Diurnal and weekly variation of anthropogenic heat emissions in singapore

DIUR AL A D WEEKLY VARIATIO OF A THROPOGE IC HEAT EMISSIO S I SI GAPORE QUAH KHAI LI A E B.A (Hons.), University of Canterbury A THESIS SUBMITTED FOR THE DEGREE OF MASTER OF SOCIAL SCIE CES DEPARTME T OF GEOGRAPHY ATIO AL U IVERSITY OF SI GAPORE 2010 i ACK OWLEDGEME TS This thesis was a collaborative effort The guidance and assistance of all people involved must be mentioned My deepest gratitude is to my supervisor, Associate Professor Matthias Roth At each stage of the thesis, I was very fortunate to have received his expertise, constructive comments and encouragement, all of which were instrumental in completing this piece of work I would also like to thank the following people and organisations who have assisted in the provision of data, namely, Ms L.T Soh, Ms S.P Lim, Ms C Ang, Mr D Phoon and Mr M Chew from the Land Transport Authority, Professor L Norford from the Massachusetts Institute of Technology, the Ministry of National Development, Mr E Toh, Mr J Chin, Ms L Ganesh from the Energy Market Authority, Ms C Ng from the West Coast Town Council, Mr H.J, Cheng from the Bukit Panjang Town Council, Mr Goh from Clementi Community Centre, Ms M Chung from Faith Montessori Centre, Ms I Leong from the National University of Singapore (Office of Estate Development) and Mr Y.Y Choon from the National Environment Agency I am also thankful to the many friends who have supported and encouraged me through this challenging period Most importantly, none of this would have been possible without the love, patience and encouragement of my parents and brother i TABLE OF CO TE TS ACK OWLEDGEME TS…………………………………………………… i TABLE OF CO TE TS……………………………………………………… ii SUMMARY……………………………………………………………………… v LIST OF TABLES……………………………………………………………… vii LIST OF FIGURES……………………………………………………………… ix LIST OF SYMBOLS……… …………………………………………………… xii CHAPTER 1: I TRODUCTIO ……………………………………………… 1.1 Context and Rationale…………………………………………… 1.2 Objectives……………………………………………………… 1.3 Structure of Thesis……………………………………………… CHAPTER 2: REVIEW OF RESULTS FROM A D METHODS USED I PAST A THROPOGE IC HEAT FLUX DE SITY STUDIES……………………………………………………… 2.1 Magnitude of Anthropogenic Heat Flux Densities in Different Cities…………………………………………………………… 2.2 Methods for Estimating Anthropogenic Heat Flux Density…… 2.2.1 2.2.2 2.2.3 2.2.4 Overview of inventory-based approach ………………… Inventory-based bottom-up modelling approach ……… Inventory-based top-down modelling approach……… Energy budget closure approach.………………………… 12 12 13 20 25 2.3 Discussion of Inventory-Based and Energy Budget Closure Approaches …………………………………………………… 26 2.3.1 2.3.2 Comparisons between the inventory-based and energy budget closure approaches.…………………………… 26 Comparisons between the bottom-up and top-down modelling approaches…………………………………… 27 ii CHAPTER 3: BACKGROU D I FORMATIO O STUDY AREAS…… 29 3.1 Geography and Population……………………………………… 29 3.2 Land Use Patterns ……………………………………………… 30 3.3 Climatic Setting……… ……………………………………… 32 3.4 City-wide Energy Consumption Patterns and Trends.…………… 34 3.5 Location and Description of the Selected Study Areas………… 37 CHAPTER 4: CO CEPTUAL FRAMEWORK A D ITS APPLICATIO TO ESTIMATE A THROPOGE IC HEAT FLUX DE SITY……………………………………………………… 43 4.1 Overview of Modelling Framework ……………… ………… 43 4.2 Anthropogenic Heat Emissions from Vehicular Traffic ……… 4.2.1 Estimation of vehicle numbers.………………………… 4.2.2 Estimation of vehicle energy usage.…………………… 4.2.3 Estimation of distance travelled by vehicles.…… …… 45 46 50 53 4.3 Anthropogenic Heat Emissions from Buildings.