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A LiDAR-based urban metabolism approach to neighbourhood scale energy and carbon emissions modelling Christen, Andreas; Coops, Nicholas C.; Kellett, Ronald; Crawford, Ben; Heyman, Eli; Olchovski, Inna; Tooke, Thoreau Rory; van der Laan, Michael Tije 2010-12-31

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Knight St.  5453551  5452601  Victoria Dr.  Fraser St.  E 41st Ave  E 49th Ave  200 400m A 0LiDAR-Based Urban Metabolism Approach to Neighbourhood Scale Energy and Carbon Emissions Modelling Andreas Christen, Assistant Professor of Geography (Principal Investigator) Nicholas Coops, Professor of Forestry and Canada Research Chair in Remote Sensing (Co-Investigator) Ronald Kellett, Professor of Landscape Architecture (Co-Investigator) Contributing authors: Ben Crawford, Eli Heyman, Inna Olchovski, Rory Tooke, Michael van der Laan University of British Columbia, 2010  495240  493340  5451651  494290  E 54th Ave  This report by the University of British Columbia was supported by the CanmetEnergy division of Natural Resources Canada with funding from the Program of energy Research and Development. Natural Resources Canada makes no warrantees or representations, express or implied, as to the accuracy or completeness of the report and does not assume any liability arising from the use of any information contained in this report. The conclusions, opinions and recommendations contained herein do not necessarily represent the views of the Government of Canada.  table of content Executive Summary    1  Part I - Concepts 1.1 1.2 1.3 Part I  The Context of Neighbourhood-scale Emission Modelling The Urban Metabolism Approach The Urban Cycle References  4 9 12 17  Part II - Methods 2.1 2.2 2.3 2.4 2.5 2.6 2.7 Part II  The ‘Sunset’ Neighbourhood in Vancouver Remote Sensing Data Inputs for Emission Modelling A Building Typology Approach to Carbon Emission Modelling Transportation Modeling Accounting for Carbon Cycling in the Human Body, Food, and Waste Vegetation and Soil The Use of Direct Carbon Flux Measurements for Model Validation References  18 21 26 39 45 48 52 59  Part III - Results 3.1 Carbon Emissions from Buildings 3.2 Carbon Emissions from Transportation 3.3 	 Carbon Emissions from Human Metabolism, Food and Waste 3.4 Carbon Emissions from and Uptake by Soils and Vegetation 3.5 Integrated Modelled Carbon Cycle 3.6 Comparison of Current Model with Direct Carbon Flux Measurements Part III References  61 65 67 70 79 84 91  Part IV - Conclusions / Scenarios 4.1 Carbon Emissions Scenarios 4.2 Scenario Discussion Part IV References  92 101 103  Acknowledgements & List of Authors  104    3  executive summary A LIDAR-BASED URBAN METABOLISM  0.0.1	 Key Model Results  APPROACH TO NEIGHBOURHOOD SCALE  •	 Carbon imports: Based on project urban  ENERGY AND CARBON EMISSIONS  metabolism scope and methods, the study area  MODELLING prototypes a remote sensing-  imports approximately 6.69 kg C m-2 year-1  based means to neighbourhood-scale energy and  (or 1.04 t C cap-1) in form of fuels, food and  carbon modelling. Building on a Vancouver case  materials and uptakes 0.49 kg C m-2 year-1 from  study neighbourhood for which remote sensing,  the atmosphere though photosynthesis of urban  atmospheric carbon flux, urban form, energy  vegetation.  and emissions data have been compiled and  •	 Carbon exports and sequestration: Sources  aggregated, the project demonstrates a replicable  within the study area emit 6.22 kg C m-2 year-1  neighbourhood-scale approach that illustrates:  (0.97 t C cap-1) or 87% of the imports to the atmosphere, and 0.87 kg C m-2 year-1 (0.14 t  •	 Holistic, systems-based and context-sensitive  C cap-1) or 12% of the imports are exported  approaches to urban energy and carbon  laterally by waste. 1% of the imported carbon,  emissions modelling.  or 0.09 kg C m-2 year-1 (0.01 t C cap-1) is  •	 Methods of deriving energy- and emissionsrelated urban form attributes (land use, building  sequestered in urban soils and biomass. •	 Relevant emission processes: Out of  type, vegetation, for example) from remote  all local emissions from the study area to the  sensing technologies.  atmosphere, 2.47 kg C m-2 year-1 (40%) are  •	 Methods of integrating diverse emission and  originating from buildings, 2.93 kg C m-2 year-1  uptake processes (combustion, respiration,  (47%) from transportation, 0.49 kg C m-2 year-1  photosynthesis), on a range of scales and  (8%) from human respiration and 0.33 kg C  resolutions based on spatial and non-spatial  m-2 year-1 (5%) from respiration of soils and  data relevant to urban form, energy and  vegetation. Emissions attributable to fuels,  emissions modelling.  resource and food production, transport or  •	 Scalable, type-based methods of building energy modeling and scenario-building. •	 Benchmark comparisons of modelled estimates  transmission, and waste management outside the study neighborhood were not considered. •	 Fossil fuel emissions: Out of the local fossil  with directly measured energy consumption  fuel emissions in the study area, 46% originate  data and two years of directly measured carbon  from the building sector (natural gas), and 54%  fluxes (emissions) on a research tower above  are attributable to transportation uses (gasoline,  the neighbourhood.  diesel). Out of the transportation emissions, 11% (0.31 kg C m-2 year-1) are attributable to carbon emitted on trips generated within the study area and 89% (2.62 kg C m-2 year-1) to carbon emitted on trips passing through the study area.  1  executive summary •	 Renewable carbon cycling: Photosynthesis  provide an effective means to scale building  and human, soil and vegetation respiration take  to neighbourhood energy modelling. These  up / emit renewable carbon. These processes  methods also facilitate definition of crucial  have potential to offset (take-up) carbon from  morphological and performance attributes  other sources as well as generate (emit) carbon  through which to filter remote sensing data  when carbon pools are disturbed, by urban land  and to scope potential mitigation strategies and  use change and (re-)development, for example.  scenarios. •	 Comparison of measured with modelled  •	 Benchmark to direct emission measurements: Two years of measurements  emissions: Direct carbon flux measurements  on a carbon flux tower in the centre of the study  on urban flux towers are demonstrated to be  area allow a comparison of modelled results  a method of validation of fine-scale emission  to directly measured carbon emissions. The  inventories / models. Given the prototype  modelled and measured emissions agreed very  nature of the approach and methods, close  well i.e. 6.71 kg C m year were measured vs.  agreement between tower measurements and  7.46 kg C m-2 year-1 modelled (refers to a subset  model results in this study is a successful and  of the study area weighted by the turbulent  promising outcome.  -2  -1  •	 Limitations: While promising, the urban  source are of the tower). The model is slightly overestimates actual emissions by 0.75 kg C m  metabolism approach demonstrated has also  year-1 (or 11%) which is mostly attributed to  been necessarily limited in several ways. Only  the lack of vehicle speed representation in the  one metabolic aspect — mass balance of  transportation model.  carbon, has been considered and measured.  -2  The spatial scale and complexity is modest — a  0.0.2	 Key Findings on Project Methodology  2km square ‘neighbourhood’ of moderate land  •	 Remote sensing: Remote sensing  carbon emissions generated in the production of  use and urban form diversity. Out of study area  technologies such as LiDAR and multispectral  food or consumer goods or the extent of local  satellite imagery have been demonstrated to be  origin trips has not been considered.  an effective means to generate, spatialize inputs fine scales (down to 1 m). These urban form  0.0.3 Key Findings from Illustrative Scenarios  attributes and data provide the inputs necessary  •	 Material emissions reduction targets:  and extract urban form and land cover data at  to energy and emission modelling tasks in  Illustrative scenarios demonstrate that, on a per  the building sector and to quantify vegetation  capita basis, local origin carbon emissions in the  emissions / uptake.  Sunset study area could meet British Columbia’s  •	 Building-type approach: Type-based modelling methods, data limitations aside,  2020 carbon reduction goal (33% below 2007 levels) with full adoption of current best practice 2  executive summary space conditioning and vehicle fuel efficiency  goal (80% below 2007 levels) would depend  standards. However, progress toward greater  on full adoption of these best practice urban  emissions reductions beyond that goal require  form strategies in combination with significant  greater population and employment density  additional technological improvement in the  in compact and mixed use, pedestrian- and  energy efficiency of buildings, vehicles and  transit-oriented patterns of urban form. Meeting  infrastructure as well as significant human  British Columbia’s 2050 carbon reduction  behaviour change toward less energy intensive lifestyles. Emissions  5.6  0.31  2.62  Other  Local traffic  Through traffic  0.28  0.49  0.05  -0.49 Photosynthesis  0.32  Vegetation -0.16  Human respiration  Soil respiration  2.15 Residential  Transportation 2.93  Above-ground respiration  Buildings 2.47  Tower measurements  Natural Gas 2.47  Vegetation  Diesel and gasoline 2.93  Export  6.7  Import  1.55 Soils  0.9  3.34 Garden waste 0.07  0.06 Human body  Food 0.97  Human waste 0.19 Food waste 0.29  Buildings / households Construction materials 0.15  Other waste 0.32  Paper 0.11  13.06  Plastics 0.06  Neighborhood  1 kg C m-2 year-1  Flux densities in kg C m-2 year-1  1 kg C m-2  Pools in kg C m-2  Figure 0.0.1: Integral modelled carbon cycle (fluxes and pools) in the study neighbourhood. Numbers denote carbon fluxes in kg C m-2 year-1 or carbon pools in kg C m-2. Fluxes leaving the neighbourhood system on top are local carbon emissions into the atmosphere and uptake of atmospheric carbon. Fluxes entering the neighbourhood system on the left hand side of the diagram are lateral imports of carbon, and fluxes leaving the neighbourhood on the right hand side are lateral exports.  3  PART I: CONCEPTS 1.1	 The Context of Neighbourhoodscale Emission Modeling  and carbon-dioxide (CO2) emissions. As much as  This section establishes an urban planning and  are attributable to urban form attributes such as  policy context for the study. It outlines the  density, land use mix, settlement pattern, building  complexity and interdependence of urban emissions  type and vegetation. Salat (2007, 2008) and Baker  sources and their fundamental relationship to many  (2000, 2003) and Steemers (2003), for example,  physical attributes of urban form — spatial patterns,  argue that urban form variation can influence urban  intensity, diversity, distribution and proximity of land  building energy demand by factors up to 2.5x and  uses, buildings, streets and mobility functions and  urban energy systems performance by factors  their associated energy sources and infrastructure.  up to 2x. Urban form also significantly impacts  Systems-based (urban metabolism) understandings  opportunity for renewable energy strategies and  of the many relationships and interactions between  technologies such as solar thermal, photovoltaic  urban form, energy demand and carbon emissions  electricity, natural ventilation and daylighting.  at neighbourhood- and greater scales are necessary  Likewise in transportation, CMHC’s Comparing  if cities are to improve their energy efficiency  Neighbourhoods for Sustainable Features (2005),  and reduce their carbon footprints to meet  California Bill 375 Redesigning Communities to  challenging legislated targets. However, patterns  Reduce Greenhouse Gases (2008) and recent urban  and attributes of urban form are difficult and costly  energy mapping projects (Center for Neighborhood  to define and measure using conventional methods  Technology, 2009 for example) demonstrate that  and technologies, Advanced remote sensing  urban form attributes significantly influence travel  technologies increase the speed and decrease the  and transportation energy demand by factors up to  cost of the means by which urban form attributes  3 times.  50% of that consumption and resulting emissions  and related data are acquired and organized. Integrated with an urban metabolism- and building  As many urban form attributes fall within the  typology-based approaches and modelling methods,  regulatory domain of local governments, planning  these technologies offer new opportunities to  policy and regulation in Canadian communities will  increase the speed, sensitivity and accuracy of  inevitably depend on urban form-based strategies  neighbourhood scale energy and carbon emissions  to meet challenging energy- and emission-reduction  modelling. Access to measured neighbourhood  targets, targets. British Columbia, for example,  scale carbon flux data affords additional opportunity  has recently completed a community energy and  to validate and compare measured and modelled  emissions inventory (CEEI) process applicable  results.  to all 180+ municipalities in the province. Local governments must now act on knowledge gained  1.1.1	 Motivation  from these inventories to integrate CEEI measures  Urban areas cover only 2% of the land surface yet  with Official Community Plans (OCPs) that link local  account for about 75% of energy consumption  planning policies, practices and regulations with 4  PART I: CONCEPTS Urban metabolism  Carbon emission Carbon uptake  Transportation  Buildings  Human body, food, and waste  Vegetation and soils  Figure 1.1.1: Simplified components of the urban metabolism controlling CO2 emissions and uptake. Arrow direction indicates carbon flow —emissions into the atmosphere (up) or uptake by the surface (down). See also Section 1.3.2 for a detailed description of the processes involved.  their energy and emissions implications.  This can soon change. Researchers and energy  In order to effectively consider and act upon  agencies around the world (Sustainable Urban  these linkages, communities will need to be able  Metabolism for Europe — SUME (2009), Sustainable  to characterize, model and represent energy  Urban Planning Decision Support Accounting for  and carbon emissions baselines and targets in  Urban Metabolism — BRIDGE (2009), Metabolism  reference to local land use, density and building  of Boston, for example), have begun to represent  policies or regulations. However, communities  and model energy in cities as systems and  are currently challenged by the data-, expertise-  patterns of sources, distribution networks,  and resource-intensive tasks of documenting,  conversion infrastructure, end uses, waste and  characterizing and modelling the energy- and  related human behaviour as ‘whole’, linked,  emissions-related attributes of urban planning  dynamic, interdependent systems of natural and  alternatives. A particular challenge has been an  anthropogenic processes.  affordable, accurate means to characterization and measurement of the surface, volumetric and spatial  In parallel, recent advances in remote sensing  attributes of urban form elements (land use, street,  technology have significantly improved the  buildings, open space and vegetation types) and  accessibility and accuracy of high resolution, site-  patterns that are the inputs to energy and emissions  specific spatial data that can support an urban  models. Typically these attributes must be inferred  metabolism approach to energy- and carbon  or extrapolated from existing mapped and tabular  emissions modelling. Two technologies in particular  data, often too coarse and imprecise for effective  hold significant promise. Light Detection and  analysis. Alternatively more appropriate data must  Ranging (LiDAR) is an active remote sensing  be created from labour- and resource-intensive  technology that emits and receives laser pulses  survey measurement and field observation.  from which three-dimensional data (see Section 5  PART I: CONCEPTS  Figure 1.1.2: Example of modelling capacity gained from high spatial resolution remote sensing datasets. (a) A traditional orthophoto and (b) the same area where building and tree heights have been extracted from LiDAR observations and vegetation type characterized from satellite imagery. In this illustration, solar potential is modelled using this spatial data and a solar radiation interception model. Tooke et al. 2009.  2.2) can be quickly and accurately extracted.  performance attributes were assigned and modelled  Integration of LiDAR data with other high-resolution  to create energy profiles (see Figure 1.1.3 and  spatial imagery facilitates accurate classification and  Section 2.3)  measurement of complex, energy and emissions significant, urban form and surfaces where in-situ  Finally there is the opportunity presented to validate  measurements are difficult to acquire.  modelled results against multiple years of measured carbon emissions data collected at a tower at the  As the complexity and diversity of urban form  centre of the study area (see Section 2.7) and utility  and buildings, and their resultant energy  consumption data in its buildings (see Section 3.7).  performance, present a methodological challenge  Effectively integrated and combined, this approach  to neighbourhood scale energy- and emissions  and its methods (urban metabolism + remote  modelling, remote sensing expedites and sharpens  sensing + typology-based modelling + calibration  the processes (Figure 1.1.2) through which inputs  against measured data — see Figure 1.1.3) can  to energy and emissions models can be derived  significantly improve the speed, accuracy and  or assigned on a parcel or building scale. As  efficacy with which neighborhood scale energy and  building types typically replicate many times at a  emissions models can be derived (Section 2) and  neighbourhood scale, data can be more accurately  modelled (Section 3).  scaled up to represent multiple others like them. In this project for example, remote sensing data was used to identify and locate study area building types based on morphological attributes (geometry, area, height, volume, for example). To these age, use, building systems, envelope and other energy 6  PART I: CONCEPTS DUPLEX, ATTACHED  BUILDING ENERGY MODELING (BEM) parcel attributes  Hydronic Heating  t 33ft typical parcel width, by 120ft or 100ft deep t XX% of site is impervious with some open green space as front and backyard planting 2  percentage of buildings in the Sunset Neighbourhood classified as SFD between 1965-1990  78%eff. (36% share) Electrical  2.1wall RSI 5.0roof RSI 1.1foundation RSI  82%eff.  Natural Gas  construction - RSI Values  car  bon  con t  ribu  tion  envelope attributes t 180m2 skin area t 28m2 aperture (window) area t 0.16 FWR (aperture-to-wall ratio)  NG Boiler  space heating system  use / occupancy attributes  2%  100%eff. (4% share)  2  average floor area  t 205m2 average floor area t typically two storeys high t 797m3 average building volume  t privately owned, typically detached garages t often secondary suites or additional living space are situated above the garage t average of 3 occupants  Baseboard  205m / 2207ft  morphological attributes 51.00’  78%eff. (60% share)  3.0avg. ppl  s  55%eff.  hot water system  1.61  kg C m-3 year-1 bldg-1  1.49  carbon emitted:  1.61  ROW  DUPLEX  0.0 kg C m-2 yr-1 bldg-1  3.0 kg C m-2 yr-1 bldg-1  heating system attributes Electrical or natural gas systems are typically used for space heating. Key values for energy modeling are the proportions of heating type and the efficiency of heating methods.  1.60  SFD POST 1990  -3  1.54 kg C m  -3  local contributions (natural gas)  Source: Google Streets, retrieved on May 17th, 2010  INPUTS - CHARACTERISTICS & PERFORMANCE Source: elementsdb, LIDAR, field work, GIS, ecoEnergy, BC assessment, BC-Hydro, CMHC, & SHEU  external contributions (electricity)  energy consumed:  NRCAN  construction attributes Thermal efficiency is typically evaluated by the thermal resistance, or RSI value, of the enclosing surfaces. RSI values for wall, roof and foundation are determined for energy modeling.  2.90  SFD PRE 1965  0.07 kg C m  hot water system attributes Natural gas systems are typically used for domestic hot water heating. Key values for energy modeling are the system type and the efficiency of heating methods.  2.38  SFD 1965-1990  0.0 MJ m-2  xxx.xx  0.0 MJ m-2  597.86  SFD PRE 1965 OUTPUTS & BENCHMARKS Hot2000, & Sunset Tower, and NRCAN  Figure 1.1.3: Example of a building typology approach to energy modelling. Morphological remote sensing identifiers are associated with energy and emissions relevant use, design and construction attributes and modelled to estimate energy demand and carbon emissions benchmarks.  7  PART I: CONCEPTS  MODEL INPUTS  Urban weather station  1 year  Satellite data  1x1m  SUBMODELS  Air and soil temperatures Water content of soil Solar radiation  MODEL OUTPUTS  LiDAR  Lawn extent Leaf area  1x1m  Tree location Leaf area Shading  Census and assessment data  Parcel or DA  Building typologies Building volumes Building shell area  Tansportation data  Arterial roads  Population Land-use Employment data  Traffic counts Trip diaries  Vegetation and soils  Buildings  Waste, food, and human body  Trasportation modelling  Ecosystem carbon model accounts for soil respiration and photosynthesis of lawns and trees.  LIDAR-informed building energy models quantify carbon emissions in a bottom-up approach.  Estimated waste production and human respiration based on census data.  Top-down modelling of traffic emissions based on splitting-up traffic counts and trip-diaries.  Maps Per area emissions 50 x 50 m raster Adding Components  MODEL VALIDATION  ?  ?  Consumption statistics Independent, top-down approach of energy consumption through natural gas and electricity  All  Flux tower data Independent, direct measurement of carbon emissions (2 years) on a tall tower using the eddy-covariance approach.  Figure 1.1.4: Diagrammatic representation of project concept and modelling approach. At the centre (grey band) is the project’s urban metabolism, systems-based approach in which carbon is attributed to four metabolism components or sub-models — vegetation and soils, buildings, people and transportation. At the top, are the data sources from which sub-model inputs are derived and aggregated. Below are model outputs which are modelled carbon emissions estimates expressed in quantitative (how much) and spatial (where) terms. At the bottom is a model validation step in which energy consumption statistics, modelled outputs and field-measured results are compared.  8  PART I: CONCEPTS 1.2  Urban Metabolism  waste and goods. The urban ecosystem is the  This section introduces the concept of an ‘urban  urbanized area where those materials cycle through  metabolism approach’. It defines urban ecosystems  and includes various components such as urban  as dynamic, open systems of material fluxes and  vegetation, atmosphere, water, soils, human bodies,  material storage on a range of spatial and temporal  goods and buildings.  scales. Further, the challenges of defining system scales are discussed.  1.2.2.	Sustainability of Urban Ecosystems  1.2.1	 The Metabolism Analogy  is clear that no city can function without supply  In analogy to the metabolism of an organism, the  of energy, carbon, water etc. from its hinterland  processes accompanying the functioning of a city  (surroundings). In this way, cities alone are not  involving the intake of resources such as power,  sustainable as they rely on constant intake of  carbon, water and food and the release (emissions)  resources from the city’s watershed, power grid,  of heat, greenhouse-gases, pollutants, products  economic network, or food-shed. If assessing  and waste can be described as urban metabolism  the environmental sustainability of a given urban  (Wolman, 1965; Kennedy et al., 2007). Figure 1.2.1  system, we should consider all processes associated  shows a schematic urban metabolism with the  with a given change as part of the metabolism,  inputs in form of food, water, raw materials and  including effects that take place beyond the city’s  power, and the outputs such as greenhouse gases,  limits.  boundaries and comparability between different  While most natural ecosystems are self-maintaining,  Greenhouse gases  Food  Water  Greenhouse gases  Fuels and raw materials  Greenhouse gases  Power  Hinterland  Carbon storage and emissions  City  Soild waste  Degraded water  Manufactured goods  Figure 1.2.1: Schematic of an urban system depicting the urban metabolism with its continuous intake of food, water, raw materials and power, and the conversion into products such as goods, greenhouse gases and waste (modified from Oke et al., 2010).  9  PART I: CONCEPTS 1.2.3.	Scales and System Boundaries  1.2.4.	Modelling the Urban Metabolism  Practically, the complexity of resource and product  The metabolism approach has been applied to  dependencies in a globalized economy makes  whole metropolitan areas at the city-scale (Table  the isolation of a single city and its hinterland  1.2.1) relying mostly on a top-down approach  challenging, if not impossible. Therefore, most  based on consumption, census and economic  studies of material and resource flows, including  information available at coarser scales. For example,  greenhouse gas emission inventories, are only  Table 1.2.1 illustrates that per-capita emissions of  possible on coarse scales (e.g. Environment  carbon dioxide (CO2) in various cities range broadly  Canada, 2009), or omit direct processes outside the  and are likely controlled by climate, efficiency  system’s limits. For example, Figure 1.2.2 illustrates  of transportation systems, sources of power  the various scales at which greenhouse gas budgets  generation, urban form, and density.  