A mobile sensor network to map carbon dioxide emissions in urban environments

Abstract

A method for directly measuring carbon dioxide (CO2) emissions using a mobile sensor network in cities at fine spatial resolution was developed and tested. First, a compact, mobile system was built using an infrared gas analyzer combined with open-source hardware to control, georeference and log measurements of CO2 mixing ratios on vehicles (car, bikes). Second, two measurement campaigns, one in summer and one in winter (heating-season) were carried out. Five mobile sensors were deployed within a 1 x 12.7 km transect across the City of Vancouver, BC, Canada. The sensors were operated for 3.5 hours on pre-defined routes to map CO2 mixing ratios at street level, which was then averaged to 100 x 100 m grids. The grid-averaged CO2 mixing ratios were 417.9 ppm in summer and 442.5 ppm in winter. In both campaigns, mixing ratios were highest in the downtown core and along arterial roads and lowest in parks and well vegetated residential areas. Third, an aerodynamic resistance approach to calculating emissions was used to derive CO2 emissions from the gridded CO2 mixing ratio measurements in conjunction with mixing ratios and fluxes collected from a 28-m tall eddy-covariance tower located within the study area. These measured emissions showed a range of −12 to 226 kg CO2 ha−1 hr−1 in summer and of −14 to 163 kg CO2 ha−1 hr−1 in winter, with an average of 35.1 kg CO2 ha−1 hr−1 (summer) and 25.9 kg CO2 ha−1 hr−1 (winter). Fourth, an independent emissions inventory was developed for the study area using buildings energy simulations from a previous study and routinely available traffic counts. The emissions inventory for the same area averaged to 22.06 kg CO2 ha−1 hr−1 (summer) and 28.76 kg CO2 ha−1 hr−1 (winter) and was used to compare against the measured emissions from the mobile sensor network. The comparison on a grid-by-grid basis showed linearity between CO2 mixing ratios and the emissions inventory (R2 = 0.53 in summer and R2 = 0.47 in winter). 87 % (summer) and 94 % (winter) of measured grid cells show a difference within ±1 order, and 49 % (summer) and 69 % (winter) show an error of less than a factor 2. Although associated with considerable errors at the individual grid cell level, the study demonstrates a promising method of using a network of mobile sensors and an aerodynamic resistance approach to rapidly map greenhouse gases at high spatial resolution across cities. The method could be improved by longer measurements and a refined calculation of the aerodynamic resistance.

Full text

A mobile sensor network to map carbon dioxide emissions in urban
environments
Joseph K. Lee1, Andreas Christen1, Rick Ketler1, and Zoran Nesic1,2
1Department of Geography / Atmospheric Science Program, The University of British Columbia, Vancouver, BC, Canada
2Biometeorology Group, Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC, Canada
Correspondence to: A. Christen (andreas.christen@ubc.ca)
Abstract. A method for directly measuring carbon dioxide (CO2) emissions using a mobile sensor network in cities at fine
spatial resolution was developed and tested. First, a compact, mobile system was built using an infrared gas analyzer combined
with open-source hardware to control, georeference and log measurements of CO2mixing ratios on vehicles (car, bikes).
Second, two measurement campaigns, one in summer and one in winter (heating-season) were carried out. Five mobile sensors
were deployed within a 1×12.7km transect across the City of Vancouver, BC, Canada. The sensors were operated for 3.55
hours on pre-defined routes to map CO2mixing ratios at street level, which was then averaged to 100 ×100 m grids. The
grid-averaged CO2mixing ratios were 417.9 ppm in summer and 442.5 ppm in winter. In both campaigns, mixing ratios
were highest in the downtown core and along arterial roads and lowest in parks and well vegetated residential areas. Third, an
aerodynamic resistance approach to calculating emissions was used to derive CO2emissions from the gridded CO2mixing
ratio measurements in conjunction with mixing ratios and fluxes collected from a 28-m tall eddy-covariance tower located10
within the study area. These “measured” emissions showed a range of -12 to 226 kg CO2ha−1hr−1in summer and of -
14 to 163 kg CO2ha−1hr−1in winter, with an average of 35.1 kgCO2ha−1hr−1(summer) and 25.9 kg CO2ha−1hr−1
(winter). Fourth, an independent emissions inventory was developed for the study area using buildings energy simulations
from a previous study and routinely available traffic counts. The emissions inventory for the same area averaged to 22.06
kg CO2ha−1hr−1(summer) and 28.76 kg CO2ha−1hr−1(winter) and was used to compare against the measured emissions15
from the mobile sensor network. The comparison on a grid-by-grid basis showed linearity between CO2mixing ratios and
the emissions inventory (R2= 0.53 in summer and R2= 0.47 in winter). 87% (summer) and 94% (winter) of measured grid
cells show a difference within ±1 order, and 49% (summer) and 69% (winter) show an error of less than a factor 2. Although
associated with considerable errors at the individual grid cell level, the study demonstrates a promising method of using a
network of mobile sensors and an aerodynamic resistance approach to rapidly map greenhouse gases at high spatial resolution20
across cities. The method could be improved by longer measurements and a refined calculation of the aerodynamic resistance.
1 Introduction
Cities and the cumulative processes of urbanization are key drivers of local and global environmental change (Mills, 2007;
Grimmond, 2007). As cities are the centers of increasing population growth and resource consumption, they are also the domi-
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nant source of greenhouse gas emissions - in particular carbon dioxide (CO2) - into the atmosphere (Rosenzweig et al., 2010).
On the global scale, urban emissions are estimated to contribute up to 20% directly and 80% indirectly to the total anthro-
pogenic CO2emissions footprint (Satterthwaite, 2008) and are thus responsible for a major proportion of the anthropogenic
greenhouse gas emissions that are intensifying positive atmospheric radiative forcing of the troposphere contributing to global
climate change (IPCC, 2013).5
In cities, the major sources of CO2are the combustion of fossil fuels for heating, ventilation, and cooling systems (HVAC),
transportation, industrial processes, and power generation (Kennedy et al., 2009). Theses fossil fuel emissions are combined
with CO2emitted from biological sources, namely soil, plant and human respiration and in part taken up by photosynthesis
of urban vegetation (Christen et al., 2011). Overall, fossil fuel sources dominate in cities, and the uptake by photosynthesis
on an annual scale is usually minor, but can be more relevant in summer (Peters and McFadden, 2012; Weissert et al., 2014).10
The dominance of emissions results in increased concentrations of CO2in the urban boundary layer (UBL) relative to rural or
pristine air (Idso et al., 2001; Grimmond et al., 2002; Vogt et al., 2006). The enrichment of CO2in the UBL links directly to
emissions which are controlled by urban form and function.
With more than 50% of the global population now living in cities (United Nations, 2014), cities are also the place where
effective mitigation of climate change, driven by policy, design, and bottom up citizen engagement is possible. IPCC (2014)15
conclude that the urban scale has the highest potential for agile and sustained implementation of mitigation efforts. Central
to the reduction of urban CO2emissions is the availability of reliable emissions information and inventories and methods of
validating city-scale emissions estimates and reduction efforts. While there are a growing number of methods of quantifying
emissions in urban areas, there are disconnects between the current spatial and temporal resolution of emissions models, the
ever-evolving urban form and function, and block to neighborhood-scale measurements which inform and validate emissions20
models (Pataki et al., 2009; Kellett et al., 2013). It further remains a challenge to directly measure emissions at fine urban
scales and separate emission CO2measurements in the urban atmosphere into different fossil fuel emissions and biological
sources (Christen, 2014).
The overall research goal of this contribution is to develop, apply and test a new methodology to map CO2emissions in
complex urban environments. Our hypothesis is that by data collected from a network of mobile sensors and from an urban25
eddy-covariance tower can be combined with the aerodynamic resistance approach to calculate and map emissions at fine
scales (blocks to neighborhoods) in cities.
Mobile measurement methods have been used in the past for studying the spatial variability of greenhouse gases in cities
(Jimenez et al., 2000; Idso et al., 2001; Henninger and Kuttler, 2007; Crawford and Christen, 2014). In general, mobile moni-
toring methods for greenhouse gases rely on a single, high cost, high precision and accuracy, and bulky sensor systems carried30
in specialized measurement vehicles (e.g. Brantley et al., 2014). Studies such as those by Tao et al. (2015) and Crawford and
Christen (2014) demonstrate mobile systems for monitoring CO2, but most of these systems are still bulky and limited by their
cost and installation needs. Therefore most urban studies using mobile approaches utilize sensors that are generally designed
for specialized transport vehicles (Bukowiecki et al., 2002; Elen et al., 2013; Crawford and Christen, 2014). While these sys-
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tems have the advantage that they can be well equipped with additional components such as calibration tanks or computers,
they do not allow for easy deployment and various modes of transport.
