Environ Monit Assess (2014) 186:2053–2066 DOI 10.1007/s10661-013-3517-4
Aircraft mass budgeting to measure CO2 emissions of Rome, Italy Beniamino Gioli & Maria F. Carfora & Vincenzo Magliulo & Maria C. Metallo & Attilio A. Poli & Piero Toscano & Franco Miglietta
Received: 14 June 2013 / Accepted: 28 October 2013 / Published online: 12 November 2013 # Springer Science+Business Media Dordrecht 2013
Abstract Aircraft measurements were used to estimate the CO2 emission rates of the city of Rome, assessed against high-resolution inventorial data. Three experimental flights were made, composed of vertical soundings to measure Planetary Boundary Layer (PBL) properties, and circular horizontal transects at various altitudes around the city area. City level emissions and associated uncertainties were computed by means of mass budgeting techniques, obtaining a positive net CO2 flux of 14.7±4.5, 2.5±1.2, and 10.3 ± 1.2 μmol m−2 s−1 for the three flights. B. Gioli (*) : P. Toscano : F. Miglietta National Research Council, Institute of Biometeorology (Cnr-Ibimet), Via Caproni 8, 50145 Firenze, Italy e-mail:
[email protected] M. F. Carfora National Research Council, Istituto per le Applicazioni del Calcolo “M. Picone” (Cnr-Iac), Via Pietro Castellino 111, 80131 Napoli, Italy V. Magliulo National Research Council, Institute for Agricultural and Forestry Systems in the Mediterranean (Cnr-Isafom), Via Patacca 85, 80040 Ercolano (Na), Italy M. C. Metallo Environmental System Analysis Srl, Via Trento 3, 00062 Bracciano (Rm), Italy A. A. Poli Asia Pacific Air Quality Group Pte Ltd, 16 Collyer Quay Level 20, 049318 Singapore, Singapore F. Miglietta FoxLab, Fondazione E.Mach, Via E. Mach 1, 38010 S. Michele all’Adige (TN), Italy
Inventorial CO2 fluxes at the time of flights were computed by means of spatial and temporal disaggregation of the gross emission inventory, at 10.9±2.5, 9.6±1.3, and 17.4±9.6 μmol m−2 s−1. The largest differences between the two dataset are associated with a greater variability of wind speed and direction in the boundary layer during measurements. Uncertainty partitioned into components related to horizontal boundary flows and top surface flow, revealed that the latter dominates total uncertainty in the presence of a wide variability of CO2 concentration in the free troposphere (up to 7 ppm), while it is a minor term with uniform tropospheric concentrations in the study area (within 2 ppm). Overall, we demonstrate how small aircraft may provide city level emission measurements that may integrate and validate emission inventories. Optimal atmospheric conditions and measurement strategies for the deployment of aircraft experimental flights are finally discussed. Keywords Aircraft mass budgeting . SkyArrow ERA . Emission inventory validation
Introduction Effective emission mitigation policies for atmospheric greenhouse gases (GHG) require the involvement of different players at supra-national, national, and subnational scales. The enhancement of natural sinks, such as forests, requires the implementation of multiscale actions from the farm to the region to the nation (Schulze et al. 2002). Similarly, the reduction of emission sources should
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involve industrial districts, transportation, and cities, where most GHG are in fact emitted (Huang et al. 2011). At present, reliable measurements of the carbon footprint of urban areas are lacking in the literature, impairing our comprehension of societal drivers and mechanisms regulating urban metabolism (Sovacool and Brown 2010). The deployment of observational systems designed to monitor city level GHG domes is needed to verify emission inventories (Duren and Miller 2012). Signatories to the United Nations Framework Convention on Climate Change (UNFCC) are required to report annual greenhouse gas inventories at national level. Such GHG emissions estimates are typically accomplished by means of inventorial and statistical methodologies like the CORINEAIR (European Environmental Agency 2009) and are based on the coupling of gross inventorial information such as road traffic intensity and fuel combustion amounts, with emission factors associated to the pathway of each GHG (Dios et al. 2010; Cetin et al. 2007; Kim et al. 2006). The temporal and spatial resolution of this type of approach is necessarily coarse and can be improved by developing specific downscaling frameworks (Gregg and Andres 2008; Maes et al 2009; Nasser et al. 2013). However, increasing spatial or temporal resolution by means of disaggregation comes normally with a decrease in accuracy (Rayner et al. 2010; Rypdal and Winiwarter 2001) especially if there is a lack of high-resolution emission