or inventories are modelled and possible emission reduction actions that are informed by those results.  To assess the environmental sustainability of various urban development patterns (land-use mix, urban  The finer the scale of interest, the more the system  form), the neighbourhood-scale is more appropriate,  depends on resources and energy supply from  as density and land-use change considerably within  outside. Hence buildings, neighbourhoods and  a city. However, with finer scale (increasing spatial  cities must be modelled as open systems as most  resolution), quantifying consumption and emissions  buildings or neighbourhoods do not function without  becomes a more challenging undertaking because  the majority of their energy supplied from outside  of reduced data availability at finer scales, and  the system. Although, regions and nations can be  the fact that systems are more open and dynamic  independent in some networks (e.g. food), they  (mobility of goods, vehicles and humans within  might still depend on the outside the system (e.g.  an urban area). Although a neighbourhood (and a  fossil fuels). The only closed system in some regard  city) is by no means self-maintaining, it is possible  is the global scale system, although there is import  to model material and energy flows as the result  of solar energy from the sun.  of a dynamic, interrelated system of natural and  Neighborhood  City  Region / Nation  Globe  Building design and technology Behaviour  Urban form Land-use mix  Transportation systems Density and land-use mix  Resources and land management Economy and carbon taxes  International agreements World economy  per area  Building  per capita  5 - 50 m  0.5 - 5 km  x 1000s  open system  5 - 500 km  x100’000s  500 - 5000 km  x 1’000’000s  40’000 km  x 7’000’000’000  closed system  Figure 1.2.2: Scales of urban and regional greenhouse-gas inventories and scale-dependent decisions (technological, behavioural and policy) that could reduce emissions.  10  PART I: CONCEPTS Table 1.2.1: Per capita carbon emissions (in form of CO2) in various metropolitan areas or cities (modified from Pataki et al., 2006).  Latitude (o)  Per Capita Carbon Emissions (t CO2 cap-1 year-1)  Tokyo, Japan  360N  4.3  Hanya & Ambe (1976)  Hong-Kong, China  220N  4.8  Warren-Rhodes & Koenig (2001)  Vancouver, Canada  49 N  5.1  BC Ministry of Environment (2009)  London, UK  0  52 M  5.5  Chartered Institute of Wastes Management (2002)  Brussels, Belgium  500N  5.9  Duvigneaud & Denayeyer-De smet (1977)  Sydney, Australia  34 N  9.1  Newman (1999)  Toronto, Ontario  43 N  14.0  Sahely et al. (2003)  City  0  0 0  Source  anthropogenic processes at the neighbourhood scale using a combination of bottom-up (upscaling) and top-down (downscaling) approaches. For example, Codoban and Kennedy (2008) analyzed the urban metabolism of four representative urban neighbourhoods in the Greater Toronto area to assess the sustainability of urban development strategies. At the finest scale, we can model the metabolism of a single building. This scale again is well suited for modelling, and for many applications, detailed information on a particular building is known. At the building scale, mostly urban form, technology and behaviour of the occupants control the energy and materials input and waste output, including greenhouse gas emissions. There are many materials or forms of energy that could potentially be tracked using the urban metabolism approach, including food, water, etc. The current study focuses on the metabolism of carbon at the neighbourhood scale.  11  PART I: CONCEPTS 1.3  The Urban Carbon Cycle  systems ground area (in kg C m-2 year-1). Lateral  This section is applying the urban metabolism  fluxes are in most cases anthropogenic processes  approach to carbon budgeting within urban  and import carbon as fossil fuel (gasoline, diesel,  systems. It discusses the concept of lateral and  natural gas), food, construction material, etc. into  vertical carbon fluxes, carbon pools and associated  the urban ecosystem, and export carbon in form  conversion processes involved in the urban carbon  of goods, solid or liquid waste through supply  cycle.  and management channels (Churkina, 2008). Lateral fluxes typically transport carbon in form of  1.3.1	 The Carbon Budget  carbohydrates, not carbon dioxide.  An urban carbon cycle can be defined for any scale of urban ecosystem (city, neighbourhood, building).  1.3.3.	Vertical Fluxes  For a given system with its spatial boundaries,  Vertical fluxes are essentially emissions of carbon  carbon can be conceptually tracked through  dioxide into the atmosphere although the term  quantifying inputs and outputs (e.g. emissions  also incorporates carbon-dioxide uptake by urban  into the atmosphere), as well as storage changes  vegetation (photosynthesis). Vertical fluxes are  within the systems (Figure 1.3.1). The magnitude  fluxes at the system’s upper boundary (land-  of carbon fluxes or pools can be informed by either  atmosphere interface) and result from chemical  direct measurements at system boundaries, top-  processes in the system, namely combustion,  down, or bottom-up modelling. As the total carbon  respiration, and photosynthesis:  is conserved, we can write for any system:  (Carbon inputs) = (Carbon outputs) - (Storage changes in carbon pools)  Combustion – Combustion is the controlled burning (oxidation) of fossil fuels in engines and heating systems. The process of combustion is resulting in  This essentially is a material flow analysis for carbon  a release of carbon dioxide while gaining energy  and means that we quantify all internal storage  in form of electricity, heat, or mechanical energy.  changes (pools), inputs and outputs (fluxes) for the  To date, the dominant fossil fuels used in an urban  system. Inputs and outputs can happen at different  ecosystem are natural gas, gasoline and diesel,  boundaries, and we conceptually separate lateral  historically also wood and coal were important.  fluxes from vertical fluxes (Figure 1.3.1).  Mass flux densities of fossil-fuel combustion can be  1.3.2	 Lateral Carbon Fluxes  conceptually separated into emissions from vehicles, emissions from buildings due to space heating and  Lateral fluxes refer to the mass of carbon imported  emission from industrial processes. Those emissions  (or exported) per time into (or out of) a system  follow diurnal, weekday and seasonal human  at the lateral (side) boundaries. Lateral fluxes can  activity cycles (traffic, heating requirements).  be expressed in kg C year-1 or normalized by the  12  PART I: CONCEPTS Respiration – is the oxidation of carbohydrates by  normalized by the system’s ground area (in kg C  living organisms associated with the release of  m-2 year-1). Other studies have reported emissions  energy. The process of respiration is resulting in a  in kg CO2 year-1, but as vertical fluxes are the result  release of carbon dioxide while gaining energy for  of the chemical transformation processes between  the metabolism to maintain living functions. We  carbohydrates and carbon dioxide it is more useful  distinguish between autotrophic respiration (trees,  to conserve carbon, not carbon dioxide when  humans) and heterotrophic respiration (microbes) in  providing budget equations.  soils and waste. Respiration rates are depending on the activity of the organism involved, which can be  1.3.4	 Carbon Pools  in the case of plants and microbes be controlled by  The term carbon pool refers to locally stored  temperature and water availability.  deposits of organic carbon in the urban ecosystem. Carbon pools are vegetation, soils, buildings,  Photosynthesis – is the biological process of  furniture, and human bodies. For example, if urban  carbohydrate formation by fixation of atmospheric  trees sequester carbon through photosynthesis,  carbon dioxide by plants. This happens in the  this carbon is stored locally in the urban ecosystem  chlorophyll-containing tissues of leaves, needles  in tree biomass and soil. As tree biomass and soil  and other plant parts under input of sunlight  pools grow over time, the accumulation of carbon in  (photosynthetic active radiation in the range 400 –  those pools has to be protected to sustainably offset  700 nm). Urban vegetation (trees, lawns, gardens  emissions. Carbon pools can be expressed in kg C  etc.) can be expected to show slightly higher rates  or normalized by the ground area (in kg C m-2)  of photosynthesis due to (i) decreased water stress due to additional water availability by irrigation,  The assessment of carbon pools is important to  (ii) generally warmer and more conservative  quantify potential and limits of carbon sequestration  temperatures in urban ecosystems (urban heat  and estimate emissions following urban land-  island) that extend the vegetation period and  cover and form changes (i.e. development,  reduce frost damage, and (iii) fertilization by  redevelopment, removal of soil, increase in  elevated atmospheric nitrogen deposition and  vegetation). Emission reduction strategies and  elevated carbon dioxide concentration in cities  policies must not only focus on changes in steady-  (Trusilova and Churkina, 2008). On the other hand,  state vertical and lateral fluxes but also incorporate  air pollution can also lead to significant physiological  disruptions to carbon pools.  stress and damage and reduce photosynthetic rates  Some carbon pools are closely coupled to the  in areas where in particular ozone concentrations  vertical or lateral fluxes and their magnitude ebbs  are high.  and flows according to the inputs and outputs. For example, carbon in biomass and soils changes  Similar to lateral fluxes, vertical fluxes (and hence  on daily and seasonal cycles following input by  emissions) are also expressed in kg C year-1 or  photosynthesis and output by respiration. Other 13  PART I: CONCEPTS or renewable carbon (wood fires) into carbon  Vertical fluxes  Inputs  Uptake  Form of carbon  Carbohydrates  processes. The detailed methods to estimate Carbon-dixode  Emissions  building-related emissions in this study are described in Section 2.3. Waste  Transportation – Mobile combustion sources  Storage change  transform fossil fuels (gasoline, diesel) or potentially renewable carbon (biofuels) into carbon dioxide. The challenge with estimating the magnitude of Lateral fluxes  this component is accounting for the relatively extreme variation in space and time, and attributing  Food Natural gas Gasoline  emissions to a selected urban activity (see also section 1.3.6). The detailed methods to estimate  Inputs  Outputs  Form of carbon  transportation-related emissions in this study are  Carbohydrates  described in Section 2.4.  Carbon-dixode  Figure 1.3.1: Conceptual representation of internal storage changes, lateral and vertical carbon fluxes for a neighbourhoodscale urban ecosystem.  Waste  eral fluxes  Outputs  dioxide for space heating or cooling, and industrial  Atmosphere  Human body, food and waste – This component includes both stationary and mobile processes of respiration as a result of the human metabolism and  carbon pools in an urban ecosystem are essentially immobile (e.g. buildings) and their carbon is rarely released to the atmosphere (e.g. in the unlikely event of a building fire, or on longer time-scales after demolition). The turnover rate of carbon in a given carbon pool is a measure of its activity.  1.3.5	 Components of the Urban Carbon Cycle  heterotrophic (microbial) decomposition of waste. Although the carbon cycled through this component is entirely renewable (the human food chain starts with plants that sequester atmospheric carbon), there are external emissions associated with food production, and potentially accompanying methane emissions resulting from urban waste management that still cause disruptions to the global climate system.  In this study, vertical fluxes (i.e. emissions) are conceptually separated into four components that  Urban vegetation and soils – This component can  are illustrated in Figure 1.3.2  act as sink or source through photosynthesis and respiration. As it entirely involves renewable carbon  Buildings and Industry – This component includes  it is not accounted for in most studies. However,  stationary combustion sources transforming  the ability of urban vegetation to sequester fossil  mostly fossil fuel carbon (natural gas, oil, coal)  fuel carbon in urban biomass and soils make this 14  PART I: CONCEPTS  Physical system boundary External emissions due to local activities  Local emissions due to local activities  Local emissions due to external activities  Community GHG modelling Flux tower data Figure 1.3.2: The concept of local and external emissions for a given urban ecosystem.  component relevant in urban metabolism studies.  the release (or uptake) of carbon dioxide within  Fluxes are highly variable in time and space and  the neighbourhood (system boundaries) due to  largely influenced by the management of urban  combustion, respiration or photosynthesis.  vegetation. The detailed methods to estimate  •	 External emissions due to local activities -  uptake and emissions from vegetation in this study  refers to the release of carbon dioxide outside  are described in Section 2.5.  the neighbourhood (system boundaries) due to combustion or respiration associated with  1.3.6	 Local and External Emissions Some of the components discussed in section  activities within the neighbourhood. •	 Local emissions due to external activities -  3.1.5 are mobile (cars, trucks, humans), others  refers to the release of carbon dioxide within  use energy in form of electricity, materials or food  the neighbourhood (system boundaries) due  that result in carbon emissions outside the system  to combustion and respiration from objects or  boundary. Figure 1.3.3 underlines the spatial  humans travelling through the neighbourhood,  differences between emissions that can be locally  but not associated with activities in the  detected within the urban system (e.g. using a  neighbourhood (e.g. through-traffic).  flux-tower, Section 2.6) and the desired deliverables for community-scale carbon modelling that include  Table 1.3.1 lists for each component (buildings,  emissions due to activities in a specific sector that  transportation, human body, vegetation and soils)  might happen outside the system:  – emission processes in each of the three above  •	 Local emissions due to local activities – refers to  cases. 15  PART I: CONCEPTS Table 1.3.1: Examples of external and local carbon dioxide emissions in neighbourhoods.  External Emissions due to Local Activities  Local Emissions due to Local Activities  Local Emissions due to External Activities  Buildings  Power generation emissions associated with power consumption within the neighbourhood  Natural gas combustion of buildings within the neighbourhood  (none)  Transportation  Trips out of the neighbourhood to work, school, recreation, etc.  Trips within community  Through-traffic  Human Body  Human respiration outside the neighbourhood by residents  Human respiration  Human respiration of people travelling through  Vegetation and Soils  Garden waste disposal outside system  Photosynthesis and respiration of plants within the neighbourhood  (none)  16  PART I: CONCEPTS References: Baker, N., Steemers, K. (2000). Energy and environment in architecture: a technical design. New York: E&FN Spon.  Ministry of the Environment, Province of British Columbia (2009). Community Energy and Emissions Inventory Reports. Retreieved on June 20, 2010 from http://www. env.gov.bc.ca/cas/mitigation/ceei/index.html  BC Ministry of Environment (2009). Community Energy & Greenhouse Gas Emissions Inventory: 2007. Government of British Columbia.  Newman, P. W. G. (1999). Sustainability and cities: extending the metabolism model. Landscape and Urban Planning, 44, 219–226.  Bory, B., & Shremmer, C. (2009). SUME — Sustainable Urban Metabolism for Europe. Retreived on June 20, 2010 from http://www.corp.at/corp_relaunch/papers_txt_suche/ CORP2009_143.pdf  Office of the Governor, State of California (2008). Senate Bill 375: Redesigning Communities to Reduce Greenhouse Gases. Retreived on June 20, 2010 from http://gov.ca.gov/ fact-sheet/10707  BRIDGE (2010). Sustainable Urban Planning Decision Support Accounting for Urban Metabolism. Retreived on June 20, 2010 from http://www.bridge-fp7.eu/  Oke, T. R., Mills G., Voogt J., & Christen A. (2010, in prep.). Urban Climates. Cambridge University Press.  Canada Mortgage and Housing Corporation (2005). Comparing Neighbourhoods for Sustainable Features. Retreived on June 20, 2010, from http://www.cmhc-schl. gc.ca/en/co/buho/sune/index.cfm Centre for Neighborhood Technology Energy (2009). Chicago Regional Energy Snapshot. Retreived on June 20, 2010, from http://www.cntenergy.org/media/ChicagoRegional-Energy-Snapshot.pdf Chartered Institute of Wastes Management (2002). A Resource Flow and Ecological Footprint Analysis of Greater London. Best Foot Forward, London. Churkina G. (2008). Modeling the carbon cycle of urban systems. Ecol Model, 216(2), 107-113. Codoban, X., & Kennedy C. (2008). Metabolism of neighbourhoods. Journal of Urban Planning D-Asce, 134(1), 21-31. Duvigneaud P., & Denayeyer-De Smet S (1977). Traveaux de la Section Belge du Programme Biologique International, Bruxelles. In L’Ecosystéme Urbs. In L’Ecosystéme Urbain Bruxellois (eds Duvigneaud P, Kestemont P), pp. 581–597. Environment Canada (2009). National Inventory Report 1990 – 2007: Greenhouse Gas Sources and Sinks in Canada. Greenhouse Gas Division of Environment Canada. Hanya T., & Ambe Y. (1976). A study on the metabolism of cities. In Science for a Better Environment (ed. HESC Science Council of Japan), pp. 228–233. Kyoto. Kennedy C., Cuddihy, J., & Engel-Yan J. (2007). The Changing Metabolism of Cities. Journal of Industrial Ecology, 11(2), 43-59. Kaufmann, Robert and Boston University (2009), Collaborative Research on the Metabolism of Boston: Clean Energy and Environmental Sustainability Initiative. Retreived on June 20, 2010 from http://www.bu.edu/ energy/research/projects/metabolism-of-boston  Pataki, D., Alig, R., Fung, A., Golubiewski, N., Kennedy, C., McPherson, E., Nowak, D., Pouyat, R., & Lankao, P. (2006). Urban ecosystems and the North American carbon cycle. Global Change Biology, 12(11) 2092-2102. Sahely, H. R., Dudding, S., Kennedy, C. A. (2003). Estimating the urban metabolism of Canadian cities: greater Toronto Area case study. Canadian Journal of Civil Engineering, 30, 468–483. Salat, S. (2007). Energy and Bioclimatic Efficiency of Urban Morphologies: Towards a Comparative Analysis of Asian and European Cities. In Proceedings of the International Conference on Sustainable Building Asia (pp. 161-166). Seoul: Fraunhofer IRB. Retreived on June 20, 2010 from http://www.irbdirekt.de/daten/iconda/CIB8015.pdf. Salat, S. (2008). Density - Energy Consumption - Urban Texture. URBA 2000. Retreived on June 20, 2010 from http://www.urba2000.com/club-ecomobilite-DUD/IMG/pdf/ SALAT.pdf. Salat, S., & Morterol, A. (2008). Factor 20: A Multiplying Method for Dividing by 20 the Carbon and Energy Footprint of Cities: The Urban Morphology Factor. Chamber of French Commerce and Industry in China. Retreived on June 20, 2010 from www.ccifc.org/index.php/fre/content/ download/1470/19204/file/Factor%2020,%20the%20 Urban%20Morphology%20Factor%20-%20Serge%20 Salat%20(21%20May%202008).pdf. Steemers, K. (2003). Energy and the city: density, buildings and transport. Energy and Buildings, 35(1), 3-14. Trusilova, K. & Churkina, G. (2008). The response of the terrestrial biosphere to urbanization: land cover conversion, climate, and urban pollution. Biogeosciences, 5(6), 15051515. Warren-Rhodes, K., & Koenig, A. (2001). Escalating trends in the urban metabolism of Hong Kong: 1971–1997. Ambio, 30, 429–438. Wolman, A. (1965). The metabolism of cities. Scientific American, 179-190.  17  PART II: methods 2.1	 The ‘Sunset’ Neighbohrood in Vancouver This section introduces the case study site and its principal population, land use, buildings and transportation related attributes.  2.1.1	 The Sunset Neighbourhood Case Study Sunset neighbourhood in south central Vancouver — an area bounded by E. 41st Avenue and the Fraser River to the north and south and Ontario Street and Knight Street to the west and east — is one of the city’s most ethnically diverse neighbourhoods. The majority of the population live primarily in single-family residential areas (typically RS-1S, a single family zone in which any dwelling  2.1.2	 Study Area Attributes Most buildings are residential and lower density (approximately 12 dwelling units hectare-1). Within the study area are 4155 detached dwellings. Approximately 55% have secondary suites. At an average of 3.7 persons, the residential dwellings support a population of 23168 persons. Approximately 37% of dwellings were built before 1965, 38% between 1965 and 1990, and 25% post 1990. The majority of single family homes are heated from natural gas. In addition, there are a smaller number of nonresidential — commercial, mixed use, light industrial and institutional building types that accommodate  may add a rental suite). The area’s commercial and higher density residential nodes and corridors are located primarily along arterial streets such as Fraser and Knight (N-S) and 41st, 49th, 57th and SE Marine Drive (E-W). Within Sunset, this project’s study area is an approximately 2 km square area (red area in figure 2.1.1) at the western edge of  Vancouver  the neighbourhood. At the centre of this study is a carbon flux instrumented tower (see Section 2.7). Study area  Sunset Neighbourhood  0  1  2km  Figure 2.1.1: Location of study area (Sunset neighbourhood highlighted in light grey). Approximate outline of study area in red.  18  PART II: methods  Figure 2.1.2: Illustrative photos of principal dwelling types within study area. Clockwise from upper left: pre-1965 single family bungalow; 1965 – 1990 single family with secondary suite option; duplex; rowhouse; apartment; and, mixed use. Images generated using Google Maps and Streetview (http://maps.google.com/).  Figure 2.1.3: Illustrative photos of principal commercial and other non-residential types within study area. Clockwise from upper left: smaller scale retail and restaurants; local grocery; mixed use, office, light industry, school. Images generated using Google Maps and Streetview (http://maps.google.com/).  The street network in the study area is an atypical  and major truck route to major employment areas  pattern of one very high volume north-south arterial  (Surrey and Richmond for example) on the south  street (Knight Street), one high volume east-  side of the Fraser River with connections to the  west arterial street (East 41st Avenue), four more  U.S. border truck crossing. Arterial trip loads and  moderate volume arterials (Fraser and Victoria  with them transportation related energy demand  north-south, and East 49th and East 54th / 57th  and carbon emissions throughout the study area  east-west) and many low volume local streets  are much higher than would be anticipated of a  between. Knight Street, the central north south  neighbourhood of this type.  corridor in the study area serves a significant regional transportation as a commuting corridor  19  Victoria Drive  Fraser Street  Knight Street  PART II: methods  E. 41st Ave.  E. 49th Ave.  E. 57th Ave. E. 54th Ave. 0  250  500  1000  2000meters  Figure 2.1.4: Figure-ground plan of study area from Figure 2.1.1. The Sunset study area highlighted is centred around a carbon flux tower at the intersection of Knight and East 49th Avenue.  Figure 2.1.5: Illustrative photos of arterial streets within study area. Left to right by row from upper left: north-south arterials are Fraser Street, Knight Street, Victoria Drive; middle left: east-west arterials are East 41st Avenue, East 49th Avenue and East 57th Avenue; and lower left: local streets are Fleming Streets, East 47th Avenue and Elgin Street. Images generated using Google Maps and Streetview (http://maps.google.com/).  20  PART II: methods 2.2	 Remote Sensing Data Inputs for Emission Modelling  Remote sensing technologies are divided into two  This section describes the remote sensing  light reflected off a surface to produce an image;  techniques applied to extract buildings and  and 2) active sensors, that generate and receive  vegetation characteristics over the Sunset study  their own source of energy to produce data. Both  site. LiDAR data provides a raw three-dimensional  of these technologies play an important role in  surface from which individual objects (buildings and  the analysis of land cover in urban environments.  trees) can be extracted using geometric algorithms.  Traditionally, passive sensors have been used almost  Those features with limited three-dimensional  exclusively for urban planning purposes in the  structure, such as ground vegetation, are not  form of aerial- and ortho- photographs. However,  discernable using LiDAR data; therefore additional  recent advances in active remote sensing devices  analysis of satellite imagery is used to extract  can provide topographic information at a scale  lawns, bushes, gardens, and ornamental plants as  appropriate for urban analysis. Light detection  ‘ground vegetation’. Individual trees in the study  and ranging (LiDAR) devices represent the leading  area were also extracted using manual aerial photo  technology in active remote sensing.  categories of sensors: 1) passive sensors, that use  interpretation integrated with height information from the LiDAR data. The features derived from the  Functioning on the principle of echo-returns, LiDAR  remote sensing products are used to inform further  provides an accurate calculation of the distance  analysis on the contribution of buildings (Section  to objects by emitting a pulse of laser energy and  2.3) and vegetation (Section 2.5) to neighbourhood-  recording the time it takes that same pulse to return  level carbon and energy fluxes.  