We used a commercially available carbon dioxide infrared analyzer (IRGA) (Li-820, Licor Inc., Lincoln, NE, USA). The Li-820 is a compact (23.23 × 15.25 × 7.62 cm, 1 kg), low maintenance (approx. 2 years of continuous use), and low noise (±1 ppm) CO2 analyzer . The Li-820 uses a single path infrared light to determine the CO2 mixing ratio within a closed path by detecting the amount of absorption of the light from the path. The Li-820 was operated with a nominal sampling rate (data output) of 1 Hz but the actual time constant of the system was determined to be 3.2 s (see Appendix ). The gas analyzer was coupled with an Arduino microcontroller (Arduino CC, Ivrea, Italy). The Arduino platform is capable of communicating digitally with the IRGA, a Global Positioning System (GPS) unit (Adafruit Ultimate GPS Logger Shield with GPS Module, Manhattan, New York, USA) unit, and a digital temperature thermometer (Maxim Integrated One Wire Digital Temperature Sensor – DS18B20, San Jose, CA, USA). A custom hardware board was developed to connect all of the components together to distribute the correct amount of power to each of the hardware components and to allow for compact hardware and sensor input. The portable CO2 system was named the “Do-It-Yourself-Sensor-CO2”, or “DIYSCO2” system (Fig. a)

The DIYSCO2 system reports CO2 as mixing ratios (r) in ppm, geoposition (latitude/longitude, speed, altitude, and satellite strength), and internal and external air temperature which are logged onto a micro secure digital (microSD) card at 1 s intervals. Air is drawn into the DIYSCO2 system through a 3 m long inlet tube (6.35 mm diameter, Synflex, polyethylene–aluminum composite, Eaton, Eden Prairie, MN, 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 DIYSCO2 system (e.g. insects) and then through a Balston disposable filter unit (DFU) (Parker Hannifin Corporation, Lancaster, NY, USA) at the end of the 3 m tube. The flow rate is regulated by a Swagelok needle valve at 700 cc min-1 as recommended by Licor to minimize the effect of internal cell pressure changes on the CO2 measurements. The entire DIYSCO2 system is 35.8 cm × 27.8 cm × 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.

Within the range of typical ambient mixing ratios of CO2 between 400 and 550 ppm the DIYSCO2 system showed strong linearity (R2 of 0.9999) and a root mean square error (RMSE) of 0.233 ppm relative to four different mixing ratios (six tanks) of reference gases (see Appendix ). The maximum sensor drift over 3 h (the duration of the campaign, see below) under controlled conditions was in the range of -0.31 and +0.51 ppm (see Appendix ). In the configuration used, the DIYSCO2 had a time lag of 18.2 s between measurement intake and analysis (see Appendix ).

Appendix 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, the sample inlet tube was run out through the passenger side window of the vehicle. The sampling line inlet was 70 cm over the vehicle's roof and 2.2 m above the road surface (Fig. b). In order to deploy the DIYSCO2 on a bicycle, the setup requires a 40 L backpack to carry the sensor and a 7 amp-hour, 12 V 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.

The study area is a 12.7 km × 1 km quadrangle within the city of Vancouver, BC, which spans from the northern-most tip of the city in forested “Stanley Park” (49∘18′45.17′′ N, 123∘09′29.10′′ W, WGS-84) to the city's south eastern neighbourhood “Victoria – Fraserview” (49∘12′59.00′′ N, 123∘03′46.90′′ W) (Fig. ). It includes dominant urban land uses – the downtown core, medium density residential, single detached residential, light industrial development, parks, and forest. The study area encompasses 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 light detection and ranging (lidar) measurements of urban form used for building emission simulations in previous research , the availability of detailed traffic counts, and the location of a 30 m tall eddy-covariance tower.

The eddy-covariance flux tower “Vancouver-Sunset” (ID: Ca-VSu, ) is located near the south east corner of the study area (49∘13′34.0′′ N, 123∘04′42.2′′ W). On the flux tower, a CSAT-3 ultrasonic anemometer–thermometer (Campbell Scientific Inc., Logan, UT, USA) measured continuously sensible heat flux (H), wind direction, and wind velocity. Further, air temperature (Ttower) was measured with a shielded HMP 45 thermometer/hygrometer (Vaisala Inc., Vanta, Finland). All four radiation components, including long-wave upwelling radiation (L↑), were measured by a CNR-1 net radiometer (Kipp & Zonen, Delft, the Netherlands). Carbon dioxide molar mixing ratios rtower were measured near tower top (28 m) using a tube that pumps air to a TGA200 closed path analyzer (Campbell Scientific Inc.). In addition, CO2 mixing ratios were measured by a Licor-7500 open path IRGA (Licor Inc., Lincoln, NE, USA) co-located with the ultrasonic anemometer–thermometer. The TGA200 was calibrated every 10 min against three WMO-traceable tanks of known CO2 mixing ratios to ensure an accuracy of < 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 . Measurements on the flux tower made it possible to link mobile measurements with data from above the city and determine aerodynamic resistances for the calculation of emissions (see Sect. )

