Regression-based predictive modeling of summer urban microclimate: Quantifying contributions from urban design and urban heat emissions
Yuan Chen, Yupeng Wang, Dian Zhou, Xilian Luo
Abstract
The formation of urban microclimates is a complex process influenced by urban morphology and anthropogenic heat emissions (AHEs). While the combined effects of urban morphology and AHEs remain underexplored. In this study, air temperature (AT), relative humidity and dew point temperature were measured in five representative districts in Xi'an, China, during typical summer days. AHE from buildings (AHEb) was simulated using EnergyPlus, while AHE from traffic (AHEt) was calculated from traffic flow data. Seven urban morphological indices were used to develop partial least squares regression models. Results show that in the average daily AT, the contributions of AHE and two-dimensional morphological indicators are similar, both around 39 %. The contribution of AHEb (20.2 %) is higher than that of AHEt (18.3 %). For the average daytime AT, AHEb contributes less than GCR and SVF. However, during the peak AT hours, AHEb becomes the dominant contributor at 26.7 %. Each 100 W/m 2 increase in HVAC emissions raises hourly AT by 1.0 °C during the day and 4.8 °C at night. The predictive modeling approach supports microclimate assessment and cooling strategy development in high-density urban areas.