City-Scale Building Anthropogenic Heating during Heat Waves

Xuan Luo,Pouya Vahmani,Tianzhen Hong,Andrew Jones

doi:10.3390/atmos11111206

Abstract

More frequent and longer duration heat waves have been observed worldwide and are recognized as a serious threat to human health and the stability of electrical grids. Past studies have identified a positive feedback between heat waves and urban heat island effects. Anthropogenic heat emissions from buildings have a crucial impact on the urban environment, and hence it is critical to understand the interactive effects of urban microclimate and building heat emissions in terms of the urban energy balance. Here we developed a coupled-simulation approach to quantify these effects, mapping urban environmental data generated by the mesoscale Weather Research and Forecasting (WRF) coupled to Urban Canopy Model (UCM) to urban building energy models (UBEM). We conducted a case study in the city of Los Angeles, California, during a five-day heat wave event in September 2009. We analyzed the surge in city-scale building heat emission and energy use during the extreme heat event. We first simulated the urban microclimate at a high resolution (500 m by 500 m) using WRF-UCM. We then generated grid-level building heat emission profiles and aggregated them using prototype building energy models informed by spatially disaggregated urban land use and urban building density data. The spatial patterns of anthropogenic heat discharge from the building sector were analyzed, and the quantitative relationship with weather conditions and urban land-use dynamics were assessed at the grid level. The simulation results indicate that the dispersion of anthropogenic heat from urban buildings to the urban environment increases by up to 20% on average and varies significantly, both in time and space, during the heat wave event. The heat dispersion from the air-conditioning heat rejection contributes most (86.5%) of the total waste heat from the buildings to the urban environment. We also found that the waste heat discharge in inland, dense urban districts is more sensitive to extreme events than it is in coastal or suburban areas. The generated anthropogenic heat profiles can be used in urban microclimate models to provide a more accurate estimation of urban air temperature rises during heat waves.

Highlights

High temperatures have occurred globally and concurrently in the past several years.The July 2019 global land and ocean surface temperature’s departure from average was the highest forJuly since global records began in 1880 [1]
We focused on the 22 September 2009, heat wave as it maintained a high intensity over an extended period of time, which represents the characteristics of mid-century heat waves under a changing climate, as reported by Vahmani et al [43]
The Weather Research and Forecasting (WRF)-Urban Canopy Model (UCM) modeling system used in this study relied on a predetermined and fixed diurnal anthropogenic heat profile to represent building heat emissions feedback to the urban environment, as an extra sensible heat flux term which was added to the surface energy balance over urban grid cells [31,32]

Summary

Introduction

High temperatures have occurred globally and concurrently in the past several years. Since global records began in 1880 [1]. The record-hottest month in 2019 precipitated an increase in intensity and frequency of extreme heat events over many regions, including North America, Asia, and Europe [2]. The recurrence and increasing severity of these extreme weather events are becoming a growing concern for city policymakers who must provide critical services to preserve human life and properties during natural disasters [3]. Atmosphere 2020, 11, 1206 temperature rise can be exacerbated due to the UHI effect, and intensify the variability of microclimates [4]. Risk analysis and prioritization of mitigation strategies to address these events in cities will require a deep and quantitative understanding of the combined effects of urban microclimate and heat waves [5]

Objectives

Results

Discussion

Conclusion