to the device. While the direction of the laser path is stationary, LiDAR devices are often attached to  2.2.1	 LiDAR in the Urban Environment  a vehicle to enable the acquisition of ranging data  Remote sensing devices offer a wide range of  across geographic space. In the case of urban  opportunities to automatically derive land cover  analysis, the LiDAR device is typically mounted to  features across varying terrain. Current remote  a fixed-wing or helicopter platform. Recording the  sensing campaigns in urban areas utilize numerous  exact location and orientation of the platform at  technologies from conventional photographic image  the time that each laser pulse is emitted enables  acquisition to more sophisticated devices that  an accurate extraction of elevation-above-sea-level  measure the three-dimensional form of the urban  for features intercepting the laser pulse. Current  surface. Data captured by remote sensing devices  LiDAR devices emit more than 100,000 laser pulses  has the capacity to reveal a wide-range of processes  per second and have the ability to record multiple  active in an urban setting, and analysis of this data  returns for a single pulse. Secondary returns can  can provide planners and decision-makers with  be used to provide additional height measures for  critical information for the management of urban  those objects that do not reflect the entire signal on  ecosystems.  first contact with the pulse (e.g. trees). 21  PART II: methods 2.2.2	 Extraction of Building Typologies and Characteristics  environments involved isolating the LiDAR returns  LiDAR data was provided through the Environmental  LiDAR last return layer was examined. Since the  Prediction in Canadian Cities (EPiCC) project  solid form of building rooftops typically reflects the  with funding from the Canadian Foundation for  entire laser pulse, few building returns tend to be  Climate and Atmospheric Sciences (CFCAS) and  represented in the last return layer, while gaps in  was acquired in March 2007 using a TRSI Mark  the three-dimensional structure of trees allows the  II discrete-return sensor attached to a fixed-wing  laser pulse to produce a second signal return lower  platform with an average point density of 0.7m2. To  in the canopy. Nonetheless, some urban features,  precisely estimate the height of objects using LiDAR  such as building edges and powerlines, also  technology requires an accurate representation  generate second pulse returns; therefore a spatial  of the ground surface as the height of objects  filtering step was applied. A 9 by 9 pixel moving  intercepting the laser beam are calculated relative  kernel was used to iteratively assess the number  to the ground topography. Returns pre-classified as  of pixels containing second returns from a series  ground were used to generate a digital elevation  of projected vectors between 1 and 180°. When  model (DEM) with a 1 m pixel size using the natural  the number of cells intercepting a given vector  neighbour interpolation algorithm (Sambridge et al.,  was greater than 4 they were classified as linear  1995; Sibson, 1981). This DEM was then subtracted  features (generally representing building edges) and  from the non-ground returns transforming the  subsequently removed. Finally, a second filter was  elevation data into height above ground surface.  applied to indentify clusters of trees by establishing  Furthermore, a maximum height surface model was  a threshold where 80% of the total pixels within the  developed using the highest LiDAR return heights  kernel were identified as tree pixels. These clusters  (above ground surface) and the natural neighbour  were then grown by reassigning pixels with non-  algorithm to form a continuous 1m surface. A 3-by-  tree pixels in the moving window as the tree class.  3-pixel median filter kernel was then applied to the  The resulting layer is a binary image of LiDAR pixels  image and subtracted from the original gridded  representing the planimetric extent of trees. The  layers to smooth holes and spikes in the raster  extracted tree layer is used to mask the original  images resulting from lack of data, edge effect,  first return LiDAR grid producing a height layer  water absorption and birds. Pixels with a difference  of non-tree features. An area threshold of 40m2  of 50m were selected as erroneous and replaced  and a height threshold of 3m were applied to each  using Delaunay triangulation.  remaining object to select buildings from the LiDAR  that interact with vegetation. To do this, the  dataset. The output result from this technique The procedure used to extract building and tree  provides a classification layer that describes the  features from the LiDAR data follows the techniques  spatial extent and number of 1) tree crowns or  of Goodwin et al. (2009). The first step to quantify  clusters of tree crowns and 2) buildings (Figure  the structure of trees and buildings in urban  2.2.1). 22  PART II: methods After all buildings were extracted from the LiDAR  (n = 83) following the removal of commission  data, a set of post-processing steps were required  errors. This suggests that building area estimates  to: remove secondary buildings including garages  are comparable to aerial photography when  and sheds, smooth building edges, and associate  LiDAR can separate individual buildings from the  buildings to relevant land parcels. The secondary  surrounding features (Goodwin et al., 2009).  building removal technique used a polygonal layer of all buildings with three Boolean operations to  After successful extraction of building polygons  determine the non-dwelling/non-commercial edifices  using the previously mentioned techniques, a suite  on each lot. The operations included the removal  of building morphological features were extracted to  of structures: less than 50m in area, less than  inform typologies for existing energy consumption  75m2 and intersecting a 4m buffer of an alleyway,  models. A number of metrics related to roof slope  and not intersecting a 25m buffer of a street.  morphology, roof shape morphology, volume, area,  After the secondary build features were removed,  and surface area were calculated and multivariate  a polygon simplification algorithm was applied  statistics were used to determine the metrics that  (Bayer, 2009) to smooth the pixilation artifacts from  represented distinct attributes for further analysis.  the raster building extraction technique (Figure  Result of a factor analysis (Table 2.2.1) indicate  2.2.2). The last step in deriving the buildings  the appropriate division of categories into roof-  involved separating buildings according to parcel  orientation, roof-slope and building volumetric  data. The LiDAR extraction technique cannot  categories, from which percent North- and East-  separate adjoining buildings (e.g. ground-level  facing roof, percent of roof slope under 20 degrees,  commercial structures), therefore post-processing  and volume are the preferred variables to represent  is required to divide building polygons based on  the determined categories.  2  cadastral land parcel data provided by the City of Vancouver. Goodwin et al. (2009) undertook a comparison of building areas derived from LiDAR and compared them to areas as interpreted from aerial photography. The comparison demonstrated the approach is accurate (r2= 0.73, p<0.001) however some buildings had significant errors due to adjacent buildings not being discriminated (i.e. commission errors). When these outliers were removed the r2 value increased to 0.96. Commission errors (i.e. buildings that were not identified) were also evident within some residential areas with the overall percentage difference between estimates equal to 35% (n = 98), which reduced to 16%  2.2.3	 Extraction of Urban Vegetation\ Characteristics In the previous subsection the extraction of trees was introduced as a preliminary step in the procedure for deriving buildings, and this layer provided the necessary information for the accurate representation of trees across the study area. However, lawns, bushes, gardens, and ornamental ground vegetation also represent a substantial portion of surface cover in urban areas required for estimating carbon sequestration and soil respiration (section 2.5). While the geometrically distinct characteristics of trees facilitates the extraction of these features using LiDAR data, lawns and soils are  23  PART II: methods Table 2.2.1: Factor analysis of building morphological features  Variable  Orientation Factor  Volumetric Factor  Slope Factor  North-facing roof (%)  -0.89  0.14  0.05  South-facing roof (%)  -0.88  0.18  0.04  East-facing roof (%)  0.84  -0.25  0.01  West-facing roof (%)  0.79  -0.45  0.01  Volume (m3)  -0.26  -0.78  0.43  Surface area (m2)  -0.36  -0.74  0.43  Slope under 20 (%)  -0.37  -0.45  -0.73  0  Slope 20 to 30 (%)  0.27  0.39  0.57  Slope over 300 (%)  0.27  0.33  0.39  Proportion of Total Variance Explained  0.27  0.18  0.14  0  0  inseparable on the basis of geometry alone. As a  (normalized using the digital elevation model of  result, supplementary remote sensing datasets were  bare ground) less than 1m were used for analysis.  used to provide the spectral properties necessary to  After determining ground, a normalized difference  accurately derive ground vegetation estimates.  vegetation index (NDVI) was produced from the red and near-infrared bands of the Quickbird image  In this project, satellite imagery was also used. A  with a threshold of 0.2 to help discern vegetated  Quickbird image was acquired over the study area  from non-vegetated surfaces. The combined result  on June 28th 2008. The image was calibrated to  provides a layer of ground vegetation for the study  radiance values using the provided metadata and  area (Figure 2.2.1) (Tooke at al. 2009).  then orthorectified using the rational polynomial  The derivation of individual trees and relevant  coefficients (RPCs) provided from DigitalGlobe  attributes (e.g. height, crown diameter) in an urban  and a 25m DEM of the area. To further ensure  setting is not currently possible using existing  accurate alignment with the LiDAR data a secondary  technology and extraction algorithms. Although  geographic registration was performed using a 2004  studies have successfully derived these metrics for  orthophotograph and the panchromatic Quickbird  homogeneous forest stands, the heterogeneous  band producing a RMS error of 0.66m.  composition of urban areas and the variety of tree  The procedure involved in extracting the ground  species and shapes found in the city prohibit the  vegetation (predominantly lawns) involved  extension of forestry-based techniques to urban  integration of both the Quickbird multispectral  analysis. Therefore, to extract individual trees  data and the LiDAR first returns layer. The LiDAR  across the study area for this project, manual  layer provided the necessary information to  aerial photo interpretation was performed on 10cm  distinguish ground from non-ground features. In  orthoimagery collected in 2008. Crown diameter  this step only those areas where first return values  was estimated directly from the imagery, while 24  PART II: methods  a) a)  b) Extractedb)Building Polygonsb)  a)  Land Parcels Extracted Building Polygons Extracted Building Polygons Land Parcelsof the extraction techniques to derive primary buildings, trees, and ground vegetation using LiDAR data and Figure 2.2.1: Results Land Parcels Quickbird satellite imagery.  Buildings  Buildings Buildings  Buildings  Trees  Trees  Ground Vegetation  Figure 2.2.2: area that displays a) resulting building polygons from the LiDAR extraction technique, and b) resultTreesA city block in study Ground Vegetation Trees ing building polygons after post-processing to simplify polygon shapes, remove secondary structures, and separate buildings by land Ground Vegetation parcel boundaries.  Ground Vegetation  heights were derived from the LiDAR. To mitigate  used to extract the underlying height value. These  interpreter error and to ensure the top of the tree  crown diameter and height metrics were derived for  crown was extracted, the LiDAR height layer was  a 400m buffer around the tower and values were  filtered for maximum values within a 5-by-5-cell  extrapolated outwards to the rest of the study area.  kernel and the interpreted stem locations were 25  PART II: methods 2.3	 A Building Typology Approach to Carbon Emission Modelling  of building energy, typically provides power for  At present the relationship between building  cooling or may entirely replace natural gas as the  morphology, energy use and carbon emissions  primary source of energy. Although emissions in BC  is not well defined. This has been in large part  resulting from electricity production are significantly  due to the difficulty in accurately characterizing  less than natural gas due to the large proportion  urban form, which is both time consuming and  of hydroelectric power, electricity produced from  labour intensive. Cities however, are assemblages  diesel and natural-gas-fired power plants drive-  of development patterns, which are in turn made  up the emissions factor to 1.67 kg C GJ-1 (BC  up of replicated parts such as building types.  GHG Assessment Guide, 2008). Although onsite  These types often share similar characteristics,  renewable energy production may offset carbon  for instance morphological and performance  emissions, the existence of such equipment was not  attributes. A typology approach, which includes  observed during field visits and thus excluded from  these differences, can better inform policy decisions  the current modelling framework.  building lighting, auxiliary equipment and space  by describing the important factors influencing carbon emissions within a development pattern. This section describes a building typology approach informed by LiDAR data to estimate building carbon emissions at the Sunset neighbourhood.  2.3.2	 A Building Typology Approach to Carbon Emissions Modelling Past research that estimates the spatial and temporal pattern of building energy use has  2.3.1	 Carbon Emissions Associated with Building Energy Use  typically followed one of two routes: top-down  Building construction, morphology and urban  and bottom-up approaches that focus on individual  density all influence building energy demand,  buildings and scale-up to larger areas (Heiple and  and ultimately the amount of carbon emissions  Sailor, 2008). A bottom-up typology approach was  released into the atmosphere. The demand for  used to document, classify and estimate the portion  energy is a result of space heating, domestic hot  of tower measured carbon emissions attributable to  water (DHW), lighting, space cooling, and auxiliary  buildings. However, local data was supplemented  equipment loads. In Vancouver, a large part of  by inventory estimates when neighbourhood  residential energy use is dedicated to space heating,  data was not available. The typology approach  of which the majority is powered by natural gas.  describes the Sunset neighbourhood through  The combustion of natural gas produces C as a  a series of prototypes that are characteristic of  by-product and for every GJ of energy produced  the existing building stock, which assumes a  from natural gas approximately 13.9 kg of C is  correlation between building use, physical form  released into the atmosphere (BC GHG Assessment  and energy performance. Through fieldwork  Guide, 2008). Electricity, another significant source  and a synthesis of precedent energy modelling  approaches that use aggregate data to scale down  studies (e.g. CanmetENERGY urban archetypes)  26  PART II: methods  Fossil fuels  Electricity External emissions due to local activities  Physical system boundary  Local emissions due to local activities  Figure 2.3.1: Conceptual representation of building carbon emissions from local and external sources.  two overarching building categories emerged for  What is the structure of the building typology? The  the Sunset neighbourhood (residential and ‘other,’  typology concept focuses on building attributes  non-residential) along with thirteen sub-types. For  relevant to energy consumption; these include  each sub-type the important morphological and  (a) morphological attributes of buildings, such as  performance measures were collected to inform  heated volume, and window, wall and roof areas  the carbon emissions intensities modelled (kg C m  (b) energy performance attributes such as heating  year-1).  system types and RSI values (thermal resistance).  -3  These attributes, along with building use were  Why a building typology method? A typology  identified as key energy performance indicators  method is a systematic approach to synthesize  and formed the basis of the building typology  and visualize otherwise complex variations in the  categorization (Figure 2.3.2).  urban ecosystem. The study site included 4558 buildings and the challenge was to compose a  What types were used in the Sunset  representative sample that described the diversity  neighbourhood? The development of each typology  of building types and their variation in energy  involved the synthesis of several data sets in  use. The resulting types represent replicable and  a geographical information system (GIS). First  replaceable instances, meaning types representative  cadastral data available from the City of Vancouver  of the Sunset neighbourhood allow for a quick  was synthesized with BC Assessment data. During  overview of the energy performance of various  this process each building parcel was associated  building types. This can ultimately lead to the  with a property identifier (PID). This was used to  assessment of alternative development scenarios,  join land use and sub-type data to each residential  and the benchmarking of carbon emissions in a  and ‘other’ parcel. Further information was  neighbourhood.  documented for residential parcels, which included finished floor area, vintage and heating system. A spatial join was then applied in GIS between the parcel data and each building extracted from LiDAR  27  TYPOLOGY  SYNTHESIS LINKING EXISTING FORM WITH KEY ENERGY MEASURES  PERFORMANCE ATTRIBUTES  MORPHOLOGICAL ATTRIBUTES  SECONDARY SUITES  YEAR BUILT  RESIDENTIAL TYPES  ECO-ENERGY, BC ASSESSMENT NRCAN URBAN ARCHETYPES BC HYDRO CONSERVATION POTENTIAL REVIEW (2007) CEI REPORT CIBEUS REPORT  LOCAL  REGIONAL  NATIONAL  GIS PARCEL DATA (MUNICIPAL) & BC ASSESSMENT Land use, year built, building totals per use, and secondary suites  Source:  Level:  LIDAR volume, roof slope & orientation, surface area, building footprints specific to the study area, and LIDAR integrated population data  2  OTHER BUILDING TYPES (NON RESIDENTIAL) CIVIC  COMMERCIAL  ROW HOUSES  EXTENDED CARE  RETAIL  APARTMENTS  DUPLEX  MIXED USE  INDUSTRIAL/ WAREHOUSE  SUNSET NEIGHBOURHOOD TYPOLOGY FOR BUILDING ENERGY MODELING (BEM) OTHER BUILDING TYPES RESIDENTIAL TYPES  BUILDING TYPOLOGY METHOD  WITH SECONDARY SUITE  PRE 1965  OFFICE  WITH SECONDARY SUITE  1966-1990  SINGLE FAMILY HOMES  WITH SECONDARY SUITE  POST 1990  PART II: methods  Figure 2.3.2: A Schematic representation of the typology used in the Sunset neighbourhood. Building sub-types were classified in three steps that described use, form and construction vintage.  28  PART II: methods data, which included the associated morphological  types other than residential were assigned default  attributes (see section 2.2).  values provided by OEE Screening tool. Although  Step 1: Building Use - Building land use was  this is a modelling limitation, accurate estimates of  the first step in the categorization; this included  employment in the area were not available through  residential and ‘other’ (non-residential) types.  assessment data.  ‘Other’ building types included extended care, warehouse, civic, office, retail, commercial and  2.3.3	 Building Energy Modelling  mixed use. Of the 4558 buildings sampled in the  Building volumes provide an estimate of the space  Sunset neighbourhood, 95% were residential, a  to be heated. This information alone however, is  significant percentage necessitating a more rigorous  not sufficient to assign an energy demand and thus  categorization.  the integration of typology attributes was key in quantifying building carbon emissions. To estimate  Step 2: Building Form - The second categorization  emissions both data sets were integrated into  further divided the residential buildings stock based  two building energy models (BEM), one dedicated  on form (stacked, detached and attached), resulting  to ground oriented residential sub-types and  in four additional categories: apartments, row  another program for ‘other’ building types. The  houses, duplexes and single family detached. Of the  energy consumption of ground oriented residential  total number of residential buildings in the study  typologies was simulated using the HOT2000  area 97% are single family dwellings (SFD).  energy analysis program and for all other typologies the OEE Screening tool was used. HOT2000 applies  Step 3: Building Vintage - The third categorization  a bin-based method to calculate energy use and  to further divide the SFD was based on year of  has been widely used in North America. The model  construction, this resulted in three additional  has undergone extensive validation (Haltrecht and  categories: SFD built before 1965, SFD built  Fraser, 1997) and is primarily used in performance  between 1965-1990, and SFD built post 1990.  evaluation programs by researchers, utilities and  These categories were selected based on energy  governmental agencies. Through campaigns such as  performance differences and house characteristics,  the ecoENERGY retrofit program, accurate libraries  such as building volume, window areas and roof  of construction details have been collected and  slope (see section 2.3.5). Additionally, a significant  input into HOT2000, these now represent a range  proportion of these SFD have secondary suites  of Canadian archetypes. In order to provide the  (data provided by BC Assessment) and thus  HOT2000 wizard with additional locally relevant  each sub-type was assigned a derivative. These  inputs for Sunset neighbourhood several steps  derivatives were modelled with larger occupancies  were taken to extract the morphological and  (see section 2.3.4) and adjusted morphological and  performance attributes needed. First, building  system attributes accordingly. Occupancy of building  volume for each typology was derived from LiDAR  29  PART II: methods  Figure 2.3.3: (a) LiDAR derived building volume plotted against the finished floor area of ground-oriented residential and (b) Building volume plotted against construction year for ground-oriented residential.  data and associated with a heated floor area (Figure  followed the BC Greenhouse Gas Emissions  2.3.3). Then, a representative model was created  Assessment Guide, 2008.  in a 3D modelling software. The morphological  In order to capture the variability in energy use  characteristics from this model, where matched  of each typology throughout the neighbourhood  to the sub-type characteristics from the LiDAR  a sensitivity analysis was performed. In this  data. Further details, such as window, wall and  evaluation building orientation, space heating and  roof areas where imported alongside the heated  DHW systems were changed in the BEM. A total of  floor area into each HOT2000 BEM. The associated  eight simulations were run for each typology and  energy performance indicators of the BEM, such  the final energy use and emissions were weighted  as insulation values and air exchange rates were  according to the primary and secondary heating  taken directly from the ecoENERGY retrofit data  system (share provided through BC-Assessment  of local homes where available. Unfortunately, out  data). The calculation of each sub-types emissions  of the 4500 homes in the neighbourhood only 10  was estimated:  ecoENERGY retrofit files were available. Although few records were aquired and the current procedure  CST = CI • Vb/ Ab  to obtain these files was arduous, the ecoENERGY data source provides a tremendous resource for  Where CST = the sub-type carbon flux. CI is the  local energy and emissions mapping. Where no  carbon intensity modelled in the BEM. Vb is the  local data was available municipal and provincial  building volume and Ab is the building footprint.  estimates were used in place. All emissions factors  30  hot water system  hot water system  on  rb  1.03  ns  bu tio  OUTPUTS & BENCHMARKS  CBIP Screening Tool & Sunset Tower, and NRCAN  1.48  3  on  CIVIC  1.74  1.81  COMMERIAL  kg C m-3 yr-1  ns  tio  bu  co nt ri  1.74  hot water system  69%eff.  SFD DUPLEX POST 1990  RETAIL  1.81  CIBEUS Domestic Hot Water1  space heating system  89%eff.  1.49 1.60 1.61 ROW  2  CIBEUS Space Heating1  construction - RSI Values  2.2 wall RSI 2.4 roof RSI 0.4 window-to-wall ratio  average volume  10437m  rb  ca  2  average floor area  2689m / 28944ft  CIVIC  carbon contributions per m-3 yr-1 bldg type-1  OFFICE  1.33  kg C m-3 yr--1  APARTMENTS  co nt ri  69%eff.  1. Average efficiencies for space heating: electricity (100%) and natural gas (78%) and domestic hot water: electricity (82%) and natural gas (55%) were applied using the Commercial and Institutional Building Energy Use Survey (CIBEUS-3000).  WAREHOUSE / INDUSTRIAL  0.46  kg C m-3 yr--1  ns  tio  bu  co nt ri  kg C m-3 yr-1  on  69%eff.  MIXED USE  CIBEUS Domestic Hot Water1  CIBEUS Domestic Hot Water1  0.46  space heating system  space heating system  2.73  CIBEUS Space Heating1  CIBEUS Space Heating1  89%eff.  construction - RSI Values  construction - RSI Values  89%eff.  2.2 wall RSI 2.4 roof RSI 0.4 window-to-wall ratio  3  2925m  2  2.2 wall RSI 2.4 roof RSI 0.4 window-to-wall ratio  ca  2  average floor area  753m / 8105ft average volume  rb  0.0  ca  2  WAREHOUSE / INDUSTRIAL  average volume  3  3326m  average floor area  2  857m / 9225ft  EXTENDED CARE  ‘OTHER‘ TYPES  2  3  2  on  co nt rib  SFD 1965-1990  2.38  ca rb  s  ut io n  2.73  EXTENDED CARE  kg C m-3 yr--1  1.33  hot water system  69%eff.  