Structural indicator synergy for mitigating extreme urban heat island effects in industrial city: Simulation and verification based on machine learning
Shidong Liu, Jianjun Zhang, Ke Wang, Xia Wu, Wei Chen, Sen Liang, Yaping Zhang, Fu Su
Abstract
Industrial areas emit large amounts of extremely high temperatures and aggravate the extreme urban heat island (E-UHI) effect, which has become a non-negligible environmental issue worldwide. Due to the lack of in-depth understanding of the relationship between E-UHI and surface structural synergy, few studies explored the causes and mitigation measures of E-UHI. Therefore, this study accordingly defined the E-UHI effect at a finer scale based on the thermal characteristics and human thermal perceptions, and a multi-indicator’ method based on machine learning was proposed to quantify internal causes and collaborative mitigation measures. The results revealed that the average E-UHI scale shows a quadratic growth in Wu’an. Artificial heat from production space (PS) is the main cause of E-UHI formation. The synergy between green space (GS), PS and shape of industrial land determines the development of E-UHI. Although both the decrease of PS and the increase of GS are ways to mitigate high land surface temperature, when PS is higher than 50%, the cooling effect of GS is not obvious. When GS is less than 2.5%, the adjustment of PS is invalid for improving the thermal environment. For mitigating E-UHI, a factory with high PS needs to increase its shape index. The synergy between the structural indicators is a novel model to solve the shortcoming of adjusting a single factor in mitigating complex E-UHI. The results provide theoretical guidance for mitigating E-UHIs.