data and their partitioning for different sources, or if gross inventories that serve as a model basis are not updated frequently (Frey 2007). These types of emission inventories are nowadays available for the entire continent such as the EU-25 (Ciais et al. 2010), as well as for many cities (Sovacool and Brown 2010; Fan et al. 2012), but direct validations at the whole city level are rare because of intrinsic spatial and temporal scale issues associated with the differences in resolutions between an inventorial methodology and an observational framework (Brondfield et al 2012). Winiwarter et al. (2003) assessed a set of methods to compare gridded emission inventories using differing techniques for Milan (Italy) and the Greater Athens area (Greece), reporting that none of the methods implemented is able to fully cover all the differences between inventories, but a combination of methods needs to be applied according to the respective needs. Due to the significant discrepancies in emission inventories reported in various regions of the world, Vivanco and Andrade (2006) evaluated the official emission inventories in the Sao Paulo Metropolitan Area (Brazil) using an observation-based approach (top–down)
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in combination with a photochemical model to investigate inaccuracies in the mobile emissions data included in the official inventory. In a recent review of CO2 emissions from fossil-fuel combustion, Andres et al. (2012) concluded that while those emissions continue to increase over time and much is known about their overall features, most has still to be learned about temporal and spatial disaggregation. When dealing with CO2 the picture is further complicated by the presence, within an urban landscape, of both urbanized and green areas, the latter offsetting emissions by photosynthetic uptake (Nordbo et al. 2012). Surface exchange of CO2 between the biosphere and atmosphere can be directly measured by means of the eddy covariance (EC) micrometeorological technique, which has been proven to be a reliable tool to assess local carbon budgets also in urban landscapes (Grimmond et al. 2002; Velasco et al. 2005; Vogt et al. 2006; Matese et al. 2009; Järvi et al. 2012; Liu et al. 2012). Eddy covariance flux measurements are typically made by a fixed measurement station and are therefore representative of a restricted surface area, providing data at high temporal resolution, but limiting the possibility to upscale measured fluxes to the landscape scale. Conversely, larger spatial scales can be addressed with research aircraft, by probing the Planetary Boundary Layer (PBL) and the free troposphere and allowing emissions to be determined at the regional to continental scale (Choi et al. 2008). Micrometeorological methods based on the mass balance approach, initially developed to assess trace gas emissions from strong point sources in agricultural systems (Denmead et al. 1996, 1998; Harper et al. 1999) and later validated for use in small plots (Magliulo et al. 2004), may be applied to the PBL to derive estimates of energy and mass exchange of the underlying surface (Levy et al. 1999; Raupach et al. 1992; Laubach and Fritsch 2002; Alfieri et al. 2010). These techniques have been applied to quantify gaseous emissions of single chimneys (Toscano et al. 2011), large industrial facilities (de Gouw et al. 2007; Brooks et al. 1997), agricultural landscapes (Laubach et al. 2012), and entire towns (Fowler et al. 1996; Lind and Kok 1999; Kalthoff et al. 2002; Wratt et al.; 2001; Carras et al. 2002; Mays et al. 2009; Cambaliza et al. 2011). The advantage of these approaches is their capability to integrally sample relatively large domains, while the main limitation remains the sporadic nature of this type of measurement. In this work, we apply the mass balance concept by means of high-resolution airborne measurements, to assess the CO2 emission strength of the city of Rome
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(Italy), and compare it with available emission inventories for the year 2005. A small environmental research aircraft (SkyArrow ERA) performed a series of three experimental flights around the city area, deriving overall city net emission. We assess and discuss the performance and limitations of this experimental methodology, which can easily be deployed in different urban areas to measure emission strength at city level, as a support tool that may be used to verify emission reduction policy actions, and to validate inventorial emission databases.
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flights is 74 % urbanized, and 24 % vegetated (i.e., cropland, urban forest, and green spaces).