………………… 4.3.1 Estimation of electricity consumption: EUI approach… 4.3.2 Estimation of electricity consumption: Household-cumcommon area electricity usage approach………………… 4.3.3 Estimation of electricity consumption: Mixed approaches 4.3.4 Mapping electricity load profile data to electricity consumption estimates…………………………………… 54 59 61 65 66 4.4 Anthropogenic Heat Emissions from Human Metabolism……… 69 4.4.1 Calculation of metabolic heat production values……… 70 4.4.2 Estimation of hourly population………………………… 71 CHAPTER 5: TEMPORAL VARIATIO OF A THROPOGE IC HEAT FLUX DE SITY 76 5.1 Anthropogenic Heat Emissions from Vehicular Traffic………… 5.1.1 Diurnal variation of QV………………………………… 5.1.2 Weekly variation of QV………………………………… 76 76 78 5.2 Anthropogenic Heat Emissions from Buildings………………… 5.2.1 Diurnal variation of QB………………………………… 5.2.2 Weekly variation of QB………………………………… 78 78 81 5.3 Anthropogenic Heat Emissions from Human Metabolism……… 81 iii 5.3.1 5.3.2 Diurnal variation of QM………………………………… 81 Weekly variation of QM………………………………… 83 5.4 Total Anthropogenic Heat Flux………………………………… 5.4.1 Diurnal variation of QF………………………………… 5.4.2 Weekly variation of QF………………………………… 84 84 87 CHAPTER 6: DISCUSSIO …………………………………………………… 89 6.1 Overview………………………………………………………… 89 6.2 Factors Influencing Diurnal and Weekly Estimates of QF…… … 6.2.1 Influence of traffic volume……………………………… 6.2.2 Influence of building electricity consumption patterns.… 6.2.3 Influence of population numbers………………………… 6.2.4 Influence of QV, QB and QM on diurnal and weekly QF estimates………………………………………………… 89 89 93 98 100 6.3 Comparison of QF against Net All-Wave Radiation Flux Density and UHI Intensities……….……………………………………… 106 6.3.1 Comparison with net-all wave radiation flux density…… 107 6.3.2 Implications for UHI intensities……………………… … 108 CHAPTER 7: CO CLUSIO ………………………………………………… 111 7.1 Summary of Results……………………………………………… 112 7.2 Recommendations for Future Research and Final Thoughts…… 114 REFERE CES………………………………………………………………… 116 APPE DIX A: RAW DATA FOR CALCULATI G QF…………………… 126 APPE DIX B: EQUATIO FOR CALCULATI G E ERGY USE I TE SITY…………………………………………………… 133 APPE DIX C: GROSS FLOOR AREA CALCULATIO S……………… 135 APPE DIX D: ESTIMATIO OF BUILDI G ELECTRICITY CO SUMPTIO USI G MIXED APPROACHES .… 138 APPE DIX E: ELECTRICITY LOAD PROFILES………………………… 144 APPE DIX F: BODY SURFACE AREA CALCULATIO ……………… 154 APPE DIX G: APPROACH FOR ESTIMATI G WORKER, SHOPPER A D PEDESTRIA POPULATIO S AT COM… ……… 156 iv SUMMARY The anthropogenic heat flux density, QF, is unique to urban environments and can potentially be an important component of the energy balance of the building-air volume, particularly in densely populated cities with high energy demands Through its role in the energy balance, QF will influence a city’s thermal environment, ambient air quality and other attributes of the urban climate system, albeit to different extents as anthropogenic heat emissions are not uniform across the city The present study estimates the temporal variability of QF for three common land use types found in Singapore, namely, (i) commercial, (ii) high-density public housing and (iii) low-density private housing, between October 2008 and March 2009 QF is estimated by considering separately the three major sources of waste heat in urban environments, which are heat release from vehicular traffic, buildings and human metabolism, respectively These components of QF are calculated by applying a combination of the top-down and bottom-up modelling approaches of energy consumption within the local setting In order to place the emission of this anthropogenic heat in a wider context, QF is compared against other components of the energy balance of the building-air volume and with urban heat island intensities observed in Singapore Results show that over a 24 hour period, magnitudes of mean hourly QF reach maximum values of 113 W m−2 in the commercial, 18 W m−2 in the high-density public housing and 13 W m−2 in the low-density private housing areas, respectively Buildings are found to be the major source of anthropogenic heat in each study area, contributing to between 49–83% of QF on weekdays and 46–82% on weekends The v spatial and temporal variations of QF are attributed to differences in traffic volume, building energy consumption and population density Comparisons show that QF is equivalent to 87% of the net-all wave radiation flux density at the commercial site whereas this percentage is considerably smaller for the two residential areas The detailed