Abstract
Abstract. Thermal emissions – or anthropogenic heat fluxes (QF) – from human activities impact urban climates at a local and larger scale. DASH considers both urban form and function in simulating QF through the use of an agent-based structure that includes behavioural characteristics of urban residents. This allows human activities to drive the calculation of QF, incorporating dynamic responses to environmental conditions. The spatial resolution of simulations depends on data availability. DASH has simple transport and building energy models to allow simulation of dynamic vehicle use, occupancy and heating–cooling demand, and release of energy to the outdoor environment through the building fabric. Building stock variations are captured using archetypes. Evaluation of DASH in Greater London for periods in 2015 uses a top-down inventory model (GQF) and national energy consumption statistics. DASH reproduces the expected spatial and temporal patterns of QF, but the annual average is smaller than published energy data. Overall, the model generally performs well, including for domestic appliance energy use. DASH could be coupled to an urban land surface model and/or used offline for developing coefficients for simpler/faster models.
Highlights
The anthropogenic heat flux, QF, the thermal emissions arising from metabolic, chemical, and electrical energy use, is an additional energy source in the urban surface energy balance
We adopt the OA as the agent spatial unit (i.e. AN ) in the model runs, with AN nested within four coarser spatial units (B): lower-layer super output area (LSOA), middle layer super output area (MSOA), local authority (LA), and city/region as data are aligned to one or more of these spatial units
In London there are 25 053 OA that vary in size from 1.56 × 10−4 to 12.3 km2, 4835 LSOA, 983 MSOA, and 33 LA within one Greater London Authority region (Table 1)
Summary
The anthropogenic heat flux, QF , the thermal emissions arising from metabolic, chemical, and electrical energy use, is an additional energy source in the urban surface energy balance. The terms of Eq (2) vary with land use and activity within an area resulting in spatial and temporal heterogeneity of QF This impacts the urban surface energy balance (Eq 1). We present a new bottom-up model for QF (DASH, Dynamic Anthropogenic activitieS impacting Heat emissions) that captures city features (i.e. place), variations in building type (e.g. thermal properties), peoples’ activities and the variability in these with demographics, transport energy use, and heat release. The DASH model allows the impacts of activities and their interactions across a wide range of spatial and temporal scales to be explored by taking an agent-based approach With both the heterogeneity of city energy use and dynamics of the whole city captured by DASH, comparisons to top-down inventories or other data with coarser spatial and temporal scale resolutions are possible. DASH is applied (Sect. 3) and evaluated (Sect. 4) in Greater London using inventory-based results (Gabey et al, 2019)
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