There are increasing successes to develop innovative methods for monitoring urban climate and air pollution using pervasive
computing and low-cost distributed sensor networks. Top-down data mining approaches using crowd-sourced smart-phone
data have shown the advantage of scalability and data density. For example, Overeem et al. (2011) derived measures of rain-5
fall for the entire Netherlands using the attenuation of a cell phone sender signal to its receiver station. In another example,
Overeem et al. (2013) developed methodology to derive fine-scale air temperature measurements using cell phone battery
temperatures to examine the urban heat island. Bottom-up approaches using distributed sensor networks have become possi-
ble in recent years with the increasing availability of low cost climate and air pollution sensors, open source programmable
microcontrollers, and improvements in networking infrastructure. For example, Meier et al. (2015) used sensor data from a10
commercial consumer-grade weather station network to examine fine-scale urban heat island effects in the city of Berlin. In
another example, Chapman et al. (2015) developed a road sensor network to monitor road surface temperatures to optimally
salt roads during the winter months in Birmingham. Given this growing interest in distributed and mobile sensing systems and
the advances in low-cost open- and micro technologies, could there be new opportunities for the fine-scale mapping of CO2
emissions in cities? Furthermore, could new methods be developed that are scalable and flexible enough to be integrated into15
existing infrastructure such as bikes, car-sharing cars, taxis, or even autonomous flying vehicles? Hence, the key considerations
for developing new mobile CO2emission monitoring systems must be around scalability (how many can be built and for what
cost?), system extendability (can the system be built upon?), accuracy and precision, temporal resolution, accessibility (e.g
open source or proprietary?), and the mobile platform on which the sensor is to be mounted.
The overall research question for this contribution asks whether it is possible to map greenhouse gas emissions, specifically20
CO2, at a spatial resolution of neighborhoods / blocks across the city with a portable network of mobile sensors that could be
routinely implemented on car-sharing platforms, public transit or random vehicles.
In order to address the research question, four major objectives were outlined and developed:
1. Sensor Development: Develop and test a compact, mobile, and multi-modal CO2sensor for bikes and cars.
2. Measurement Campaign: Deploy the sensors in a targeted measurement campaign.25
3. Methodology development: Calculate emissions from measurements of CO2mixing ratios and aerodynamic resistance
(in the following called “measured emissions”)
4. Analysis and Evaluation: Compare the measured emissions to fine-scale traffic and building emissions inventories. Can
we find agreement between the spatial patterns in the inventories and measured emissions?
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2 Methods
2.1 The DIYSCO2system
2.1.1 System requirements
A mobile CO2monitoring system was required to address the project’s need for multiple, low cost, yet accurate sensors
capable of measuring mixing ratios and position at high frequency (≥1 Hz) and easily deployable on bikes and various cars5
with a compact design. A mobile monitoring system with such specifications is necessary to cover large geographic areas
within limited time scales (hours) at sufficiently fine resolution that are representative of typical urban emission patterns.
Sensor systems with many of these specifications do already exist, but few, if any, were designed to be carried on and easily
interface with various types of mobile platforms; all studies using high accuracy CO2sensors either have been stationary or
have primarily used specialized vehicles because of the weight, power consumption, and size of the sensors being used and are10
highly costly.
2.1.2 System design
Components from the Arduino platform (Arduino CC, Ivrea, Italy), an opensource programmable microcontroller, were cou-
pled with Licor’s proprietary Li-820 IRGA (Licor Inc., Lincoln, NB, USA) - a compact (23.23 cm x 15.25 cm x 7.62 cm, 1 kg),
low maintenance (approx. 2 years of continuous use) and high accuracy CO2(±1 ppm) single-path IRGA built for various15
CO2monitoring applications including agriculture (Li-Cor, 2015) - to prototype a portable CO2analyzer. The IRGA uses in-
frared light to determine the CO2mixing ratio within a closed path by detecting the amount of absorption of the light from the
path. With low cost compact components, open code base, and flexible hardware interfacing, the Arduino platform provided a
lightweight and modular prototyping environment capable of communicating digitally with the IRGA, a GPS (Adafruit Ulti-
mate GPS Logger Shield with GPS Module, Manhattan, New York, USA) unit, and digital temperature thermometers (Maxim20
Integrated One Wire Digital Temperature Sensor - DS18B20, San Jose, CA, USA). A custom hardware board was developed
to connect all of the components together in a way that: 1. distributes the correct amount of power to each of the hardware
components, 2. allows for hardware and sensor input, and 3. keeps the sensor hardware centralized, organized, and compact.
The portable CO2analyzer was named the “Do-It-Yourself-Sensor-CO2”, or “DIYSCO2” system (Fig. 1a)
The DIYSCO2system reports CO2as mixing ratios (r) in ppm, geoposition (latitude/longitude, speed, altitude, and satellite25
strength), and internal and external air temperature which are logged onto a micro-Secure Digital (SD) card at 1-second
intervals. Air is drawn into the DIYSCO2system through a 3 m long inlet tube (6.35mm diameter, Dekoron Bendable Tubing,
Mt. Pleasant, Texas, USA) using a small KNF NMP015 Micro-Diaphragm Pump (KNF Neuberger, Inc., Trenton, NJ, USA)
first passing through a mesh filter at the sample inlet head to prevent large particles from entering the DIYSCO2system (e.g.
insects) and then through a Balston disposable filter unit (DFU) (Parker Hannifin Corporation, Lancaster, NY, USA) at the end30
of the 3 m tube. The flow rate is regulated by a Swagelok needle valve at 700 ccmin−1as recommended by Licor to minimize
the effect of internal cell pressure changes on the CO2measurements. The entire DIYSCO2system is 35.8 cm x 27.8 cm x
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Inlet for CO2
and filter
Air temperature
(a)
(b)
Infrared
Gas
Analyzer
Fan
Internal
Temperature
Sensor
GPS &
Data
Logger
Custom
Hardware
Board
External
Temperature
Sensor
Arduino
Micro-
controller
Weatherproof Case
RS-232
Serial
Connection
Filter
Pump
Figure 1. (a)Photo of the “DIYSCO2” system (case open) with components labelled. During operation, the system is enclosed in the case,
while LEDs on the box indicate system state. (b)Inlet mounted through the passenger window of a car-sharing vehicle, the “DIYSCO2”
sits in the trunk space.
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11.8 cm, weighs 2.6 kg and is contained in a weather-proof case (NANUK 910, Plasticase, Terrebonne, CA, USA). The system
is powered by a single 9-18V DC/DC input which can be supplied by battery or via car cigarette lighter socket.
2.1.3 System testing and installation
Within the range of typical ambient mixing ratios of CO2between 400 and 550 ppm the DIYSCO2system showed strong
linearity (R2of 0.9999) and a root mean square error (RMSE) of 0.233 ppm relative to six tanks of reference gases (see Ap-5
pendix A1). The maximum sensor drift over three hours (the duration of the campaign, see below) under controlled conditions
was in the range of -0.31 and +0.51 ppm (see Appendix A2). In the configuration used, the DIYSCO2had a time lag of 18.2 s
between measurement intake and analysis (see Appendix A3).
Appendix A4 discusses errors associated with mounting the inlet at different positions on the car which can lead to a
systematic bias. Generally, values on the driver side (centre of road) were higher than the passenger side. In the current work,10
the sample inlet tube was run out through the passenger side window of the vehicles at the height of 2 m (Fig. 1b). In order to
deploy the DIYSCO2on a bike, the setup requires a 40 `backpack to carry the sensor and a 7 Amp-hour, 12V gel-cell battery
and a 1.5 m long rigid mounting tube (6 mm diameter) to mount the inlet tube above the cyclist. The sensor is placed in the
backpack with the battery and worn on the back of the cyclist to reduce vibrations to the sensor system.
2.2 Measurement campaigns15
The systems were tested in two field campaigns. In each of the campaigns, a fleet of five sensors were operated simultaneously
on pre-defined routes to evaluate the potential to map emissions and compare them against inventory data.
2.2.1 Study area
The study area for testing is a 12.7 km ×1 km quadrangle of diverse urban land uses within the City of Vancouver, BC,
Canada (Fig. 2). The study area begins at the northern-most tip of the city (UTM 10,488510 E, 5451513 N) in forested “Stanley20
Park”, and extends to the city’s south eastern neigborhood called “Victoria - Fraserview” (UTM 10, 495410E, 5462213N). It
includes dominant urban land uses - the downtown core, medium density residential, single detached residential, light industrial
development, parks and forest. The study area is encompassing approximately 11.1% of the total area of the City of Vancouver,
and was selected, because of the provision of high resolution geospatial data, including LIDAR measurements of urban form
used for building emission simulations in previous research (van der Laan, 2011), the availability of detailed traffic counts, and25
the location of a 30-m tall eddy-covariance tower within the study area.
2.2.2 Tower-based measurements
The eddy-covariance tower “Vancouver-Sunset”,(ID: Ca-VSu FLUXNET (2016); Crawford and Christen (2015)) is located
at the south east corner of the study area (UTM 10, 494273 E, 5452641 E). The eddy-covariance tower was instrumented
with a CSAT-3 ultrasonic anemometer-thermomemter (Campbell Scientific Inc., Logan, UT, USA) which provides continuous30
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Burrard Inlet
City of
Vancouver
Eddy-covariance tower
Sunset study area
Start / end
Trails mapped by bike
0 500 1000 1500 2000 m
N
Vehicle 1
Vehicle 2
Vehicle 3
Vehicle 4
Vehicle 5
Stanley Park
Downtown
Fairview
Mount Pleasant
Kensington -
Cedar Cottage
Sunset
Riley
Park
Victoria -
Fraserview
Westend
Figure 2. Map of the study area, a 12.7 km x 1 km quadrangle (black outline) in the City of Vancouver, BC, Canada. Black lines refer to the
paths of each of the five DIYSCO2systems. The colored areas are the neighborhoods used in further analysis. Shown are also the location of
the eddy covariance tower and the start and end point of all paths (where all five systems were cross-checked before and after the campaign).