Two field campaigns took place, the first on 28 May 2015 (non-heating season, broadleaf vegetation with leaves emerged) and the second on 18 March 2016 (heating season, before leaf emergence). For simplicity, datasets from the two dates will be referred to as “summer” (28 May 2015) and “winter” (18 March 2016). Sampling was conducted from 10:00 to 13:30 LT (Pacific Time), when vehicular traffic and meteorological conditions are relatively constant.

Five DIYSCO2 systems were installed on vehicles. Each of the five vehicles was assigned a route to travel approximately 70 km during the study period (achieving an optimal sampling density of about 3.5 km2 h-1). Each vehicle started and ended at the southeast corner of the transect (49∘13′15.08′′ N, 123∘04′14.11′′ W; Fig. ). The routes of the five systems were drawn such that a majority of the streets and lanes in the study area would be sampled at least once in the 3.5 h time period, but ideally sampled at different times throughout the campaign. The routes were evaluated using an overlaid 100×100 m grid, confirming that nearly all of the grid cells would be crossed by at least one system if the routes were successfully completed. Furthermore, a bicycle was used to traverse trails in the forested area of “Stanley Park” to sample along pathways in the densely forested ecosystem away from roads.

Prior to the mobile measurements, all vehicles were parked on the southeastern corner of Gordon Park, away from major streets in a school parking lot. The five DIYSCO2 systems were operated for a 15 min warm-up period in their respective vehicles parked next to each other and then logged for 5 min in order to determine their relative offsets before the field campaign; this is called the “in situ comparison”. 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 h traverse, all vehicles returned to the starting location, where a second in situ comparison was performed. The data collected in the in situ comparison were 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.

The 1 Hz data from all five DIYSCO2 systems were filtered according to , so that all data were removed when the GPS recorded speeds were below 5 km h-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 specifications and calibration of the Li-820.

Vector matrix grids of 50 × 50 m, 100 × 100 m, 200 × 200 m, and 400 × 400 m were mapped onto the study area in a Geographic Information System to spatially aggregate and attribute the rmobile measured by the DIYSCO2 systems to square grid cells. The separate data analysis for the 50, 100, 200, 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 cell 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  explores the effect of using different grid sizes by comparing the results from the 100 m grid to the 50, 200, 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 neighbourhood (Fig. ) 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.

Data from the eddy-covariance tower are used in conjunction with the gridded averages of rmobile to calculate emissions for each grid cell based on the aerodynamic resistance approach, which posits that the molar flux of CO2 for a given area and time (w′c′‾ in µmol m-2 s-1) is equal to the difference of the molar concentration c (in µmol m-3) at the height above the roughness sublayer (ctower) and screen level at 2 m height (cmobile) divided by the aerodynamic resistance of CO2 (in s m-1): w′c′‾=-ctower-cmobileraC. While both ctower and cmobile are available through the measurement of r and density (considering pressure and air temperature), the challenge is that raC cannot be directly and easily measured due to the spatial heterogeneity of w′c′‾ and 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 CO2 fluxes and mixing ratios. From the tower measurements of air temperature (Ta) and surface brightness temperature we then calculate the aerodynamic resistance of sensible heat raH . raH is the integral resistance from the surface (ground, roofs) to the top of the tower. raH=ρcpTtower-T0H, where Ttower is the air temperature (K) at the height of the tower (24 m), T0 is the surface brightness temperature (in K, calculated as T0=(L↓/σ)0.25) from the long-wave radiometer, where σ=5.6×10-8 W m-2 K-4 is the Stefan–Boltzmann constant), and H is the sensible heat flux (W m-2) measured by eddy covariance.

In a next step we assume Reynolds analogy between heat and passive scalar transfer, i.e. that the aerodynamic resistance of sensible heat is equal to the aerodynamic resistance of carbon dioxide (raC) and rewrite Eq. ().

