Diurnal and weekly variation of anthropogenic heat emissions in a tropical city, Singapore
Highlights
► The anthropogenic heat flux density (QF) was estimated for different land uses in Singapore. ► The largest mean hourly QF estimate (113 W m−2) was found in the commercial area. ► Building-related emissions related to air-conditioning were the dominant contributor to anthropogenic heat. ► A distinct diurnal and weekly variability in the QF estimates was observed.
Introduction
Cities cover only about 2% of the global land area, yet they are responsible for ∼70% of the world’s energy consumption (International Energy Agency, 2008a). Energy demand by urban dwellers and activities associated with running a city are predicted to increase over the next 20 years and probably beyond (e.g. International Energy Agency, 2009). The consumption of energy for human activities produces waste heat, water vapour and pollutants, thereby directly affecting the temperature, humidity, air quality and human health in the urban environment. Heat and moisture emissions associated with energy consumption in cities has been largely ignored or overly simplified in many studies of the urban climate (Sailor, 2011). This is particularly true for cities located in tropical climate, where understanding of the physical processes operating in the urban atmosphere in general is still rudimentary (Roth, 2007).
In order to effectively address the climate-related environmental issues that urban areas face as a consequence of their energy consumption patterns, it is essential to first have a better understanding of the nature of their physical climatology. Fundamental to this understanding is the building-air volume formulation of the urban (surface) energy balance (UEB) which is expressed as (Oke, 1987):where Q* is the net all-wave radiation flux, QH and QE are the turbulent sensible and latent heat fluxes, respectively, ΔQS is the net heat storage flux and ΔQA the net horizontal advective heat flux. QF is the anthropogenic heat flux and focus of the present study. Positive values on the left-hand side of Eq. (1) are inputs to the system, while positive values on the right-hand side are outputs or losses.
The majority of UEB studies which in the past have been carried out primarily in temperate (e.g. Grimmond and Oke, 1999, Spronken-Smith, 2002) and in fewer cases tropical (e.g. Roth, 2007) cities, neglect the QF term, which is essentially the energy released from human sources such as vehicles, commercial and residential buildings, industry, power plants and human metabolism. Disregard for QF stems primarily from its modest magnitude relative to the other fluxes in Eq. (1) and also from difficulties in assessing the magnitude of this particular term (e.g. Garcia-Cueto et al., 2003, Spronken-Smith et al., 2006, Sailor, 2011). Nevertheless, the interest in quantifying QF has been growing over the last few years, especially for densely populated mid-latitude cities such as Tokyo, Seoul and London. This trend is perhaps the result of an increased awareness of the findings of past QF studies, which have shown that for densely populated cities with high energy demands, QF can potentially be an important or even dominant component of the UEB with the potential to influence urban climates. For example, Grimmond (1992) observed that more than 10% of the winter-time energy input (Q* + QF) in Vancouver was accounted for by QF, while Ichinose et al. (1999) estimated QF in central Tokyo to reach 908 W m−2 in the daytime during summer months and as much as 1590 W m−2 during the early morning hours in winter. Temperature simulations by Fan and Sailor (2005) suggested that in winter, QF contributes about 2–3 °C to the nighttime urban heat island (UHI) of Philadelphia. In addition, numerical simulations by Chen et al. (2009) concluded that QF contributes 43.6% (54.5) in summer (winter) to the UHI intensity in Hangzhou City. The same study also concluded that waste heat emissions via QF strengthen the UHI circulation and improve near-surface turbulent activity, with stronger effects observed at night than during daytime (Chen et al., 2009).
The magnitude of QF varies greatly not only between cities but also within cities depending on per capita energy use, population density, meteorological conditions and background climate. According to Oke (1988) the mean annual magnitude of QF for large cities ranges from 20 to 160 W m−2. For US cities, values between 20 and 40 W m−2 in summer and 70–210 W m−2 in winter have been reported (Taha, 1997). An updated summary of QF estimates from mostly mid-latitude cities located in the northern hemisphere shows higher winter (cold season) values compared to those estimated for the summer (where data exists), and extreme peak values under certain conditions (e.g. winter time and/or in densely built areas) when QF can exceed the net radiation input into the system (Table 1).