QF data obtained from the present study can be included in urban climate models to allow researchers to quantify and gain further insights into the way in which QF affects the local climate of cities and its potential contribution to urban heat islands vi LIST OF TABLES Table 2.1 Summary of mean anthropogenic heat flux density (QF) for different cities …………………………… ………………… ……………… Table 2.2 Net heat combustion and fuel density values of vehicles in a Vancouver suburb according to fuel type ……………………… … 15 Table 4.1 Inventory-based modelling approaches used to calculate the magnitude of the components of QF………………………………… 43 Table 4.2 Fuel type according to vehicle class………………………………… 51 Table 4.3 Net heat combustion and fuel density of unleaded petrol and diesel… 51 Table 4.4 Mean fuel economy of different vehicle classes in Singapore……… 52 Table 4.5 Representative values of the energy used per vehicle according to the vehicle class………………………………………………………… 53 Table 4.6 Building categories and sub-categories and the corresponding number of buildings……………………………………… ………… 58 Table 4.7 Average EUIs of non-residential buildings according to building category/sub-category………………………………………………… 60 Table 4.8 Mean monthly electricity consumption per household according to household type…….………………………………………………… 62 Table 4.9 Mean hourly normalised common area electricity consumption according to block type……………………………………………… 65 Table 4.10 Temporal scale at which electricity consumption estimates of the various building categories/sub-categories were available……… … 67 Table 4.11 Building categories/sub-categories and household types without representative load profile data and their matched building category/sub-category and household type with available load profile data…………………………………………………………… 69 Table 4.12 Metabolic heat production values of activity types most commonly carried out in COM, HDB and RES………………………………… 71 Table 4.13 Estimated population of COM, HDB and RES at any hour of the ‘sleep’ and ‘active’ periods, on weekdays and weekends………… 72 Table 4.14 Total number of non-working residents, workers, shoppers and pedestrians for COM on weekdays and weekends…………… …… 74 vii Table 6.1 QF magnitudes of various city-centres/commercial areas … ……… 101 Table 6.2 QF magnitudes of various suburban residential areas……………… 104 Table 6.3 Weekday and weekend mean hourly QF values for COM, HDB and RES…………………………………………………………………… 106 viii LIST OF FIGURES Figure 1.1 Growth of urban population by region, 1950–2050…………… Figure 2.1 Temporal profiles of traffic counts for major and minor roads in a Vancouver suburb………………………………………………… 14 Figure 2.2 Mean hourly heat release from vehicles in a Vancouver suburb…… 15 Figure 2.3 Weekly and daily normalised electricity load profiles for Toulouse 17 Figure 2.4 Mean hourly heat release from human metabolism in a Vancouver suburb……………………………………………………………… 19 Figure 2.5 Diurnal metabolic rates representative of Greater Manchester…… 20 Figure 2.6 Daily normalised traffic flow profiles for various US cities and states, Toulouse and the Gyeong-In region of South Korea ……… 22 Figure 2.7 Weekly normalised traffic flow profile for Toulouse…………… Figure 2.8 Representative summer and winter electricity load profiles for various service regions across the US…… ……………………… 24 Figure 3.1 Location map of Singapore…………………………… ………… Figure 3.2 Land use map of Singapore in 1958 and 2005…………… ……… 31 Figure 3.3 Climograph of Singapore based on measurement carried out between 1982 and 2008…………………………………………… 32 Figure 3.4 Distribution of Singapore’s end-use energy consumption in 2007 according to consumption sector…………………………………… 35 Figure 3.5 Distribution of energy consumed in Singapore for 2006 according to types of energy…………………………………………………… 35 Figure 3.6 Distribution of electricity consumed by a typical household in Singapore according to type of appliance………………………… 37 Figure 3.7 Location of study areas………………… ………………………… 38 Figure 3.8 Land use patterns and spatial extent of the three study areas……… 39 Figure 3.9 Photographs