Accepted Manuscript A multi-method and multi-scale approach for estimating city-wide anthropogenic heat fluxes Winston T.L. Chow, Francisco Salamanca, Matei Georgescu, Alex Mahalov, Jeffery M. Milne, Benjamin L. Ruddell PII:
S1352-2310(14)00746-8
DOI:
10.1016/j.atmosenv.2014.09.053
Reference:
AEA 13285
To appear in:
Atmospheric Environment
Received Date: 30 June 2014 Revised Date:
17 September 2014
Accepted Date: 19 September 2014
Please cite this article as: Chow, W.T.L., Salamanca, F., Georgescu, M., Mahalov, A., Milne, J.M., Ruddell, B.L., A multi-method and multi-scale approach for estimating city-wide anthropogenic heat fluxes, Atmospheric Environment (2014), doi: 10.1016/j.atmosenv.2014.09.053. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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A multi-method and multi-scale approach for estimating city-wide anthropogenic heat fluxes
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Winston T.L. Chow
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Department of Geography, National University of Singapore
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Francisco Salamanca
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School of Mathematical and Statistical Sciences, Arizona State University
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Matei Georgescu
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School of Geographical Sciences and Urban Planning, Arizona State University
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Alex Mahalov
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School of Mathematical and Statistical Sciences, Arizona State University
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Jeffery M. Milne
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School of Meteorology, University of Oklahoma
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Benjamin L. Ruddell
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Fulton Schools of Engineering, Arizona State University
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Contact details of Corresponding author:
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AS2, #03-01
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1 Arts Link, Kent Ridge
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Singapore 117570
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Email:
[email protected]
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Keywords: Anthropogenic heat, waste heat, urban climate
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ABSTRACT
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A multi-method approach estimating summer waste heat emissions from anthropogenic activities
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(QF) was applied for a major subtropical city (Phoenix, AZ). These included detailed, quality-
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controlled inventories of city-wide population density and traffic counts to estimate waste heat
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emissions from population and vehicular sources respectively, and also included waste heat
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simulations derived from urban electrical consumption generated by a coupled building energy –
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regional climate model (WRF-BEM+BEP). These component QF data were subsequently
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summed and mapped through Geographic Information Systems techniques to enable analysis
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over local (i.e. census-tract) and regional (i.e. metropolitan area) scales. Through this approach,
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local mean daily QF estimates compared reasonably versus (1.) observed daily surface energy
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balance residuals from an eddy covariance tower sited within a residential area and (2.) estimates
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from inventory methods employed in a prior study, with improved sensitivity to temperature and
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precipitation variations. Regional analysis indicates substantial variations in both mean and
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maximum daily QF, which varied with urban land use type. Average regional daily QF was ~ 13
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W m-2 for the summer period. Temporal analyses also indicated notable differences using this
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approach with previous estimates of QF in Phoenix over different land uses, with much larger
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peak fluxes averaging ~50 W m-2 occurring in commercial or industrial areas during late summer
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afternoons. The spatio-temporal analysis of QF also suggests that it may influence the form and
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intensity of the Phoenix urban heat island, specifically through additional early evening heat
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input, and by modifying the urban boundary layer structure through increased turbulence.
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1. Introduction
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A significant aspect of the growth of cities is the urban metabolism function, which is the sum of
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all materials and commodities produced and/or utilized in order to sustain a city’s inhabitants
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(Wolman 1965). The generation of waste heat, water and pollutants into the atmosphere from the
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metabolism directly alters near-surface urban climates, especially over different spatial and
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temporal scales (Oke 2006). One prominent feature in urban meteorological research is the input
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of anthropogenic heat and moisture emissions associated with energy consumption from various
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sources within cities. These can originate through consumption of electricity and heating fuels
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originating from buildings or industrial activities, combustion of fuel from vehicular traffic, and
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activities associated with human metabolism; these inputs are hereafter referred to as QF (Sailor
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2011).
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The influence of QF on local-scale urban climates can be considerable. Within mid- or high-latitude cities during winter, waste heat from urban metabolism can be significant in urban
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heat island (UHI) development (e.g. Magee et al. 1999; Fan and Sailor 2005). This feature arises
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due to smaller geographical input of solar radiative fluxes relative to low-latitude cities, as well
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as comparatively thinner winter urban boundary layers when compared to warmer, more
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turbulent summer conditions. This impact of QF on surface air temperatures may not be restricted
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to cities and their nearby environs. Given the rapid rate of global urbanization, and
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corresponding rates of energy consumption, there is a possibility that combined waste heat from
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multiple large cities could also increase winter nocturnal near-surface temperatures at regional
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scales e.g. the North American and European continents (Zhang et al. 2013).
