Estimating anthropogenic heat flux by assimilating meteorological observations with a Kalman filter approach.

Anthropogenic heat (AH) emissions in urban environments alter the surface energy budget and significantly influence urban climates. However, these emissions vary greatly in both time and space, leading to considerable uncertainty in their estimation. As remote sensing in the urban environment advances, where the remotely sensed urban surface temperatures are becoming increasingly available, such as those retrieved from satellite observations and thermal cameras. Yet, assimilating these observations into surface energy modeling for AH estimation has not been fully explored. In this study, a model for AH estimation based on the Kalman filter and surface energy balance is developed (KF-SEB model). Urban meteorological data, including air temperature and building surface temperature, are assimilated into the Kalman filter, yielding sensible heat flux and building heat storage. AH is subsequently calculated using the SEB equation. The KF-SEB model is evaluated using a forward model with predefined AH emissions. The forward model employs a simple SEB approach at the building exterior surface and adopts a 1-D heat conduction equation for the wall. The results show that the KF-SEB model accurately captures the magnitude and temporal variation of AH, with reduced uncertainties compared to previous studies. This study offers a novel approach of AH estimation based on urban meteorological data and provides important insights into the feedback between urban microclimates and anthropogenic energy use.

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.

Urban land use and anthropogenic heat (AH) emission can considerably influence the human thermal comfort during extreme heat events. In this study, a spatially heterogeneous AH emission data and updated urban land use data are integrated into the Weather Research and Forecasting model to simulate the physical processes of urban warming during summer. Simulations conducted in the Yangtze River Delta (YRD) of east China suggest that the mean urban heat island intensity reaches 1.49 °C in urbanized areas during summer, with AH emission making a considerable contribution. The warming effect due to urban land use is intensified during extremely hot days, but in contrast, the AH effects are slightly reduced. Urban development increases the total thermal discomfort hours by 27% in the urban areas of YRD, with AH and urban land use contributing nearly equal amount. By limiting the daytime latent heat release, urban land use reduces the daily maximum heat stress particularly during extremely hot days; however, such alleviations can be offset by the AH emission. Strategies for mitigation of urban heat island effect and heat stress in cities should therefore include measures to reduce AH emission.

Estimating spatial effects of anthropogenic heat emissions upon the urban thermal environment in an urban agglomeration area in East China

Abstract. Anthropogenic heat (AH) emissions from human activities caused by urbanization can affect the city environment. Based on the energy consumption and the gridded demographic data, the spatial distribution of AH emission over the Yangtze River Delta (YRD) region is estimated. Meanwhile, a new method for the AH parameterization is developed in the WRF/Chem model, which incorporates the gridded AH emission data with the seasonal and diurnal variations into the simulations. By running this upgraded WRF/Chem for 2 typical months in 2010, the impacts of AH on the meteorology and air quality over the YRD region are studied. The results show that the AH fluxes over the YRD have been growing in recent decades. In 2010, the annual-mean values of AH over Shanghai, Jiangsu and Zhejiang are 14.46, 2.61 and 1.63 W m−2, respectively, with the high value of 113.5 W m−2 occurring in the urban areas of Shanghai. These AH emissions can significantly change the urban heat island and urban-breeze circulations in the cities of the YRD region. In Shanghai, 2 m air temperature increases by 1.6 °C in January and 1.4 °C in July, the PBLH (planetary boundary layer height) rises up by 140 m in January and 160 m in July, and 10 m wind speed is enhanced by 0.7 m s−1 in January and 0.5 m s−1 in July, with a higher increment at night. The enhanced vertical movement can transport more moisture to higher levels, which causes the decrease in water vapor at ground level and the increase in the upper PBL (planetary boundary layer), and thereby induces the accumulative precipitation to increase by 15–30 % over the megacities in July. The adding of AH can impact the spatial and vertical distributions of the simulated pollutants as well. The concentrations of primary air pollutants decrease near the surface and increase at the upper levels, due mainly to the increases in PBLH, surface wind speed and upward air vertical movement. But surface O3 concentrations increase in the urban areas, with maximum changes of 2.5 ppb in January and 4 ppb in July. Chemical direct (the rising up of air temperature directly accelerates surface O3 formation) and indirect (the decrease in NOx at the ground results in the increase in surface O3) effects can play a significant role in O3 changes over this region. The meteorology and air pollution predictions in and around large urban areas are highly sensitive to the anthropogenic heat inputs, suggesting that AH should be considered in the climate and air quality assessments.