Aircraft measurements
This experiment is focused on the Rome city area, composed by a densely populated historic central area, surrounded by residential, industrial, and rural neighborhoods (Fig. 1). Rome is ringed by a heavy traffic transit highway (GRA), which quite neatly separates more densely inhabited internal areas from peri-urban and rural zones on the outside (Fig. 1). The GRA was therefore chosen as the delimiting perimeter of the study area, and flight paths were designed along its 68 km route. The land use in the area encompassed by the
Aircraft measurements have been performed with the SkyArrow ERA, a certified small aircraft platform for environmental research (Gioli et al. 2006). The SkyArrow is equipped with a Mobile Flux Platform (MFP) system, consisting of a pressure sphere, GPS, and INS devices (Crawford and Dobosy 1992). The MFP is capable of deriving a 3-D wind vector at 50 Hz time resolution, by measuring wind angles of attack with the pressure sphere and aircraft movement by GPS (mod. OEM4, Novatel Inc) and INS (mod. AT4, Javad Inc) systems. Air temperature is measured by a fast response thermocouple placed on top of the pressure sphere, with a time response of 0.02 s. CO2 and water vapor mass densities are measured at 50 Hz by an open path IRGA (mod. LI7500, Licor Inc). Raw data are synchronized and recorded on board, while actual wind components are retrieved in post processing. More information on the aircraft instrumentation can be found in Gioli et al. 2004, while a recent review of the MFP wind retrieval procedure and accuracy may be found in Vellinga et al. 2013.
Fig. 1 Land cover classification map of the study area derived from aggregations of Corine Land Cover data, with indications of the flight path (black line), vertical profiles locations (PRF1,
PRF2, PRF3, and PRF4), and weather station of Roma Urbe airport (WS). Administrative boundaries of Rome area and flight path are showed in the right box
Materials and methods Study area
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A total of three experimental flights were made in the winter season of 2004/2005. All the flights (F1, F2, and F3) were designed as approximately circular paths along the GRA, and made at 3 different altitudes (Table 1) within and above the PBL to adequately resolve the vertical variability: the first two levels were always inside the PBL, while the 3rd was always in the free troposphere. A series of four vertical soundings, sampling from 50 m above ground level (AGL) to the free troposphere, were taken on each flight, to detect PBL properties across different positions of the experimental domain. Each flight started with aircraft takeoff from Urbe airport. An upward vertical sounding was then performed in PRF1, followed by a descending one in PRF2. An ascending and a descending vertical sounding in PRF3 and PRF4 then closed the sequence. The bottom level legs were then flown, followed by the intermediate and uppermost level, before landing at Urbe airport. Flights took on average 2 h and were made in the late morning/midday conditions (Table 1), so as to encounter a welldeveloped PBL and likely stationary emissions at the ground, away from typical transitions of early morning and late afternoon. Flights were made on weekdays, in overall fair weather conditions. Cloud cover and meteorological data at the ground were recorded by the weather station at Rome Urbe airport, located within the GRA area (Fig. 1). Highfrequency data have been averaged over transects or subportions of transects to derive basic statistics. Unbiased mean and variance of wind direction have been computed as described in Farrugia and Micallef 2006 (Table 1).