The 1.9 ×1.9 km box labelled “Sunset study area” refers to the domain of previous research, including the fine scale emission inventory
developed by Christen et al. (2011) and Kellett et al. (2013), and 24 hour measurements of CO2storage by Crawford and Christen (2014).
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measurements of sensible heat flux (H), wind direction, wind velocity. Further a shielded HMP 45 thermometer / hygrometer
(Vaisala Inc., Vanta, Finland) provided air temperature (Ttower). A CNR-1 net radiometer (Kipp & Zonen, Delft, The Nether-
lands) measured all four radiation components including long-wave upwelling radiation (L↑). Carbon dioxide molar mixing
ratio rtower was measured near tower top (28 m) using a tube that pumps air to a TGA200 closed path analyzer (Campbell
Scientific Inc.) and additionally by a Licor-7500 open path IRGA (Licor Inc., Lincoln, NK, USA). The TGA200 is calibrated5
every 10 minutes against three WMO-traceable tanks of known CO2mixing ratios to ensure an accuracy of about <0.15 ppm.
The Licor-7500 is calibrated twice a year in the lab. Further details of the site location, instrument exposure and data processing
are discussed in Crawford and Christen (2015). The availability of this tower made it possible to link mobile measurements
with data from above the city and determine aerodynamic resistances for the calculation of emissions (see Section 2.4.1)
2.2.3 Mobile measurements10
Two field campaigns took place, one on 28 May 2015 (non-heating season, broadleaf vegetation with leaves emerged) and one
on 18 March 2016 (heating season, before leaf emergence), both between 10:00 and 13:30 local time. For simplicity, data sets
from the two dates will be referred to as “summer” (28 May 2015) and “winter” (18 March 2016). The measurement period
was set between 10:00 to 13:30 because this time period was identified to show relatively consistent traffic counts throughout
the transect as well as relatively stationary meteorological conditions.15
In order to ensure that the study area was comprehensively sampled during the duration of the measurement campaign,
transects were predefined for each of the five DIYSCO2systems (Fig. 2). Taken together, the routes were drawn such that the
DIYSCO2would not only sample some of the same street segments at different times throughout the campaign, but also that
a majority of the streets and lanes in the study area would be sampled at least once in the 3.5 hour time period. The predefined
routes were evaluated using an overlaid 100 m ×100 m grid, confirming that nearly all of the grid cells would be traversed by20
at least one system if the routes were successfully completed. Each vehicle was assigned a path to travel approximately 70 km
during the study period (achieving an optimal sampling density of about 3.5 km2hr−1). Each vehicle started and ended at the
southeast corner of the transect (UTM 10, 494860 E, 5452010 N, Fig. 2). Furthermore, a bike was used to traverse trails in the
forested area of “Stanley Park” to sample in the densely forested ecosystem away from roads.
Five DIYSCO2systems were installed on vehicles and recorded CO2mixing ratios rmobile, air temperature and GPS25
location at 1 Hz. Prior to the mobile measurements, all vehicles were parked on the South-Eastern corner of Gordon Park,
away from major streets in a school parking lot. The five DIYSCO2systems were operated for a 15 minute warm up period
in their respective vehicles parked next to each other, and then logged for 5 minutes in order to determine their relative offsets
before the field campaign; this is called the “in-situ calibration”. During the test, all people moved away and 30 m downwind
of the vehicles to avoid contamination from human exhaust and all engines were turned off. After the 3.5 hour traverse, all30
vehicles returned to the starting location, where a second “in-situ calibration” was performed. The data collected in the in-situ
calibration was used to determine offsets and drift of the sensors during the campaign. The slope of the senors was determined
in the lab the day before each campaign using two reference tanks.
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2.3 Data analysis
2.4 Data post-processing and griding
The 1 Hz-data from all five DIYSCO2systems were first filtered following the methods in Crawford and Christen (2014). Data
were omitted if the GPS speeds were below 5 kmh−1(to avoid self contamination by vehicle exhaust when idling). Data were
also removed where the IRGA cell temperature and pressure were below 45 ◦C and 96 kPa, to measure within the specifications5
and calibration of the Li-820.
Vector matrix grids of 50 m ×50 m, 100 m ×100 m, 200 m ×200 m, and 400 m ×400 m were mapped onto the study
area in a Geographic Information System to spatially aggregate and attribute the rmobile measured by the DIYSCO2systems
to square grid cells. The separate data analysis for the 50 m, 100 m, 200 m, and 400 m grids provided a way to determine
the effects of grid size on emissions estimates. In the results section, the 100 m grid is selected, because the 100 m grid cell10
size was determined to be significantly large enough to avoid most micro-scale horizontal advection of emissions while also
still attributing emissions at a traceable scale to individual arterial roads and features. Appendix C explores the effect of using
different grid sizes by comparing the results from the 100 m grid to the 50 m, 200 m, and 400 m grids.
For each cell, the summary statistics were computed for all valid data points intersecting it. The summary statistics included
the mean, median, maximum, minimum, range, skewness, and variance. The gridded data were also classified by neighborhood15
(Fig. 2) to enable comparisons of rmobile for areas of different urban form and density. Only grid cells with actual measurements
were retained for the analysis. All of the grid cells that did not fall “completely within” the boundaries of the study area were
withheld from the analysis.
2.4.1 Emission calculation and comparison
Data from the eddy covariance tower is used in conjunction with the gridded averages of rmobile to calculate emissions for each20
grid cell based on the aerodynamic resistance approach which posits that the molar flux of CO2for a given area and time (w0c0
in µmolm−2s−1) is equal to the difference of the molar concentration c(in µmolm−3) at the height above the RSL (ctower)
and screen level at 2 m height (cmobile) divided by the aerodynamic resistance of CO2(in sm−1):
w0c0=−ctower −cmobile
raC
(1)
While both, ctower and cmobile are available through the measurement of rand density (considering pressure and air tem-25
perature), the challenge is that raC cannot be directly and easily measured due to the spatial heterogeneity of w0c0and cmobile.
Hence, to make the approach more robust, it uses the availability of sensible heat flux H(W m−2), air temperature at 24 m
height (Ta) and surface brightness temperatures (T0). This is possible because a city is a relatively homogeneous source of
sensible heat and temperatures are more uniform than CO2fluxes and mixing ratios. From the tower measurements of air
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temperature (Ta) and surface brightness temperature we then calculate the aerodynamic resistance of sensible heat raH (Kanda
et al., 2007). raH is the integral resistance from the surface (ground, roofs) to the top of the tower.
raH =ρcp
Ttower −T0
H(2)
where Ttower is the air temperature (K) at the height of the tower (24 m), T0is the surface brightness temperature (in K,
calculated as T0= (L↓/σ)0.25 ) from the long-wave radiometer, where σ= 5.6×10−8W m−2K−4is the Stefan-Boltzmann5
constant), and His the sensible heat flux (W m−2) measured by eddy covariance.
In a next step we assume Reynolds analogy, which assumes equivalency of scalar transfer, i.e. that the aerodynamic resistance
of sensible heat is equal to the aerodynamic resistance of carbon dioxide (raC ) and rewrite Eq. 1.
In order to convert the molar flux w0c0(in µmolm−2s−1) to a mass flux Fcconsistent with inventories (in kg CO2ha−1hr−1),
we rewrite:10
Fc=−Mcbabtbobm
ctower −cmobile
raH
(3)
where Mcis the molar mass of CO2(44.01 g mol−1), bais a factor for converting m−2to ha−1(i.e. ba= 104m2ha−1),
btis a factor for converting s−1to hr−1(i.e. bt= 3600s hr−1), bois the factor for converting µmol to mol (i.e. bm=
10−6µmolµmol−1) and bmis the factor for converting gto kg (i.e. bm= 10−3kgg −1).
Equation 3 was applied to each grid cell in the two measurement campaigns, where cmobile varied for each grid cell and15
each time, while raH and ctower varied only over time. The calculated emissions Fcare then compared to independent gridded
building and traffic emissions estimates to test the feasibility and accuracy of the method (the derivation of the independent
emissions inventories is documented in Appendix B.
In summary, this procedure to calculate emissions from mobile and tower measurements is only valid under the following
key assumptions:20
1. CO2concentrations in the well mixed UBL (the tower location) at daytime will not change dramatically over a short
time period or space (e.g. over 30 min time periods are long enough where urban fluxes are well represented) given
the same meteorological conditions and are therefore in an equilibrium. In other words, the measurements of ctower are
representative of the UBL above each grid cell at any time.
2. The flux at the height of the tower is directly related to the flux at the surface, hence concentration changes over time25
in the layer between surface and tower are negligible at day (i.e. no storage flux). This assumption is supported for
the daytime by independent measurements documented in Crawford and Christen (2014) for the current study area and
in Helfter et al. (2011) for a higher-density area in Central London, UK. Bjorkegren et al. (2015) and Crawford and
Christen (2014) and also conclude that this assumption is severely violated at night and in the early to mid morning, so
the proposed approach would only work midday or afternoon.30
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Published: 20 June 2016
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Table 1. Summary of weather conditions during the two campaigns (from 09:00 to 13:00 PST) measured on top of the urban climate tower
“Vancouver-Sunset” (Ca-VSu) located within the study transect
Summer Winter
28 May 2015 18 March 2016
Surface temperature 31.0 ◦C15.2 ◦C
Relative humidity (26.0 m) 71.5% 36.2%
Solar irradiance (26.2 m) 817 W m−2475 W m−2
Net radiation (26.2 m) 680 W m−2323 W m−2
Sensible heat flux (28.8 m) 390 W m−2120 W m−2
Wind speed (28.8 m) 2.6 ms−11.9 m s−1
Wind direction (28.8 m) 237◦70◦
CO2mixing ratio (28.8 m) 396.6 ppm 420.2 ppm
3. Reynolds analogy applies to raC =raH and raH and therefore raC is constant across all the urban densities/local climate
zones (LCZs) in the study area/city. Despite the fact that there are varying urban densities throughout a city, the idea is
that the resistance will not change significantly.