In order to convert the molar flux w′c′‾ (in µmol m-2 s-1) to a mass flux Fc consistent with inventories (in kg CO2 ha-1 h-1), we rewrite Fc=-Mcbabtbobmctower-cmobileraH, where Mc is the molar mass of CO2 (44.01 g mol-1), ba is a factor for converting m-2 to ha-1 (i.e. ba=104 m2 ha-1), bt is a factor for converting s-1 to h-1 (i.e. bt=3600 s h-1), bo is the factor for converting µmol to mol (i.e. bm=10-6 µmol µmol-1), and bm is the factor for converting g to kg (i.e. bm=10-3 kg g-1).

Equation () was applied to each grid cell, where cmobile varied for each grid cell and each time while raH and ctower varied only over time. The calculated emissions Fc are 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 ).

In summary, this procedure to calculate emissions from mobile and tower measurements is only valid under the following key assumptions:

CO2 concentrations 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.

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 h window after filtering. Figure  shows the frequency distribution of the filtered 1 Hz rmobile measured by all five DIYSCO2 systems alongside the mixing ratio on the tower (rtower).

In summer, the measured 1 Hz rmobile ranged from 380.2 to 918.1 ppm with a median and average r of 408.5 and 419.5 ppm (SD 32.35 ppm), respectively, for the entire dataset. The lowest rmobile (< 400 ppm) was measured in 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. ) and “West Georgia St.” (Highway 99). In winter, overall r was higher for both tower and mobile system. In winter, the measured 1 Hz rmobile ranged from 401.4 to 918.5 ppm with a median and average rmobile of 432.7 and 443.9 ppm (SD 34.77 ppm).

During the summer and winter campaigns, 2 % and 16 % of the measured rmobile were lower than the tower (rtower) and 3 % and 7 % were higher than 500 ppm, respectively.

For the 100 × 100 m grid cells that could be traversed, in summer 91.31 % of the grid cells contained more than 10 samples per grid cell (1 sample equals one 1 Hz measurement), 69.24 % of cells contained more than 20 samples, and 28.32 % of cell contained more than 50 samples. At the average vehicle speed of 20 km h-1, this corresponds to a typical spatial spacing of 5.5 m. 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, which resulted in 30.8 % of all cells being removed in the summer campaign and 27.4 % in the winter campaign. Generally, grid cells along major roads tended to have more sample counts because they were traversed at different times, often by different vehicles.

Of the 1332 grid cells that could be traversed by a car or bicycle, 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. . Table  summarizes the measured mixing ratios separated by neighbourhood. In summer, the grid-averaged rmobile of all valid grid cells in the entire transect ranged between 393.1 and 518.0 ppm, averaging 417.9 ppm, and had a median of 410.0 ppm. In winter, the grid-averaged rmobile ranged between 408.4 and 560.5 ppm, averaging 442.5 ppm. Three percent of all grid cells in summer and 8 % in winter showed a rmobile that was lower than rtower; the majority of those cases were located in the forested “Stanley Park” in both campaigns (Table ). Selected cells in the residential parts of “Riley Park/Kensington – Cedar Cottage” neighbourhood also showed a rmobile that was lower than rtower.

Both campaigns showed considerable variation of rmobile between grid cells in the same neighbourhoods. 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 neighbourhoods, “Kensington-Cedar Cottage/Riley Park” exhibited the lowest and “Downtown” the highest average rmobile in both campaigns (Table ).

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 a positive skewness which means there are intra-grid peaks in measured CO2 mixing ratios.

The aerodynamic resistance raH for each measurement campaign was calculated by averaging H, averaging T0, and averaging Ttower over the 3.5 h of the field campaign. The resulting raH was 34.14 s m-1 in summer and 56.12 s m-1 in winter.

The measured CO2 emissions calculated using Eq. () showed a range of -12.0 kg CO2 ha-1 h-1 (net uptake) to 225.6 kg CO2 ha-1 h-1 in the summer campaign and -13.7 to 162.4 kg CO2 ha-1 h-1 in winter. The median and average emissions were, respectively, 20.1 and 35.0 kg CO2 ha-1 h-1 for the summer campaign and 17.1 and 25.6 kg CO2 ha-1 h-1 for the winter campaign. Highest emissions in general were located in “Downtown” and along the major transport corridors and intersections (Fig. , Table ).

