Diurnal and weekly variation of anthropogenic heat emissions in a tropical city, Singapore

Abstract

The present study estimates the temporal variability of the anthropogenic heat flux density (Q_F) for three common land use types found in Singapore between October 2008 and March 2009. Q_F is estimated by separately considering the major sources of waste heat in urban environments, which are heat release from vehicular traffic, buildings and human metabolism, respectively. The individual components of Q_F are calculated by using a combination of top-down and bottom-up modelling approaches of energy consumption applied to the local context. Results show that over a 24-h period, magnitudes of mean hourly Q_F reach maximum values of 113 W m⁻² in the commercial, 17 W m⁻² in the high-density public housing and 13 W m⁻² in the low-density residential areas, respectively. Buildings are found to be the major source of anthropogenic heat (primarily related to space cooling) in each study area, contributing to between 49–82% of Q_F on weekdays and 46–81% on weekends. The spatial and temporal variations of Q_F are attributed to differences in traffic volume, building energy consumption and population density. This is one of the first anthropogenic heat studies carried out in a tropical city and the results show that Q_F can be substantial and of the same order of magnitude as calculated for city centres in mid-latitude cities during winter time.

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):

$$Q^{*}+Q_{\text{F}}=Q_{\text{H}}+Q_{\text{E}}+\Delta Q_{\text{S}}+\Delta Q_{\text{A}}\left[\text{W}{\text{m}}^{-2}\right]$$

where Q* is the net all-wave radiation flux, Q_H and Q_E are the turbulent sensible and latent heat fluxes, respectively, ΔQ_S is the net heat storage flux and ΔQ_A the net horizontal advective heat flux. Q_F 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 Q_F 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 Q_F 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 Q_F 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 Q_F studies, which have shown that for densely populated cities with high energy demands, Q_F 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* + Q_F) in Vancouver was accounted for by Q_F, while Ichinose et al. (1999) estimated Q_F 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, Q_F 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 Q_F 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 Q_F 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 Q_F 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 Q_F 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 Q_F 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 Q_F can exceed the net radiation input into the system (Table 1).

Most studies estimate Q_F using either the inventory-based or energy balance closure approach. Estimating Q_F 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 Q_F 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 Q_F as a residual term of the UEB (Eq. (1)). However, it has the inherent problem of accumulating errors arising during the measurement of Q_H and Q_E and modelling of ΔQ_S and ΔQ_A (which cannot be directly measured) in the residual Q_F. Therefore, only few studies have used the energy balance closure method to estimate Q_F (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:

• Estimate the diurnal and weekly variations of Q_F in this tropical city where the magnitude of Q_F can potentially be quite large owing to strong demand for air-conditioning throughout the year;

• Analyse the results with respect to land use types and energy consumption patterns; and

• Provide an assessment of the potential impact of Q_F on the urban climate of Singapore.