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1. Prioritizing environmental determinants of urban heat islands: A machine learning study for major cities in China

   https://doi.org/10.1016/j.jag.2023.103411  

   Hou, Haoran; Longyang, Qianqiu; Su, Hongbo; Zeng, Ruijie; Xu, Tianfang; Wang, Zhi-Hua (August 2023, International Journal of Applied Earth Observation and Geoinformation)

   The exacerbated thermal environment in cities, the urban heat island (UHI) effect as a prominent example, has been the source of many adverse urban environmental issues, including the increase of health risks, degradation of air quality and ecosystem services, and reduced resiliency of engineering infrastructure. Last decades have witnessed tremendous efforts and resources being invested to find sustainable solutions for urban heat mitigation, whereas the relative contributions of different UHI attributes and their patterns of spatio-temporal variability remain obscure. In this study, we employed the random forest (RF) method to quantify the relative importance of four categories of urban surface characteristics that regulate the surface UHI, namely the urban greenery fraction, land surface albedo, urban morphology, and level of human activities. We selected seventeen major cities from six megaregions in China as our study areas, with the RF training and test sets obtained from multi-sourced remote sensing and observational data products. It is found that the urban greenery coverage manifests as the most important environmental determinants of UHI, followed by surface albedo. The results are informative for urban planners, policymakers, and engineering practitioners to design and implement sustainable strategies for urban heat mitigation. 

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2. An integrated cyberGIS and machine learning framework for fine-scale prediction of Urban Heat Island using satellite remote sensing and urban sensor network data

   https://doi.org/10.1007/s44212-022-00002-4  

   Lyu, Fangzheng; Wang, Shaohua; Han, Su Yeon; Catlett, Charlie; Wang, Shaowen (September 2022, Urban Informatics)

   Abstract Due to climate change and rapid urbanization, Urban Heat Island (UHI), featuring significantly higher temperature in metropolitan areas than surrounding areas, has caused negative impacts on urban communities. Temporal granularity is often limited in UHI studies based on satellite remote sensing data that typically has multi-day frequency coverage of a particular urban area. This low temporal frequency has restricted the development of models for predicting UHI. To resolve this limitation, this study has developed a cyber-based geographic information science and systems (cyberGIS) framework encompassing multiple machine learning models for predicting UHI with high-frequency urban sensor network data combined with remote sensing data focused on Chicago, Illinois, from 2018 to 2020. Enabled by rapid advances in urban sensor network technologies and high-performance computing, this framework is designed to predict UHI in Chicago with fine spatiotemporal granularity based on environmental data collected with the Array of Things (AoT) urban sensor network and Landsat-8 remote sensing imagery. Our computational experiments revealed that a random forest regression (RFR) model outperforms other models with the prediction accuracy of 0.45 degree Celsius in 2020 and 0.8 degree Celsius in 2018 and 2019 with mean absolute error as the evaluation metric. Humidity, distance to geographic center, and PM_2.5concentration are identified as important factors contributing to the model performance. Furthermore, we estimate UHI in Chicago with 10-min temporal frequency and 1-km spatial resolution on the hottest day in 2018. It is demonstrated that the RFR model can accurately predict UHI at fine spatiotemporal scales with high-frequency urban sensor network data integrated with satellite remote sensing data. 

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3. Lab-Controlled Experimental Evaluation of Heat-Reflective Coatings by Increasing Surface Albedo for Cool Pavements in Urban Areas

   https://doi.org/10.3390/coatings12010007  

   Lu, Yang; Rahman, Md Asif; Moore, Nicholas W.; Golrokh, Aidin J. (January 2022, Coatings)