4. Lateral Advection of CO2between the surface and the height of the tower in-between grid-cells are negligible, or at least
add random (unbiased) noise.5
3 Results
3.1 Field campaign
Weather conditions on both dates were cloudless, convective and steady. Table 1 summarizes the weather and environmental
conditions for the two campaigns.
3.1.1 Raw data points10
A total of 41,027 1 Hz-measurements were available in summer and 42,786 measurements in winter from the 5 DIYSCO2
systems during a 3.5 hour window after filtering. Fig 3 shows the frequency distribution of the filtered 1 Hz rmobile measured
by all five DIYSCO2systems alongside the mixing ratio on the tower (rtower).
In summer, the measured 1 Hz rmobile were ranging from 380.2 ppm to 918.1 ppm with a median and average rof 408.5
ppm and 419.5 ppm (std. dev. 32.35 ppm) respectively for the entire dataset. The lowest rmobile (<400 ppm) were measured in15
the forest at “Stanley Park”, in select well vegetated residential streets, and in a large cemetery. The highest values (>800 ppm)
were measured in “Downtown” and along the major transport corridors such as “Knight St.” (Fig. 4) and “West Georgia St.”
11
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350 400 450 500 550 600 650 700
rmobile (ppm)
rtower
rtower
0
25
50
75
100
Cumulative frequency (%)
450.4 ppm
422.3 ppm
432.7 ppm
408.5 ppm
422.8 ppm
403.7 ppm
Figure 3. Cumulative frequency distribution for raw 1-second rmeasured by all five mobile systems in the summer (red) and winter (blue)
campaign. The thin vertical lines correspond to the average ron top of the tower during the period of the campaign. The colored numbers on
the horizontal lines refer to the 25%, 50% and 75% percentiles for summer (red) and winter (blue).
(Highway 99). In winter, overall rwere higher for both tower and mobile system. In winter, the measured 1 Hz rmobile were
ranging from 401.4 ppm to 918.5 ppm with a median and average rmobile of 432.7 ppm and 443.9 ppm (std. dev. 34.77 ppm).
2% and 16% of the measured rmobile were lower than the tower (rtower) during the summer and winter campaign, respec-
tively. 3% and 7% were higher than 500 ppm in summer and winter, respectively.
3.1.2 Grid sample counts5
For the 100 m ×100 m grid cells that could be traversed, in summer 91.31% of the grid cells contained more than 10 samples
per grid cell, 69.24% of cells contained more than 20 samples, and 28.32% of cell contained more than 50 samples. For the
winter campaign, 90.85% of the grid cells contained more than 10 samples, 72.64% contained more than 20 samples, and
27.36% contained more than 50 samples. Grid cells with less than 10 samples were removed from further analysis. Generally,
grid cells along major roads tended to have more sample counts because they were traversed at different times, often by different10
vehicles.
3.1.3 Grid averaged statistics
Of the 1332 grid cells that could be traversed by a car or bike, the case study covered 1024 in summer and 1037 in winter, of
which 821 and 856 were further used (based on the condition of more than 10 samples). The maps of gridded rmobile for the
summer and winter campaign are shown in Fig. 5. Table 2 summarizes the measured mixing ratios separated by neighborhood.15
12
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Figure 4. 3D-visualization of all raw rmobile measurements from all systems (summer campaign) in the “Sunset / Victoria-Fraserview”
neighborhood. The visualization is illustrating the high density of measurements taken along streets, laneways, and in parks. The linear area
with many higher mixing ratios is the busy 6-lane “Knight St”. with ≈50,000 vehicles per day. Image visualized in Google Earth.
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Table 2. Grid-averaged mixing ratios (rmobile), standard deviation of all grid cell means in the neighborhood, and fraction of cells with
rmobile < rtower per neighborhood
Neighborhood LCZ(a)Mean mixing ratio Std. dev. of rmobile Fraction of cells Number of
rmobile (ppm) (ppm) with rmobile < rtower grid cells
Summer Winter Summer Winter Summer Winter Summer Winter
Stanley Park A 413.7 435.6 19.1 24.3 4% 28% N=78 N=86
West End 1 416.1 442.7 15.1 15.9 1% 4% N=102 N=111
Downtown 1 437.8 474.9 19.2 26.5 0% 0% N=117 N=115
Fairview / Mount Pleasant 6 & 8 421.2 446.2 19.0 17.6 0% 0% N=136 N=144
Kensington-C. C. / Riley Park 6 411.0 432.3 13.5 15.1 1% 11% N=225 N=245
Sunset / Victoria-Fraserview 6 413.3 434.7 14.2 16.0 0% 8% N=163 N=155
(a)“LCZ” refers to the dominant local climate zones in the neigborhood according to Stewart and Oke (2012).
In summer, the grid averaged rmobile of all valid gird cells in the entire transect ranged between 393.1 ppm and 518.0 ppm,
averaged 417.9 ppm, and had a median of 410.0 ppm. In winter, the grid averaged rmobile ranged between 408.4 ppm and
560.5 ppm, averaged 442.5 ppm. 3% of all grid cells in summer, and 8% in winter were showing a rmobile that was lower than
rtower, the majority of those cases were located in the forested “Stanley Park” in both campaigns (Tab. 2). Selected cells in the
residential parts of “Riley Park / Kensington - Cedar Cottage” neighborhood were also showing a rmobile that was lower than5
rtower.
Both campaigns showed considerable variation of rmobile between grid cells in the same neighborhoods. Overall, the grid
cells covering major arterial roads and downtown core showed the highest maximum, minimum, median and mean rmobile.
Conversely, the grid cells covering residential streets and forested trails exhibited the lowest rmobile for the same statistics.
Of all neighborhoods, “Kensington-Cedar Cottage / Riley Park” exhibited the lowest, and “Downtown” the highest average10
rmobile in both campaigns (Tab. 2).
Similarly, standard deviations within each 100 m grid cell (not shown) are highest along the major arterial roads and in
“Downtown”. In contrast, the residential areas have lower standard deviations within grid cells indicating less variability in
rmobile for less busy roads. The trends are similar in the winter campaign except that there is overall higher standard deviation
in the residential areas compared to the summer campaign. Over 65.98% of the cells in summer and 66.80% in winter had a15
positive skewness which means there are intra-grid peaks in measured CO2mixing ratios.
3.1.4 Measured emissions
The aerodynamic resistance raH for each measurement campaign was calculated by averaging H, averaging T0, and averaging
Ttower over the 3.5 hours of the field campaign. The resulting raH was 34.14 s m−1in Summer and 56.12 s m−1in winter.
The measured CO2emissions calculated using Eq. 1 showed a range of -12.0 kg CO2ha−1hr−1(net uptake) to 225.620
kg CO2ha−1hr−1in the summer campaign and -13.7 to 162.4 kg CO2ha−1hr−1in winter. The median and average
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Published: 20 June 2016
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5451000
5454000
5457000
5460000
5463000
5451000
5454000
5457000
5460000
5463000
lat
(a) Summer
(b) Winter
400
450
500
550
Measured CO
2
Mixing Ratios
r
mobile
(ppm)
Stanley Park
Downtown
Fairview Mount Pleasant
Kensington -
Cedar Cottage
Sunset
Riley
Park
Knight St.
E 41
st
Ave.
Victoria -
Fraserview
Westend
Main St.
Cambie St.
Fraser St.
E 57
th
Ave.
E King Edward
12
th
Ave.
Burrard St.
Denman St.
Hwy 99
Figure 5. Map of grid-averaged CO2mixing ratios (rmobile) for (a) summer and (b) winter campaign using the same scale. The grid size is
100 ×100 m.
emissions were respectively 20.1 and 35.0 kg CO2ha−1hr−1for the summer campaign and 17.1 and 25.6 kg CO2ha−1hr−1
for the winter campaign. Highest emissions in general were located in “Downtown” and along the major transport corridors
and intersections (Fig. 6, Tab. 3).
3.2 Comparison to emissions inventory
3.2.1 Characteristics of emissions inventories5
The gridded traffic emissions inventory at 100 m ×100 m resolution (see Appendix B1 and Fig.7a) showed median and mean
emissions respectively of 2.37 and 12.50 kg CO2ha−1hr−1for the summer campaign and 2.17 and 12.19 kg CO2ha−1hr−1
for the winter campaign. As expected, the major roads and the areas with the densest road network (e.g. “Downtown”) exhibited
the highest emissions, all of which were greater than 18 kg CO2ha−1hr−1. The greatest traffic emissions in a single grid cell
was 123.60 kg CO2ha−1hr−1.10
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(a) Summer (b) Winter
0
-5
-10
40
80
120
160
Measured CO
2
Emissions
(kg CO
2
ha
-1
hr
-1
)
Net EmissionNet Uptake
Knight St.