Mass budget modeling The modeling approach is based on three steps: (1) defining a virtual box containing the study area, (2) applying interpolation procedures to reconstruct wind components and scalar concentration fields at the virtual box edge surfaces, and (3) applying a mass budget conservation equation at the box edge surfaces and top surface. The computational domain consists of a fixed volume for each flight, where the bottom surface corresponds to the ground, the top surface is a conveniently located virtual horizontal plane, and the lateral faces are aligned to the mean aircraft flight path. The fundamental equation for the budget technique is the scalar conservation equation, which is integrated over the computational domain. ∂ρ þ ∇⋅ F ðρÞ ¼ 0 ∂t
ð1Þ
where ρ is the mass density of the scalar, ∇ is the divergence operator, F is the flux. The rate of accumulation of a nonreactive chemical component is given by the difference between the inflow and outflow rates to and from the control volume. Referring to CO2 as the scalar species and assuming a constant emission rate from the surface during the measurement period, the total net CO2 exchange F CO2 over the control volume B is given by F CO2 ¼ ∫B ∇⋅ ρCO2 q dV ¼∫ΩB ρCO2 q⋅n dS ð2Þ where, at every point on the volume boundaries, ρCO2 is CO2 density, q=(u, v, w) is the wind velocity vector, while n=(nx, ny, nz) is the wind component normal to the
Table 1 Summary table of horizontal level aircraft transects. Three levels per flight were flown (bottom, mid, and high) Flight
Date
Time UTC
Day
Tair (K)
CO2 (ppm)
U (m s−1)
Udir (deg)
Z (m)
F1
02/12/2004
11:45–14:11
Thu
291.6±0.6
366±4
5.4±1.2
181±12
192±33
Bottom
290.6±0.6
361±3
4.8±1.1
183±9
314±10
Mid
287.4±0.5
362±2
4.1±0.6
195±27
774±8
High
285.1±0.9
403±11
2.1±0.9
308±38
179±32
Bottom
285.2±0.3
382±7
3.3±1.2
291±24
364±34
Mid
282.4±0.1
374±1
5.2±1.7
321±17
862±7
High
283.4±1.0
401±13
1.8±0.6
73±26
162±29
Bottom
284.2±0.2
381±2
1.4±0.4
67±18
365±3
Mid
282.0±0.2
381±1
3.9±0.9
240±11
754±5
High
F2
F3
14/12/2004
07/01/2005
10:22–13:01
9:35–12:13
Tue
Fri
Means and standard deviations of high-frequency measurements are reported for air temperature (Tair), CO2 concentration (CO2), wind speed (U), wind direction (Udir), and flight altitude above sea level (Z)
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boundary surface ΩB. The ground projection of the flight paths, has been approximated by a polygon of N=12 segment sides (Fig. 1), thus defining 12 vertical planes as edge surfaces, spanning from the ground to the top surface. Geometric properties of such a domain are easily computed, so that the application of Eqs. (1) and (2) is straightforward. As our flight data represent a sparse dataset on the lateral surfaces, a convenient estimate of the q and ρ functions at every surface point is needed. We adopted a nonparametric inverse distance regression method, based on the Shepard (1968) function, which is often used to represent sparse environmental data in space. The regression function in a target point is a weighted average of the values assumed by experimental data over the entire volume, with weights decreasing with the distance of the data from the target point. The approximated value bf of a function f at a point x is given by N X x−xi −β f ðxi Þ
bf ðxÞ ¼
i¼1 N X
ð3Þ jx
xi
j−β
i¼1
where |x–xi ·| represents distance between the target point x and the data points xi in the usual Euclidean norm, and β is a positive parameter that regulates the degree of smoothing: higher values assign greater influence to data points closest to x, while giving sharp peaks to the approximation. We used the commonly adopted value of β=2, that ensures a smooth interpolation. Eq. (3) is applied on a local Cartesian coordinate system constructed on each face of the box, and a numerical grid with varying vertical resolution coherent with the distance between two consecutive legs. CO2 mass density, air density and wind speed at the center of each grid cell xi, j, k have then been determined by Eq. (2). Several increasing horizontal resolutions for the computation of lateral fluxes have been tested, showing that fluctuations in results tend to level off at a resolution of about 50 m, therefore we adopted a conservative value of 2,000 cells on each horizontal level, corresponding to a horizontal grid resolution of 35 m. The flux across the top of the control volume is estimated as the product of a mean value of CO2 concentration and a mean vertical wind. The mean CO2 concentration has been derived as the mean of the four
vertical profiles at the chosen altitude for the top surface. The mean vertical wind was computed by applying the continuity equation to the entire control volume (Eq. 1). With such a methodology the vertical flux across the top surface is computed as the value for which the mass conservation on the entire control volume is fulfilled. A more detailed description of this method can be found in Alfieri et al. (2010). Vertical soundings A convenient value for the top boundary vertical location (ztop) is condition dependent, since different PBL depths (zi) may be encountered. We adopted a purely empirical strategy based on observations: at the beginning of each flight a complete PBL sounding was made in four points of the domain. The height zi of the boundary layer over the area of interest was determined based on measured vertical profiles of potential temperature, water vapor, CO2 concentration, and