Both urban land cover (ULC) change and anthropogenic heat (AH) emission are important causes of urban heat island, but their relative contributions to the changes in urban precipitation and the related mechanism remain unclear. Based on numerical simulations utilizing the latest realistic urban fraction and AH data over the Yangtze River Delta urban agglomeration, we found that ULC and AH resulted in nearly opposite effects on precipitation. Various dynamical and thermodynamic processes were involved according to the atmospheric moisture budget analyses. AH increased precipitation particularly during afternoon, and the increases were stronger during heavy precipitation events because of the enhanced moisture convergence effect together with the release of moisture storage previously accumulated in the atmosphere. Differently, ULC reduced mean precipitation mainly due to suppressed evaporation. During weak precipitation events, the suppressed evaporation was largely balanced by the intensified moisture convergence, but during heavy events, ULC caused more pronounced precipitation reduction because the moisture convergence response disappeared and failed to offset the evaporation effect. The relative contributions of different dynamical and thermodynamic processes such as those related to circulation, moisture gradient, and background moisture availability to the temporal variation in the total moisture convergence were further quantified. Overall, our results help better understand the relative roles of different aspects of urbanization on precipitation, and suggest that compared to ULC, reduction in AH emission that is tightly related to the energy consumption structure could be more efficient for mitigating the risk of extreme precipitation.

Abstract. Anthropogenic heat (AH) emissions from human activities can change the urban circulation and thereby affect the air pollution in and around cities. Based on statistic data, the spatial distribution of AH flux in South China is estimated. With the aid of the Weather Research and Forecasting model coupled with Chemistry (WRF/Chem), in which the AH parameterization is developed to incorporate the gridded AH emissions with temporal variation, simulations for January and July in 2014 are performed over South China. By analyzing the differences between the simulations with and without adding AH, the impact of AH on regional meteorology and air quality is quantified. The results show that the regional annual mean AH fluxes over South China are only 0.87 W m−2, but the values for the urban areas of the Pearl River Delta (PRD) region can be close to 60 W m−2. These AH emissions can significantly change the urban heat island and urban-breeze circulations in big cities. In the PRD city cluster, 2 m air temperature rises by 1.1° in January and over 0.5° in July, the planetary boundary layer height (PBLH) increases by 120 m in January and 90 m in July, 10 m wind speed is intensified to over 0.35 m s−1 in January and 0.3 m s−1 in July, and accumulative precipitation is enhanced by 20–40 % in July. These changes in meteorological conditions can significantly impact the spatial and vertical distributions of air pollutants. Due to the increases in PBLH, surface wind speed and upward vertical movement, the concentrations of primary air pollutants decrease near the surface and increase in the upper levels. But the vertical changes in O3 concentrations show the different patterns in different seasons. The surface O3 concentrations in big cities increase with maximum values of over 2.5 ppb in January, while O3 is reduced at the lower layers and increases at the upper layers above some megacities in July. This phenomenon can be attributed to the fact that chemical effects can play a significant role in O3 changes over South China in winter, while the vertical movement can be the dominant effect in some big cities in summer. Adding the gridded AH emissions can better describe the heterogeneous impacts of AH on regional meteorology and air quality, suggesting that more studies on AH should be carried out in climate and air quality assessments.

The energy consumption due to urbanization and man-made activities has resulted in production of waste, heat, and pollution in the urban environment. These have further resulted in undesirable environmental issues such as the production of excessive Anthropogenic Heat Emissions (AHE), thus leading to an increased Urban Heat Island (UHI) effect. The aim of this study was to estimate the total AHE based on the contribution of three major sources of waste heat generation in an urban environment, i.e., buildings, vehicular traffic, and human metabolism. Furthermore, a comparison of dominating anthropogenic heat factor of Darwin with that of other major international cities was carried out. Field measurements of microclimate (temperatures, humidity, solar radiation, and other factors of climate measures) were conducted along Smith Street, Darwin City. Then, surveys were conducted to collect information regarding the buildings, vehicle traffic and Human population (metabolism) in the study area. Each individual component of AHE was calculated based on a conceptual framework of the anthropogenic heat model developed within this study. The results showed that AHE from buildings is the most dominant factor influencing the total AHE in Darwin, contributing to about 87% to 95% of total AHE. This is followed by vehicular traffic (4–13%) and lastly, human metabolism (0.1–0.8%). The study also shows that Darwin gains an average of 990 Wm−2 solar power on a peak day. This study proves that building anthropogenic heat is the major dominating factor influencing the UHI in tropical urban climates.