E 41
st
Ave.
Main St.
Cambie St.
Fraser St.
E 57
th
Ave.
E King Edward
12
th
Ave.
Burrard St.
Denman St.
Hwy 99
Stanley Park
Downtown
Fairview Mount Pleasant
Kensington -
Cedar Cottage
Sunset
Riley
Park
Victoria -
Fraserview
Westend
Figure 6. Measured emissions (calculated from mixing ratios using the aerodynamic resistance approach in Eq. 1) for (a) summer and (b)
winter campaign at a resolution of 100 ×100 m.
The building emissions inventory (see Appendix B2), is shown in Fig.7b. In summer, the data for the 100 m grid showed a
median and mean of 6.69 and 10.19 kg CO2ha−1hr−1, respectively. In winter, the data for the 100 m grid showed a higher
median and a higher mean of 13.08 and 20.44 kg CO2ha−1hr−1, respectively. The maximum rate of building emissions was
located in “Downtown”. The building emissions inventory only covers a subset of the transect area (Fig. 7b). Data for part of
“West End” and for “Stanley Park” are not available.5
The total emissions inventory is the sum of the building and traffic emissions estimates (Fig.7c). For the summer campaign,
the median and mean of the total emissions estimates were 10.15 and 22.06 kg CO2ha−1hr−1, respectively. Overall, for the
area with both inventories available, 59% of the emissions were estimated from traffic and 41% from buildings. For the winter
campaign, the total emissions estimates were 15.87 and 28.76 kg CO2ha−1hr−1, respectively, and 41% of the emissions were
estimated from traffic and 59% from buildings. The fraction of traffic emissions is higher in the detached residential areas (LCZ10
6 and 8) and lower in “Downtown” (Tab. 3).
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Table 3. Comparison of measured emissions, with inventory emissions, separated by neighborhood based on a 100 ×100 m grid.
Neighborhood Measured Emission Relative Mean absolute Fraction Grid
emissions inventory error (RE) error (MAE) of traffic cells
(kg CO2(kg CO2(kg CO2
ha−1hr−1)ha−1hr−1ha−1hr−1)
Summer
West End 47.6 30.4 +56% 29.3 34% N= 21
Downtown 75.1 63.3 +19% 28.9 54% N= 90
Fairview / Mount Pleasant 41.4 27.4 +51% 19.7 70% N= 136
Kensington-C. C. / Riley Park 21.9 14.5 +51% 10.9 60% N= 225
Sunset / Victoria-Fraserview 26.5 13.3 +99% 15.3 73% N= 162
Winter
West End 30.1 43.4 -31% 24.8 22% N= 24
Downtown 65.3 92.1 -29% 41.6 35% N= 92
Fairview / Mount Pleasant 30.3 34.7 -13% 14.6 52% N= 142
Kensington-C. C. / Riley Park 14.0 19.4 -28% 10.1 40% N= 244
Sunset / Victoria-Fraserview 16.8 17.1 -2% 12.4 56% N= 155
3.2.2 Mixing ratios vs. emissions inventory
First, measured rmobile were compared to the emissions estimates to identify if there is a direct relationship between measured
mixing ratios and hourly emissions estimates from the emissions inventory. It is observed that as emissions in the inventory
increase, the range of the measured rmobile becomes greater. The relationship between measured rmobile and traffic shows
generally a linear correlation (Fig. 8a and b). Further, measured rmobile and building emissions are also positively correlated,5
but with more scatter (Fig. 8c and d). Best agreement is achived when comparing rmobile to the total (i.e. traffic + building)
emissions (Fig. 8e and f). The linear equations given in Fig. 8e show R2= 0.53 in summer and R2= 0.47 in winter.
3.2.3 Measured emissions vs. emissions inventory
Figure 9a and b show the measured emissions as a function of the traffic emissions inventory. The data show that 86.71% of
the measured emissions are within a factor of ±10 of the traffic emissions estimates for 100 m grids for the summer campaign10
(grey shaded area in 9). For the winter campaign, 93.74% of the measured emissions are within a factor of ±10 of the traffic
emissions estimates for 100 m grids. In particular in areas with lower traffic emissions and where the urban density is lower
(e.g. “Sunset / Victoria-Fraserview”) the measurements are higher than the emission inventory (note that building emissions
are not considered in Fig. 9a and b). The measured emissions and the traffic emissions inventory were found to be correlated
positively by 77.87% for the 100 m grid in the summer campaign and 71.75% in the winter campaign.15
17
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Published: 20 June 2016
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5451000
5454000
5457000
5460000
5463000
lat
5451000
5454000
5457000
5460000
5463000
lat
5451000
5454000
5457000
5460000
5463000
(a) Traffic
(b) Buildings
(c) Traffic + Building
no data
no data
Inventory of CO
2
Emissions
(kg CO2 ha-1 hr-1)
0
50
100
150
200
Figure 7. Emission inventory for (a) traffic emissions, (b) local building sector emissions, and (c) total (traffic + buildings) emissions for the
time of the winter campaign. The equivalent emission inventory for the summer date (not shown) does not look significantly different, but
has overall lower building emissions. Note that the building inventory, available from a previous study, did not extend into the Northern part
of the transect (label "no data") due to lack of high-resolution LIDAR data in this part of the city.
In Fig. 9c and d measured emissions and the building emissions inventories are compared for each grid-cell. Building
emissions are clustered by neighborhood with the lowest urban density (LCZ 6) of “Sunset / Victoria-Fraserview” exhibiting
the lowest emissions and “Downtown” with the highest urban density (LCZ 1) exhibiting the highest building emissions.
Across all neighborhoods, the measured emissions are higher than the building emissions only (note that traffic emissions are
not considered in Fig. 9c and d). The measured emissions and the building emissions estimates were found to be correlated5
positively by 35.91% for the 100 m grid in the summer campaign and 32.42% in the winter campaign.
Last, Fig. 9c shows the measured emissions as a function of the total emissions (building + traffic) inventory. For the summer
campaign the data show that 86.71% of the measured emissions are within a factor of ±10 of the total emissions estimates
for 100 m grid. The measured emissions and the total emissions inventory were found to be correlated positively by 77.87%
18
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110 100
Inventory building emissions (kg CO
2
hr
−1
ha
−1
)
350
400
450
500
550
600
110 100
Inventory building emissions (kg CO
2
hr
−1
ha
−1
)
−20
0
20
40
60
80
100
120
r
mobile
− r
tower
(ppm)
110 100
Inventory traffic + building emissions (kg CO
2
hr
−1
ha
−1
)
−20
0
20
40
60
80
100
120
r
mobile
− r
tower
(ppm)
r
mobile
(ppm) r
mobile
(ppm) r
mobile
(ppm)
r
mobile
= 0.47 E
inventory
+ 429.2
rmobile
= 0.56 E
inventory
+ 405.6
110 100
Inventory traffic + building emissions (kg CO
2
hr
−1
ha
−1
)
350
400
450
500
550
600
110 100
Inventory traffic emissions (kg CO
2
hr
−1
ha
−1
)
−20
0
20
40
60
80
100
120
r
mobile
− r
tower
(ppm)
110 100
Inventory traffic emissions (kg CO
2
hr
−1
ha
−1
)
350
400
450
500
550
600
(a) Traffic emissions (b) Traffic emissions
(c) Building emissions (d) Building emissions
(e) Traffic + building emissions (f) Traffic + building emissions
rtower
rtower
rtower
rtower
rtower
rtower
Winter
Summer
90%
10%
25%
50%
75%
mean
Winter
Summer
Figure 8. (left column: a,c,e) Comparison of inventory (traffic only, building emissions only, and total emissions) against grid-averaged
mixing ratios (rmobile) where each dot is a 100 ×100 m grid cell. Note that the x-axis is logarithmic. The curves in (e) are linear fits (Right
column: b,d,f). Comparison of inventory (traffic only, building emissions only, and total emissions) to the difference between grid-averaged
mixing ratio rmobile and the mixing ratio measured at the tower.
19
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10 100 1000
Inventory building emissions (kg CO
2
hr
−1
ha
−1
)
1
10
100
1000
Measured emissions (kg CO
2
hr
−1
ha
−1
)
10 100 1000
Inventory traffic emissions (kg CO
2
hr
−1
ha
−1
)
0.1 1
0.1
0.1 1
0.1
1
10
100
1000
Measured emissions (kg CO
2
hr
−1
ha
−1
)
Stanley Park
West End
Downtown
Sunset
Victoria-Fraserview
Kensington-Cedar Cottage
Riley Park
Fairview
Mount Pleasant
10 100 1000
Inventory traffic + building emissions (kg CO
2
hr
−1
ha
−
1
10
100
1000
Measured emissions (kg CO
2
hr
−1
ha
−1
)
10 100 1000
Inventory traffic + buildings emissions (kg CO
2
hr
−1
ha
−1
)
1
10
100
1000
Measured emissions (kg CO
2
hr
−1
ha
−1
)
10 100 1000
Inventory traffic emissions (kg CO
2
hr
−1
ha
−1
)
1
10
100
1000
Measured emissions (kg CO
2
hr
−1
ha
−1
)
10 100 1000
Inventory buildings emissions (kg CO
2
hr
−1
ha
−1
)
1
10
100
1000
Measured emissions (kg CO
2
hr
−1
ha
−1
)
n = 819
n = 634
n = 634
n = 849
n = 659
n = 657
0.1 1
0.1
0.1 1
0.1
0.1 1
0.1
0.1 1
0.1
(a) Summer (traffic only) (b) Winter (traffic only)
(c) Summer (buildings only) (d) Winter (buildings only)
(e) Summer (traffic + buildings) (f) Winter (traffic + buildings)
Figure 9. Comparison of inventory emissions and measured emissions on a grid-by-grid basis plotted with double logarithmic axes. The black
line is the 1:1 curve and the grey area shows data within one order of magnitude of each other. Grid cells with less than 0.1 kg CO2ha−1hr−1
in the emission inventory and/or measured emissions are not shown. nrefers to the number of grid cells included in the comparison.