The gridded traffic emissions inventory at 100 × 100 m resolution (see Appendix  and Fig. a) showed median and mean emissions, respectively, of 2.37 and 12.50 kg CO2 ha-1 h-1 for the summer campaign and 2.17 and 12.19 kg CO2 ha-1 h-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 CO2 ha-1 h-1. The greatest traffic emissions in a single grid cell was 123.60 kg CO2 ha-1 h-1.

The building emissions inventory (see Appendix ) is shown in Fig. b. In summer, the data for the 100 m grid showed a median and mean of 6.69 and 10.19 kg CO2 ha-1 h-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 CO2 ha-1 h-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. b). Data for part of “West End” and for “Stanley Park” are not available.

The total emissions inventory is the sum of the building and traffic emissions estimates (Fig. c). For the summer campaign, the median and mean of the total emissions estimates were 10.15 and 22.06 kg CO2 ha-1 h-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 CO2 ha-1 h-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 (LCZ 6 and 8) and lower in “Downtown” (Table ).

First, measured rmobile was 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. a and b). Further, measured rmobile and building emissions are also positively correlated, but with more scatter (Fig. c and d). Best agreement is archived when comparing rmobile to the total (i.e. traffic + building) emissions (Fig. e and f). The linear equations given in Fig. e show R2=0.53 in summer and R2=0.47 in winter.

Figure a 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 campaign (grey shaded area in Fig. ). 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. a 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.

In Fig. c and d measured emissions and the building emissions inventories are compared for each grid cell. Building emissions are clustered by neighbourhood, with the lowest urban density (LCZ 6) of “Sunset/Victoria-Fraserview” exhibiting the lowest emissions and the highest urban density (LCZ 1) of “Downtown” exhibiting the highest building emissions. Across all neighbourhoods, the measured emissions are higher than the building emissions only (note that traffic emissions are not considered in Fig. c and d). The measured emissions and the building emissions estimates were found to be correlated positively by 35.91 % for the 100 m grid in the summer campaign and 32.42 % in the winter campaign.

Lastly, Fig. c 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 the 100 m grid. The measured emissions and the total emissions inventory were found to be correlated positively by 77.87 % 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 kg CO2 ha-1 h-1 as compared to 22.06 kg CO2 ha-1 h-1 of the emissions inventory. In winter, the measured emissions averaged to 25.92 kg CO2 ha-1 h-1 as compared to 28.76 kg CO2 ha-1 h-1 of 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 inventory. For both the summer and winter campaigns, emission measurements are higher than the inventory in grid cells along major arterial roads whereas the measurements are lower than the inventory in residential areas and in “Downtown”.

The mean absolute error (MAE) for all grid cells in the entire transect between measured and modelled total emissions is 17.1 kg CO2 ha-1 h-1 in summer and 16.6 kg CO2 ha-1 h-1 in winter. The median absolute error for the entire transect is 9.6 in summer and 9.9 in winter. Table  lists the MAE by neighbourhood. The MAE is about a factor of 2 larger in “Downtown” and “West End” compared to the residential and industrial neighbourhoods.

The relative error (RE) is defined as the difference between a grid cell's measured emission and the same cell's emissions 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 an RE within a factor of ±1. As expected, locations with higher REs 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 CO2 ha-1 h-1) in the inventory, 80 % of the grid cells in summer and 91 % in winter have an RE with a magnitude of less than ±1.

In terms of methodology, raH is calculated using Ttower and T0 at a single location and is 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 in 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 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 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 Fc change when using different methods of estimating raH.

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 to average those finer grid cells to lower grid resolutions as done in . 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.

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 r and 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 of the day, however, studies (e.g. ) show that most building occupancy (and therefore energy use) occurs between 09:00 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 . Building energy use intensity might be modelled by season 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  is used to map traffic count values across the residential streets to overcome the missing traffic counts, but 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 CO2 per litre of fuel burned. More precise estimates of emissions factor in the differences in the emissions factor by vehicle type and fuel type . Last, the traffic count data do not indicate the amount of emissions from idling that occur as a result of traffic jams, which introduces another aspect of possible uncertainty within the traffic emissions inventory and can be substantially higher in urban contexts.

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 CO2 emissions could come from human activities such as landscaping (e.g. lawnmowers and leaf blowers) and construction. For example, a study by showed that, in a 1.9 km × 1.9 km study area around the “Vancouver-Sunset” tower (see Fig. ), emissions from human respiration and vegetation and soils can account for 8 and 5 %, respectively, of the total emissions.

Data-driven models in combination with urban surface databases (urban form, traffic) could be used to further improve the information in the post-processing and hence assist the derivation of more realistic emission maps e.g..