of land-use characteristics at the three study areas…… 40 Figure 4.1 Mean diurnal traffic count profiles for COM, HDB and RES… … 47 23 29 ix E-1: Monthly electricity load profiles The monthly electricity load profiles illustrated in Figure AE.1 were developed based on actual data obtained from the energy audit reports No substantial variability in the monthly electricity consumption values were observed, like in the mid-latitude city of Toulouse (Pigeon et al., 2007) for example, where demand for energy increases significantly in winter due to heating needs In order to obtain monthly electricity consumption estimates, the available annual electricity consumption data of buildings in the study areas were mapped to the monthly electricity consumption fraction corresponding to October This particular month was selected to coincide with the period for which traffic count data was obtained Furthermore, October is considered to be a relatively typical month in terms of monthly electricity usage compared to some other months, such as December which is the time when offices and schools shut down, and shopping centres extend their opening hours for the holiday season; thus, resulting in either lower/higher monthly electricity usage levels (depending on building type) relative to the ‘normal’ months 145 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 Office (2002) Shopping Centre (2006) 0.01 Dec Nov Oct Sep Aug Sep Oct Nov Dec Sep Oct Nov Dec Aug Jul Jun May Apr Mar Feb Jan Dec Nov Oct Sep Aug Jul Hospital (2004) Jun May Apr Mar Feb Jan Month 0.10 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 Primary School/ International School (2003) Recreation Club (2004) Month Aug Jul May Apr Mar Feb Jan Dec Nov Oct Sep Aug Jul Jun Apr May Mar Feb Jan 0.00 Jun Fraction of Yearly Electricity Consumption Hotel (2002) Month Fraction of Yearly Electricity Consumption Jul Month Month 0.10 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0.00 Jun May Apr Mar Feb Jan Dec Oct Nov Sep Aug Jul Jun May Apr Mar Feb 0.00 Jan Fraction of Yerrly Electricity Consumption 0.10 Month Figure AE.1 Representative monthly electricity load profiles of various building types, expressed as a fraction of total electricity consumed over year The load profiles are based on data obtained for the year indicated (in parenthesis) on the graphs (Sources: Ministry of Education, 2002; Shangri-La Hotel Singapore, 2002; National Institute of Education, 2003; Tan Tock Seng Hospital, 2004; The American Club Singapore, 2004; Plaza Singapura; 2006) 146 E-2: Daily electricity load profiles While the load profiles displayed in Figure AE.2 are actual data obtained from the Energy Market Authority (personal communication, 2009), some assumption had to be made in order to develop the load profiles shown in Figure AE.3 because of data availability issues In particular, for office buildings, daily electricity consumption was assumed to remain the same on weekdays (i.e Monday–Friday) whereas the weekend values reflect actual data With regard to the daily electricity consumption of hotels, hospitals, primary/international schools and recreation clubs, their daily values available for a specific weekday and weekend were considered to be uniform for Monday to Friday, and Saturday and Sunday, respectively Given that daily electricity consumption data of shopping centres, MRT stations and bus terminal were available only for one day of the week, their daily electricity load profiles were assumed to exhibit no variability, which is a fairly reasonable assumption as these buildings are likely to consume somewhat similar levels of electricity throughout the week 147 Fraction of Weekly Electricity Consumption 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 HDB 3-room 0.02 HDB 4-room 0.00 Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Fri Sat Sun Fri Sat Sun Day Day Fraction of Weekly Electricity Consumption Thu 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 Landed House HDB 5-room 0.02 0.00 Mon Tue Wed Thu Day Fri Sat Sun Mon Tue Wed Thu Day Figure