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for the 100 m grid. For the winter campaign, the data show that 92.58% of the measured emissions are within a factor of ±10
of the total emissions estimates for 100 m grid. The measured emissions and the total emissions inventory were found to be
correlated positively by 71.75% for the 100 m grid.
Across all valid grid cells in the study area, the measured emissions in summer averaged to 35.11 kgCO2ha−1hr−1as
compared to 22.06 kg CO2ha−1hr−1of the emissions inventory. In winter, the measured emissions in averaged to 25.925
kg CO2ha−1hr−1as compared to 28.76 kg CO2ha−1hr−1of the emissions inventory.
In summer, 73% of the grid cells show measured emissions that are greater than the corresponding grid cells of the total
emissions inventory. For the winter campaign, only 35% of the measured emissions are greater than the total emissions inven-
tory. For both the summer and winter campaigns, emission measurements are higher than inventory in grid cells along major
arterial roads whereas the measurements are lower than the inventory in residential areas and in “Downtown”.10
The mean absolute error (MAE) for all grid cells in the entire transect between measured and modelled total emissions
is 17.1 in summer and 16.6 in winter. The median absolute error for the entire transect is 9.6 in summer and 9.9 in winter.
Table 3 lists the MAE by neighborhood. The MAE is about a factor 2 larger in “Downtown” and “West End” compared to the
residential and industrial neighborhoods.
The relative error (RE) is defined as the difference between a grid cell’s measured emission and the same cell’s emissions15
inventory divided by the cell’s emissions inventory. The data for the 100 m grid show that 62% of the grid cells in summer
and 81% in winter have a RE within a factor of ±1. As expected, locations with higher relative errors were locations in which
the building and traffic emissions inventories estimated almost zero but measured emissions were higher. When excluding grid
cells with emissions <10 kg CO2ha−1hr−1) in the inventory, 80% of the grid cells in summer, and 91% in winter have a RE
with a magnitude of less than ±1.20
4 Discussion
4.1 Assessment of the measurement methodology
Overall, the developed approach lead to realistic and consistent results. The spatial patterns of measured emissions are plausible
and match generally the fine-scale inventories of traffic and buildings although at the scale of an individual grid cell, large errors
up to an order of magnitude are observed. The study was also able to replicate in the winter campaign the spatial patterns and the25
magnitude found in summer. The results demonstrate the potential to apply an aerodynamic resistance approach to measuring
emissions using a network of mobile sensors and data from an urban climate tower.
Building and traffic emissions are both good predictors of rmobile measured in a city at ground level. This implies that
values of rmobile, from microscale to neighborhood scales, are related the CO2emissions being generated at those scales (and
presumably this also holds for primary, less reactive air pollutants). This suggests that it is possible to link rto emissions across30
a complex landscape under specific, stationary atmospheric conditions. Nevertheless, several challenges remain.
Overall, the building emissions were less clearly correlated with the spatial variability in rthan traffic emissions which
were a better predictor. Building emissions of CO2(natural gas burning) are most likely injected into the atmosphere at roof
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level (chimneys), where higher winds blend them in the process of downward mixing into streets and laneways where mobile
sensors were operated. As a result of this blending, the signal of rmight show less spatial variability if emissions originate
from buildings (far from sensor) compared to situations near ground-level emissions (car exhaust on arterial roads). Measured
emissions generally tend to underestimate the inventory in “Downtown” where there are a high density of tall buildings that
vent their emissions usually at higher storeys, likely decoupled from the grid cells at ground. Consequently, the observed peaks5
in rare more likely to be a result of traffic emissions alone.
Despite these differences data aligned relatively well with an independent previous study by Christen et al. (2011) that
measured and modelled emissions for the 1.9 ×1.9 km study area surrounding the “Vancouver-Sunset” tower (see Fig. 2).
In the study area, the annual total CO2emissions were modelled to be 26.87 kg CO2ha−1hr−1and validated using direct
eddy-covariance measurements of CO2, which were on average 25.96 kg CO2ha−1hr−1over the year. The current study10
estimates emissions for the “Sunset / Victoria-Fraserview” neighborhood (that overlaps with the area in (Christen et al., 2011))
as 21.65 kg CO2ha−1hr−1(average of summer and winter campaigns). Note that the time scales of the two studies disagree.
Christen et al. (2011) report annual and monthly emissions, while the current study is restricted to weekdays between 10:00
and 13:30.
In selected areas negative net ecosystem exchange (NEE) were detected, such as in the forest at “Stanley Park”, in some15
highly vegetated urban residential areas and the lawn area of a cemetery. This is plausible, because most grid cells have likely
some uptake by photosynthesis of urban vegetation, but in many cells the emissions from combustion and respiration combined
are greater than photosynthesis. In comparing our lowest measured emissions from “Stanley Park” (-12 kgCO2ha−1hr−1)
to a study by Humphreys et al. (2006) who measured NEE for a forest with similar stand composition (Douglas Fir forest on
Vancouver Island, 200 km to the W) in April and June in the same latitude. We find that our measured emissions were within20
a factor of 2 of those observed in a typical forest at the same time of day and year.
4.2 Possible refinements and errors
Ultimately, the comparison of measured emissions and the emissions inventories showed where there might be close alignment
or divergences between the datasets and suggests promising new research opportunities for improving the proposed methodol-
ogy and/or emissions inventories.25
4.2.1 Aerodynamic resistance
In terms of methodology, raH is calculated using Ttower and T0at a single location, likely not representative for the entire
city. There is evidence of varying aerodynamic resistances across the study area. For example in the narrow street canyons
of “Downtown” and in forested “Stanley Park”, it is likely that the aerodynamic resistance is higher, because of the sheltered
nature of the deep canyons and forest canopy, respectively. Generally, measured emissions could possibly be overestimated in30
streets with a denser tree canopy regardless if the canopy is vegetation or buildings. An area with a dense tree canopy may
actually reduce mixing (Jin et al., 2014) and as a result, the measured rmobile might be higher than emissions propose with a
constant raH across the study area. It would therefore be beneficial to consider variable aerodynamic resistances and to use
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models that relate canopy porosity to create maps of variability in raH. Further experiments should be done to determine how
raH and consequently the resulting Fcchange when using different methods of estimating raH .
4.2.2 Averaging procedure
A methodology to improve the grid averaging would be to sub-sample larger grid cells using a finer scale grid (e.g. 20 m ×
20 m or less) and then averaging those finer grid cells to lower grid resolutions as done in Crawford and Christen (2014).5
This would help to reduce some errors at two critical moments. First, it may be possible to average out some of the extreme
values within a grid cell that may be contributing to an over- or -underestimation of emissions within a grid cell due to a spatial
sampling bias. Second, it offers a possibility to determine the representativeness of the grid cell sample and attribute a certainty
or weight to each cell. Because the current methodology simply spatially attributes any point(s) to the grid cell in which it
intersects, we do not account for the degree in which point measurements represent the spatial mean of grid cells.10
4.2.3 Emission inventories
Several factors may account for the differences due to errors in the emission inventories. First, the emissions inventories
were not based on real-time models of the data for the period of the measurement campaign. The building emissions inventory
presents a challenge when comparing the grid averaged rand the measured emissions because the building emissions inventory
is downscaled to an hourly average from a yearly estimate. This hourly average is assumed to be constant over the course of15
the day, however, studies (e.g. Martani et al. (2012)) show that most building occupancy (and therefore energy use) occurs
between 9:00 and 19:00, with peaks around 13:00 and 16:00. Furthermore, this does not address the fact that spatially, building
energy use changes throughout the day as people go to and from work and home. Future work might attempt to quantify the
spatial ebb and flow of people using a combination of surveys, census data, and methods using call detail records to derive
home versus work locations as shown in Holleczek et al. (2014). Building energy use intensity might be modeled by season20
and diurnally based on factors such as building occupancy, building age, form, and function.
To explain differences in the traffic emissions inventory, we must account for the fact that the traffic emissions inventory
was derived from spatially and temporally disaggregated samples of short-term traffic counts. As a result, the traffic emissions
inventory may compound errors over time and space. Spatially, the traffic count dataset covers mostly the major roads which
leaves much of the residential areas unsampled. The method described in Appendix B1 is used to map traffic count values across25
the residential streets to overcome the missing traffic counts, however more validation is necessary to determine whether this
method is appropriate. Temporally, the traffic emissions inventory is not a real-time representation of the traffic counts during
the measurement campaign. Furthermore, the traffic emissions are generated using an emissions factor that is a fleet average
for the emitted CO2per liter of fuel burned. More precise estimates of emissions factor in the differences in the emissions
factor by vehicle type and fuel type (Kellett et al., 2013). Last, the traffic count data does not indicate the amount of emissions30
from idling that occur as a result of traffic jams and thus introduces another aspect of possible uncertainty within the traffic
emissions inventory, and can be substantially higher in urban contexts.