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QF is a component of the urban Surface Energy Balance (SEB), and SEB is quantified by the following equation of flux densities (Oke 1988): (W m-2) [1]
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where Q* = net all-wave radiation, QH = sensible heat, QE = latent heat, ∆QS = net heat storage,
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and ∆QA = turbulent heat advection into the urban system
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Existing urban SEB work is primarily focused on observation, e.g. through eddy covariance
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(EC), or modeling e.g. through numerical or physics-based models of other terms in Equation [1]
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apart from QF (e.g. Grimmond et al. 2010). In contrast, there are three extant approaches to
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directly estimating QF. First, by the inventory of electrical consumption across different urban
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sectors, such as from buildings, industry and transport. Second, via QF based on accurate
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parameterizations of coupled building energy models that simulate waste heat rejected into the
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atmosphere of regional or global climate models. Third, through derivation of waste heat from
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closure of urban SEB using EC flux data residuals. Sailor (2011) reviewed several
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methodological limitations with respect to each of these methods, such as the lack of data
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availability over differing spatio-temporal resolutions for the inventory of QF arising from
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buildings, industry and transport; the relative complexity and computational cost of running
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coupled building energy-regional climate models; and the high cost of installing and logistical
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difficulties in operating an urban EC tower for estimating the urban SEB, as well as its implicit
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error uncertainties with respect to EC data residual accuracy. Notably, he suggested that future
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research should consider utilizing a combination of these approaches in estimating QF.
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Despite these limitations, research originating from several European (e.g. Christen and Vogt 2004; Offerle et al. 2005; Pigeon et al. 2007; Allen et al. 2010; Iamarino et al. 2012;
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Bohnenstengel et al. 2013; Lindberg et al. 2013; Ward et al. 2013), Asian (e.g. Ichinose et al.
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1999; Dhakal and Hanaki 2002; Lee et al. 2009; Quah and Roth 2012), Australian (Simmonds
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and Keays 1997), and North American cities (e.g. Taha 1997; Sailor and Lu 2004; Fan and
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Sailor 2005; Grossman-Clarke et al. 2005; Salamanca et al. 2013; Salamanca et al. 2014) enable
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us to infer several observations about QF:
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Spatial variations in urban land-use are important in QF with largest mean fluxes
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originating from areas with commercial or industrial land-uses, which generally have
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greater energy demand and consumption compared to residential land-uses. The former
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land-uses are usually concentrated in urban downtown core areas, while the latter
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corresponds with suburban areas or the urban-rural boundary. Coincidentally, this spatial
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pattern of QF matches the typical distribution of the UHI intensity across a city (Oke
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1982). Further, areas adjacent to major traffic thoroughfares (e.g. highways/freeways)
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were also associated with relatively higher QF compared to areas with local roads due to
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greater volume of vehicle heat and moisture emissions.
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Temporal variations are also significant in QF dynamics. In sub-diurnal or hourly
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timescales, peak fluxes tend to coincide with both morning and evening rush hour
periods, and can be a large component of the urban energy balance relative to other
radiative (i.e. incoming short-wave and outgoing long-wave with respect to the urban
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surface) or turbulent transfer (i.e. sensible and latent heat) terms. On longer timescales
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(daily, monthly or annual), this may not be the case. Taha (1997) noted that daily mean
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QF flux in city centers (e.g. 20-40 W m-2 in summer) are much smaller compared to mean
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incident shortwave radiation, which is usually an order of magnitude larger.
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The seasonal impact of QF largely depends on a city’s geographical location. On average, QF fluxes are greater during winter in mid/high-latitude cities, where waste heat from
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residential heating is greater than those generated from summer air-conditioning in low-
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latitude cities. For most cities, more energy is consumed through heating, rather than
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cooling, to maintain a comfortable ambient temperature for human thermoregulation (Hill
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et al. 2013). For instance, Ichinose et al. (1999) noted in their model simulations in Tokyo
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(35.6 °N) that a remarkable peak winter hourly QF of ~1590 W m-2 occurred in the
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downtown/commercial area of the city, with most of this waste heat originating from
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building hot water supplies for indoor heating. In contrast, Quah and Roth (2012)
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estimated that a maximum hourly QF in downtown Singapore (1.4 °N), where building
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interior cooling via air conditioning is prevalent, was ~113 W m-2.
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In the majority of previous research, QF estimates were restricted to a single method (i.e.
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assessing waste heat through inventory, energy balance residual from an EC tower, or through
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extant building energy models). Although Offerle et al. (2005) and Pigeon et al. (2007) were
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notable exceptions as they used multiple methods in estimating QF for Lodz and Marseille
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respectively, their analyses were restricted to a single, local spatial scale ( 45 W m-2) and HIR (> 35 W m-2), and the
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dual-peak pattern in the morning and evening are seen in all three urban classes; however, the
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strong positive skew of QFT in both residential classes indicates that much lower traffic counts
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(especially from non-highway traffic) are the norm within the majority of residential areas in
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Phoenix. These differences in traffic waste heat within residential areas should be considered in
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subsequent research, especially when previous inventory methods assume equal distribution of
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QFT throughout an urban area. The daily sinusoidal QFB trend of is apparent for all three urban
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classes, but the spread, specifically the observed strong negative skew, is more significant in both
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LIR and HIR. This feature can be associated with lower electrical consumption in areas around
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the margins of the metropolitan area, where population densities are relatively lower. Lastly,
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although there are some exceptions from areas with higher population densities, waste heat from
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human metabolism is a minor contributor towards total QF, with average magnitudes of hourly
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QF within all urban classes of