CIBEUS Domestic Hot Water1  space heating system  89%eff.  CIBEUS Space Heating1  construction - RSI Values  2.2 wall RSI 2.4 roof RSI 0.4 window-to-wall ratio  average volume  3063m  average floor area  789m / 8493ft  OFFICE  2.90  SFD PRE 1965  3.0 kg C m-3 yr-1  BUILDING ENERGY MODELING (BEM)  PART II: methods  Figure 2.3.4: Building typology with appropriate Building Energy Modeling (BEM) attributes and corresponding carbon contributions in m-3 year-1 building type-1  31  2  2  hot water system  hot water system  on  rb  1.03  ns  bu tio  APARTMENTS  co nt ri  69%eff.  OUTPUTS & BENCHMARKS  CBIP Screening Tool & Sunset Tower, and NRCAN  OFFICE  1.33  1.48  MIXED USE  3  on  CIVIC  1.74  1.81  COMMERIAL  kg C m-3 yr-1  ns  tio  bu  co nt ri  1.03  hot water system  69%eff.  SFD DUPLEX POST 1990  RETAIL  1.81  CIBEUS District Hot Water1  space heating system  89%eff.  1.49 1.60 1.61 ROW  2  CIBEUS Space Heating1  construction - RSI Values  2.2 wall RSI 2.4 roof RSI 0.4 window-to-wall ratio  average volume  11512m  rb  ca  2  average floor area  2965m / 31915ft  APARTMENTS  carbon contributions per m-3 yr-1 bldg type-1  1. Average efficiencies for space heating: electricity (100%) and natural gas (78%) and domestic hot water: electricity (82%) and natural gas (55%) were applied using the Commercial and Institutional Building Energy Use Survey (CIBEUS-3000).  WAREHOUSE / INDUSTRIAL  0.46  kg C m-3 yr--1  ns  tio  bu  co nt ri  kg C m-3 yr-1  on  69%eff. kg C m-3 yr--1  CIBEUS District Hot Water1  CIBEUS District Hot Water1  1.80  space heating system  space heating system  1.81  CIBEUS Space Heating1  CIBEUS Space Heating1  89%eff.  construction - RSI Values  construction - RSI Values  89%eff.  2.2 wall RSI 2.4 roof RSI 0.4 window-to-wall ratio  2.2 wall RSI 2.4 roof RSI 0.4 window-to-wall ratio  3  5359m average volume  ca  2  average floor area  1380m / 14854ft  COMMERCIAL  average volume  3  2090m  rb  0.0  ca  2  average floor area  538m / 5791ft  RETAIL  ‘OTHER‘ TYPES  2  3  2  on  co nt rib  SFD 1965-1990  2.38  ca rb  s  ut io n  2.73  EXTENDED CARE  kg C m-3 yr--1  1.48  hot water system  69%eff.  CIBEUS District Hot Water1  space heating system  89%eff.  CIBEUS Space Heating1  construction - RSI Values  2.2 wall RSI 2.4 roof RSI 0.4 window-to-wall ratio  average volume  1953m  average floor area  503m / 5414ft  MIXED USE  2.90  SFD PRE 1965  3.0 kg C m-3 yr-1  BUILDING ENERGY MODELING (BEM)  PART II: methods  Figure 2.3.4: Continued from previous page  32  0.24  FWR window-to-wall ratio  55%eff. (4% share) 82%eff. (96% share)  0.46  OUTPUTS & BENCHMARKS  ca  2  0.16  FWR window-to-wall ratio  rb on  1.03  ns  bu tio  1.48  on  rb  ca  CIVIC  1.74  1.81  COMMERIAL  kg C m-3 yr-1  ns  tio  bu  co nt ri  ROW  SFD DUPLEX POST 1990  1.49 1.60 1.61  carbon contributions per m-3 yr-1 bldg type-1  OFFICE  1.33  kg C m-3 yr--1  APARTMENTS  co nt ri  1.61 MIXED USE  55%eff. (4% share) 82%eff. (96% share)  hot water system  Electrical  Natural Gas  space heating system  78%eff. (60% share) Baseboard 100%eff. (4% share) NG Boiler 78%eff. (36% share) Hydronic Heating  construction RSI Value  2.1wall RSI 5.0roof RSI 1.1foundation RSI  average floor area  HOT2000 & Sunset Tower, and NRCAN  WAREHOUSE / INDUSTRIAL  ns  kg C m-3 yr--1  1.49  tio  bu  co nt ri  kg C m-3 yr-1  0.0  rb on  ca  hot water system  Electrical  Natural Gas  space heating system  Hydronic Heating  share) 78%eff. (60% Baseboard 100%eff. (4% share) NG Boiler 78%eff. (36% share)  construction RSI Value  2.1wall RSI 5.0roof RSI 1.1foundation RSI  average floor area  2  205m / 2207ft  2  205m / 2207ft  2  3.0avg. ppl  DUPLEX  3.0avg. ppl  ROW  RESIDENTIAL, ROW & DUPLEX  RETAIL  1.81  on  co nt rib  SFD 1965-1990  2.38  ca rb  s  ut io n  -3  yr-1  -3  yr-1  2.73  EXTENDED CARE  2.90  SFD PRE 1965  3.0 kg C m-3 yr-1  external contributions (electricity)  0.07 kg C m  external contributions (electricity)  0.07 kg C m  kg C m-3 yr--1  yr-1 local contributions (natural gas) -3  1.54 kg C m  DUPLEX  yr-1 local contributions (natural gas) -3  1.42 kg C m  ROW  LOCAL AND EXTERNAL CONTRIBUTIONS  BUILDING ENERGY MODELING (BEM)  PART II: methods  Figure 2.3.4: Continued from previous page  33  2  55%eff. (100% share)  0.46  OUTPUTS & BENCHMARKS  1.76  rb on  1.03  MIXED USE  1.48  2.45  kg C m-3 yr-1  on  CIVIC  1.74  ROW  SFD DUPLEX POST 1990  1.49 1.60 1.61  0.12  FWR window-to-wall ratio  1.81  COMMERIAL  kg C m-3 yr-1  55%eff. (100% share)  RETAIL  1.81  SFD PRE 1965 with SECONDARY SUITES  2.90 ns  tio  bu  co nt ri  Natural Gas  hot water system  rb  ca  2  5.0  avg. ppl with secondary Suites  78%eff. (93% share) 78%eff. (7% share)  space heating system  NG Boiler  NG Furnace  construction RSI Value  1.4wall RSI 1.8roof RSI 1.2foundation RSI  average floor area  2  150m / 1615ft  3.0avg. ppl  SFD, PRE 1965  carbon contributions per m-3 yr-1 bldg type-1  OFFICE  1.33  SFD 1965-1990 with SECONDARY SUITES  kg C m-3 yr--1  2.38 ns  bu tio  0.12  FWR window-to-wall ratio  55%eff. (100% share)  APARTMENTS  co nt ri  Natural Gas  hot water system  kg C m-3 yr-1  ca  2  6.0  avg.ppl with secondary Suites  78%eff. (70% share) 78%eff. (30% share)  space heating system  NG Boiler  NG Furnace  construction RSI Value  1.8wall RSI 2.3roof RSI 1.6foundation RSI  average floor area  2  224m / 2411ft  HOT2000 & Sunset Tower, and NRCAN  WAREHOUSE / INDUSTRIAL  ns  SFD PRO 1990 with SECONDARY SUITES  kg C m-3 yr--1  1.60  tio  bu  co nt ri  kg C m-3 yr-1  0.0  rb on  ca  hot water system  Natural Gas  0.14  FWR window-to-wall ratio  78%eff. (76% share) 78%eff. (24% share)  space heating system  NG Boiler  NG Furnace  construction RSI Value  2.1wall RSI 5.0roof RSI 1.1foundation RSI  average floor area  2  220m / 2207ft  4.0avg. ppl  6.0  5.0avg. ppl  avg. ppl with secondary Suites  SFD, 1965-1990  SFD, POST 1990  RESIDENTIAL, SFD  on  2.38  co nt rib  SFD 1965-1990  kg C m-3 yr-1  3.02  ca rb  s  ut io n  -3  yr-1  -3  yr-1  -3  yr-1  2.73  EXTENDED CARE  2.90  SFD PRE 1965  3.0 kg C m-3 yr-1  external contributions (electricity)  0.06 kg C m  external contributions (electricity)  0.07 kg C m  external contributions (electricity)  0.10 kg C m  kg C m-3 yr--1  yr-1 local contributions (natural gas) -3  1.54 kg C m  SFD, POST 1990  -3  yr-1 local contributions (natural gas)  2.31 kg C m  SFD, 1965-1990  yr-1 local contributions (natural gas) -3  2.80 kg C m  SFD, PRE 1965  LOCAL AND EXTERNAL CONTRIBUTIONS  BUILDING ENERGY MODELING (BEM)  PART II: methods  Figure 2.3.4: Continued from previous page  34  PART II: methods Population Densi ty (Nighttime) a) 50 x 50m Raster  b) Dissemination Area (DA)  5453551  5453551  800 Inh. / ha  E 41st Ave  E 41st Ave  Tower  5452601  5452601  E 49th Ave  Memorial S. Park  Tecumseh Park  600  Victoria Dr.  Knight St.  Tecumseh Park  Fraser St.  Memorial S. Park  Victoria Dr.  Knight St.  Fraser St.  700  500  Tower  400  E 49th Ave  300 Gordon Park  Gordon Park  200 100  200  400 m  495240  494290  5451651  493340  494290  493340  5451651  0  50 0  E 54th Ave  495240  E 54th Ave  N  Figure 2.3.5: Population density (inhabitants ha-1) shown by (a) 50 x 50m raster that combines Statistics Canada Dissemination Area (DA) census data, BC Assessment data and LiDAR volume data, and (b) DA-level Statistics Canada population data.  2.3.4.	LiDAR volume-based building occupancy distribution  Step 1: Firstly, Census data was used to separate  By distributing nighttime population accurately to  inhabitants living in apartment dwellings vs. ground-  buildings, the sources of human respiration can  oriented dwellings was calculated separately for  be estimated spatially (see section 3.3.1) and  each DA - as this information is not directly provided  the energy load for different building types can  by the census data (only the approx. number of  be assigned. The source for population values  dwellings by type and the total population are  is Statistics Canada’s 2006 census, but this only  provided). This was done by plotting Ptotal /Dtotal vs.  allows for population information down to the  Dapt /Dtotal for each DA separately, where Ptotal is the  dissemination areas (DA) that cover multiple city  total population of the DA, Dtotal is the total number  blocks and do not necessarily overlap with the  of dwellings in the given DA and Dapt is the total  boundaries of the study area. In this report, the  number of apartment dwellings in a DA (provided  spatial resolution has been downscaled to 50 x 50  by Statistics Canada). A linear regression through  m grid elements using building volumes derived  Dapt /Dtotal vs. Ptotal /Dtotal with data from all DAs  from LiDAR and land assessment data as proxy data  allowed for an allocation of the average number  for the distribution.  of people living in each apartment dwelling vs.  population based on dwelling type. The number of  average number of people living in ground-oriented dwellings for the entire study area.Although the 35  PART II: methods actual number of inhabitants living in an apartment  volume of each building type by the total number  dwelling vs. the average number of inhabitants  of inhabitants in the corresponding dwelling type  living in a ground-oriented dwelling could vary from  (both from step 3).These values were then used  one DA to another, the ratio between the two was  to create attribution factors that allowed for a  assumed to remain constant across the study area.  ‘volume adjustment’ in order to make the volume  This means that within each DA the total number  occupied by a person in a ground-oriented dwelling  of people living in each of the two dwelling types  the same as that of someone in an apartment. This  can be calculated from the total population, number  adjustment allows for the population of buildings to  of apartment dwellings and the number of ground-  be based on an ‘adjusted volume’ regardless of their  oriented dwellings, all of which are provided by  type.  Statistics Canada. Step 5: The LiDAR volume of each individual Step 2: This information was then associated with  building in the study area, including those excluded  building typology data in GIS (see section 2.3.2)  in step 4, was multiplied (‘adjusted’) by the  which contained the needed building volume and  attribution factor based on the building’s type. As a  land use to spatialize population.  result, all non-residential buildings were assigned a volume of zero, while the volume of apartments was  Step 3: In order to account for partial DAs near the  increased by a factor of 1.30 and the volume of all  site area border a subset of the DAs were selected.  ground oriented dwellings decreased by a factor of  This subset included those (i) completely within  0.70. The adjusted volumes for each building type  the study area (i.e. LiDAR data available) and (ii)  were totaled for each DA, complete or incomplete.  contained at least one apartment building according to the BC Assessment data. For complete DAs with  Step 6: The adjusted volume of each residential  apartments, the total number of inhabitants per  building was divided by the total adjusted volume of  dwelling type (step 1) and the corresponding total  all buildings in the particular DA. This corresponds  volume per building type (step 2) were extracted.  to the modeled fractional population of the building  The resulting numbers were summed for all  in the DA.  complete DAs. Step 7: For all buildings in each complete DA, the Step 4: Using the totals for all complete DAs  fractional population was multiplied by the census  from step 3, a series of global (study area-wide)  total population of the entire DA to obtain a value  parameters were calculated in order to establish  for the population of the building. For buildings in  the average volume occupied by a person (m3 Inh-1)  incomplete DAs, the adjusted volume of the building  living in either an apartment or ground-oriented  was divided by the global volume/inhabitant value  dwelling. This was done by dividing the total  to obtain an estimate. 36  PART II: methods 2.3.5.	Density Effects and Up-Scaling to the Neighbourhood  performance attributes are important indicators of  Once all sub-type’s energy and carbon intensities  buildings and vegetation additionally impact energy  had been documented the results were made  demand. Although general relationships were  spatial in GIS. This distribution followed one of two  established for shading impacts in the BEM, these  methodologies explored, which were based on the  neighbouring effects have not been fully accounted  hypothetical amount of information available. The  for in the current methodology.  carbon emissions other impacts from surrounding  first, with which the current results are presented (see section 3.1), followed a scenario where  2.3.6	 Carbon Storage in Buildings  detailed building information was available. The  Organic carbon is stored in buildings in substantial  second scenario explored the possibility of using  amounts. This carbon is incorporated in the  LiDAR to automatically classify the buildings stock.  building’s structure, but also in furniture and books (Churkina, 2009).  Scenario 1: GIS informed mapping In this step the GIS synthesized data set is used to  Carbon stored in furniture  inform the spatial pattern of carbon emissions. The  The carbon stored in furniture and books was  defined sub-types carbon intensity (kg m-3 year-1)  estimated:  Cbf,n = P • fc • mf  is mapped to the associated buildings and total emissions is derived using the LiDAR volume.  Where P is population density, mf is the average Scenario 2: LiDAR informed mapping  estimate of the mass of furniture and books per  In this step a reduced level of information  capita (300 kg Inh-1), fc is the fraction of carbon  is assumed and an automatic classification  matter per dry mass in wood and construction  of buildings attempted. Using fieldwork data  materials (0.5 kg C kg-1). Cbf,n is the neighbourhood-  collected for approximately 200 homes, three SFD  wide pool of carbon stored in furniture and books in  typologies were defined (SFD outlined earlier).  kg C per m2 urban area.  The morphological characteristics of these homes were used as a training data set for the entire  Carbon stored in building structure  neighbourhood. This data was input into a decision  The storage in residential buildings structure  tree regression and showed promising results where  materials (framing, flooring, roofing, and walls) was  73% of the homes were properly classified using  estimated:  volume as an indicator. Lastly, it is important to note that although morphological characteristics and energy  Cbs,n = rbw • fc • lR rbw is the wood mass per unit of floor area which was estimated rbw = 97 kg m-2 in a standard US 37  PART II: methods residential home (based on Keoleian, 2000). fc is the fraction of carbon matter per dry mass in wood (0.5 kg C kg-1). Cbs,n is the neighbourhood-wide pool of carbon stored in building structures in kg C per m2 urban area. lR is the ratio of total floor area of residential buildings AR to total ground area of the neighbourhood AG:  lR = AR / AG AF was available for residential ground-oriented buildings through BC assessment. For apartments and non-residential buildings it was calculated from LiDAR volume Vb based on a regression against residential ground-oriented buildings:  AR = cr • Vb where cr = 0.2441 m2 / m3 was determined empirically by comparing Vb and AR for all ground oriented buildings. Commercial buildings were accounted for separately following the above approach, but based on Chrukina (2009) it was assumed that they contain only 10% of the carbon stored in residential buildings.  38  PART II: methods 2.4  Transportation Modelling  Vancouver’s web-based spatial data set.  This section describes the approach and methods  Within the project study area were 13 directional  used to derive carbon emissions from the  traffic count sites with one or more sets of recent  transportation urban metabolism component.  (since 2005) 24-hour weekday traffic counts, 9 intersection traffic movement counts with a few  2.4.1	 Calculating Carbon Emissions from Transportation  hours of peak travel data and 1 vehicle class traffic  The challenge to the transportation component  data. Data gathered at these points were used to  of the urban metabolism was to estimate the  estimate a weekday traffic profile for the study area.  proportion of tower measured carbon emissions  In this profile all vehicle trips (including local origin  attributable to fossil fuels burned in vehicle  or destination trips) were assumed to enter or exit  trips to, from and through the case study  the study area along one of the arterials for which  area. That challenge was complicated by the  data was available. To simplify the method, only  absence of consistent, sufficiently detailed and  trips exiting the study area were considered. Count  appropriately scaled neighbourhood scale travel  sites with more than one data set after 2005 were  and transportation data upon which to compile  averaged (see Table 2.4.1).  intersection movement count with 12 hours of  an annual travel snapshot of how many trips, of what type (to from or through) and length, by how  To convert average weekday trips to annual total  many vehicles, of what type, using how much of  trips, each weekday number was prorated by a  which fuels — and converting answers to those  multiplier of 346.75 (weekday to annual) based  questions to carbon units. Each question had to be  on the measured distribution of traffic by day  approached separately and incrementally.  elsewhere in Vancouver (personal conversation City of Vancouver transportation staff). This factor  How many trips? Traffic monitoring through the City  is based on an average daily traffic distribution  of Vancouver is done on a piecemeal, intermittent  as follows: Monday = .95, Tuesday, Wednesday,  basis. From time to time, automatic directional  Thursday = 1.0, Friday = 1.05, Saturday = 0.9 and  traffic counting devices are placed for 24 hour or  Sunday = 0.75.  greater periods on arterials and major roadways. Less frequently, manual counts of traffic movements  To, from or through? No neighbourhood scale  (right turn, left turn, through etc.) are taken at  mode split data are available. However, according  significant intersections at peak traffic periods  to the last available regional trip diary survey  — usually a few hours in a few days. Even less  (Ministry of Transportation and Greater Vancouver  frequently, manual counts of movements by vehicle  Transportation Authority, 2004) average trip rates  type or class are taken at the most significant  and mode splits in the City of Vancouver segment  intersections — usually 12 hours of a single day.  of the Metro region are 3 trips per capita, .605 of  All of these are posted to VanMap — the City of  which are by automobile. These rates were used 39  PART II: methods  Figure 2.4.1: Simplified plan diagram of average weekday arterial traffic by vehicle class. Numbered line segments are directional traffic count sites. Numbered dots are intersection traffic movement 12-hour manual count sites. A 12-hour manual traffic count at the Knight Street and East 41st Avenue intersection (noted as 230750) provided vehicle class data. Source: City of Vancouver. 40  PART II: methods Table 2.4.1: Traffic count data by acquisition location (see Figure 2.4.1). Source: City of Vancouver.  41  PART II: methods to estimate the local portion of annual trips as  Avenue.  follows: Study area population = 23,135 x 3 trips  By what trip length? All local origin and destination  per capita x .605 by automobile = 41,990 weekday  trips were considered to be an average of 1 km —  trips. Prorated against total non-freight vehicle trips  approximately the distance from the centre of the  estimated above, the local trip share was estimated  study area to an arterial exit point. All through  at 24% and through trips at 76% of total trips  trips were considered to be an average of 2 km  throughout the study area.  — approximately the distance from entry to exit along a study area arterial. Using these average trip  By what kinds of vehicles? The carbon emissions  lengths, total annual kilometers travelled, by vehicle  intensity from transportation sources depends in  class, was estimated within the study area.  significant part on vehicle type and its associated  How much of which fuel? Average fuel efficiencies  fuel and fuel efficiency — average gasoline fueled  (NRCan, 2007) were used to estimate the amount  passenger vehicles emitting approximately one third  of fuel combusted for each trip and vehicle type.  the carbon of a diesel fueled truck and trailer per  Light vehicles were estimated to average 11.5  kilometer. Trips by vehicle were estimated using  litres of gasoline / 100 km. Medium freight vehicles  a four type classification (Figure 2.4.3 and Table  were estimated to average 26.5 litres of gasoline  2.4.2) of light vehicles and transit vehicles primarily  / 100 km. Heavy freight vehicles and buses were  moving people to jobs and services, medium freight  estimated to average 39 litres of diesel fuel / 100  vehicles primarily moving goods and materials to  km. Total annual fuel consumed in the study area  out-of-study area local destinations, and heavy  was estimated for each vehicle class / fuel type.  freight vehicles primarily moving goods and materials to more distant destinations.  How much carbon? Total annual fuel consumed in  How many of which types of vehicles on which  the study area was converted to tCO2e using the  study area streets was more challenging. As one  conversion factors of 241 gCO2e / liter for gasoline  intersection traffic count by vehicle class was  and 276 gCO2e / liter for diesel fuel (NRCan, 2007).  available (at Knight Street and East 41st Avenue  CO2e was converted to gC using a factor of 0.273  at the northern centre of the study area) that  (Ministry of Community Services, 2008). The  distribution was used to estimate vehicle classes on  resulting values were proportionally attributed to  arterials as follows. At this location, Knight Street,  each arterial corridor and local streets based on  being a principal truck freight route in the region,  km travelled (Table 2.4.2) and vehicle class (Table  accommodates a greater share of medium and  2.4.3)  heavy freight vehicles than did East 41st Avenue as follows: Based on these proportions, freight and transit traffic was allocated to Knight Street as calculated. All other arterials were assumed similar to East 41st 42  PART II: methods  Figure 2.4.3: Project vehicle classes referenced to US Federal Highways Administration vehicle classes  43  PART II: methods Table 2.4.2: Estimated proportion of vehicle types by class at Knight Street and East 41st Avenue over 12 hours. Source: ‘All vehicles’ average from City of Vancouver Thursday-Friday traffic counts 21, 22 July 2005 and 15-16 February 2007. Share by vehicle type from City of Vancouver manual vehicle class count at Knight Street and East 41st Avenue (date).  Vehicle Type  Knight Street  All Vehicles (both directions) Medium Freight Heavy Freight Transit  East 41st Avenue 29,958  25,159  1,091  268  3.5%  1%  2,081  137  7%  0.5%  202  342  0.6%  1.3%  Table 2.4.3: Vehicle type classification by size, fuel type, fuel efficiency and carbon intensity.  Fuel  Fuel Efficiency L/100km  gC / km  FHWA Class  Light Passenger  Gasoline  11.5  75.6  FHWA 1-3 Two axle, four tire vehicles passenger cars, motorcycles  Medium Freight  Gasoline  26.5  174.3  FHWA 5 Single frame, two axle, dual rear wheel (delivery type)  Heavy Freight  Diesel  39.3  296.0  FHWA 6-13 Multiple axles truck, truck and trailer, truck and multiple trailers  Transit  Diesel  39.3  296.0  FHWA 4 Two or more axle, six or more tire busses  Vehicle Type  44  PART II: methods 2.5	 Accounting for Carbon Cycling in the Human Body, Food and Waste  faeces) and therefore laterally exported out of the neighbourhood through sewer systems.  This section discusses the methods used in this study to estimate carbon storage and fluxes related  In this study, for simplicity reasons, greenhouse  to carbon cycling though the human metabolism  gas emissions outside the neighbourhood system  and related food and waste products.  due to production and transport of food, or carbon emissions related to waste processing are excluded.  2.5.1.	Carbon Flows Associated with the Human Metabolism  They might play important roles on a community  Figure 2.5.1 illustrates simplified carbon fluxes  emissions. We also note that carbon cycled through  associated with the human metabolism and food  this component is renewable carbon, as it originated  consumption. In contrast to natural ecosystems,  from plants photosynthesizing atmospheric carbon-  where food is grown within the system, it is clear  dioxide at the bottom of the human food-chain.  scale, in particular when incorporating methane  that in an urban ecosystem with anthropogenic food distribution systems, the majority of food and hence carbon - is imported from outside the  2.5.2.	Carbon storage in the human body  system. With the current urban land-use patterns  The neighbourhood-wide carbon pool in the human  the human food-web is spatially disconnected from  body was calculated based on Churkina et al.  the local urban vegetation component, and urban  (2009):  agriculture and gardening play negligible roles in  Ch,n = P • mh • fd • fc  the carbon cycle at the neighbourhood scale. Where P is population density (in Inh. m-2), mh Part of the carbon contained in food products that  is the average mass of the human body (60 kg,  is imported to the neighbourhood is lost in the  including water), fd is the fraction of dry mass per  preparation and/or storage of meals. This carbon is  total body mass (0.3), fc is the fraction of carbon  then laterally exported (garbage and liquid waste  matter per dry mass in the human body (0.5 kg  water). In this study we only account for food  C kg-1). Although humans are highly mobile, for  losses in distribution and residential households in  attributing the values to the spatial domain, the  the neighbourhood, not in agriculture or industrial  nighttime residential population from the Statistics  processing.  