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The total emissions inventory factors only building and traffic emissions and excludes other sources of emissions such as
those from human, animal, and plant and soil respiration. Additional sources of CO2emissions could come from human
activities such as landscaping (e.g. lawnmowers and leafblowers) and construction. For example, a study by Kellett et al.
(2013) showed that, in a 1.9 km ×1.9 km study area around the “Vancouver-Sunset” tower (see Fig. 2), emissions from human
respiration and vegetation and soils can account for 8% and 5% respectively of the total emissions, respectively.5
5 Conclusions
Several studies have measured racross transects through cities (Jimenez et al., 2000; Idso et al., 2001; Henninger and Kuttler,
2007; Crawford and Christen, 2014), however no study to date has deployed multiple mobile CO2sensors simultaneously, and
no study has used the measured rin combination with a tower to determine emissions across a city.
A portable, mobile sensor system called the DIYSCO2was developed an tested. Five DIYSCO2’s were deployed across a10
12.7 km2study area over a period of 3.5 hours; the average sampling density was about 40 samples ha−1. Of the 11.7 km2
study area that could be traversed, 8.5 km2in summer and 8.2 km2in winter were sampled with >10 samples per grid cell.
Hence, excluding the grid cells with <10 samples, the sampling density was roughly 0.5 km2sensor−1hr−1over the 3.5 hour
period for the 5 sensors. If it is assumed that this sampling density is appropriate for representing urban scale processes, it
would require 230 coordinated mobile sensors on predefined routes to be deployed across the entire City of Vancouver (11515
km2) to measure CO2emissions across the city during the same time – obviously an effort that is not realistic.
However as sensor parts will become cheaper in the future, possibilities exist to integrate mobile sensor systems into op-
erational vehicles such as taxis (e.g. 600 in the City of Vancouver) and mobility-on-demand services (e.g. currently there are
>1000 carshare vehicles in the City of Vancouver). Alternatively, the time frame could be extended and using proper data
selection, one could create composite maps from rmobile measured on different days under similar conditions. It would take 1020
days in a coordinated effort to cover the entire City of Vancouver similar to the current transect.
A further question to be explored is whether the current number of samples (>10 s) per grid cell is sufficient to represent
the typical emissions in the cell given the intermittent traffic and the fact that large coherent structures are mostly responsible
for mixing of pollutants out of the urban canopy layer (Salmond et al., 2005; Christen et al., 2007). Would a higher density of
points (including multiple campaign days) improve the correlation between measured and inventory emissions?25
The method to map emissions based on the aerodynamic resistance approach is sensitive to the measurements that are used
to derive the aerodynamic resistance of heat and requires that a number of assumptions and conditions are met, yet, the work
shows that the aerodynamic resistance approach can be used reasonably on a scale of 100 ×100 m grid cells to derive emissions
from measures of aggregated mixing ratios. The measured emissions across the study area ranged from -12 kg CO2ha−1hr−1
to 225 kg CO2ha−1hr−1per grid cell, thus showing the possibility for this methodology to detect negative emissions (net30
uptake), where photosynthesis is greater than the combined combustion and respiration emissions.
The research presented is proof of concept for a future in which atmospheric sensing is integrated into urban mobility. We
haves shown the successful development of new technology and methodology for monitoring and mapping CO2mixing ratios
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and emissions in complex urban environments, at much finer scale than previously possible. Despite the simplicity of the
methodology, the study demonstrated that it is possible to measure emissions across a complex landscape with a fleet of mobile
sensors, an eddy-covariance tower, and the use of the aerodynamic approach to calculating emissions.
The data gained cannot be only used to map and validate emissions but could be integrated into regional efforts using
observations and inverse modelling (Newman et al. (2013)) or even with total column measurements of CO2from satellites.5
Further, the concept can and should be translated to the mapping of other trace gases and air pollutants, air and surface
temperature, and other environmental variables that affect human health, comfort, and safety. The development of smaller, more
affordable, mobile sensor systems can facilitate new methodological approaches to monitoring the urban environment. With
a fleet of mobile sensors and the methodologies for processing the derived datasets, the possibility to map and consequently
validate emissions inventories is promising, as is the derivation or real time pollution and climate data in cities.10
Appendix A: Testing of sensor system
Several key system specifications of the DIYSCO2were evaluated during the prototyping, namely: linearrity, accuracy and
drift, measurement lag time, between sampling and measurement, and the effects of inlet location on measurement variability.
A1 Sensor precision
The accuracy of the Li-820 is ensured using a two-point calibration, usually performed in the lab using a zero-gas and a span15
gas in the range of assumed measurement. However, precision and linearity of the Li-820 sensor is in particular relevant in
the range 400 to 500 ppm to enable comparisons between different DIYSCO2’s operated simultaneously and also to properly
compare rmobile −rtower.
To test the accuracy and linearity, a 6-point calibration was performed using six tanks of known mixing ratios of CO2
between 399.08 and 503.77 ppm. All standard tanks have been calibrated against CDML / NOAA WMO traceable tanks with20
a typical error in rof <0.1ppm. To perform this test, all Li-820 sensors were first left running for 2 hours to ensure significant
warm up time. After the warm up period, the DIYSCO2’s were connected to a calibration gas using a Union Tee connector.
For each of the six gases, the calibration protocol called for an initial two minute system flush and then a recording of the
values for at least 1 minute each. A minimum of 60 points per gas sample were used to calculate the average mixing ratios per
tank measured by the system. The data were recorded directly by the DIYSCO2data logger.25
The Li-820 contained in the DIYSCO2showed strong linearity (R2of 0.9999) and a root mean square error (RMSE) of
0.233 ppm for the six tanks of known CO2mixing ratios. This indicates that the IRGA is operating well within its factory
specifications of 1 ppm when calibrated and linearity is not a limiting factor for this type of study.
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A2 Sensor drift
Sensor accuracy and drift is assessed to determine the DIYSCO2’s ability to properly resolve the variability of mixing ratios
during the duration of the campaign. Sensor drift was tested over the course of 7 days, with 5 sensors drawing in air from the
same point outdoors at ≈3 m in an urban context.
The RMSE between the five system at a 1-minute resolution ranged between 0.2 and 3 ppm for the seven day period and5
is therefore time dependent. Given that the field campaign was planned to be 3 to 3.5 hours long, the maximum drift of any
sensor in any 3 hours was determined at most -0.31 ppm and 0.51 ppm relative to the mean of all 5 sensors. The drift was up
to ±3.32 ppm per day for individual sensors and days.
A3 Measurement lag time
The system measurement lag time is the time delay from when a measurement first enters the sample inlet of the system to when10
the signal is registered by the sensor. The DIYSCO2’s measurement lag time is important to correctly attribute measurements
to their geographic space.
For a given tube length and flow rate, the lag time will differ and therefore affect the system response characteristics. The
values here are for a tube length of 3 m. Lab measurements were performed in which a solenoid switch was used to pass
nitrogen gas with 0 ppm CO2into the sample tube inlet while simultaneously logging the exact second in which the solenoid15
was triggered. To calculate the lag time value for the system, the number of seconds were counted from when the sample enters
the sample tube until 50% of the change was reached.
The measurement lag time of the DIYSCO2system was determined to be 18.2 s. It took on average 16 seconds for the
sample to travel from the inlet to the IRGA and 2.2 seconds for the IRGA to register 50% of the step change. We consequently
used a value of 18 seconds in the post processing to shift the GPS and observed rmobile time series to properly attribute20
measurements spatially to locations.
A4 Effects of inlet location
Two tests were performed to examine possible sampling biases due to different sample inlet locations on a vehicle. First, a
test was done with five DIYSCO2in the same vehicle, where all the inlet tubes were bundled together measuring at the same
location of the vehicle (referred to as “Grouped Inlet Test”). A second test was done with each of the inlet tubes located at25
different locations on the same vehicle (referred to as “Ungrouped Inlet Test”). Locations tested were all at 2 m height: One
each above the driver’s side front, driver’s side back, passenger side front, and passenger side back window.
Both test were performed in the City of Vancouver using a Toyota Tacoma Truck along a route with traffic volumes ranging
from 300 to 850 vehicles per hour. In areas with a well-mixed atmosphere and on roads with little traffic, the DIYSCO2systems
for the grouped inlet test showed a range within ±0.5ppm of the mean all five sensors for 1 second data. For the ungrouped30
inlet test under those same conditions, the accuracy deteriorated to ±5ppm of the mean. With observations of higher CO2
mixing ratios, the standard deviation between all five of the DIYSCO2locations increases for the 1 s data. This is the case for
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both the grouped and ungrouped inlet tests. With inlets grouped together, 48.9%, 81.16%, and 90.14% of the one second data
have an error within 5, 15, and 25 ppm. While this indicates that more than half of the 1-s data measured by the sensors are
within 5 ppm of each other, the test also shows that we can expect a majority of the data (>88.85%) to have errors up to 15
ppm depending on where on the car the inlet is mounted. When examining the error of the observed values for the 1 min data,
we observed that 86.3% and 98.63% of the data have an error within 5 and 25 ppm.5
Appendix B: Emissions inventories
This Appendix described the derivation of the independent building and traffic emissions inventory that were compared against
the measured CO2emissions.