Energy consumption in the urban environment impacts the urban surface energy budget and leads to the emission of anthropogenic sensible heat and moisture into the atmosphere. Anthropogenic heat and moisture emissions vary significantly both in time and space, and are not readily measured. As a result, detailed models of these emissions are not commonly available for most cities. Furthermore, most attempts to quantify anthropogenic emissions have focused on the sensible heat component, largely ignoring moisture emissions and invoking assumptions—such as the equivalence of energy consumption and anthropogenic sensible heating—which limit the accuracy of the resulting anthropogenic heating estimates. This paper provides a historical perspective of the development of models of energy consumption in the urban environment and the associated anthropogenic impacts on the urban energy balance. It highlights some fundamental limitations of past approaches and suggests a roadmap forward for including anthropogenic heat and moisture in modelling of the urban environment. Copyright © 2010 Royal Meteorological Society

Large-eddy simulations of an idealized diurnal urban heat island are performed using the Weather Research and Forecasting Model. The surface energy balance over an inhomogeneous terrain is solved considering the anthropogenic heat contribution and the differences of thermal and mechanical properties between urban and rural surfaces. Several cases are simulated together with a reference case, considering different values of the control parameters: albedo, thermal inertia, roughness length, anthropogenic heat emission, and geostrophic wind intensity. Spatial distributions of second-moment statistics, including the turbulent kinetic energy (TKE) budget, are analyzed to characterize the structure of the planetary boundary layer (PBL). The effect of each control parameter value on the turbulent properties of the PBL is investigated with respect to the reference case. For all of the analyzed cases, the primary source of TKE is the buoyancy in the lower half of the PBL, the shear in the upper half, and the turbulent transport term at the top. The vertical advection of TKE is significant in the upper half of the PBL. The control parameters significantly influence the shape of the profiles of the transport and shear terms in the TKE budget. Bulk properties of the PBL via proper scaling are compared with literature data. A log-linear relationship between the aspect ratio of the heat island and the Froude number is confirmed. For the first time, the effect of relevant surface control parameters and the geostrophic wind intensity on the bulk and turbulent properties of the PBL is systematically investigated at high resolution.

The surface energy balance (SEB) model is a physically based approach in which aerodynamic principles and bulk transfer theory are used to estimate actual evapotranspiration. A wide range of different methods have been developed to parameterize the SEB equation; however, few studies addressed solutions to the SEB considering the land surface temperature (LST). Therefore, in the current review, a clear and comprehensive classification is provided for energy-based approaches considering the key role of LST in solving the energy budget. In this regard, three general approaches are presented using LSTs derived by climate and land surface models (LSMs), satellite-based data, and energy balance closure. In addition, this review surveys the concepts, required inputs, and assumptions of energy-based LSMs and SEB algorithms in detail. The limitations and challenges of aforementioned approaches including land surface temperature, surface energy imbalance, and calculation of surface and aerodynamic resistance network are also assessed. According to the results, since the accuracy of resulting LSTs are affected by weather conditions, surface energy closure, and use of vegetation/meteorological information, all approaches are faced with uncertainties in determining ET. In addition, for further study, an interactive evaluation of water and energy conservation laws is recommended to improve the ET estimation accuracy.

With the rapid development of metropolises worldwide, the urban heat island (UHI) effect is becoming a serious environmental problem in recent years. The rapidly increasing anthropogenic heat (AH) from human activities has more significant impacts on urban microclimate which aggravates the UHI effect. The characteristics of AH emissions at different scales may vary according to different natural backgrounds. Therefore, the calculation of AH is complicated and uncertain due to the temporal and spatial variation. This review presented different methods of AH calculation according to specific case studies at home and abroad. We summarized the scales of different methods and required data set as well as the certainty of error sources. Last we discussed the advantages, limitations, and potential improvements for different approaches. By the review, we suggested that the AH research should first choose a reasonable calculation method based on spatial and temporal scales to guarantee the accuracy. The calculation of AH could provide useful information to better understand the AH emissions of specific areas, which bring more potentials to improve the living environment through rational urban planning.