Canada 2006 census was used. The resulting Ch,n is the neighbourhood-wide pool of carbon stored in  The human body ingests the remaining carbon to  the human body in kg C per m-2. The m-2 refers to  gain energy and temporarily stores that carbon. Part  the urban plan area in the study area.  of the carbon contained in food products is respired (oxidized) in the process of human respiration. Another part is lost though human waste (urine and 45  PART II: methods Atmosphere Respiration Atmosphere 76.3  2.5.4.	Carbon Fluxes due Food Imports Form of carbon Outputs and Food Waste Carbohydrates  Inputs  Respiration 76.3  The flux of carbon through food into the Human waste 29.9  Human body  Human waste 29.9  Human body  Carbon-dixode  Food losses (waste) 45.4 Food losses (waste) 45.4  neighbourhood was estimated based on nationwide food consumption statistics (Statistics Canada, 2004). Statistics used include per capita consumptions on a mass basis for selected various food groups (Table 2.5.1). For each food category,  Food consumed 106.2  Food  Food consumed 106.2  Food  Food purchases 151.6  lost” was converted to dry matter content using  System boundaries  a category-specific dry matter content fdr. Dry  System boundaries  matter was converted to carbon mass C(cat) using  Food purchases 151.6 Inputs  “mass of food consumed” and “mass of food  an average carbon content of fc = 0.5 kg C kg-1 dry matter:  Outputs  Form of carbon  C(cat) = m(cat) • fdr (cat) • fc (cat)  Carbohydrates  Summing the different categories, results in a total  Carbon-dixode  per capita carbon consumption of 106 kg C cap-1  Figure 2.5.1: Conceptual representation of the carbon fluxes and storages involving the human body. Numbers refer to per capita estimates in kg C cap-1 year-1 as described in this section.  year-1 and food losses of 45.4 kg C cap-1 year-1 (Table 2.5.1). It is interesting to note that the major portion of carbon lost is carried by the liquid waste  2.5.3	 Respiration by the Human Body  flow (not solid garbage) which can be explained  The injection of carbon-dioxide by human  by the high dry matter content of cooking water,  respiration into the atmosphere was calculated by  disposed through kitchen sinks (Codoban and  Eh,n = P • fr  Kennedy, 2008).  P is population density (in Inh. m-2). fr is the annual respiration of a human body in kg C year-1 and  The numbers were multiplied by the nighttime  set to 76.3 kg C year-1 cap-1 according to Moriwaki  (residential) population density P (in Inh. m-2) to  and Kanda (2004). They estimated the respiration  retrieve per-area flux densities of food imported and  of carbon dioxide per person of 8.87 mg CO2 s-1  food lost in kg C cap-1 year-1:  based on medical literature. Global-scale estimates  Ff = C • P  are about 0.57 Gt C year-1, which translates to 93.4 kg C year-1 cap-1 on the global average (Prairie and Duarte, 2006). The resulting Eh,n is the neighbourhood-wide carbon flux (emission) released by human respiration in kg C m-2 year-1. 46  PART II: methods Table 2.5.1: Consumption statistics and carbon content of various food products (modified from Codoban and Kennedy (2008), using data from Statistics Canada (2004) and Baccini and Brunner (1991))  Dry Matter Food Category Context  fdr %  Mass of Food Entering System m(cat) kg C cap-1 year  Mass of Food Consumed m(cat,c) -1  kg C cap-1 year  Mass of Carbon Consumed C(cat,c)  Mass of Food Losses m(cat,l) -1  kg C cap-1 year  -1  kg C cap-1 year  Mass of Carbon Lost C(cat,l) -1  kg C cap-1 year  Cereal Products  88  88.6  65.3  23.3  28.7  10.3  Fruits  15  81.2  50.5  30.7  3.8  2.3  Vegetables  15  183.4  112.0  71.4  8.4  5.4  Pulses  90  7.7  6.8  0.9  3.1  0.4  Red Meat  35  64.0  28.3  35.7  5.0  6.2  Poultry  30  35.3  13.2  22.1  2.0  3.3  Fish  20  9.5  6.7  2.8  0.7  0.3  Milk  12  91.3  67.5  23.8  4.1  1.4  Cheese  60  12.1  8.9  3.2  2.7  1.0  Other Dairy  36  24.1  5.1  19.0  0.9  3.4  Eggs  25  9.2  7.5  1.7  0.9  0.2  Sugars & Syrups  95  40.8  30.5  10.3  14.5  4.9  Oils & Fats  99  30.7  22.0  8.7  10.9  4.3  Tree Nuts  80  1.5  1.3  0.2  0.5  0.1  Alcoholic Beverages  10  102.2  99.5  2.7  5.0  0.1  Nonalcoholic Beverages  10  339.4  302.7  36.7  15.1  1.8  1121  828  293  106.2  45.4  Total per Capita  -1  2.5.5	 Carbon Fluxes due to Human Waste  matter losses in the literature, which are given  The carbon exported from the system in form of  This corresponds to 31 kg C year-1 cap-1, when  human waste was determined as the difference  applying fc = 0.5. To convert this value to a per-area  of the carbon consumed (106.2 kg C cap year ,  estimate, similar to 2.5.4, the value was multiplied  section 2.5.4) minus the carbon respired (76.3  by the nighttime (residential) population density P  kg C cap year , section 2.5.3). This is justified  (in Inh. m-2).  with 52 kg year-1 cap-1 (Baccini and Brunner, 1991).  -1  -1  -1  -1  because the adult human body does not accumulate biomass. The carbon flux due to human waste out of the system was calculated 29.9 kg C cap-1 year-1. This number comes close to estimated dry  47  PART II: methods 2.6	 Vegetation and Soils A Terrestrial Ecology Approach to Urban Carbon Cycling  the plan area fraction of pervious surfaces lb to estimate the neighbourhood-wide carbon pool Cs,n  This section describes methods used to quantify  Cs,n = Cs • lp  carbon storage, carbon emissions and carbon uptake by urban vegetation and soils. Results are found in Section 3.4.  2.6.2	 Carbon Storage in Vegetation The carbon storage in vegetation was estimated separately for tree biomass (above-ground and  2.6.1	 Carbon Storage in Soils  below ground, leaf biomass, needle biomass) and  Although organic matter is often present in the soil  lawn biomass.  to a depth of 1 or 1.5 m, most is in a surface layer of from 1 to 20 cm (Buringh, 1984). Carbon storage  Carbon storage in trees:  in soils in the study area was estimated based on  Carbon stored in trees and specific parts was  lab analysis of soil organic matter mass fraction  calculated based on literature urban-specific  mo (in kg kg ). A total of 20 soil cores were taken  allometric relationships (Nowak, 1996). Bushes  in 2008 on four lawn lots in the neighbourhood  (< 2m) are not considered in this study.  -1  (Christen et al., 2010). For each lot, soil organic content was measured separately in the lab for  Leaf area index, leaf biomass, and tree woody  0-3, 3-6, 6-10, 10-15 and 15-20 cm depth layers.  biomass were calculated for all trees on public and  Those cores excluded tree root material (which  private land in a circle of 400m around the carbon  is accounted for under ‘vegetation biomass’). For  flux tower. In this circle, all trees were manually  each layer, this was converted to an organic matter  digitized based on air photos, and their height was  density in the soil fo using measured average soil  extracted from the LiDAR dataset (Table 2.6.1). For  bulk-density (rs = 0.94 Mg m ), and the ro was  a subset of this dataset, also tree crown diameter  then converted to the carbon density rc (kg m )  was measured, and to partition total biomass into  assuming that fb = 58% of the mass of organic  above-ground and below-ground biomass (roots),  matter content is carbon (van Bemmelen factor):  a root-to-shoot ratio of 0.26 was applied (Cairns et  -3  -3  rc = mo • rs • fb An exponential function was fitted through the four depth layers of rc. This function rc was integrated  al., 1997). To upscale from the 400m to the entire study area, LiDAR derived tree volume was used.  over the profile (down to 1 m depth) to retrieve  To calculate lateral carbon export from the  total organic carbon in the soil. We assumed that  neighbourhood through biomass removal we  topsoil that contains organic carbon is only found  assumed a typical life-span of urban trees of Tl = 75  below pervious surfaces (i.e. has been removed  years. The annual exported of carbon in tree woody  under buildings and streets). So Cs is multiplied by  biomass by tree removal / death was estimated as 48  PART II: methods Table 2.6.1: Tree characteristics in a circle of 400 m around Sunset tower.  Density (stems/ha)  Average Tree Height (m)  Average Total Biomass (kg)  All Trees (n=95)  19.20  9.04  1662  Coniferous (n=23)  4.44  11.13  2620  Broadleaf (n=742)  14.76  8.41  1374  1/Tl of the total carbon pool. We further assume  in the study area (Figure 2.6.1). Details of the  that two-thirds of all leaves of deciduous trees are  measurement and modelling procedure can be  removed out of the study area in the fall season.  found in Liss et al. (2009).  Carbon storage in lawns: The estimation of the carbon stored in above-  Respiration from above-ground biomass (Rtree)  ground biomass of residential lawns was based  was modelled based on measurements from a  on destructive samples of the grass cover in four  portable photosynthesis measurement system  different lots in the neighbourhood. Dry matter  (Li-6400, Licor Inc., Lincoln, Nebraska, USA). A  mass of those samples was measured in the lab  total of 12 urban trees and shrubs in the study area  and on average the dry-matter density of the grass  were sampled including Sugar Maple, Purple Leaf  canopy was rvl = 1.05 kg m-2 (0.65 – 1.61 kg m-2,  Flowering Plum, Dwarf and Rosebay rhododendron,  included mosses). To convert to carbon density,  Cherrylaurel, American Chestnut, Oregon Ash,  a generic factor of the carbon fraction in biomass  European Beech, American Elder and Silver Maple.  of fo = 0.5 (kg C kg ) was applied. This value was  The photosynthesis measurement system was  multiplied by the plan area fraction of lawns in the  programmed to determine dark respiration of leaves  neighbourhood:  as a function of air temperature. Average dark  -1  Cvl,n = rvl • fo • lvl  respiration at 25ºC was determined 1.03 µmol s-1 m-2 leaf area. Above-ground respiration was scaled using modelled leaf area index (see Section 3.4,  2.6.3	 Respiration by Urban Soils, Lawns and Trees  seasonally changing) in the neighbourhood. Using year-round air temperature measurement, data was  Respiration from soils and lawns (Rsoil+lawn) was modelled based on approx 280 closed-chamber measurements with an opaque chamber on residential lawn in the neighbourhood. Rsoil+lawn was then modelled over the year based on yearlong observations of soil volumetric water content and soil temperatures of four residential lawns  49  PART II: methods integrated in 5-min steps over the complete year  on modelled PAR irradiance (Ogren and Evans, 1993)  2009. Details of the measurement equipment can  taking into account soil volumetric water content in  be found in Liss et al. (2010).  the study area. Details of the modelling procedure can be found in Christen et al. (2009).  2.6.4	 Carbon Uptake by Photosynthesis  Photosynthesis  Photosynthesis by residential lawns (Plawn) was  based on measured light-response curves from 12  modelled based on closed-chamber measurements  representative trees in the neighbourhood (same  with a clear chamber and co-located photosynthetically  as listed in Section 2.6.3), and in combination with  active radiation (PAR) measurements (Figure 2.6.2).  a multi-layer radiation transmission model run at 1  Using a LiDAR-derived DEM, for each hour of the  m3 resolution in the study area over the year 2009.  year 2009, PAR irradiance of was distributed on a 1  Similarly to the lawn photosynthesis mode, the tree  x 1 raster for all lawn surfaces (including shading by  photosynthesis model was driven by measured short-  trees and buildings) based on measured short-wave  wave irradiance from the carbon flux tower.  by  trees  (Ptrees)  was  modelled  irradiance on tower top. Plawn was then modelled based  20  15 Rlawn (µmol m-2 s-1)  T = 35%  Average Volumetric Water Content (0-12cm) 0% - 10% 10% - 20% 20% - 30% 30% - 40% > 40%  T = 25% T = 15%  10  T = 5% 5  0 0  5  10  15 20 Soil temperature T at 5cm (°C)  25  30  35  Figure 2.6.1 Measured (dots) and modelled (lines) lawn respiration as a function of soil temperature and soil volumetric water content (from Christen et al. 2009).  50  PART II: methods 20 Average Volumetric Water Content (0-12cm) 0% - 10% 10% - 20% 20% - 30% 30% - 40% > 40%  Plawn (µmol m-2 s-1)  15  T = 35%  10  5 Water stress  0 0  500  1000 PAR (µmol m-2 s-1)  1500  2000  Figure 2.6.2: Measured (dots) and modelled (line, for water content at 35%) lawn photosynthesis as a function of photosynthetically active radiation (PAR) and soil volumetric water content (from Christen et al. 2009).  51  PART II: methods 2.7	 The Use of Direct Carbon Flux Measurements for Model Validation  used to quantify the exchange (vertical flux, see  This section describes the direct and continuous  EC is currently used at several hundred research  measurement of carbon emissions over a two-  sites worldwide to continuously monitor CO2 fluxes  year period in the study area. The methodology of  between various natural and managed ecosystems  tower-based carbon flux measurements, including  and the atmosphere, including farm land,  infrastructure and data post-processing procedures,  grasslands, tundra, and forests (e.g. Baldocchi,  are described. The results from these tower-based  2008). The EC approach allows for a quantification  measurements will be compared to modelled  of emissions as it measures the net mass of CO2  emissions in Section 3.6.  exchanged per unit area of surface over a given  section 1.3.3) of greenhouse gases including CO2.  time (a flux density in g C m-2 s-1). There are  2.7.1	 The Eddy Covariance Method  significant theoretical and practical limitations to  Eddy covariance (EC) is a method to continuously  the method, which can be overcome by properly  and directly measure surface-atmosphere  choosing a measurement location and technology.  exchanges of energy and trace gases. EC can be AnÊultrasonicÊanemometerÊmeasuresÊsmallestÊmotionsÊofÊwindÊbyÊmeansÊ ofÊultrasoundÊwaves.ÊSoundÊwavesÊtravelÊslightlyÊfasterÊifÊtheyÊareÊcarriedÊ withÊtheÊwindÊasÊopposedÊtoÊtravelÊagainstÊwind.  AÊcombinationÊofÊanÊultrasonicÊanemometerÊandÊanÊinfraredÊ gasÊanalyzerÊcanÊbeÊusedÊtoÊdirectlyÊcalculateÊhowÊmuchÊcarbonÊ dioxideÊisÊexchangedÊbetweenÊtheÊurbanÊsurfaceÊandÊtheÊatmosphere.ÊForÊexample,ÊweÊfindÊstatisticallyÊthatÊupwardÊmovingÊairÊ isÊslightlyÊcarryingÊmoreÊcarbon-dioxdieÊthanÊairÊthatÊcomesÊfromÊ higherÊlevelsÊofÊtheÊatmosphere.Ê  AnÊinfraredÊgasÊanalyzerÊmeasuresÊconcentrationsÊofÊcarbonÊ dioxideÊinÊtheÊair.ÊTheÊinstrumentÊoperatesÊbyÊshiningÊanÊinfraredÊ lightÊsourceÊacrossÊaÊshortÊpath.ÊThisÊhigh-performanceÊinstrumentÊmeasuresÊ150ÊtimesÊaÊsecond.  AnalyzerÊelectronicsÊcontrolÊtheÊtheÊ measurementsÊandÊstoreÊtheÊdataÊforÊ post-processing.  Figure 2.7.1: Tower-based EC instrumentation consisting of an ultrasonic anemometer and a fast trace-gas analyzer. This set-up was operated at the carbon flux tower in the study area as part of the CFCAS network ‘Environmental Prediction in Canadian Cities’ from May 2008 to April 2010 (see Section 2.7.3).  52  PART II: methods The EC method fundamentally rests on principles  EC instrumentation is also sensitive to inclement  of mass conservation and directly measures  weather. Rain, snow, or dew on the sensing  atmospheric turbulence and trace gas concentration  elements of the instruments can compromise wind  fluctuations in the atmosphere above the surface  velocity and trace gas concentration measurements.  of interest. Measurements are done at frequencies  Typically, data from rainy or snowy periods is  between 20 and 5 Hz to fully capture the highly  withheld from analysis.  dynamic state of the turbulent atmosphere (Tropea et al, 2007). These rapid measurements are coupled with simultaneous, co-located CO2 concentration measurements, which allows for in situ quantification of the transport of CO2 by turbulent eddies between the surface and atmosphere. The specific instrumentation required for EC is a fast anemometer to measure vertical wind velocities coupled with a fast gas analyzer to measure CO2 concentration fluctuations (Figure 2.7.1). To make representative readings, EC systems need to be installed on towers significantly above the surface of interest. Typically, a measurement height of 2-3 times the tallest surface objects is sufficient to ensure that mixing and air flow is representative. Ideally, towers are slender and/or latticed to minimize flow disturbances, yet rigid enough to minimize tower swaying and instrument movement. The ideal measurement surface is extensive and flat with homogeneous surface cover to maintain representative measurements for a range of conditions and avoid a major restrictive situation advection. Advection is a horizontal flux of CO2 into the measurement volume transported by flow from an upwind surface with different CO2 characteristics. With proper tower citing and instrument choices, however, advection can be avoided and EC measurements at a single point above a surface are representative of the CO2 exchange for an entire ecosystem.  2.7.2. Carbon Flux Measurements in Urban Areas In urban areas, the extreme spatial variability of urban cover and form make representative citing of EC instruments difficult (Grimmond et al. 2002). The limitations set by logistical and experimental difficulties encountered in urban environments are much higher even than they are for ecosystems with a more uniform structure and form. Nevertheless, in recent years, numerous EC sites used to measure carbon fluxes have been implemented, including four towers in Canadian cities (Christen et al., 2009). In order to measure representative carbon fluxes at the neighbourhood scale, instruments must be mounted 2-3 times the average building and vegetation height to ensure air flow is not influenced by individual buildings or trees and is representative of a large area (i.e. an entire neighbourhood). Additionally, the surface in the primary upwind direction should show uniform building, vegetation, and road characteristics (e.g. density, geometry, cover). This was the motivation to erect UBC’s urban climate research tower in the Vancouver-Sunset neighbourhood in 1978, following an extensive analysis of urban form and topography. The Vancouver-Sunset neighbourhood was identified as an extraordinarily flat, and  53  PART II: methods wind EC system  90%  50%  turbulent source area isopleths  Figure 2.7.2: Conceptual diagram of a flux source area model. The source area is oriented along the direction of the mean wind and the isopleths are the contributions of the surface to the measured flux signal.  homogeneously developed area.  Data used in this study was sampled from May 2008  The surface from which EC instrumentation  to April 2010 as part of research in the network  measures carbon exchange (or other trace-gases)  ‘Environmental Prediction in Canadian Cities’  is called the flux source area and its orientation and  funded by the Canadian Foundation for Climate and  extent are primarily controlled by wind direction and  Atmospheric Sciences (CFCAS). Previous work on  atmospheric stability (e.g. Schmid, 1994). Source  the urban carbon cycle at this research site includes  areas constantly change and reshape with changing  carbon-dioxide concentration (Reid and Steyn,  wind direction and atmospheric conditions. Source  1997) and flux measurements in 2001 (Walsh,  areas at any time are oriented along the axis of the  2005).  mean wind direction. Source area models have been developed to delimit the surface area contributing to  The instrumentation consists of a sonic anemometer  flux measurements. Figure 2.7.2 shows how model  (CSAT 3-d sonic anemometer, Campbell Scientific,  results are visualized as surface isopleths. Isopleths  Logan, UT, USA) and an open-path infrared-gas  are showing the probability of the surface of origin  analyzer (Li-7500, Li-Cor Inc., Lincoln, NE, USA)  of the measured signal for the given situation.  (Figure 2.7.3). Three dimensional wind velocities and CO2 concentrations are sampled at 20 Hz and  2.7.3. Carbon Flux Tower ‘Vancouver-Sunset’ Site and instrumentation - The EC system used in this study is located in the centre of the chosen study area on a tower 28.8 m above the local ground surface (494290, 5452601, UTM-10). The tower is located within the Mainwaring Power Substation of BC hydro, and is operated by UBC Geography / the UBC Soil Water Air Laboratory.  are collected on a data logger (CR3000, Campbell Scientific, Logan, UT, USA).  Data processing – Vertical fluxes of CO2 were calculated as the covariance of fluctuations from the mean of vertical wind velocities and CO2 concentrations. 30-minute averaging periods were chosen to capture the circulation periods of large eddies and fulfil requirements for signal 54  PART II: methods Table 2.7.1: Monthly summary of data coverage for carbon fluxes measured at the tower from May 1, 2008 – April 30, 2010.  Month  Total Hours Observed (n)  Data Coverage (%)  January  1680  56.5  February  1983  73.8  March  2163  72.7  April  2130  74.0  May  2284  76.7  June  1980  68.8  July  2612  87.8  August  2184  73.4  September  2340  81.2  October  2114  71.0  November  1668  57.9  December  1390  46.7  24528  70.0  Total  stationarity. First, the coordinate system of the  Meixner 2001) that was run for all 30 min periods  wind velocity components is rotated two times so  between May 1, 2008 and April 30, 2010 at a 2m  that it is aligned with the mean wind direction and  grid resolution over a domain of 2000 by 2000m.  the mean vertical wind is zero. Then, 30-minute  Following the procedure described in Chen et  covariances of fluctuations of vertical wind and CO2  al. (2009), the long-term integrated source area  concentrations are calculated and corrections are applied to account for changes in air density (Webb, et al. 1980) and spatial separation between gasanalyzer and anemometer (Moore, 1986). Fluxes undergo several quality control checks, with all details given in Crawford et al. (2010). Using this procedure, 30-minute CO2 fluxes were calculated for the full 2-year cycle (May 2008 – April 2010). Data coverage for the 2-year period is 70.0% (Table 2.7.1). Of the missing data, 26.6% is due to weather (i.e. rain, snow, or dew on the instruments) or failure to meet quality control standards, and 3.4% is from EC system failure.  Source areas - The long-term source area of the EC system is shown in Figure 2.7.6. This map was calculated using a 2-dimensional gradient diffusion and crosswind dispersion model (Kormann and  Figure 2.7.3: Photo of the EC instrumentation on top of the carbon flux tower taken with a fisheye lens. The sonic anemometer is on the left, the gas analyzer on the right. Both instruments are mounted 28.8 m above the local surface (Photo by R. Ketler, UBC).  55  PART II: methods was calculated as the average of all individual  calculated by averaging all situations when wind  (changing) 30-min source areas during that period.  was from a given sector and within a selected hour.  Figure 2.7.6 shows that 50% of the measured flux  Hours without any occurrence of the wind direction  signal is from within approximately 400 m of the  from that sector were linearly interpolated for up to  tower. The source area includes the intersection of  3 hours. The average diurnal course of that month  Knight Street and 49th Avenue.  was then integrated over a full day to retrieve a daily carbon flux density (g C m-2 day-1) for each  Calculation of annual total flux - For annual flux  wind sector and each month. For the annual flux  densities, 30-minute fluxes were integrated and  density, monthly values from the given sector were  spatially sorted to account for different emission  integrated and weighted by the number of days in  characteristics across the source area. Each valid  the given month. This procedure was separately  30-min measurement is sorted by (i) month of  applied for weekdays and weekends. Flux densities  the year, by (ii) hour of the day, and by (iii) wind  integrated for the entire neighbourhood were  direction (vector average) into one of four sectors  calculated as the average flux from each of the four  (NE (0-90º), SE (90º-180º), SW (180º-270º), and  wind sectors.  NW (270º-360º). For each month and each wind sector, a typical diurnal course of the flux is then  Summer (June-August)  Winter (December - February) 0o  0o  2%  270o  6%  10%  90o  180o  10%  90o  Fall (September - November)  0o  2%  180o  6%  180o  Spring (March-May)  270o  2%  270o  0o  6%  10%  90o  2%  270o  6%  10%  90o  180o  Figure 2.7.4: Seasonal distributions of wind directions measured at the carbon flux for May 2008 – April 2010. Data are 5-minute vector means binned into 10o segments.  56  PART II: methods  300  20  200  10  100  0  Precipitation (mm)  30  Precipitation (mm/month)  Temperature (oC)  Sunset Tower 2009 Monthly Air Temperature and Precipitation  0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec  Figure 2.7.5: Observed mean air temperatures (solid line is mean air temperature, dashed lines are mean daily maximum and minimum air temperature) at the carbon flux tower during 2009. Precipitation data (bars) were measured during 2009 at Vancouver International Airport, located approximately 10 km south west of the study area.  57  PART II: methods  Knight St.  5453551  5452601  Victoria Dr.  Fraser St.  E 41st Ave  E 49th Ave  0  494290  493340  5451651  200  495240  E 54th Ave  400m  Figure 2.7.6: Map of the long-term integrated flux source area for the carbon flux tower between May 1, 2008 and April 30, 2009 superimposed over the LiDAR / Quickbird derived land-cover map (subset of study area).  