B1 Traffic emissions inventory
The fine-scale gridded traffic emissions inventory was based on hourly averaged directional traffic count data from 2008 - 201310
provided by the City of Vancouver (City of Vancouver, 2015).
For each hour of the day, traffic counts were spatially attributed to the Open Street Map road network. The City of Vancouver
provides traffic counts collected from pneumatic road tubes which are attributed to an approximate address of where the traffic
counters were located. The traffic counts do not distinguish between different vehicle classes and are aggregated to the street
level, meaning that, for this analysis, the traffic counts did not take into account the direction of travel.15
The City also provides a geospatial representation of the locations of the traffic counters with the address, but without the
count data attached. The geospatial data were merged with the count data. However, because spatial traffic counts do not align
with the OSM road network, the centroids of the spatial traffic count data were computed and then “snapped” to the OSM road
network. Before joining the traffic count data by the matching locations of the two datasets, the OSM road network was split
into segments using the 50 m ×50 m vector grid. A small (0.5 m) buffer was applied to the traffic count centroids to ensure20
that they spatially match onto the OSM road network and then were merged to the OSM dataset.
An algorithm was used to match the street names in the traffic count dataset to those in the OSM street network. Manual
mapping of traffic counts was necessary to attribute traffic counts to streets that were not sampled in the traffic counts. A rule
of proximity and local understanding of the traffic patterns for each of the streets was used to manually map the traffic counts
to the unsampled streets. Using the OSM street classifications, traffic counts for paths unnavigable by vehicles were given a25
value of “0” traffic counts, namely “steps”, “trail”, “footpath”, and “service”. Lastly, the traffic counts for forked roads in the
dataset which would have doubled the count for a particular street were divided in half.
With a complete model of the traffic counts for the transect, it was then possible to generate a gridded traffic emissions
inventory map of CO2(now referred to as “traffic emissions inventory”). The length of each of the street segments which had
been split in the earlier steps were calculated and then summed up per 50 m, 100 m, 200 m, and 400 m grid cell. Next, the30
length of navigable roads per grid cell were multiplied by the hourly traffic counts along each road, resulting in an estimate
of total distance of vehicle traveled per grid cell. Each grid cell’s hourly travel distance was then multiplied by the NRCAN
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fleet standard fuel comsumption (Natural Resources Canada, 2014) for urban driving (12.9 `100km−1) and after by a CO2
emissions factor (2.175 kg `−1fuel burned) (Environment, 2014) to generate the traffic emissions estimate map of CO2. In
this study, the traffic count data provided by the City of Vancouver is averaged across all of the years that the traffic count data
have been collected. The data are then scaled by a factor 0.9985 and 1.0216 to reflect the seasonally changing relative traffic
volumes for March and May based on automatic and continuous highway counts (weekday only) at 5 locations throughout5
Metro Vancouver.
B2 Building emissions inventory
The fine-scale gridded building emission inventory was developed in previous research and is documented in detail in van der
Laan (2011). It integrates Light Detection and Ranging (LiDAR) data, building simulation software and a building typology
database to model CO2emissions attributed to building energy use; The original building emissions inventory is on a per-10
building scale in carbon dioxide equivalent (CO2e, reported in kg CO2e yr−1). In this research, it is assumed that CO2eand
CO2are the same for building heating systems. This is then then converted to a 1m raster using building footprints derived
from LiDAR and property permeters. The 1 m raster was then averaged to the 50 m, 100 m, 200 m, and 400 m vector grids and
scaled to their estimated hourly values for both campaigns.
Because the inventory by van der Laan (2011) reports annual estimates (in kg CO2e m−2yr−1), a scaling factor based on15
monthly city emissions inventory was used in this study to account for the winter and summer building emissions fraction. In
the month of March and May, the building emissions for a sample of the City of Vancouver was estimated to be 99.85% and
63.63% of the annual average building emissions (reported in (Christen et al., 2011)). The final building emissions inventory
were reported in kg CO2ha−1hr−1. In this case, it is assumed that the building emissions are constant over the course of the
day.20
Each grid cell of the total emissions inventory is simply the sum of the building emissions inventory and the traffic emissions
inventory in kg CO2ha−1hr−1. Other emission processes such as human respiration or biological processes are not considered
in the inventory.
Appendix C: Effect of grid size
In addition to the 100 ×100 m grid, the raw data points were also gridded to 50 m, 200 m, and 400 m vector grids for both the25
winter and summer campaigns to explore the sensitivity of choosing different grid sizes.
C1 Effects on spatially averaged mixing ratios
Changes in grid size affected the study area mean rmobile by 6.1 ppm in the summer and only 1.1 ppm in the winter. Table 4
summarizes the statistics for different grid cell sizes. The grid maximum values for the 50 m, 100 m, 200 m, and 400 m grids
were 529.8, 518.0, 488.2, and 447.7 ppm respectively for the summer and 643.1, 560.5, 529.4, and 492.5 ppm respectively for30
the winter.
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Table 4. Summary data of the measured mixing ratios for all grid sizes for the summer and winter campaigns. The table shows the mean,
minimum, median, maximum CO2mixing ratio rmobile for the gridded data.
Grid Size Min Median Mean Max
(ppm) (ppm) (ppm) (ppm)
Summer
50m 393.1 409.4 417.3 529.8
100m 393.1 410.0 417.9 518.0
200m 397.0 412.9 419.6 488.2
400m 399.6 417.5 419.0 447.7
Winter
50m 408.4 434.5 442.6 643.1
100m 408.4 435.0 442.5 560.5
200m 408.4 436.8 443.7 529.4
400m 420.5 441.9 443.2 492.5
The highest grid maximums were observed in the 50 m grid size. This is expected because the most extreme rmobile are
spatially averaged out by larger grid cell sizes.
C2 Effects on spatially averaged emissions
In the summer campaign, the differences between the measured emissions and the inventory emissions increases as the grid size
increases (Tab. 5). The least amount of difference is seen in the 50 m grid at 6.88 kg CO2ha−1hr−1. In the winter campaign,5
the differences between the measured and inventory emissions are smallest in the 100 m (2.84 kg CO2ha−1hr−1) and 200 m
(0.9 kg CO2ha−1hr−1) grid sizes and are greatest in the 50 m grid size at 7.8 kg CO2ha−1hr−1.
In both campaigns, the spatial error (expressed RMSE) between measurements and inventory decreases as grid sizes become
coarser. In the summer campaign 80.05%, 86.71%, 85.31%, and 95.45% of the cells have measured emissions that are within
a factor of ±10 of the total emissions inventory for the 50 m, 100 m, 200 m, and 400 m grids, respectively. In the winter10
campaign, 91.16%, 93.74%, 94.20%, and 100% of the cells have measured emissions within ±10 of the total emissions
inventory.
For the winter campaign, we observe as grid size increases, the mean bias, i.e. differences between the mean measured
emissions and the mean inventory emissions decreases, presumably because more sampling points mean we average out random
errors in individual cells. The best match is found at 200 m resolution. Of course this is very sensitive to the calculated15
aerodynamic resistance and should not be interpreted as a generality.
For the summer campaign, however, there is an increasing difference between the mean measured emissions and the mean
total emissions. This may be best explained by the bias towards roads in the sampling methodology. In the summer campaign,
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Table 5. Mean measured emissions versus mean inventory emissions for the winter and summer campaigns
Grid size Measured emissions Inventory emissions Relative difference RMSE
(kg CO2ha−1hr−1) (kg CO2ha−1hr−1) (kg CO2ha−1hr−1)
Summer
50m 34.06 27.18 +29% 32.54
100m 35.11 22.06 +59% 27.91
200m 38.30 19.73 +94% 29.01
400m 37.26 15.27 +144% 28.57
Winter
50m 25.67 33.47 -23% 34.23
100m 25.92 28.76 -10% 25.39
200m 27.21 26.31 +3% 19.58
400m 26.60 23.33 +14% 17.71
the dominant source are vehicles constrained to roads. The difference between the average measured emissions and the total
emissions inventory is relatively small for the 50 m grid because the measurements are made mostly along roads and therefore
do not include traffic-free areas such as in the backyards of homes and within large street blocks which can have significantly
lower concentration of traffic-related pollutants (Weber and Weber, 2008). As a result, when comparing the average measured
emissions to the average of the total emissions inventories for the 100 m, 200 m, and 400 m grids, we see that a sampling5
bias becomes more apparent. The 50 m grid cell size is a more appropriate resolution for griding the point measurements
collected using this methodology when traffic emissions dominate. Additional sampling along alleys and laneways and more
representative sampling using alternative mobility options such as bikes or autonomous flying vehicles may help to improve
the relationship between measured emissions and the emissions inventory when griding at coarser resolutions.
Acknowledgements. This funding was supported through an NSERC Discovery Grant (“Direct measurement of greenhouse gas exchange10
in urban ecosystems”, A. Christen). Sensor development and tower infrastructure were funded in part through the Canada Foundation for
Innovation (Grants 17141 and 33600). Scholarship to J. K. Lee was provided through NSERC CREATE and through the “Mozilla Science
Lab”, . Experiment vehicles were sponsored by “moovel lab”, Stuttgart, Germany. We thank A. Black, R. Kellett, S. Lapsky, L. Lavkulich
(all UBC) for their guidance, support, and help.
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