Heat emissions from buildings in many cities play an important role for the urban surface energy balance (USEB) and the urban micro-climate. Heat generated indoors from human activities (e.g., use of electrical appliances, space heating, metabolic rate) is conducted through the building fabric and affects the USEB through long-wave radiation and turbulent sensible heat flux. The radiative and thermal properties of materials used in the building’s structural components (e.g., external walls, roof, windows) determine the storage of heat in the building volume and therefore the rate and timing of heat exchange between indoor and outdoor environments. This also affects the overall indoor thermal comfort.We use a building energy model (STEBBS – Simplified Thermal Energy Balance for Buildings Scheme) to simulate and compare the anthropogenic heat flux from residential buildings in the city centres of London, UK, and Berlin, Germany, in different seasons. Occupancy schedules and timings of indoor heat gains for STEBBS can be determined from the agent-based model DAVE (Dynamic Anthropogenic actiVities and feedback to Emissions; McGrory et al. – this meeting). Both cities feature a diverse set of building types (determined by common morphological attributes) and a range of typical construction types that are derived from information on building age bands, building regulations and building typologies (EPISCOPE, TABULA). This poster discusses the generation of characteristic and comparable building archetypes for the two cities, considering and discussing differences in morphology markers (e.g., building height, volume, exposed walls), building age, construction materials (e.g., presence or not of wall insulation, roof types) and the typical state of refurbishment / retrofitting of the residential building stock. Seasonal diurnal variations of the building energy balance in terms of energy consumption, turbulent sensible heat flux and net storage heat flux are compared for common building archetypes in the two cities to assess the main controls on the anthropogenic heat emissions into the urban canopy layer.Funding supporting this work includes ERC urbisphere, NERC APEx, and EPSRC CREDS.

Surface ozone levels are strongly influenced by temperature, with elevated concentrations commonly observed during summer. However, the ozone-temperature relationship requires further investigation due to the non-linear mechanisms governing ozone formation. In urban areas, anthropogenic heat emissions (AHE) contribute to local temperature increases, yet their effect on ozone levels remains uncertain and has not been fully quantified.This study pioneers the coupling of distributed urban parameters in WRF's Single-Layer Urban Canopy Model (SLUCM) with atmospheric chemistry in WRF-Chem version 4.6. This version enables the representation of AHE and spatially varying urban morphological parameters (roughness length for momentum, displacement height, and sky-view factors). The Model for Ozone and Related Chemical Tracers (MOZART) coupled with the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) was used for gas-phase chemistry and aerosol representation. Chemical concentrations were initialized using output from the Whole Atmosphere Community Climate Model (WACCM), while biogenic emissions were derived from the Model of Emissions of Gases and Aerosols from Nature (MEGAN). Anthropogenic emissions were incorporated from the EDGAR HTAP_v3 inventory. Using this configuration, we examine the role of urban effects in driving surface ozone formation over the Kanto region of Japan for one week in August 2021, with a finest domain resolution of 1.5km.Comparing simulations with and without AHE, we confirm that AHE significantly influences ozone transformation. In urban areas, AHE generally leads to an increase in ozone concentration. Notably, the increase in temperature (△T) and ozone (△ozone) reach their maximum in the evening, but their peaks do not coincide. The peak in △T occurs first, followed by the peak in △ozone after a lag of several hours. This temporal offset suggests complex interactions between AHE, meteorology, and atmospheric chemistry, which this study aims to determine by analyzing the mechanisms driving these interactions and their implications for urban air quality.

In air quality forecasting systems, failure to consider the considerably large anthropogenic heat emissions generated daily in the Beijing megacity by intensive human activities is one of the major causes of model failure. In this paper, we employ the nested air quality prediction model system coupled with the weather research and forecasting model and an urban canopy model to integrate anthropogenic heat emissions over Beijing into the modeling system and exhaustively evaluate their potential effects on air quality forecast by analyzing the wind field, boundary layer structure (height and atmospheric circulation), and surface and vertical distribution of pollutants. Consequently, the effects of anthropogenic heat on the boundary layer structure, greatly pronounced in urban areas, exhibited substantial variability at different levels depending on the time. The effects were evident during both daytime and night, but played a more prominent singular role in the night in the absence of solar short-wave radiation. Basically, anthropogenic heat acts not only by directly inducing the ascent of a warm air mass from the low parts of the atmosphere over urban areas to the top of the boundary layer, but also by indirectly driving wind convergence and inducing the descent of a cooled air mass from a high altitude to the boundary layer through a complex atmospheric circulation process. Incorporating anthropogenic heat emissions into the modeling system was effective in improving predictions by reducing the normalized mean bias by 20%–30% (for wind speed) and root mean square error by 361–558 m (for boundary layer height) and by 10–23 µg m–3 (for surface PM10), with a significant reduction in the underestimation of ozone concentration by approximately 20 ppb at urban sites. This paper is expected to provide new insights into the improvement of model accuracy for air quality forecasts over megacities.