   Many studies were conducted to find possible strategies for reducing the urban heat island (UHI) effect during the hot summer months. One of the largest contributors to UHI is the role that paved surfaces play in the warming of urban areas. Solar-reflective cool pavements stay cooler in the sun than traditional pavements. Pavement reflectance can be enhanced by using a reflective surface coating. The use of heat-reflective coatings to combat the effects of pavements on UHI was pre-viously studied but no consistent conclusions were drawn. To find a conclusive solution, this work focuses on the abilities of heat-reflective pavement coatings to reduce UHI in varying weather conditions. Within this context, both concrete and asphalt samples were subject to a series of per-formance tests when applied to a heat-reflective coating, under the influence of normal, windy, and humid conditions. During these tests, the samples were heated with a halogen lamp and the surface temperature profile was measured using an infrared thermal camera. The air temperature was recorded with a thermometer, and the body temperature at multiple depths of the samples was measured using thermocouples. The results from these tests show that the effectiveness of the heat-reflective coating varies under different weather conditions. For instance, the coated samples were about 1 °C cooler for concrete and nearly 5 °C cooler for asphalt, on average. However, this temperature difference was reduced significantly under windy conditions. As such, the findings from this work conclude that the heat-reflective coatings can effectively cool down the pavement by increasing the surface albedo, and thus might be a viable solution to mitigate UHI impacts in the city/urban areas. 

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4. Lab-Controlled Experimental Evaluation of Heat-Reflective Coatings by Increasing Surface Albedo for Cool Pavements in Urban Areas

   https://doi.org/doi.org/10.3390/coatings12010007  

   Lu, Yang; Rahman, Md Asif; Moore, Nicholas W.; Golrokh, Aidin J. (December 2021, Coatings)

   Many studies were conducted to find possible strategies for reducing the urban heat island (UHI) effect during the hot summer months. One of the largest contributors to UHI is the role that paved surfaces play in the warming of urban areas. Solar-reflective cool pavements stay cooler in the sun than traditional pavements. Pavement reflectance can be enhanced by using a reflective surface coating. The use of heat-reflective coatings to combat the effects of pavements on UHI was pre-viously studied but no consistent conclusions were drawn. To find a conclusive solution, this work focuses on the abilities of heat-reflective pavement coatings to reduce UHI in varying weather conditions. Within this context, both concrete and asphalt samples were subject to a series of per-formance tests when applied to a heat-reflective coating, under the influence of normal, windy, and humid conditions. During these tests, the samples were heated with a halogen lamp and the surface temperature profile was measured using an infrared thermal camera. The air temperature was recorded with a thermometer, and the body temperature at multiple depths of the samples was measured using thermocouples. The results from these tests show that the effectiveness of the heat-reflective coating varies under different weather conditions. For instance, the coated samples were about 1 °C cooler for concrete and nearly 5 °C cooler for asphalt, on average. However, this temperature difference was reduced significantly under windy conditions. As such, the findings from this work conclude that the heat-reflective coatings can effectively cool down the pavement by increasing the surface albedo, and thus might be a viable solution to mitigate UHI impacts in the city/urban areas. 

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5. Comparing land surface temperature and mean radiant temperature for urban heat mapping in Philadelphia

   https://doi.org/10.1016/j.uclim.2023.101615  

   Li, Xiaojiang; Chakraborty, TC; Wang, Guoqing (September 2023, Urban Climate)

   Many cities are experiencing more frequent extreme heat during hot summers. With the rise of global temperature, the thermal comfort in urban areas become even worse. Quantitative information of the spatial distributions of urban heat has become increasingly important for resilience and adaptation to climate change in cities. This study compares satellite-derived land surface temperature (LST) and urban microclimate modeling-based mean radiant temperature (Tmrt) for mapping the urban heat distributions in Philadelphia, Pennsylvania, USA. The LST was estimated based on Landsat 8 thermal imagery with a spatial resolution of around 100 m, while the Tmrt was simulated based on high resolution LiDAR and national aerial imagery program multispectral aerial imageries with a spatial resolution of 1 m. Result shows that both LST and Tmrt show a similar general pattern of the urban heat across the study area, while the Tmrt presents much more details of the heat variations street by street and neighborhood by neighborhood. The LST tends to have a stronger relationship with the Tmrt on building roofs, which are usually not the place for human activities. This studyprovides evidence for choosing more appropriate metrics in urban heat-related studies. 

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