58  PART II: methods References: Baccini, P., Brunner P. H. (1991). Metabolism of the anthroposphere. Springer, Berlin. Baldocchi, D. (2008). Breathing of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Australian Journal of Botany, 56, 1-26. Bayer, T. (2009). Automated building simplification using a recursive approach. In Cartography in Central and Eastern Europe (Eds. G. Gartner., & F. Ortag.) Springer: Berlin, 121-146. Buringh, P. (1984). Organic Carbon in Soils of the World. In The Role of Terrestrial Vegetation in the Global Carbon Cycle: Measurement by Remote Sensing (Eds. G. M. Woodwell, 1984 SCOPE). Published by John Wiley & Sons Ltd. Cairns, M.A., Brown, S., Helmer, E.H., & Baumgardner, G.A., 1997. Root biomass allocation in the world’s upland forests. Oecologia 111, 1–11. Chen, B., Black, T.A., Coops, N.C., Hilker, T. Trofymow, J.A., & Morgenstern, K. (2009). Assessing tower flux footprint climatology and scaling between remotely sensed and eddy covariance measurements. Boundary-Layer Meteorol, 130(2), 137-167. Christen, A., Coops, N., Crawford, B., Liss, K., Oke, T. R., & Tooke, R. (2009). The role of soils and lawns in urbanatmosphere exchange of carbon dioxide. 7th International Conference on Urban Climate, Yokahama, Japan, June 29 - July 3, 2009. Christen, A., Crawford, B., Liss K., Siemens, C. (2010). Soil properties at the Vancouver EPiCC experimental sites. EPiCC Technical Report No. 2, 28pp. Retreived on June 20, 2010 from http://www.geog.ubc.ca/~epicc/reports/ Vancouver-EPiCC-Tech-Report-2.pdf Christen, A., Grimmond, C. S. B., Roth, M., & Pardyjak, E. (2009). The IAUC Urban Flux Network - An international network of micrometeorological flux towers in urban ecosystems. Eos Trans. American Geophysical Union, 90(52), Fall Meet. Suppl., Abstract B31C-06 Churkina, G., Brown, D. G., & Keoleian, G. (2009). Carbon stored in human settlements: the conterminous United States. Global Change Biology, 16(1), 135-143 Codoban, N., & Kennedy, A. C. (2008). Metabolism of neighbourhoods. Journal of Urban Planning, D-Asce, 134(1), 21-31. Natural Resources Canada (2009). Communities | Urban Archetypes Project. Retreived in June 20, 2010 from http:// canmetenergy-canmetenergie.nrcan-rncan.gc.ca/eng/ buildings_communities/communities/urban_archetypes_ project.html  City of Vancouver, Traffic and intersection counts as referenced in VanMap. City of Vancouver web-based mapping system (2010). Retreived on June 20, 2010 from http://vancouver.ca/vanmap Crawford, B. Christen, A., Ketler, R. (2010). EPiCC Technical Report 1: Processing and quality control procedures of turbulent flux measurements during the Vancouver EPiCC experiment. Retreived on June 20, 2010 from http:// www.geog.ubc.ca/~epicc/reports/Vancouver-EPiCC-TechReport-1.pdf ecoENERGY, Natural Resources Canada (2007). 2005 Canadian Vehicle Survey Summary Report. Ministry of Community Services, Province of British Columbia (2008), Greenhouse Gas Emission Assessment Guide For British Columbia Local Governments. Goodwin, N.R., Coops, N.C., Tooke, T.R., Christen, A., and Voogt, J.A. (2009). Characterising urban surface cover and structure with airborne LiDAR technology. Canadian Journal of Remote Sensing 35, 297-309. Grimmond, C.S.B., King, T.S., Cropley, F.D., Nowak, D.J., & Souch, C (2002). Local-scale fluxes of carbon dioxide in urban environments: methodological challenges and results from Chicago. Environmental Pollution, 116, S243-S254. Haltrecht, D., & Fraser, K. (1997). Validation of HOT2000 using HERS BESTEST. IBPSA, Natural Resources Canada’s CanmetENERGY. Retreived on June 20, 2010 from http:// www.ibpsa.org/proceedings/BS1997/BS97_P009.pdf Heiple, S., & Sailor, D. (2008). Using building energy simulation and geospatial modeling techniques to determine high-resolution building sector energy consumption profiles. Energy and Buildings, 40(8), 14261436. Keoleian, G. A., Blanchard, S., & Reppe, P. (2000). Life-cycle energy, costs, and strategies for improving a single-family house. Journal of Industrial Ecology, 4, 135–156. Kormann, R., & Meixner, f. X. (2001). An analytical footprint model for non-neutral stratification. Boundary-Layer Meteorology 99, 207-224. Liss, K., Crawford, B., Jassal, R., Siemens, C., & Christen A. (2009). Soil respiration in suburban lawns and its response to varying management and irrigation regimes. Proc. of the AMS Eighth Conference on the Urban Environment, Phoenix, AZ, January 11-15, 2009. Liss, K., Tooke, R., Coops, N., & Christen, A., (2010). Vegetation Characteristics at the Vancouver EPiCC experimental sites. EPiCC Technical Report No. 3, 38 pp. Retreived on June 20, 2010 from http://www.geog.ubc. ca/~epicc/reports/Vancouver-EPiCC-Tech-Report-3.pdf  59  PART II: methods Ministry of Transportation, Province of British Columbia and Greater Vancouver Transportation Authority (TransLink) (no date), Greater Vancouver Trip Diary Survey, 2004  Sibson, R. (1981). A brief description of natural neighbourhood interpolation. In Interpreting Multivariate Data (Ed. Barnett, V.). Wiley: Chichester, 21–53.  Moore, C.J. (1986). Frequency response corrections for eddy correlation systems. Boundary-­Layer Meteorology, 37, 17-­35.  Statistics Canada (2004). Food statistics’ Catalogue No. 21–020-XIE, Vol., 4, No. 1. Agriculture Division, Ottawa.  Moriwaki, R., & Kandaz, M. (2004). Seasonal and diurnal fluxes of radiation, heat, water vapor, and carbon dioxide over a suburban area. Journal of Applied Meteorology, 43, 1700-1710. Nowak, D. J. (1996). Estimating leaf area and leaf biomass of open-grown deciduous urban trees. Forest Sciences, 42(4), pp. 504-507 Ogren, E., & Evans, J. R. (1993). Photosynthetic lightresponse curves: The influence of CO2 partial pressure and leaf inversion. Planta, 189, 182-190. Reid, K. H., & Steyn, D. G. (1997). Diurnal variations of boundary-layer carbon dioxide in a coastal city - Observations and comparison with model results. Atmospheric Environment, 31(18), 3101-3114.  Tooke, R., Coops, N.C., Goodwin, N.R., & Voogt J.A. (2009). The influence of Vegetation Characteristics on Spectral Mixture Analysis in an Urban Environment. Remote Sensing of Environment, 113, 398-407 Walsh, C.J. (2005). Fluxes of radiation, energy, and carbon dioxide over a suburban area of Vancouver. BC. M.Sc. Thesis, Department of Geography, University of British Columbia. Webb, E.K., Pearman, G. I., & Leuning , R. (1980). Correction of flux measurement for density effects due to heat and water vapour transfer. Quarterly Journal of the Royal Meteorological Society, 106, 85-­100.  Prairie, Y. T., & Duarte C. M. (2006). Direct and indirect metabolic CO2 release by humanity. Biogeosciences Discussion, 3, 1781-1789. Province of British Columbia (2008). BC GHG Emissions Assessment Guide. Retreived on June 20, 2010 from http:// www.townsfortomorrow.gov.bc.ca/docs/ghg_assessment_ guidebook_feb_2008.pdf Sambridge, M., Braun, J., & McQueen, H. (1995). Geophysical parametrization and interpolation of irregular data using natural neighbours. Geophysical Journal International 122, 837-857. Schmid, H. P. (1994). Source Areas for Scalars and Scalar Fluxes. Boundary-Layer Meteorology, 67, 293−318. Tropea, C., Yarin A. L., & Foss J. F. (2007). Springer Handbook of Experimental Fluid Mechanics. Springer, Berlin, 1557 p.  60  PART III: results 3.1  Carbon Emissions from Buildings  The total modelled carbon flux attributable to  This section describes modelled results of energy  buildings was 2.58 kg C m-2 (see section 2.3).  and carbon emissions attributable to the building  Locally emitted carbon made up the majority of  urban metabolism component.  this total (2.46 kg C m-2) due to the large number of detached homes heated by natural gas. Table  3.1.1	 Carbon emissions from buildings  3.1.1 lists the fluxes for the entire study area and  The energy intensity of residential sub-types ranged  the different sectors. Building carbon emissions  from 517-1017 MJ m of finished floor area. These  are estimated to total approximately 40% of all  values compare relatively well with canmetENERGY  modelled LOCAL emissions (46% of all local fossil  urban archetypes project, although energy totals  fuel emissions).  -2  for single family dwellings post 1965 were slightly larger than expected (modelled = 596-804 MJ m-2, canmetENERGY urban archetypes = 434-812 MJ m-2). This is in part due to the increased occupancy modelled in each sub-type when compared to the urban archetypes study.  Table 3.1.1 Modelled building carbon emissions per area  All Sectors (kg C m-2 year-1)  NE (kg C m-2 year-1)  SE (kg C m-2 year-1)  Local  2.46  2.56  External  0.11  0.11  Total  2.58  2.67  SW (kg C m-2 year-1)  NW (kg C m-2 year-1)  2.40  2.77  2.14  0.11  0.11  0.10  2.52  2.89  2.24  Table 3.1.2 Modelled building carbon emissions per capita  All Sectors Total Population Total (kg C cap-1 year-1)  NE  SE  SW  NW  23,168  6,430  5,493  6,925  4,319  385  359  396  362  445 61  PART III: results 3.1.2	 Land use and modelled building carbon emissions  As expected energy and carbon emissions intensity  Table 3.1.3 summarizes building carbon fluxes  emissions were found in detached dwellings  according to land use. The largest emissions were  followed by attached and stacked dwelling types  found in the SW sector which contains a small  (Table 3.1.5). It is clear a large proportion of  amount of green space and a larger built fraction of  emissions reduction potential exists in retrofitting  0.214 (Table 3.1.3). Emissions from non-residential  older detached dwellings, where air leakage,  buildings were significantly less than residential  poor insulation and inefficient heating systems  emissions and relatively constant among sectors  all contribute to large carbon intensities. This  due to commercial strips found along Fraser street  is highlighted by a new construction rate of  and Victoria drive. The proportion of emissions  approximately 1.04% year-1 (calculated from  from commercial buildings attributable to local  BC-Assessment data) and the fact new construction  sources was 89% due to the larger electricity share  is typically built to national performance standards.  compared to SFD.  For an extended survey on energy reduction  decreased with typology intensity. The highest  potential in existing building stocks see Harvey, 2009.  Table 3.1.3: Fraction built area (plan area occupied by buildings) of study area quadrants.  All Sectors Built Fraction (%)  19.2  NE  SE 19.5  SW 19.0  NW 21.4  17.0  Table 3.1.4: Modelled building carbon emissions for Residential and ‘Other’ land use in study area quadrants.  All Sectors (kg C m-2 year-1)  NE (kg C m-2 year-1)  SE (kg C m-2 year-1)  SW (kg C m-2 year-1)  NW (kg C m-2 year-1)  Residential Local  2.15  2.28  1.98  2.58  1.75  Residential External  0.07  0.08  0.06  0.09  0.06  Other Local  0.32  0.27  0.42  0.20  0.39  Other External  0.04  0.03  0.05  0.02  0.05 62  PART III: results 3.1.3	 Carbon Storage in Buildings Organic carbon is stored in buildings in substantial amounts, and it is not relevant in terms of emissions since it is not released unless the building is removed or catches on fire. The amount of carbon stored in buildings at Sunset was approximately three times combined storage in vegetation and soils. This carbon is incorporated in the building’s structure, but also in furniture and books (Churkina, 2010). The carbon pool in buildings is estimated to be 13.06 kg C m-2, which includes residential and non-residential building structures and furniture.  Table 3.1.5: Modelled building carbon emissions by sub-type  Fraction of Built Volume  Energy Intensity (MJ m-2 floor area year-1)  SFD (pre1965)  21.0  1017  2.95  SFD (1965-1990)  34.0  804  2.43  SFD (post 1990)  21.1  596  1.69  Duplex  1.5  598  1.61  Row  0.2  580  1.55  Apartment  4.6  517  1.03  Mixed-use  3.2  739  1.48  Extended Care  0.2  1367  2.73  Warehouse  0.1  232  0.46  Civic  6.8  870  1.73  Commercial  2.2  904  1.80  Office  1.1  665  1.33  Retail  4.1  905  1.80  Carbon Intensity (kg C m-3 year-1)  63  PART III: results (b) Residential  (b) Commercial and institutional  5453551  5453551  10 kg C m-2 year-1  E 41st Ave  9  Memorial S. Park  Tecumseh Park  Victoria Dr.  Knight St.  Fraser St.  8 7 6  Tower  5452601  5452601  E 49th Ave  Tower  5 4  Gordon Park  3 2 1  E 54th Ave  0  200  400 m  495240  0 494290  493340  5451651  495240  494290  493340  5451651  Emissions from buildings  N  5453551  Figure 3.1.1: (a) Modelled local residential carbon emissions (b) Modelled local commercial carbon emissions  Emissions from buildings  E 41st Ave  5453551 E 41st Ave  9  Memorial S. Park  Tecumseh Park  Victoria Dr.  Knight St.  Fraser St.  8 5452601  Memorial S. Park  7  Tower E 49th Ave  6  Tower  5452601  Fraser St.  10 kg C m-2 year-1  5  E 49th Ave  4 Gordon Park  3 2  E 54th Ave  400 m  N  495240  494290  493340  Figure 3.1.2:Modelled total carbon emissions attributable to buildings  200  1 0  5451651  0  494290  493340  5451651  E 54th Ave  0  200  400 m  N 64  PART III: results 3.2	 Carbon Emissions from Transportation  concentrate along Knight Street, the highest volume arterial with the greatest share of heavy freight trips. In descending order (greatest to lowest  This section describes results from calculations of  carbon emissions concentration):  carbon emissions attributable to the transportation urban metabolism component.  As arterial roads and associated traffic volumes and carbon emissions are distributed asymmetrically  3.2.1	 Carbon Emissions from Transportation  in study area quadrants (two arterials in the NW quadrant; three arterials one of which is Knight  Methods described in Section 2.4 yield estimated  Street in the NE; three arterials, one of which is  annual carbon emissions attributable to  East 49th Avenue, in the SW and 4 arterials two of  transportation sources of 11.04 tC or 3.06 kg C m-2  which are Knight Street and East 49th Avenue in  or 477.37 kg C cap-1 within the study area. These  the SE) — See Table 3.2.3. In addition as the two  emissions derive from the following types of travel  highest volume arterial roads are in the SE quadrant  and vehicles.  within 130m of the carbon flux tower at the centre of the study area, they appear to create an over  Spatially these emissions distribute along arterial  representation of measured carbon emissions in  and local roads as summarized in Table 3.2.2  that quadrant.  and illustrated in Figure 3.2.1. Most emissions  Table 3.2.1: Annual carbon emissions attributable to vehicles by type  Total: t C year-1 All Vehicles  Per area: kg C m-2 year-1 3.06kg  Per Capita: kg C m-2 year-1 477.37kg  All Light Vehicles  9,052t / 82%  Non-local Light Vehicles  7,826t / 71%  Local Light Vehicles  1,226t / 11%  53.0kg  445t / 4%  19.33kg  Transit Vehicles All Freight Vehicles  1,547t / 14% 65  PART III: results Table 3.2.3: Annual carbon emissions distributed by quadrant.  All Sectors  NE  SE  SW  NW  All Transportation Emissions per Area / Sector (t C year-1)  10,604  3,041  3,899  2,157  1,507  All Traffic Transportation (Local + Through) Emissions per Unit Ground Area (kg C m-2 year-1)  2.93  3.37  4.32  2.39  1.67  Local (kg C m-2 year-1)  0.31  0.36  0.47  0.28  0.20  Through (kg C m-2 year-1)  2.62  3.01  3.85  2.11  1.47  Emissions from transportation 5453551  Emissions from transportation  E 41st Ave  5453551  50 kg C m-2 year-1 E 41st Ave  Fraser St.  45  Memorial S. Park  Memorial S. Park  Tecumseh Park  Victoria Dr.  Knight St.  Fraser St.  40 5452601  Tower E 49th Ave  30  Tower  5452601  35  25  E 49th Ave  20 Gordon Park  15 10  E 54th Ave  400 m  N  495240  494290  493340  Figure 3.2.1: Annual carbon emissions attributable to transportation 50m rasters  200  5 0  5451651  0  494290  493340  5451651  E 54th Ave  0  200  400 m  N 66  PART III: results 3.3	 Carbon Emissions from the Human Metabolism, Food and Waste  However, it is interesting to note that the human  This section describes results from calculations  urban ecosystem in terms of throughput. With an  of carbon emissions attributable to the human  average carbon input / output of 106 kg C cap-1  metabolism.  year-1, the statistical turnover rate of carbon in the  body is also the most active carbon pool in the  human body is only 31 days.  3.3.1	 Carbon storage in the human body Carbon storage in the human body was calculated  3.3.2	 Emissions by human respiration  based on Section 2.5.2 as an average of 9 kg C  The carbon emission through human respiration  cap-1. Table 3.3.1 lists the carbon storage in the  was calculated based on Section 2.5.3. Table 3.5.2  human body per ground area. It is clear when  lists calculated values for the entire study area  comparing carbon stored in the human body to  and the different sectors. The average carbon flux  carbon stored in buildings, soils and vegetation, that  density in the study area was estimated 0.49 kg  the human body is a negligible small pool. Carbon  C year-1 m-2. This is in a similar magnitude as flux  stored in pets and animals is expected to be even  densities reported by Matese et al. (2009) for the  smaller, and is therefore not considered in this  city centre of Firenze, Italy that were 0.35 kg C  study.  m-2 year-1. With the high population density in this neighbourhood, human respiration is responsible for about 8% of the carbon dioxide emissions in  Table 3.3.1: Population, population density and calculated carbon storage in the human body for the study area.  All Sectors Total Population in Area / Sector  NE  SE  SW  NW  23168  6430  5493  6925  4319  Population Density (Inh. ha-1)  64.2  71.3  60.9  76.7  47.9  Total Carbon Stored in the Human Body (t)  209  58  49  62  39  0.058  0.064  0.055  0.069  0.043  Carbon stored in the Human Body / Unit Ground Area (kg C m-2)  Table 3.3.2. Calculated respiratory release of carbon totals and per unit ground area  All Sectors  NE  SE  SW  NW  Total Carbon Released in Area / Sector (t C year-1)  1767  491  419  528  330  Carbon Released by Human Respiration per Unit Ground Area (kg C m-2 year-1)  0.49  0.54  0.46  0.59  0.37 67  PART III: results the neighbourhood (see Section 3.5). This makes  that is emitted.  human respiration from an ecological viewpoint a relevant process. In comparison, Moriwaki and  3.3.3	 Lateral fluxes of carbon in food and waste  Kanda (2004) found for a densely populated  Lateral fluxes (imports / exports) in this component  Japanese residential neighbourhood that 38% of  were calculated based on the per capita fluxes  the total carbon emissions in summer and 17% of  discussed in Section 2.5.4 and 2.5.5 and are  that in winter were caused by human respiration.  listed in Table 3.3.3. Those lateral fluxes are not  However, human respiration should not be  emitted to the atmosphere although production and  overstated in emission reduction strategies, since  distribution of food as well as waste decomposition  it cannot be changed and as it is renewable carbon  / management will cause carbon emissions outside the neighbourhood (not included in this study).  Emissions from human respiratio 5453551  Emissions from human respiration  E 41st Ave  5453551 E 41st Ave  Memorial S. Park  Tecumseh Park  Victoria Dr.  Knight St.  Fraser St.  4  Fraser St.  5 kg C m-2 year-1  Memorial S. Park  Tower  5452601  E 49th Ave  3  Tower  5452601  E 49th Ave  2 Gordon Park  1  E 54th Ave  E 54th Ave  494290  493340  Figure 3.3.1: Calculated respiratory release of carbon based on a 50m raster.  200  400 m  N  495240  0  5451651  0  494290  493340  5451651  0  200  400 m  N 68  PART III: results Table 3.3.3. Calculated lateral fluxes of carbon related to carbon contained in food and waste.  All Sectors  NE  SE  SW  NW  Carbon Imported through Food per Unit Ground Area (kg C m-2 year-1)  0.97  1.08  0.92  1.16  0.73  Carbon Consumed by Humans per Unit Ground Area (kg C m-2 year-1)  0.68  0.76  0.65  0.81  0.51  Carbon Exported by Food Waste kg C m-2 year-1)  0.29  0.32  0.28  0.35  0.22  Carbon Exported by Human Waste kg C m-2 year-1)  0.19  0.21  0.18  0.23  0.14  69  PART III: results 3.4	 Carbon Emissions from and Uptake by Soils and Vegetation  The carbon storage in vegetation biomass in the study area was calculated separately for lawns (0.13  This section reports calculated carbon storage and  kg C m-2) and trees (1.42 kg C m-2). The sum of  emissions from urban soils and vegetation and  both is listed as Cv,n in Table 3.4.2. The NW sector  uptake by vegetation in the study area. It reports  shows highest Cv,n values, as it includes Memorial  net emissions and carbon sequestration by urban  South Park with 30 m tall deciduous trees that  vegetation.  contribute substantially to the neighbourhood’s above-ground biomass (Figure 3.4.1).  3.4.1	 Carbon Storage in Soils and Vegetation  The detailed allocation of carbon stored in different  For one square meter pervious surface the measured carbon content in the soil was Cs = 9.65 kg C m-2 (see Section 2.6.1). This is within the  vegetation parts is listed in Table 3.4.3 based on methods described in Section 2.6.1. The calculated carbon stored in vegetation biomass in the Sunset  expected range of 3.5-14 kg C m-2 reported for  study area is only half the average value reported  urban soils in Pouyat et al. (2002) based on data from five different cities in the US. If Cs is multiplied by the vegetated pervious surface fraction (35.7%, see Table 3.4.1 and Figure 3.2.1), we estimate the neighbourhood-wide average carbon storage in soils  Cs,n = 3.45 kg C m-2.  for carbon storage in urban trees of US NorthWestern cities (3.2 kg m-2), for which an average of 32.7% tree cover is reported (Nowak and Carne, 2002). This is realistic, as the study area shows only 11.3% tree cover, and a relatively low to mediumsized street tree population compared to other Vancouver neighbourhoods.  Table 3.4.1. Surface area covered by ground vegetation (lawns) and tall vegetations (trees > 2m). Land-cover was calculated on a 1 x 1 m raster using LiDAR and optical remote sensing data (Section 2.2)  Table 3.4.1: Surface area covered by ground vegetation (lawns) and tall vegetations (trees > 2m). Land-cover was calculated on a 1 x 1 m raster using LiDAR and optical remote sensing data (Section 2.2)  All Sectors  NE  SE  SW  NW  Surface Cover Fraction of Ground Vegetation (Lawns)  24.4%  20.4%  26.0%  21.7%  29.4%  Surface Cover Fraction of Trees  11.3%  11.2%  9.7%  10.4%  13.4%  Total Vegetated Surface Cover  35.7%  31.6%  35.7%  32.0%  43.2% 70  PART III: results 3.4.2	 Laterally exported carbon through maintenance biomass removal  clippings), and by autumnal removal of leaves /  The annual lateral export (flux) of carbon Fexp  of all leaves are removed). Unaccounted are the  is calculated to be 0.07 g C m year . This  export and import due to gardening activities (e.g.  -2  -1  number is the sum of carbon exported by tree  litter (0.04 g C m-2 year-1, assuming two thirds  mulching, topsoil). Carbon exported laterally can  removal / maintenance / prunning (0.01 g C m  be released (decomposed) outside the system  year-1), removal of lawn clippings (0.02 g C m-2  (composting) or sequestered in landfills.  -2  year  -1  assuming that 35% of all properties export  Table 3.4.2:. Measured and/or estimated carbon pools in soils and vegetation biomass in the Vancouver-Sunset neighbourhood for all four sectors separately and the full study area.  All Sectors (kg C m-2)  NE (kg C m-2)  SE (kg C m-2)  SW (kg C m-2)  NW (kg C m-2)  Carbon Stored in Soils (Cs,n)  3.43  3.04  3.44  3.08  4.17  Carbon Stored in Vegetation (Cv,n)  1.55  1.36  1.28  1.21  2.35  Total  4.98  4.40  4.72  4.29  6.52 71  PART III: results (a)  (b)  Ground Vegetation  Trees  5453551  Ov  5453551  1.0  E 41st Ave  Memorial S. Park  Tecumseh Park  Victoria Dr.  Knight St.  Fraser St.  0.8  0.6  Tower  5452601  Tower  5452601  E 49th Ave  0.4 Gordon Park  0.2  (c)  (d)  Ground Vegetation  Trees  5453551  495240  494290  0.0  5451651  493340  494290  493340  495240  E 54th Ave  5451651  LAI (m2/m2)  5453551  10  8  6 Tower  5452601  5452601  Tower  4  0  200  400 m  495240  0 494290  5451651  493340  494290  493340  5451651  495240  2  N  Figure 3.4.1: Maps of surface area covered by (a) ground vegetation (lawns) and (b) tall vegetations (trees > 2m), (c) leaf area index of lawns, and (d) leaf area index of trees based on remote sensing data.  72  PART III: results 3.4.3	 Carbon Emissions  The neighbourhood-wide emission due to soil and  Carbon emissions by respiration were separately  lawn respiration were calculated as 0.33 g C m-2  calculated for the process of autotrophic and  year-1 (Table 3.4.4). This value includes autotrophic  heterotrophic soil respiration (Rsoil+lawn), and  respiration of lawns and tree roots. Values are  autotrophic above-ground leaf and bole respiration  higher for the SE and NW sectors which contain  of trees (Rtree). The detailed methodology is  extensive sport-fields and park areas (Memorial  described in Section 2.6.2. Respiration from  South Park) (Figure 3.4.2a).  bushes and other low ornamental plants were not considered.  Table 3.4.3: Estimated carbon pools in different parts of the urban vegetation in the Vancouver-Sunset neighbourhood for the full study area.  Carbon Pool  Carbon Storage (kg C m-2)  Above-ground Tree (Woody) Biomass  1.03  Below-ground Tree (Root) Biomass  0.32  Leaf Biomass (Broadleaf Trees)  0.06  Needle Biomass (Coniferous Trees)  0.01  Total in Tree Biomass  1.42  Lawn Biomass  0.3  All Vegetation Components  1.55  73  PART III: results (a)  (b)  Respiration of soils and lawns (RSoil+Lawn)  Above-ground respiration of trees (RTree)  5453551  kg C m-2 year-1  5453551  1.0  E 41st Ave  Memorial S. Park  Tecumseh Park  Victoria Dr.  Knight St.  Fraser St.  0.8  0.6  Tower  5452601  Tower  5452601  E 49th Ave  0.4 Gordon Park  0.2  (c)  495240  494290  0.0  5451651  493340  494290  493340  495240  E 54th Ave  5451651  (d)  Photosynthesis of lawns (PLawn)  Photosynthesis of trees (PTree)  5453551  kg C m-2 year-1  5453551  -3.0  -2.5  -2.0 Tower  5452601  5452601  Tower  -1.5  -1.0  0  200  400 m  495240  494290  5451651  493340  494290  493340  5451651  495240  -0.5  0.0  N  Figure 3.4.2: Maps of modelled (a) soil and lawn respiration, (b) above ground respiration, (c) photosynthesis by lawns and (d) photosynthesis of trees on the 50 x 50 m raster.  74  PART III: results The calculation is based on closed-chamber  detailed methodology is described in Section 2.6.3.  