AE.2 Representative daily electricity load profiles of various household types, expressed as a fraction of total electricity consumed over week The load profiles are based on daily data obtained in February 2009 (Source: Energy Market Authority, personal communication, 2009) 148 0.16 0.14 0.12 0.10 0.08 0.06 0.04 Office (2002) 0.02 Shopping Centre (2006) 0.00 Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Sat Sun Fri Sat Sun 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 Hotel (2002) 0.02 Hospital (2004) 0.00 Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Day Day 0.18 0.16 0.14 0.12 0.10 0.08 0.06 Primary School/ International School (2003) 0.04 0.02 Recreation Club (2004) MRT Station/ Bus Terminal (2005) Day Day Sun Sat Fri Thu Mon Sun Sat Fri Thu Wed Tue Mon Sun Sat Fri Thu Wed Tue 0.00 Mon Fraction of Weeky Electricity Consumption Fri Day Wed Fraction of Weekly Electricity Consumption Day Thu Tue Fraction of Weekly Electricity Consumption 0.18 Day Figure AE.3 Representative daily electricity load profiles of various building types, expressed as a fraction of total electricity consumed over week The load profiles are based on data obtained for the year indicated (in parenthesis) on the graphs (Sources: Ministry of Education, 2002; Shangri-La Hotel Singapore, 2002; National Institute of Education, 2003; Tan Tock Seng Hospital, 2004; The American Club Singapore, 2004; Plaza Singapura; 2006) 149 E-3: Hourly electricity load profiles The hourly household load profiles shown in Figure AE.4 are actual data Similar to the daily load profiles, some assumptions were also made to obtain the hourly load profiles of the various building types illustrated in Figure AE.5 The weekday hourly profile of office buildings was developed based on actual data from one specific weekday and this profile was assumed to be the same for all weekdays; their profiles for Saturday and Sunday show actual data Available energy audit reports of hotels, hospitals, primary/international schools and recreation clubs contained only data on their respective hourly electricity consumption for one weekday and one weekend (i.e Saturday or Sunday) Thus, the values for that particular weekday and weekend were considered to be the same as all weekdays, and Saturday and Sunday, respectively Finally, the hourly load profile data for shopping centres, MRT stations and bus terminals were assumed to remain uniform for all days of the week Given that data indicating the hourly electricity usage of MRT stations was unavailable, their representative hourly load profile shown in Figure AE.5 was developed by first dividing the day into two periods, which are the hours when train services are in operation (i.e 06:00 h to midnight) and the hours when train services are not in operation (i.e 01:00 h–05:00 h) Using information on the average percentage of electricity consumed by the various electrical systems in MRT stations (e.g ventilation systems, lighting and lifts) as a whole, which was provided by Thong et al (2005), the daily electricity consumption estimates were apportioned accordingly to obtain the hourly load profile data Owing to the absence of appropriate data, the only bus terminal considered in this study was assumed to have the same hourly load profile representative of MRT stations as both building types are likely to show 150 similar hourly electricity consumption trends, considering that both have the same 0.08 HDB 3-room room 0.07 HDB 4-room 0.06 0.05 0.04 0.03 0.02 0.01 23:00 21:00 19:00 17:00 15:00 13:00 11:00 Time (h) Time (h) 0.08 HDB 5-room room 0.07 Landed House 0.06 0.05 0.04 0.03 0.02 0.01 Time (h) 23:00 21:00 19:00 17:00 15:00 13:00 11:00 09:00 07:00 05:00 03:00 01:00 23:00 21:00 19:00 17:00 15:00 13:00 11:00 09:00 07:00 05:00 03:00 01:00 Proportion of Daily Electricity Consumption 09:00 07:00 05:00 03:00 01:00 23:00 21:00 19:00 17:00 15:00 13:00 11:00 09:00 07:00 05:00 03:00 01:00 Fraction of Daily Electricity Consumption operating hours and buildin building g use (i.e for providing public transport