An official website of the United States government
Abstract Urban areas increasingly suffer from subsurface heat islands: an underground climate change responsible for environmental, public health, and transportation issues. Soils, rocks, and construction materials deform under the influence of temperature variations and excessive deformations can affect the performance of civil infrastructure. Here I explore if ground deformations caused by subsurface heat islands might affect civil infrastructure. The Chicago Loop district is used as a case study. A 3-D computer model informed by data collected via a network of temperature sensors is used to characterize the ground temperature variations, deformations, and displacements caused by underground climate change. These deformations and displacements are significant and, on a case-by-case basis, may be incompatible with the operational requirements of civil structures. Therefore, the impact of underground climate change on civil infrastructure should be considered in future urban planning strategies to avoid possible structural damage and malfunction. Overall, this work suggests that underground climate change can represent a silent hazard for civil infrastructure in the Chicago Loop and other urban areas worldwide, but also an opportunity to reutilize or minimize waste heat in the ground.
more »
« less
This content will become publicly available on May 1, 2026
Evaluating PurpleAir Sensors: Do They Accurately Reflect Ambient Air Temperature?
Low-cost sensors (LCSs) emerge as a popular tool for urban micro-climate studies by offering dense observational coverage. This study evaluates the performance of PurpleAir (PA) sensors for ambient temperature monitoring—a key but underexplored aspect of their use. While widely used for particulate matter, PA sensors’ temperature data remain underutilized and lack thorough validation. For the first time, this research evaluates their accuracy by comparing PA temperature measurements with collocated high-precision temperature data loggers across a dense urban network in a humid subtropical U.S. county. Results show a moderate correlation with reference data (r = 0.86) but an average overestimation of 3.77 °C, indicating PA sensors are better suited for identifying temperature trends but not for precise applications like extreme heat events. We also developed and compared eight calibration methods to create a replicable model using readily available crowdsourced data. The best-performing model reduced RMSE and MAE by 51% and 47%, respectively, and achieved an R2 of 0.89 compared to the uncalibrated scenario. Finally, the practical application of PA temperature data for identifying heat wave events was investigated, including an assessment of associated uncertainties. In sum, this work provides a crucial evaluation of PA’s temperature monitoring capabilities, offering a pathway for improved heat mapping, multi-hazard vulnerability assessments, and public health interventions in the development of climate-resilient cities.
more »
« less
- PAR ID:
- 10609939
- Publisher / Repository:
- Sensors
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 25
- Issue:
- 10
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 3044
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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 heatsafety policies, and exposure warning for workers.more » « less
-
Abstract The two-resistance mechanism (TRM) attribution method, which was designed to analyze the urban–rural contrast of temperature, is improved to study the urban–rural contrast of heat stress. The improved method can be applied to diagnosing any heat stress index that is a function of temperature and humidity. As an example, in this study we use it to analyze the summertime urban–rural contrast of simplified wet bulb globe temperature (SWBGT) simulated by the Geophysical Fluid Dynamics Laboratory land model coupled with an urban canopy model. We find that the urban–rural contrast of SWBGT is primarily caused by the lack of evapotranspiration in urban areas during the daytime and the release of heat storage during the nighttime, with the urban–rural differences in aerodynamic features playing either positive or negative roles depending on the background climate. Compared to the magnitude of the urban–rural contrast of temperature, the magnitude of the urban–rural contrast of SWBGT is damped due to the moisture deficits in urban areas. We further find that the urban–rural contrast of 2-m air temperature/SWBGT is fundamentally different from that of canopy air temperature/SWBGT. Turbulent mixing in the surface layer leads to much smaller urban–rural contrasts of 2-m air temperature/SWBGT than their canopy air counterparts. Significance Statement Heat leads to serious public health concerns, but urban and rural areas have different levels of heat stress. Our study explains the magnitude and pattern of the simulated urban–rural contrast in heat stress at the global scale and improves an attribution method to quantify which biophysical processes are mostly responsible for the simulated urban–rural contrast in heat stress. We highlight two well-known causes of higher heat stress in cities: the lack of evapotranspiration and the stronger release of heat storage. Meanwhile, we draw attention to the vegetation types in rural areas, which determine the urban–rural difference in surface roughness and significantly affect the urban–rural difference in heat stress. Last, we find the urban–rural contrasts of 2-m air temperature/SWBGT are largely reduced relative to their canopy air counterparts due to the turbulent mixing effect.more » « less
-
Abstract The spatio-temporal variability of temperatures in cities impacts human well-being, particularly in a large metropolis. Low-cost sensors now allow the observation of urban temperatures at a much finer resolution, and, in recent years, there has been a proliferation of fixed and mobile monitoring networks. However, how to design such networks to maximize the information content of collected data remains an open challenge. In this study, we investigate the performance of different measurement networks and strategies by deploying virtual sensors to sample the temperature data set in high-resolution weather simulations in four American cities. Results show that, with proper designs and a sufficient number of sensors, fixed networks can capture the spatio-temporal variations of temperatures within the cities reasonably well. Based on the simulation study, the key to optimizing fixed sensor location is to capture the whole range of impervious fractions. Randomly moving mobile systems consistently outperform optimized fixed systems in measuring the trend of monthly mean temperatures, but they underperform in detecting mean daily maximum temperatures with errors up to 5 °C. For both networks, the grand challenge is to capture anomalous temperatures under extreme events of short duration, such as heat waves. Here, we show that hybrid networks are more robust systems under extreme events, reducing errors by more than 50%, because the time span of extreme events detected by fixed sensors and the spatial information measured by mobile sensors can complement each other. The main conclusion of this study concerns the importance of optimizing network design for enhancing the effectiveness of urban measurements.more » « less
-
Research about urban local climate and urban heat island often relies on land surface temperature (LST) data to characterize the distribution of temperature near the surface. Although using remotely sensed data for such work has the advantage of continuous spatial coverage at regular temporal intervals, it is recognized that surface temperature is not an ideal proxy for air temperature (AT). This study's goal is to develop a spatiotemporal model revealing the relationship between LST and AT within the complexities of the urban environment. A mobile weather monitoring unit was used to collect spatially-explicit fine-scale AT data while Landsat 8 and 9 passed overhead collecting LST data. A spatiotemporal model of the relationship between LST and AT in Philadelphia was constructed with this data utilizing basis functions to account for spatial and temporal autocorrelation. The spatiotemporal model results show a strong relationship between LST and AT and indicate that it is possible to predict fine scale AT (120 m) using remotely sensed LST in an urban context (r-squared = 0.99, RMSE = 0.89 ◦C). The spatiotemporal model outperforms models that do not account for spatial and temporal autocorrelation, highlighting the importance of considering these dependencies in temperature modeling. City-wide AT predictions were generated for Philadelphia demonstrating the ability of the model to improve understanding of local urban climate.more » « less