measurements in the study area, which indicate  Photosynthesis of small bushes. garden plots, and  that emissions peak during summer and fall months,  ornamental plants was not separately considered  and are strongly controlled by temperature and  but modelled similar to lawns. Lawns are the  irrigation. Extensive and regular lawn irrigation was  predominant vegetated ground cover.  shown to control annual soil respiration by a factor two. Lawn respiration ranged between 0.81 kg C  Lawns cover 24.4% of the study plan area whereas  m (lawn) year for properties with no irrigation  trees only 11.2% (Table 3.4.2). Photosynthesis  and 1.79 kg C m-2 (lawn) year-1 for properties with  of lawns was calculated -0.48 kg C m-2 year-1 and  regular and extensive lawn sprinkling in the city  photosynthesis of trees was -0.28 kg C m-2 year-1  of Vancouver (Liss et al., 2009). However, it is  (Table 3.4.5). The NW sector with Memorial South  important to note that although lawn sprinkling  Park shows highest uptake by photosynthesis. For  reduction / regulations would reduce carbon  example, the average Plawn + Ptree of Memorial Park  emissions through Rsoil+lawn, such restriction  is modelled -1.45 kg C m-2 year-1 due to tall and  would also reduce carbon sequestration by lawn  mature freestanding trees and extensive, irrigated  photosynthesis (see section 3.4.4).  lawn surfaces (Figure 3.4.2c and d).  Autotropic above-ground respiration was modelled  It should be noted that the regular clipping of  based on leaf area. Emissions are smaller (0.05 kg  lawns increases the growth rate, and therefore  C m year ) compared to below-ground and lawn  significantly increases lawn photosynthesis  respiration (Table 3.4.4 and Figure 3.4.2b).  compared to photosynthesis of trees, but radiation  -2  -1  -2  -1  at ground level is reduced by shading (building,  3.4.4	 Carbon Uptake  trees), so lawns do not necessarily result in more  Carbon uptake by photosynthesis was calculated  efficient carbon sequestration. On average, carbon  separately for lawns (Plawn) and included private  is cycled through lawn (plant) biomass in only 226  properties, sport fields, public parks, and trees  days, whereas carbon taken up by trees resides in  (Ptree). Ptree incorporates street and private trees. The  tree biomass on average for 5 years.  Table 3.4.4:. Modelled respiration separately for soils / lawns and above-ground vegetation of trees.  All Sectors (kg C m-2 year-1)  NE (kg C m-2 year-1)  SE (kg C m-2 year-1)  SW (kg C m-2 year-1)  NW (kg C m-2 year-1)  Soil and Lawn Respiration (Rsoil+lawn)  0.28  0.24  0.30  0.25  0.34  Autotrophic Above Ground Respiration of Trees (Rtree)  0.05  0.04  0.04  0.03  0.08  Total (Rsoil+lawn + Rtree)  0.33  0.28  0.34  0.28  0.42  75  PART III: results 3.4.5	 Net emissions by the urban biosphere  m-2 year-1 which is the difference of the estimated  The total net emissions between urban soils,  and 0.07 kg C m-2 year-1 lateral export Fexp (Section  vegetation and the atmosphere, Fnet (net ecosystem  3.4.2).  Fnet = -0.16 kg C m-2 year-1 net uptake (Table 3.4.5)  exchange) was calculated by adding all components in direct exchange with the atmosphere, i.e.  This corresponds to an accumulation rate of  Fnet = Rsoil+lawn + Rtree + Plawn + Ptree  currently 1.8% per year of the total carbon  Table 3.4.6 lists the net emissions for the study  stored in the soil and vegetation pool. The carbon  area and all four sectors, and the negative values  sequestered is expected to accumulate dominantly  indicate that there is a net uptake of carbon in  in tree biomass and soils. Figure 3.4.4 visualizes  the order of -0.16 kg C m-2 year-1. Figure 3.4.3  the vegetation growth in the area over the last  shows the 50 x 50 m raster map of calculated net  30 years. It shows photos taken at exactly the  emissions, which can reach values up to -0.51 kg C  same location with obvious changes in tree volume  m year in areas of Memorial South Park.  from the 1980s to 2009. Also soils are expected  -2  -1  to accumulate carbon over time. One of the few  3.4.6	 Carbon Sequestration by the Urban Biosphere  studies that measured organic carbon in urban soils  Carbon sequestration in the study area by trees,  and land-use increased and accumulated carbon  lawns and soils was calculated by  in soil after an initial decrease due to development  after urbanization showed that urban gardening  Sequestration = Fnet - Fexp  (Golubiewski, 2006).  The urban ecosystem pool is expected to accumulate carbon (sequester carbon) at 0.09 kg C  Table 3.4.5: Modelled photosynthesis separately lawns and trees.  All Sectors (kg C m-2 year-1) Lawn (Plawn)  NE (kg C m-2 year-1)  SE (kg C m-2 year-1)  SW (kg C m-2 year-1)  -0.17  -0.24  -0.18  -0.21  NW (kg C m-2 year-1) -0.26  Tree (Ptree)  -0.28  -0.27  -0.22  -0.22  -0.39  Total (Plawn + Ptree)  -0.49  -0.44  -0.46  -0.40  -0.65  Table 3.4.6: Modelled net emissions from vegetation and soils  Total  All Sectors (kg C m-2 year-1)  NE (kg C m-2 year-1)  -0.28  -0.27  SE (kg C m-2 year-1) -0.23  SW (kg C m-2 year-1) -0.23  NW (kg C m-2 year-1) -0.37  76  PART III: results In summary, the urban biosphere sequesters carbon at 0.09 kg C m-2 year-1. This value is compared to measured values over grassland and forests in the Vancouver area in Table 3.4.7. It shows that because only 36% of the urban surface is vegetated, sequestration is significantly less to that of unmanaged grassland or even a mature forest. The urban vegetation sequesters 1.4% of the local emissions in this neighbourhood (1.7% of fossil fuel emissions).  Net-emissions from urban vegeta 5453551  Net-emissions from urban vegetation  E 41st Ave  5453551  -1.6 kg C m-2 year-1 Fraser St.  E 41st Ave  Memorial S. Park  Tecumseh Park  Victoria Dr.  Knight St.  Fraser St.  -1.4 -1.2 5452601  Tower  5452601  Memorial S. Park  Tower E 49th Ave  -1.0 -0.8  E 49th Ave  -0.6 Gordon Park  -0.4  494290  493340  5451651  E 54th Ave  -0.2 0.0 +0.1  494290  493340  5451651  495240  E 54th Ave  0  200  Figure 3.4.3:. Map of modelled net emissions (negative values mean uptake) on a 50 by 50 m raster for the study area.  0  200  400 m  N  400 m  N 77  PART III: results  Figure 3.4.4: Vegetation growth between the 1980s and 2009 (Source: UBC Geography photo archive).  Table 3.4.7: Comparison of annual carbon sequestration by urban vegetation in the study area to annual carbon sequestration by other ecosystems (grassland, forest) in the Vancouver area.  Site  Land Use  Annual Carbon Sequestration (kg C m-2 year-1)  Source  Vancouver-sunset, BC  Urban  -0.09  This Study (Modelled)  Westham Island, Delta, BC  Unmanaged Grassland -0.25  EPiCC Network, 2008 (Measured, Unpublished)  Campbell River, BC  Mature West coast Forest (60 Years Old)  Canadian Carbon Program (Measured, Krishnan et al., 2009)  -0.36  78  PART III: results 3.5  Integrated Modelled Carbon Cycle  South Park.  This section discusses the integrated emissions,  Out of all carbon emissions (renewable and fossil  which were calculated by adding all four  fuel), 2.47 kg C m-2 year-1 (40%) are originating  components (buildings, transportation, human  from buildings, 2.93 kg C m-2 year-1 (47%) from  metabolism, and vegetation/soils) in the study  transportation, 0.33 kg C m-2 year-1 (5%) from  area. We compare the magnitude of the different  human respiration and 0.48 kg C m-2 year-1 (8%)  components and link them to the local carbon cycle  from respiration of soil and vegetation. Those are  at the neighbourhood-scale by constructing a full  offset by an annual uptake of -0.49 kg C m-2 year-1  mass balance including lateral fluxes and pools of  (-9% of emissions) though photosynthesis of urban  carbon.  vegetation (lawns and trees). Out of the local fossil fuel emissions in the study  3.5.1	 Integrated Per-area Carbon Emissions  area, 46% originate from the building sector,  The different local emission components (buildings, transportation, human metabolism, and vegetation/ soils) are shown for each of the four quadrants in Table 3.5.1 and Figure 3.5.1. The SE sector shows highest integral emissions, which is explained by the higher fraction of arterial roads, and hence increased transportation emissions (see Section 3.2). Inversely, the NW sector has lowest emissions because only two arterial road segments are included (41st Ave. and Fraser St.) and building / population density is slightly lower due to Memorial  whereas 54% is from the transportation sector. 51% of all local fossil fuel emissions are of local origin, 49% are from through traffic. It is worth stating that numbers listed in Table 3.5.1 include only local emissions due to local activities and local emissions due to external activities (see Figure 1.3.2). When incorporating external emissions due to local activities (electricity consumption, food production, waste), emissions will be higher (as discussed in Section 3.1).  Table 3.5.1: Comparison of the carbon emissions and uptake from all different components and integrated emissions (total) for the complete study area and the different sectors.  All Sector (kg C m-2 year-1)  NE (kg C m-2 year-1)  Buildings  2.47  2.56  Transportation  2.93  Human Metabolism, Food & Waste  Vegetation & Soils  SE (kg C m-2 year-1)  SW (kg C m-2 year-1)  NW (kg C m-2 year-1)  2.41  2.78  2.13  3.37  4.31  2.39  1.67  0.49  0.54  0.46  0.59  0.37  0.33  0.28  0.34  0.29  0.42  -0.40  -0.65  5.64  3.93  Emissions  Uptake Vegetation & Soils  -0.49  -0.44  -0.46 TOTAL  5.74  6.31  7.07  79  PART III: results However as those emissions are not happening on  soil) are shown in Table 3.5.2. Those numbers have  the neighbourhood scale, and not measured on the  to be interpreted carefully, as total local emissions  tower, they have not been included in the maps.  are not necessarily reflecting residential activities only and include through traffic and commercial  On a spatial scale, for each raster element, all  activities. Further, they do not include activities  four components were summed and the integral  of local residents outside the neighbourhood (see  emissions (or uptake) map is shown in Figure  Sections 1.2 and 1.3 for a detailed discussion of the  3-5-2. It is evident from the map that arterial roads  problem).  are emission ‘hot spots’, where, on a relatively small area, significant emissions are found. Other components (buildings, human metabolism,  3.5.3	 Neighbourhood Carbon Cycle Figure 3.5.4 illustrates the integral carbon cycling  vegetation) are relatively uniformly distributed within the study area, with the exception of parks show dominating carbon uptake.  (fluxes and pools) in the neighbourhood for a mass balance. Numbers denote carbon fluxes in kg C m-2 year-1 or carbon pools in kg C m-2 (in italics). Fluxes leaving the neighbourhood system on top are  3.5.2	 Integrated Per-capita Carbon Emissions  local carbon emissions into the atmosphere minus  Per capita emissions from all sectors (buildings,  see Section 3.5.1). Those fluxes are detectable at  transportation, human metabolism, vegetation /  the carbon flux tower (Section 3.6) as indicated by  Buildings  uptake of atmospheric carbon (5.73 kg C m-2 year-1,  Transportation  Humans  VegetationÊ/ÊSoils  5  Local carbon emission (kg C m-2 year-1)  4.31 4 3.37 3  2.93 2.56  2.47  2.78 2.39  2.41  2.13  2  1  1.67  0.49  0.54  0.46  0.59  0.37  0 -0.11  -0.23 NW  SE  SW  -0.12  -0.16 NE  -1  AllÊSectors  -0.16  Figure 3.5.1: Comparison of the different emission components in the whole study area and for the different sectors. Vegetation shows the net-effect incorporating both, uptake and emissions.  80  PART III: results the line ‘tower measurements’. Fluxes entering the  Of all carbon that is exported laterally, 0.07 kg C m-2  neighbourhood system on the left hand side of the  year-1 (8%) are exported in form of garden waste,  diagram are lateral imports of carbon (6.69 kg C m-2  0.19 kg C m-2 year-1 (22%) in form of human waste,  year-1), and fluxes leaving the neighbourhood on the  0.29 kg C m-2 year-1 (33%) in form of food waste,  right hand side are lateral exports (0.87 kg C m-2  and 0.32 kg C m-2 year-1 (33%) in form of other  year-1). Red and blue arrows are fossil fuel carbon  waste (e.g. woodm plastic, paper). Most carbon  while green, yellow are mostly renewable carbon.  leaving the system laterally is renewable carbon.  Grey arrows denote both, fossil fuel and renewable carbon.  The system’s mass balance of carbon (in kg C m-2 year-1) can then be written as import minus exports  Of all carbon that is imported laterally, 2.47 kg C  (atmospheric net emissions plus lateral export):  6.69 - (5.73 + 0.87) = +0.09  m year (37%) are imported in form of natural -2  -1  gas, 2.93 kg C m-2 year-1 (44%) in form of gasoline or diesel (mostly through vehicles driving into the  Hence, the urban ecosystem in expected to  ‘system’), 0.97 kg C m-2 year-1 (15%) in form of  accumulate +0.09 kg C m-2 year-1 in tree and  food, and 0.22 kg C m-2 year-1 (3%) in form of  soil biomass. Table 3.5.3 shows the different  construction material, paper and plastic. Of all  carbon pools of (renewable) carbon in the study  carbon entering the system, 83% is from fossil fuel  neighbourhood, the sequestration and calculated  sources, and 17% is renewable carbon.  average turnover rates.  Table 3.5.2: Comparison of per-capita carbon emissions for the complete study area and the different sectors.  All Sectors (kg C m-2 year-1)  NE (kg C m-2 year-1)  SE (kg C m-2 year-1)  SW (kg C m-2 year-1)  NW (kg C m-2 year-1)  All Local Emissions  893  886  1162  735  821  Excluding through Traffic  485  462  527  459  510  Excluding Through Traffic but Including Building Related External Emissions  502  477  545  473  531  Table 3.5.3: Comparison of the different carbon pools of renewable carbon in the study neighbourhood and their sequestration and calculated average turnover rates.  Human Body  Lawn Biomass  Total Carbon Stored in Pool (kg C m-2)  0.06  0.13  Current Sequestration Rate (kg C m-2 year-1)  ~0  ~0  30 days  120 days  Turnover Rate  Tree Biomass  Building Structures  Soils  1.42  3.43 -0.09  5 years  13.06 ~0  8 years  90 years  81  Emissions from all sources 5453551  PART III: results E 41st Ave 5453551 E 41st Ave  Fraser St.  50 kg C m-2 year-1 45  Knight St.  Emissions from all sources  Memorial S. Park  Memorial S. Park  Tecumseh Park  Victoria Dr.  Knight St.  Fraser St.  40 35  E 49th Ave  30  Tower  5452601  Tower  5452601  25  E 49th Ave  20 15 E 54th Ave  10  493340  5451651  5  494290  493340  5451651  495240  E 54th Ave  0 -2 0  200  400 m  200  400 m  N  Emissions from all sources  Figure 3.5.2: Map of integrated carbon emissions in the study area with 50 x 50 m resolution. 0  494290  Gordon Park  5453551  N  E 41st Ave  Relative emission contributions 5453551  Tower  5452601  E 49th Ave  n  Tecumseh Park  Memorial S. Park  10  s  0%  ing  ild  E 49th Ave  Tra  Bu  nsp  0%  Tower  5452601  ort a  10  tio  Memorial S. Park  Victoria Dr.  Knight St.  Fraser St.  E 41st Ave  Knight St.  Fraser St.  100 % Respiration  Gordon Park E 54th Ave  494290  493340  5451651  494290  493340  5451651  495240  E 54th Ave  0  200  400 m  Figure 3.5.3: RGB composite map of relative contributions of local emission sources – buildings (red), transportation (blue) and respiration (green) to the total emissions from each raster cell. Raster cells drawn in black are cells with zero emissions. 0  200  400 m  N  N  82  PART III: results Emissions  5.6  0.31  2.62  Other  Local traffic  Through traffic  0.28  0.49  0.05  -0.49 Photosynthesis  0.32  Vegetation -0.16  Human respiration  Soil respiration  2.15 Residential  Transportation 2.93  Above-ground respiration  Buildings 2.47  Tower measurements  Natural Gas 2.47  Vegetation  Diesel and gasoline 2.93  Export  6.7  Import  1.55 Soils  0.9  3.34 Garden waste 0.07  0.06 Human body  Food 0.97  Human waste 0.19 Food waste 0.29  Buildings / households Construction materials 0.15  Other waste 0.32  Paper 0.11  13.06  Plastics 0.06  Neighborhood  1 kg C m-2 year-1  Flux densities in kg C m-2 year-1  1 kg C m-2  Pools in kg C m-2  Figure 3.5.4: Integral modelled carbon cycle (fluxes and pools) in the study neighbourhood. Numbers denote carbon fluxes in kg C m-2 year-1 or carbon pools in kg C m-2. Fluxes leaving the neighbourhood system on top are local carbon emissions into the atmosphere and uptake of atmospheric carbon. Fluxes entering the neighbourhood system on the left hand side of the diagram are lateral imports of carbon, and fluxes leaving the neighbourhood on the right hand side are lateral exports.  83  PART III: results 3.6	 Comparison of Current Model with Direct Carbon Flux Measurements and Consumption Inventoriess  from vegetation is expected, as well as reduced  This section presents the results from two years of  monthly minimum recorded was16.0 g C m-2 day-1  direct flux measurement on the carbon flux tower.  in August during school holidays. The annual  Spatial and temporal patterns in the measured  average determined was 18.4 g C m-2 day-1 which  emissions are discussed. Measurements are then  corresponds to 6.71 kg C m-2 year-1.  motorized traffic load and no space heating, hence lowering overall measured emissions. The  compared to model results.  3.6.1	 Direct carbon flux measurements – temporal and spatial dynamics Monthly carbon (CO2) fluxes were calculated from  sectors (Table 3.6.1). Emissions over the SE sector are highest with an annual total of 13.16 kg C m-2 year-1. This is attributable to emissions along two  the continuous flux measurements on the carbon flux tower over two years from May 1, 2008 – April 30, 2010 according to the procedure described in  arterial roads (49th Ave. / Knight St.) that intersect just 130 m upwind of the instruments to the SE. Lowest emissions are measured over the NW  Section 2.7.3.  sector where no arterial roads are encountered up to a 750m distance from the tower (2.81 kg C m-2  Figure 3.6.1 and Table 3.6.1 summarize monthly  year-1). It is also interesting to note that emissions  average fluxes in g C m day for the average of -2  -1  both years. Largest emissions were recorded in the winter months when space-heating requirements  measured over the NE sector (6.57 kg C m-2 year-1) are higher than those from the SW sector (4.31 kg C m-2 year-1), although both sectors contain  are highest and CO2 uptake from vegetation is minimal (maximum in December with 22.1 g C m-2 day-1). During summer, increased CO2 sequestration  a similar arrangement with one major arterial road. A comparable distribution of emissions was  25 22.1 20.2  15  17.0  16.3  16.8  17.8 16.0  16.4  Sep  18.2  Aug  20.0  Jul  20.9  Jun  20  19.0  10  Dec  Nov  Oct  May  Apr  Mar  0  Feb  5  Jan  Daily measured carbon emission (g C m-2 d-1)  Measured fluxes were separated into four wind  Figure 3.6.1: Monthly average emissions (g C m-2 day-1) measured on the carbon flux tower for all wind directions (equally weighted).  84  PART III: results Table 3.6.1: Monthly average emissions measured on the carbon flux tower. ‘All Wind Sectors’ results are equally weighted averages from the four wind sectors. Numbers highlighted in green are minima, highlighted in red are maxima.  Month  All Wind Sectors (g C m-2 day-1)  January  20.90  18.81  37.41  14.58  12.78  February  20.00  20.32  36.42  13.78  9.48  NE (0-900) (g C m-2 day-1)  SE (90-1800) (g C m-2 day-1)  SW (180-2700) (g C m-2 day-1)  NW (270-3600) (g C m-2 day-1)  March  20.18  19.67  37.13  14.47  9.44  April  18.19  16.50  38.37  11.20  6.67  May  17.01  18.37  35.52  9.50  4.65  June  16.30  16.81  36.66  7.66  4.07  July  16.76  16.98  35.76  10.05  4.25  August  16.00  14.03  36.27  9.42  4.28  September  16.36  16.48  33.94  9.87  5.14  October  17.83  18.38  34.42  11.55  6.96  November  19.04  19.03  34.05  13.21  9.89  December  22.14  20.68  36.86  16.35  14.70  Total (kg C m-2 year-1)  6.71  6.57  13.16  4.31  2.81  Table 3.6.2: Measured weekday and weekend emissions from the SE sector by month.  Month  Weekdays (g C m-2 day-1)  Weekends (g C m-2 day-1)  Difference Weekdays-Weekend (g C m-2 day-1)  Difference Weekday -Weekend (%)  January  39.60  33.61  5.99  15%  February  40.11  31.90  8.21  20%  March  39.17  28.57  10.6  27%  April  40.05  29.76  10.29  26%  May  38.20  28.26  9.94  26%  June  39.52  27.50  12.02  30%  July  38.69  27.23  11.46  30%  August  39.21  29.04  10.17  26%  September  35.79  26.20  9.59  27%  October  36.98  28.63  8.35  23%  November  38.49  26.35  12.14  32%  December  37.76  36.02  1.74  5%  Total  38.63  29.42  9.21  24%  85  PART III: results  Jul  May  Nov  0  Nov  0  Sep  10 Nov  10 Sep  20  Jul  20  May  30  Mar  30  Sep  40  NW 2.8 kg C m-2 yr-1  Jul  SW 4.3 kg C m-2 yr-1  May  40  Mar  0  Mar  0  Nov  10 Sep  10 Jul  20  May  20  Mar  30  Jan  30  SE 13.2 kg C m-2 yr-1  Jan  40  Jan  NE 6.6 kg C m-2 yr-1  Jan  Daily measured carbon emission (g C m-2 d-1)  40  Figure 3.6.2: Visualization of the monthly average emissions (in g C m-2 yr-1) measured from the carbon flux tower for each wind sector separately.  measured by Walsh (2005), who compiled carbon  values during early morning hours, when traffic is  flux measurements on the same tower for the  minimal, but show considerable variation during  year 2001 using independent instrumentation and  daytime. Interesting is that select hours in the  data processing procedures. She reported highest  NW sector around noon in June indicate negative  emissions from SE (9.64 kg C m-2 year-1) and lowest  values, i.e. demonstrate that uptake of carbon by  emissions from NW (2.42 kg C m-2 year-1).  photosynthesis is the dominant effect in this special case.  When stratifying emission measurements into weekends and weekdays (Section 2.7.3), emissions were found always to be lower on weekends for all wind sectors and months, with greatest differences observed for the SE sector (23.8 % weekend reduction, see Tables 3.6.2 and 3.6.4). Figure 3.6.3 illustrates the potential of using carbon flux measurements to help in understanding and quantifying the urban metabolism even over a daily cycle. Shown are hourly values for four selected months (March, June, September and December) and for all four sectors in mg C m-2 s-1. Measured carbon emissions show significant differences between nighttime and daytime. Emissions in all sectors converge to low background  3.6.2	 Modelled Values vs. Carbon Flux Measurements. Tables 3.6.3 to 3.6.5 compare carbon fluxes measured on the tower to model results from Section 3.1 to 3.5. The model results used for the comparison are aggregated in two different ways. In a simple approach (‘Radius aggregation’) all grid cells of the 50m raster that fall within a 400 m radius around the tower are considered and averaged (equally weighted). Those values are then compared to the tower measurements. A 400 m radius was chosen because this corresponds the 50% source area (i.e. more than half of the signal measured at the tower comes from this area, and 86  PART III: results the remainder comes from a larger upwind area).  pattern outisde the study area.  In a more sophisticated aggregation approach,  For the average of all emissions within a 400-m-  called ‘Source-area aggregation’, model values  radius buffer around the tower, the model results  are weighted by the turbulent source area using  agree very well with the measurements, i.e. 6.71  a detailed backward dispresion model, which is  kg C m-2 year-1 measured vs. 6.25 kg C m-2 year-1  described in Section 2.7.3. Source areas were  modelled. The model slightly underestimates actual  calculted for each 30 min step, and summed using  emissions by 0.25 kg C m-2 year-1 (or 4%). With  the same process by which fluxes were aggregated  the actual errors associated with the uncertainty of  (i.e. for each hour of the day and each month of the  the 400 m buffer source area, this is a surprisingly  year). In this approach, modelled emissions on the  successful result. The model results are split up into  50 x 50 m grid were weighted by the spatial source  different components in Table 3.6.4.  -area distribution. A small fraction of the flux (12%) is predicted to originate outside the 1900 x 1900  The different wind sectors within the 400-m-radius  study area. This fraction was assumed to represent  buffer show higher differences, in particular the  the average emission within the entire study area,  SE-sector, where measurements are significantly  assuming that the urban form continues in a similar  higher (13.16 kg C m-2 year-1) than modelled values  Mar  1.00  Jun  (mg C m-2 s-1)  0.75 0.50 0.25 0.00 -0.25  00  06  12 Hour of day  18  0  00  06  18  0  18  0  Dec  Sep  1.00  12 Hour of day  (mg C m-2 s-1)  0.75 0.50 0.25 0.00 -0.25  00  06  NE  12 Hour of day  SE  SW  18  0  00  06  12 Hour of day  NW  Figure 3.6.3: Daily courses of measured emissions on the carbon flux tower.  87  PART III: results areas are based on standard dispersion theory  (10.17 kg C m-2 year-1). This might be an over-  (see Figure 3.6.4) and blend-off to represent the  proportional contribution of traffic-related emissions  diminishing contributions of far-field areas rather  from the near-field (intersection 49th Ave. / Knight  than abruptly and unreaslistically cut-off at 400 m.  St. with traffic lights) where both idling and moving  The source-area aggregated model results are 7.46  vehicles are injecting carbon, but which is not  kg C m-2 year-1 (11% higher than the measured  accounted for in the model. In turn, in the two  6.71 kg C m-2 year-1). Figure 3.6.1a illustrates that  sectors that show arterial road segments without  the source area model combined with the emission  intersections (NE and SW), the model overestimates  model suggests that approximately 70% of all fluxes  emissions. Improving the transportation model and  that were measured at the tower (5.22 kg C m-2  incorporating speed of vehicles in each cell could  year-1) originate from transportation (restricted to  resolve some of those inaccuracies.  a narrow part of Knight and 49th Avenue). 