services) Time (h) Figure AE.4 Representative hourly electricity load profiles of various household types, expressed as a fraction of total electricity consumed over day The load profiles are based bas on hourly data obtained in February 2009 (Source: Energy Market Authority, personal communication, 2009) 151 Weekday Weekend 0.05 0.04 0.03 0.02 0.01 0.00 Fraction of Daily Electricity Consumption 0.07 Hospital (2004) 0.06 Weekday Weekend 0.05 0.04 0.03 0.02 0.01 Weekday Weekend Fraction of Daily Electricity Consumption 23:00 21:00 19:00 17:00 15:00 13:00 Time (h) Primary School/ International School (2003) 0.09 Recreation Club (2004) 0.08 Weekday Weekend 0.07 0.06 0.05 0.04 0.03 0.02 0.01 Time (h) 23:00 21:00 19:00 17:00 15:00 13:00 11:00 09:00 07:00 05:00 03:00 01:00 23:00 21:00 19:00 17:00 15:00 13:00 11:00 09:00 07:00 05:00 03:00 0.00 01:00 Fraction of Daily Electricity Consumption Time (h) 0.10 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0.00 11:00 09:00 07:00 05:00 03:00 23:00 21:00 19:00 17:00 15:00 13:00 11:00 09:00 07:00 05:00 03:00 01:00 0.00 01:00 Fraction of Daily Electricity Consumption Hotel (2002) 0.06 23:00 Time (h) Time (h) 0.07 21:00 01:00 0.00 23:00 21:00 19:00 17:00 15:00 13:00 11:00 09:00 07:00 05:00 03:00 01:00 0.00 0.01 19:00 0.01 0.02 17:00 0.02 15:00 0.03 0.03 13:00 0.04 0.04 11:00 0.05 09:00 0.06 0.05 07:00 Sunday 0.07 Shopping Centre (2006) 0.06 05:00 0.08 Saturday 0.07 03:00 Weekday Office (2002) 0.09 Fraction of Daily Electricity Consumption Fraction of Daily Electricity Consumption 0.10 Time (h) Figure AE.4 Representative hourly electricity load profiles of various building types, expressed as a fraction of total electricity consumed over day The load profiles are based on data obtained for the year indicated (in parenthesis) on the graphs (Sources: Ministry of Education, 2002; Shangri-La Hotel Singapore, 2002; National Institute of Education, 2003; Tan Tock Seng Hospital, 2004; The American Club Singapore, 2004; Thong et al., 2005; Plaza Singapura; 2006) 152 0.06 01:00 03:00 05:00 07:00 09:00 11:00 13:00 15:00 17:00 19:00 21:00 23:00 Fractionn of Daily Electricity Consumption 0.07 MRT Station/ Bus Terminal (2005) 0.05 0.04 0.03 0.02 0.01 0.00 Time (h) 153 Appendix F Body Surface Area Calculation 154 The DuBois and DuBois (1916) formula is commonly used to calculate body surface area if weight and height information is known This formula can be expressed as, Body surface area = W 0.425 × H 0.725 × 0.007184 [m2] where W is weight of a person (kg) and H is height of a person (cm) (AF.1) Weight and height measurements of about 300 adult residents in Singapore were collected for a health-related study by Deurenberg-Yap et al (2000) It was found that the mean weight and height of the female subjects were 58 kg and 156 cm respectively, and that for the male subjects were 68 kg and 168 cm respectively For the purpose of this study, the mean weight and height of the females (males) subjects were considered to be representative of the mean weight and height of the entire adult female (male) population in Singapore Subsequently, the corresponding mean weight and height values of both females and males were substituted into equation (AF.1) and the resulting mean body surface area of a female and male are calculated as 1.6 m2 and 1.8 m2 respectively In view that data on the size of the female and male population in the study areas was unavailable, the mean female and male body surface areas were averaged to obtain a mean adult body surface area of 1.7 m2 155 Appendix G Approach for Estimating Worker, Shopper and Pedestrian Populations at COM 156 G1 Estimating Worker Population Office workers and retail staff were assumed to make up the total weekday worker population at COM since offices and shopping centres are the main workplaces in this study area In making this assumption, the weekday worker population was estimated using the following equation: WD = (ORN / ORS) × ORC [persons] (AG.1) where WD is the estimated weekday worker population at COM, ORN is the estimated total number of