street canyon.

Urban thermal environment is directly linked to the health and comfort of local residents, as well as energy consumption. Drone-based thermal infrared image acquirement provides an efficient and flexible way of assessing urban heat distribution, thereby supporting climate-resilient and sustainable urban development. Here, we present an advanced approach that utilizes the thermal infrared camera mounted on the drone for high-resolution building wall temperature measurement and achieves centimeter accuracy. According to the binocular vision theory, the three-dimensional (3D) reconstruction of thermal infrared images is first conducted, and then the two-dimensional building wall temperature is extracted. Real-world validation shows that our approach can measure the wall temperature within a 5 °C error, which confirms the reliability of this approach. The field measurement of Yuquanting in Xiong’an New Area China during three time periods, i.e., morning (7:00–8:00), noon (13:00–14:00) and evening (18:00–19:00), was used as a case study to demonstrate our approach. The results show that during the heating season, the building wall temperature was the highest at noon time and the lowest in evening time, which were mostly caused by solar radiation. The highest wall temperature at noon time was 55 °C, which was under direct sun radiation. The maximum wall temperature differences were 39 °C, 55 °C, and 20 °C during morning, noon and evening time, respectively. The lighter wall coating color tended to have a lower temperature than the darker wall coating color. Beyond this application, this approach has potential in future autonomous thermal environment measuring systems as a foundational element.