27%  3.6.3 Source-area Aggregation  (2.05 kg C m-2 year-1) originate from buildings,  The source area aggregation is more meaningful  5% from human respiration and -2% (-0.17 kg C  than the 400-m-radius buffer method because  m-2 year-1) is offset by vegetation. Although in the  a significant portion of the measured emissions  entire study area, emissions from buildings and  originate beyond the 400 m boundary. The source  transportation are of approximately equal strength  Table 3.6.3: Comparison of modelled and measured carbon emissions for a 400-m-radius buffer around the tower  All Sector  NE  SE  SW  NW  Carbon flux Density Measured (kg C m-2 year-1)  6.71  6.57  13.16  4.31  2.81  Carbon Flux Density Modelled (kg C m-2 year-1) and aggregated for a 400-m-radius buffer  6.46  7.62  10.17  5.81  2.27  Difference modelled (kg C m-2 year-1)  0.25  1.05  2.99  1.50  0.54  Difference Percentage  4%  16%  23%  35%  19%  Table 3.6.4: Modelled carbon emissions for a 400m buffer around the tower. The total is compared in Table 3.6.3 against tower flux data.  All Sectors (kg C m-2 year-1)  NE (kg C m-2 year-1)  SE (kg C m-2 year-1)  SW (kg C m-2 year-1)  NW (kg C m-2 year-1)  Buildings  2.40  2.06  2.67  3.03  1.82  Transportation  3.76  5.29  7.13  2.31  0.35  Human Metabolism, Food & Waste  0.47  0.45  0.51  0.61  0.29  Vegetation & Soils  -0.16  -0.18  -0.14  -0.15  -0.19  TOTAL  6.46  7.62  10.17  5.81  2.27 88  PART III: results (40% and 47% of all emissions respectively),  Figure 3.6.4: (a) Long-term integrated turbulent  the specific location of the tower close to an  source area and (b) relative CO2 flux contribution  intersection makes the signal from transportation  (Product of Figure 3.6.2 and 3.6.4a).  in the tower signal more relevant. Interestingly, the sector where transportation is small (NW) shows  Earlier, annual measured weekend in net emissions  the best agreement between tower and model (2.81  were demonstrated to be 24% less than weekday  kg C m-2 year-1 measured vs. 2.87 kg C m-2 year-1  emissions in the SE area (Table 3.6.2). Next, we  modelled).  determine the weekend-weekday difference for the transportation emissions component only. Table  In summary, agreement between measured and  3.6.6 compares the observed emission reduction  model fluxes (in both aggregation approaches) is a  weekday – weekend in three of four sectors to  very positive outcome and the use of direct carbon  the modelled transportation sector emissions. The  flux measurements is shown to be a promising  relative emission reduction on weekends (measured,  method to validate fine-scale emission inventories  but expressed as fraction of the modelled carbon  / models. Tower measurements can be also  flux from the transportation sector) is estimated  used to identify model components that require  42%. This suggests that weekend traffic emits 42%  improvement, in this case the transportation model.  less than weekday traffic in the study area.  (a) Integral turbulent source area  (b) Relative CO2 flux contribution  5453551  1 x 10-5 m-2  5453551  1.00 kg C m-2 year-1 m-2  E 41st Ave  E 41st Ave  5452601  Memorial S. Park  Tecumseh Park  0.6 5452601  E 49th Ave  Victoria Dr.  Knight St.  Tecumseh Park  0.80 Fraser St.  Memorial S. Park  Victoria Dr.  Knight St.  Fraser St.  0.8  0.60  E 49th Ave  0.40  0.4 Gordon Park  Gordon Park  0.20  0.2  200  400 m  495240  E 54th Ave  5451651  494290  494290  493340 0  495240  0.0  493340  E 54th Ave  5451651  0.00 -0.04  N  Figure 3.6.4: (a) Long-term integrated turbulent source areas and (b) relative CO2 flux contribution (Product of Figure 3.6.2 and 3.6.4a).  89  PART III: results Table 3.6.5: Comparison of modelled and measured carbon emissions weighted by the long-term turbulent source area of the tower.  All sectors Carbon Flux Density Measured (kg C m-2 day-1)  6.71  Carbon Flux Density Modelled (kg C m-2 day-1) and Aggregated by the Long-term Source Area  7.46  Difference Measured - Modelled (kg C m-2 day-1)  0.75  Difference (%)  11  Table 3.6.6: Comparison of measured carbon emission reduction on weekends to the modelled transportation sector emissions for a 400-m-radius buffer around the tower. Not enough measurements on weekends with wind from NW were available to estimate the reduction in the NW sector.  Sectors NE, SE, & SW  NE  SE  SW  NW  Modelled Flux Density from Transportation (g C m-2 day-1)  13.15  14.1  19.1  6.2  1.1  Measured Difference Between WeekdayWeekend (g C m-2 day-1)  5.71  7.31  9.79  5.59  n/a  Percentage of Measured Weekend Reduction to Total Modelled Transportation Flux (%)  42%  38%  48%  40%  n/a  90  PART III: results References: Churkina, G. (2010). Carbon stored in human settlements: the conterminous United States. Global Change Biology, 16(1), 135-143. Harvey, L. (2009). Reducing energy use in the building sector: measures, costs, and examples. Energy Efficiency, 2 (2), 139-163. Golubiewski, N. E. (2006). Urbanization increases grassland carbon pools: Effects of landscaping in Colorado’s front range. Ecological Applications, 16(2), 555-571. Krishnan, P., Black, T. A., Jassal, R.S., Chen, B., & Nesic, Z. (2009). Interannual variability of the carbon balance of three different-aged Douglas -fir stands in the Pacific Northwest. Journal of Geographysical Research (114), G04011. Liss, K., Crawford, B., Jassal, R., Siemens, C., & Christen, A. (2009). Soil respiration in suburban lawns and its response to varying management and irrigation regimes. Proc. of the AMS Eighth Conference on the Urban Environment, Phoenix, AZ, January 11-15, 2009. Moriwaki, R., & Kanda, M. (2004). Seasonal and diurnal fluxes of radiation, heat, water vapor, and carbon dioxide over a suburban area. Journal of Applied Meteorology 43, 1700-1710. Matese A., Gioli B., Vaccari F. P., Zaldei A., Miglietta F. (2009). Carbon Dioxide Emissions of the City Center of Firenze, Italy: Measurement, Evaluation, and Source Partitioning. Journal of Applied Meteorology and Climatology, 48(9), 1940-1947 Natural Resources Canada (2009). Communities | Urban Archetypes Project. Retreived in June 20, 2010 from http:// canmetenergy-canmetenergie.nrcan-rncan.gc.ca/eng/ buildings_communities/communities/urban_archetypes_ project.html Nowak, D. J., & Crane, D. E., (2002). Carbon storage and sequestration by urban trees in the USA. Environmental Pollution, 116, 381-389. Pouyat, R., Groffman, P., Yesilonis, I., L., & Hernandez, L. (2002). Soil carbon pools and fluxes in urban ecosystems. Environmental Pollution, 116, S107-S118 Walsh, C. J. (2005). Fluxes of radiation, energy, and carbon dioxide over a suburban area of Vancouver, BC. M.Sc. Thesis, Department of Geography, University of British Columbia.  91  PART IV: Scenarios 4.1  Carbon Emissions Scenarios  To streamline modeling, each scenario is built and  Four carbon reduction scenarios are extrapolated  modeled incrementally — subsequent scenarios  from measured results reported at the May 27  build off the results of prior scenarios. We will not  workshop. Their purpose is to be heuristic and  iterate or model from base assumptions.  illustrative — to demonstrate the potential and  Results are reported at total carbon per year and  limitations attributable to uniformly applied change  total carbon per capita year in the study area.  in the study area and estimate the associated  This latter category is crucial as population and  carbon reduction potential. To simplify and validate  jobs must increase and compact in the study  these illustrations with data collected, we consider  area to realize improvement in both building and  only a narrow definition of local emissions — those  transportation sectors. An illustration of scenario  attributable to local sources emitted in the study  decisions and numbers reported can be seen in  area. No attempt is made to estimate non-local  figure 4.1.1.  origin emissions of local origin emissions outside the study area.  District and renewable energy technologies Vehicle mode split  Vegetation  Land use + building typology Construction + Efficiency Total compared to baseline Totals tonnes carbon emitted and the neighbourhood per  .52  tonnes C person-1  12075tonnes C year  -1  Figure 4.1.1: Schematic of carbon reduction scenario decisions  92  PART IV: scenarios  .52  Baseline  Scenario 1: Modified Baseline  tonnes C person-1  12119 tonnes C year 64 people hectare 14 jobs hectare  -1  -1  -1  Buildings Local Carbon  8715  External Carbon  420  Total tonnes Carbon  9135  Transportation Total tonnes Carbon  1542  Human metabolism Total tonnes Carbon  1767  Vegetation and Soils Total tonnes Carbon  -325  Table 4.1.1 Modelled carbon emissions SC3 (i.e. t C year-1)  4.1.1 Scenario 1: Modified Baseline The first scenario interpolates local origin / local emission results from those previously reported. Only transportation emissions are modified from the baseline reported in previous sections. The changes to transportation illustrate only transit and local origin light vehicle trips. The modified baseline provides the framework for subsequent scenarios to be measured against.  93  PART IV: scenarios  .33  63%  Scenario 2: Optimize Sunset  tonnes C person-1  7624tonnes C year 64 people hectare 14 jobs hectare  -1  -1  -1  Buildings Local Carbon  4826  External Carbon  312  Total tonnes Carbon  5138  Transportation Total tonnes Carbon  1044  Human metabolism Total tonnes Carbon  1767  Vegetation and Soils Total tonnes Carbon  -325  Table 4.1.2 Modelled carbon emissions SC3 (i.e. t C year-1)  4.1.2 Scenario 2: Optimize Sunset  developed for each archetype, simulations were  The second scenario ‘Optomize Sunset’ illustrates  run in HOT2000 for ground oriented residential  best possible energy performance from current  typologies and NRCan Screening tool for all other  policy and regulatory standards — without spatial  typologies. The upgrades that were assumed  change to the study area. This scenario illustrates  possible are extensive and therefore may at times  the effect of elevating all existing buildings to best  not make economic sense. However, the purpose  practice envelope and space conditioning systems  of the ‘Optimize Sunset’ scenario is to illustrate  standards and elevating the engine fuel efficiency of  the potential for retrofitting the existing building  the local origin passenger and transit vehicle fleet to  stock without changing urban form or land use mix.  best 2008 practice.  The upgrades follow prescriptive retrofits outlined in CMHC (2007), Detail Practice (2008), Building  In order to estimate the potential for carbon  Insight (2008) and the R-200 standard.  reduction through building retrofits, a collection of upgrades were developed for each archetype. These collections were sensitive to building construction, age and land use. Once an upgrade package was  94  PART IV: scenarios Scenario highlights in addition to those reported in MODIFIED BASELINE:  Change to Building sector  •	 Archetypes: No change  Change to Transportation sector Efficiency: •	 Average transit and light passenger vehicle fuel  Efficiency:  efficiency increased  •	 Envelope construction: Extensive retrofit of existing buildings following the R-2000 and  Demand: •	 No change  Greening the BC building code standards. Upgrades were vintage sensitive and improved  Source: •	 No change  upon assumed CMHC Building code standards (CMHC, 2005). Included in many of the upgrades were wall, roof, and foundation  Change to Vegetation and Soils  insulation along with improved air change  •	 Green roofs: No change  values (assumed to be more difficult in older  •	 Ground vegetation: No change  dwellings) and CSA energySTAR windows.  •	 Trees: No change  •	 Space conditioning and Domestic Hot Water (system and efficiency): Systems in older dwelling were replaced with newer, more       efficient models and hot water tanks was insulated. Demand: •	 Lighting and appliances: Decreased lighting load based on predicted CFL share found in Survey for Household Energy use, 2007. Source: •	 Fuel share: No change Typology: •	 Population: No change •	 Jobs: No change  95  PART IV: scenarios  .26  48%  Scenario 3: Transit Oriented Sunset  tonnes C person-1  11309tonnes C year 123 people hectare 34 jobs hectare  -1  -1  -1  Buildings Local Carbon  5903  External Carbon  572  Total tonnes Carbon  6475  Transportation Total tonnes Carbon  1928  Human metabolism Total tonnes Carbon  3386  Vegetation and Soils Total tonnes Carbon  -480  Table 4.1.3 Modelled carbon emissions SC3  4.1.3 Scenario 3: Transit Oriented Sunset  development density has been offset by more  The third scenario illustrates the opportunity of  and lane houses take place in single family areas  future ‘Smart Growth’ and transit-oriented growth  outside of the development corridor and no net  in the study area. Population and job growth are  change to impervious ground cover is assumed.  based on a pro-rata share of the anticipated Metro  Transportation is based on favorable transit-oriented  future growth estimated in the Sustainability By  and walkable neighbourhood mode splits (again SxD  Design project (SxD). The growth pattern of this  provides guidance here) and a technology based  scenario concentrates along corridors and in nodes  engine fuel efficiency performance improvement —  in compact energy efficient building types. Energy  the California 2020 standard, for example.  intensive green roof and tree planting. Infill suites  intensity and the subsequent carbon emissions for new development are borrowed from Scenario 2. The net loss of ground vegetation due to greater  96  PART IV: scenarios Scenario highlights in addition to those reported in  Change to Transportation sector  MODIFIED BASELINE:  Efficiency: •	 Average transit and light passenger vehicle fuel  Change to Building sector Efficiency: •	 Envelope construction: Same standard applied as in Scenario2: Optimize Sunset. •	 Space conditioning and Domestic Hot Water  efficiency increased Demand: •	 Mode split adjusted to reflect a doubling of transit and pedestrians / cycling share (site Calgary source...) and subsequent reduction in  (system and efficiency): Same standard applied  private automobiles (approximately 1/3 share  as in Scenario2: Optimize Sunset.  each).  Demand: •	 Lighting and appliances: Same standard applied  Source: •	 No change  as in Scenario2: Optimize Sunset. Source: •	 Fuel share: No change Typology: •	 Population: The number of people was increased by a factor of 1.9 following the sustainable by design predictions for the neighborhood (find source...). •	 Jobs: The number of jobs was increases by a factor of two following the sustainable by design guidelines for the neighbourhood (find source...). Baseline employment was assumed to follow 13.7 jobs hectare-1 suggested by SXD. •	 Archetypes: The population and job growth was  Change to Vegetation and Soils •	 Green roofs: 5% of buildings assumed to be built with green roofs. •	 Ground vegetation: Ground vegetation decreased as a result of new development (decrease from 24.2% plan area down to 22.2%). Areas covered by semi-permeable driveways was increased from 0% to 2% of plan area. •	 Trees: An increase in areas covered by trees was assumed (increase from 10.6% to 15%).    accommodated for in new four storey mixed use buildings (40%), row houses (35%), and mid rise residential (25%). The new development was restricted to parcels bordering transit corridors (Fraser, Knight, Victoria, E. 41st, E. 49th, and E. 57th). Additional population was accommodated for by laneway housing infill in existing single family residential areas.  97  PART IV: scenarios  31%  .16  Scenario 4: Low Carbon Sunset  tonnes C person-1  7076tonnes C year 123 people hectare 34 jobs hectare  -1 -1  -1  Buildings Local Carbon  2789  External Carbon  572  Total tonnes Carbon  3361  Transportation Total tonnes Carbon  964  Human metabolism Total tonnes Carbon  3386  Vegetation and Soils Total tonnes Carbon  -635  Table 4.1.4 Modelled carbon emissions SC3  4.1.4 Scenario 4: Low Carbon Sunset The fourth scenario LOW CARBON SUNSET adds the best known technical innovations and improvements to the population and job intensification, spatial pattern and building types of TOD SUNSET. For example, this scenario includes carbon neutral transit, aggressive adoption of electrically powered private vehicles, and district energy systems. All new development along the corridors (i.e 100% saturation of district energy, similar to that assumed by Miller and Cavens, 2008) found in the TOD Sunset scenario is connected to a district energy system, such as a biomass plant or solar hot water distribution system. The district energy is assumed to accommodate all new space heating and domestic hot water loads through carbon neutral means (e.g. BC Assessment guides suggested GHG factors).  98  PART IV: scenarios Scenario highlights in addition to those reported in  Oriented Sunset.  MODIFIED BASELINE:  Change to Transportation sector  Change to Building sector  Efficiency:  Efficiency: •	 Envelope construction: Same standard applied as in Scenario2: Optimize Sunset. •	 Space conditioning and Domestic Hot Water (system and efficiency): Same standard applied as in Scenario2: Optimize Sunset. Demand: •	 Lighting and appliances: Same standard applied  •	 Same as in Scenario 3: Transit Oriented Sunset. Demand: •	 Same as in Scenario 3: Transit Oriented Sunset. Source: •	 All transit is assumed to be carbon neutral (electric powered for example). •	 Light passenger vehicle fleet is assumed to be 50% electric powered.  as in Scenario2: Optimize Sunset. Source: •	 Fuel share: It is assumed possible to provide 100% of space heating and domestic hot water loads by district energy biomass, sewer heat recovery or solar thermal in the new development along the corridor (Miller and Cavens, 2008). Typology: •	 Population: Same as in Scenario 3: Transit Oriented Sunset. •	 Jobs: Same as in Scenario 3: Transit Oriented Sunset.  Change to Vegetation and Soils •	 Green roofs: 20% of buildings assumed to be built with green roofs. •	 Ground vegetation: Ground vegetation decreased as a result of new development (decrease from 24.2% plan area down to 22.2%). Areas covered by semi-permeable driveways was increased from 0% to 5% of plan area. •	 Trees: An increase in areas covered by trees was assumed (increase from 10.6% to 20%).  •	 Archetypes: Same as in Scenario 3: Transit  99  PART IV: scenarios Table 4.1.5 Scenario attributes summary table  Scenario  Mod. Baseline  Opt. Sunset  TOD Sunset  Low Carbon  Envelope standard  /  R-2000 + BCBC  R-2000 + BCBC  R-2000 + BCBC  Space conditioning + DHW standard  /  R-2000 + BCBC  R-2000 + BCBC  R-2000 + BCBC  /  lighting load  Lighting load  Lighting load  Residential floor area (m )  923512  923512  1660787  1660787  Other floor area (m )  198708  198708  366162  366162  /  /  /  Yes  Local tonnes carbon (NG)  8715  4826  5903  2789  External tonnes carbon (Elec)  420  312  572  572  9135  5138  6475  3361  Private vehicles (L/100km)  12.0 (G)  8.6 (G)  6.7 (G)  3.4 (G)  Transit (L/100km)  39.0 (D)  28.0 (D)  21.7 (D)  10.9 (D)  Total private automobile trips / day  42050  38922  51666  51666  Total transit trips / day  7506  8688  51666  51666  Total cycling or pedestrian trips / day  8201  8688  51666  51666  Gasoline (G) litres (L)  1749697  1160681  1198536  600164  Diesel (D) litres (L)  518779  372457  1512547  756274  Carbon-neutral Fuel  0  0  0  50%  Transportation Emissions  1542  1044  1928  964  Population  23,135  23,135  44352  44352  64  64  123  123  4938  4903  12363  12363  14  14  34  34  Human Metabolism Emissions  1769  1769  3386  3386  Plan area covered by buildings  21.0%  21.0%  25.3%  25.3%  Plan area covered by ground vegetation  24.2%  24.2%  22.2%  22.2%  Plan area covered by trees  10.6%  10.6%  15.0%  20.0%  Plan area with semi-permeable driveways  0.0%  0.0%  2.0%  5.0%  Fraction of building plan area with green roofs  0.0%  0.0%  5.0%  20.0%  Vegetation + Soils Emissions  -325  -325  -480  -635  Energy efficiency  Energy demand Building  Lighting and appliance upgrade 2  2  Energy source District Energy System  Building Emissions (tonnes) Energy efficiency  Vegetation  Human  Transportation  Energy demand  Energy source  People hectare  -1  Jobs Jobs hectare  -1  100  PART IV: scenarios 4.2 Scenario Discussion  performance energy supply and conservation  With British Columbia’s Bill 27 carbon emissions  technologies not yet commercially viable or  reduction targets of 33% reduction from 2007  available at this time. TOD Sunset Scenario, for  levels by 2020 and 80% by 2050 as context, the  example, uses urban form strategies — higher  four scenarios offer one illustration of the potential  density and greater land use mix — to reduce  building and urban form implications of those  passenger vehicle trip demand in the transportation  targets, albeit a spatially constrained and primarily  sector and thermal energy demand in the building  local (with the exception of off-site hydroelectricity  sector. To achieve that densification this scenario  emissions) one. Not considered, for example, are  approximately doubles the population and jobs in  the significant fuel energy implications of travel  the study area and, in so doing, increases total  to and from the study area that are influenced by  energy consumption and carbon emissions in the  regional land use, employment and transportation  study area. However, because population and job  policy and infrastructure or fundamental change in  growth concentrates in more efficient buildings in  human behaviour with respect to travel choices and  close proximity to transit routes, building energy  energy use. Neither were food- nor waste-related  efficiencies and economies along increased transit  energy and carbon fully considered in the baseline.  ridership reduces per capita carbon emissions to 52% less than the 2007 baseline — about halfway  Using the Modified Baseline scenario as a proxy for  between the 2020 and 2050 targets.  2007 local origin emissions, on a per capita basis, the 2020 (33%) goal could be slightly exceeded  The Low Carbon Scenario illustrates that  (37% less than 2007 baseline) by the technology-  approximately 17% of the remaining 28%  based, fuel efficiency improvements of the Optimize  per capita carbon emissions gap could come  Sunset scenario. In this scenario, the greatest  from advance energy source and conservation  reduction (56% of sector baseline) is attributable  technologies such as high performance fuel efficient  to whole neighbourhood upgrades to best practice  infrastructure, buildings and vehicles and low  envelope and space conditioning standards in the  carbon fuels in combination with the urban form  building sector and a lesser, but still significant  based strategies of TOD Sunset Scenario. Widely  reduction (67% of sector baseline) attributable to  applied vegetation changes, such as the greening of  whole neighbourhood upgrades of all passenger  roof surfaces, while desirable from stormwater and  and transit vehicles to best practice fuel efficiency  hydrologic points of view, yield very minor direct  standards in the transportation sector.  improvement to study area carbon sequestration. However, other indirect effects of urban vegetation  Reductions greater than those achieved in the  such as reduction in space conditioning loads  Optimize Sunset scenario would not be achievable  attributable to shading and sheltering would  without change to the form and structure of  likely yield energy demand reduction benefits not  the study area and / or introduction of high  measurable within the scope of this study. The 101  PART IV: scenarios remaining 11% gap to an 80% carbon emissions reduction has not been accounted but potentially achieveable with greater urban form or behaviour change or higher performance technologies than those anticipated and illustrated by these scenarios.  102  PART IV: scenarios References: Builder Insight (2008). Greening the BC Building Code : First Steps. Retrieved on June, 2010 from: http://www.hpo. bc.ca/PDF/BuilderInsight/BI5.pdf Canadian Home Builders’ Association (2010). R-2000 Building Performance Based Standard. Retreived on June, 2010 from: http://r2000.chba.ca/What_is_R2000/R2000_ standard.php Canadian Mortgage and Housing Corporation CMHC (2007). Canadian Wood Frame Housing Construction. CMHC, 1967, revised 2007. Miller, N., Cavens, D. (2008). City of North Vancouver 100 Year Sustainability Vision: GHG Measurement and Mapping. Prepared for the Ministry of Environment CEEI Working group. Province of British Columbia (2008). BC GHG Emissions Assessment Guide. Retreived on June 20, 2010 from http:// www.townsfortomorrow.gov.bc.ca/docs/ghg_assessment_ guidebook_feb_2008.pdf Richarz, C., Schulz, C., Zeitler, F., (2007). Energy-Efficiency Upgrades. Detail Practice books. Birkhauser  103  acknowledgements  list of authors  Principal funding for this project has been provided  Andreas Christen, Assistant Professor of Geography  by CanmetENERGY, Natural Resources Canada,  (Principal Investigator) with Nicholas Coops,  Ottawa. Jessica Webster, project manager.  Professor of Forestry and Canada Research Chair in Remote Sensing and Ronald Kellett, Professor of  The Canadian Foundation for Climate and  Landscape Architecture (Co-Investigators) at the  Atmospheric Sciences (CFCAS) funded the  University of British Columbia.  acquisition and processing of the LiDAR data as well as the two-year measurements on the carbon  Contributing project team members, researchers,  flux tower. This was funded as part of the CFCAS  co-authors and illustrators include Ben Crawford,  network “Environmental Prediction in Canadian  Eli Heyman and Michael van der Laan, Geography,  Cities (EPiCC)”. Selected research infrastructure on  Rory Tooke, Forestry and Inna Olchovski, Landscape  the tower was supported by NSERC RTI (Christen,  Architecture.  #344541-0) and CFI / BCKDF (Christen). We acknowledge the support of BC Hydro, the City of Vancouver, Environment Canada and Terasen Gas for providing additional data and to BC Hydro for their in-kind support (tower access) in the Mainwaring substation. We further acknowledge the significant technical support of staff at the University of British Columbia including (in alphabetical order): Jonathan Bau, Kate Liss, Rick Ketler, Zoran Nesic, Julie Ranada, Chad Siemens.  104  

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