office workers and retail staff in the planning zones of COM (the planning zones being Orchard, Newton and River Valley), ORS is the total office and retail space in the planning zones of COM (which is different from the values for the actual site of COM) (m2), and ORC is the estimated total office and retail space in COM (m2) Based on manpower statistics (Ministry of Manpower, 2009), the value of ORN is estimate to be 19,549 persons Data obtained from REALIS (2008) indicates that ORS is equivalent to 840,583 m2 ORC was determined using the GFA data listed in Appendix A-3, and has an estimated value of 579 977 m2 The weekend worker population at COM consisted only of retail staff Statistics released by the Ministry of Manpower (2009) indicate that a majority (76%) of office workers were on the 5-day workweek in 2008 Hence, office workers were excluded from the calculation of weekend worker population at COM The following equation was used to estimate the total number of workers at COM on weekends: WE = (RN / RS) × RC [persons] (AG.2) 157 where WE is the estimated weekend worker population at COM, RN is the total number of retail staff in the planning zones of COM, RS is the total retail space in the planning zones of COM (m2), and RC is the estimated total retail space in COM (m2) The values of RN (11,993 persons), RS (365,074 m2) and RC (302,093 m2) were obtained from the same sources as their corresponding terms on the right-hand side of equation (AG.1) G2 Estimating Shopper Population While some of the larger shopping centres in Singapore monitor their own shopper volumes, this data was unobtainable Therefore, shopper population at COM was estimated by counting the number of shoppers in a representative shopping centre Instead of attempting to count every shopper inside the shopping centre (which would be quite tricky to carry out), the total number of shoppers on a representative floor of the selected shopping centre was counted for one weekday and one weekend Subsequently, this weekday and weekend shopper count data was multiplied by the total number of floors in the shopping centre These estimates were then divided by the total retail floor space of the selected shopping centre to derive its shopper population density for weekdays (0.04 person m−2) and weekends (0.08 person m−2), which were assumed to be representative of every shopping centre identified within COM Eventually, the weekday and weekend shopper population at COM was estimated from multiplying their respective shopper population densities by the total retail floor space in COM (i.e 302,093 m2) 158 G3 Estimating Pedestrian Population The pavements bordering the main stretch of road in COM experiences a high volume of pedestrians compared to those found along the minor roads within this area Therefore, only the pedestrian population on pavements adjacent to the main road of COM was estimated For these pavements, the number of pedestrians in one section was counted during one weekday and one weekend These numbers were then divided by the area of the selected pavement section (≈ 000 m2) The estimated pedestrian population density of the selected pavement section on weekdays (0.09 persons m−2) and weekends (0.23 persons m−2) was assumed to be representative of all pavement stretches bordering the main road of COM These pavement stretches have a total length of approximately 000 m and an average width of about m on each side of the road 159 ... vehicles and buildings was examined for the purpose of estimating the diurnal and weekly magnitudes of the anthropogenic heat flux density In consideration of this, some background information on Singapore? ??s... of offices, commercial buildings and hotels in Tokyo and the diurnal building energy use profile of COM……………………………………………………… 95 x Figure 6.4 Diurnal building energy use profiles of HDB, RES and. .. - - Inventory (B) Inventory (B) Inventory (B) Inventory (B) Inventory (B) Inventory (B) Inventory (B) Inventory (B) Inventory (B) Inventory (B) Inventory (B) Inventory (B) Inventory (B) Inventory