Most studies estimate QF using either the inventory-based or energy balance closure approach. Estimating QF based on the former is the classical and most frequently used approach (e.g. Grimmond, 1992, Klysik, 1996, Sailor and Lu, 2004, Pigeon et al., 2007, Smith et al., 2009). Its earliest application is likely that by Torrance and Shum (1975) who estimated the mean annual QF of an unspecified densely populated city as 83.7 W m−2. The inventory-based method can be further divided into top-down and bottom-up approaches using utility scale consumption data or data obtained from energy consumption surveys, respectively. The former requires data at large aggregate scales (e.g. yearly) for the purpose of downscaling to smaller scales of interest (e.g. hourly) whereas the latter uses energy consumption estimated at small-scales (e.g. individual buildings) in order to scale the information up to larger scales of interest (e.g. city-scale). The theoretically simpler and more straightforward energy balance closure approach calculates QF as a residual term of the UEB (Eq. (1)). However, it has the inherent problem of accumulating errors arising during the measurement of QH and QE and modelling of ΔQS and ΔQA (which cannot be directly measured) in the residual QF. Therefore, only few studies have used the energy balance closure method to estimate QF (e.g. Offerle et al., 2005, Pigeon et al., 2007).
Data from a range of cities is necessary to arrive at a more comprehensive understanding of the effects of anthropogenic heat emission and their effects on the urban thermal environment and other attributes of the urban climate system. Most available studies have been conducted primarily in mid-latitude regions with the exception of Hong Kong, Mexico City and São Paulo (Table 1). The objective of the present study is therefore to estimate the anthropogenic heat emissions in Singapore which is a fast-growing metropolis located in a tropical climate. The specific goals are to:
- i.
Estimate the diurnal and weekly variations of QF in this tropical city where the magnitude of QF can potentially be quite large owing to strong demand for air-conditioning throughout the year;
- ii.
Analyse the results with respect to land use types and energy consumption patterns; and
- iii.
Provide an assessment of the potential impact of QF on the urban climate of Singapore.
Section snippets
Description of study area
Singapore (1°09′N to 1°29′N, 103°36′E to 104°25′E) is an island city-state located at the southern tip of the Malay Peninsula. Due to its maritime position and proximity to the equator (∼130 km to its north) Singapore experiences a typical equatorial wet tropical climate with uniformly high temperatures (mean monthly temperatures range from 26.4 to 28.3 °C) and abundant rainfall (mean annual average ∼2192 mm) throughout the year (National Environment Agency, 2009). The most significant annual
Conceptual framework used to estimate QF
QF was estimated as the sum of the major sources of waste heat (e.g. Sailor and Lu, 2004):where QV, QB and QM are the contributions to the anthropogenic heat flux from traffic, buildings and human metabolism, respectively. The three terms on the right-hand side of Eq. (2) were determined using different inventory-based modelling approaches, viz. bottom-up for QV and QB and top-down for QM.
Although the bottom-up modelling approach is generally more accurate than the top-down
Diurnal variation of anthropogenic heat emissions from vehicles, buildings and human metabolism
Ensemble averages of the diurnal variation of QV for all 181 days of the observation period are shown in Fig. 3. QV was largest at COM at every hour of the day with a maximum value of 15 W m−2 recorded at 19:00 h. Peak diurnal QV values at HDB (8 W m−2) and RES (6 W m−2) were also observed at 19:00 h with secondary, slightly smaller peaks at 08:00 h. As expected, peak emissions correspond to the morning and evening rush hours. The systematically larger values in COM reflect the much heavier
Temporal variability of QF estimates
Given that the present study includes some of the first detailed results of anthropogenic heat emissions in a tropical city, it is useful to compare the results to similar work carried out in cites located in other climatic regions. Of particular interest are building-related emissions which usually contribute most to QF, but very much depend on heating or cooling needs which are primarily determined by climate or seasonal temperature characteristics. Table 6 summarises results obtained for
Summary and conclusions
The present paper provides one of the first detailed assessments of QF for a tropical city. Specifically, the diurnal and weekly variability of the anthropogenic heat flux density has been estimated for three land use types in Singapore (COM – commercial; HDB – high-density public housing; and RES – low-density residential) using the inventory-based modelling approaches of energy consumption for the period October 2008 to March 2009. The main findings obtained from the present study are:
- (i)
The
Acknowledgements
The authors are grateful for the financial support received from the National University of Singapore (Academic Research Fund) under grant R-109-000-091-112 for this study. The Land Transport Authority, Energy Market Authority and Housing and Development Board have supplied important secondary data.
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