wind magnitude. The method originally proposed by Heffter (1980) was adopted, based on a lapse rate criterion to identify the thermal inversion. With this, zi is defined as the height where the potential temperature vertical gradient dθ dz becomes greater than 0.5 K/100 m. Other quantities sampled over vertical profiles have been used to verify such estimates, identifying sharp transitions in humidity, CO2 or wind speed and direction. Uncertainty analysis Several sources of uncertainty are present in this methodology, which combine and propagate to the final results. We first determined uncertainties associated with instrumental performance in measurements of temperature, CO2, and wind vector. These were derived from instrumental performance data, at 0.15 K for temperature measurements, 0.5 ppm for CO2 measurements, 0.2 m s−1 for wind measurements (Alfieri et al. 2010). The uncertainty in interpolated quantities at the lateral surfaces was estimated by means of a bootstrap analysis, generating n=100 samples of each variable by adding a Gaussian noise with standard deviation corresponding to the known experimental errors, and then propagated through the numerical model. The uncertainty in prescribed values for CO2 concentration at the top surface, derived from the four vertical profiles has been estimated from the statistical 95 % confidence
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interval of the four measurement profiles at the top surface level. All these sources of uncertainty are finally combined and propagate to final emission rates computations through linear Eqs. (1)–(3). The relative impact of single sources of uncertainties on the total is also determined. Inventorial emission data CO2 emissions for Rome have been derived from a regional GHG emissions inventory, providing detailed emissions rates and source categories for the six major GHG categories (Metallo et al. 2009). The inventory was compiled in accordance with the United Nations Framework Convention on Climate Change (UNFCCC) and the European Union's Greenhouse Gas Monitoring Mechanism. The methodology is based on the use of emissions factors and socioeconomic activity indicators or proxy variables, such as fossil fuels consumption, vehicle miles traveled, and industrial production. The Regional Inventory Report was edited using the same framework as “National Inventory Report” for Italy edited by the National Environment Protection Agency. According to UNFCCC guidelines, the official presentation tables, known as Common Report Format (CRF), have been compiled, considering all the CRF sectors: Energy, Industrial processes, Solvents and other product use, Agriculture, Land Use - Land Use Change and Forestry, Waste management. The inventory has finally been spatially and temporally disaggregated: a spatial resolution of 1 km was achieved following CORINAIR classification of emission source, while industrial facility point sources have been estimated by means of a detailed industrial emission survey; a temporal resolution of 1 h was achieved by using hourly temporal patterns on a monthly basis for each emission category, both for weekdays and weekend days. Finally, inventorial emissions at the times of flight have been computed by averaging the pixels encompassed by the flight track referred to the flight time interval.
Results This section examines observations first (vertical PBL profiles and horizontal transects), then the results of the mass budgeting methodology, assessed against inventorial based estimates. Finally, we quantify overall uncertainty and the relative impact of the different uncertainty
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estimates, and discuss the general applicability limits of this methodology. Vertical PBL profiles Vertical profiles for F1 are shown in Fig. 2a for the four selected locations. PBL depth zi is clearly detectable at northern profiles PRF1 and PRF4, where it has been computed at 581 and 603 m, respectively. At southern points PRF2 and PRF3 a clear inversion is not detectable, while a careful visual examination of profiles reveals a layer associated to a change in CO2 concentration and temperature lapse rate at approximately 480 m. An altitude of 600 m has therefore been selected as top surface location ztop (Table 2). During F2, vertical profiles indicate shallow PBL at southern profiles, at 280 and 290 m for PRF2 and PRF3 (Fig. 2b), while a clear temperature inversion is not detected for northern profiles. From examination of CO2 concentration profiles (Fig. 2b), the accumulation of CO2 in the PBL is clearly detectable, while all four profiles converge to the same value of tropospheric CO2 concentration (within 2 ppm) at a level of 510 m AGL, which has therefore been selected as ztop (Table 2). During F3, PBL depth was shallow, at ∼200 m for PRF2 and ∼300 m for PRF1, PRF3, and PRF4. CO2 profiles confirm a convergence to the same value of 381 ppm in the free troposphere above 320 m AGL, which has therefore been selected as ztop (Table 2). Horizontal transects During F1, prevailing winds at the surface, measured by the Rome Urbe airport weather station, were northerly and stable at approximately 5 m s−1 (Table 1). CO2 patterns exhibit higher concentrations on the northeastern downwind section at both low and medium levels, with a difference of about 7 (low level) and 5 ppm (medium level) with respect to the southwestern upwind portions of the flight path (Fig. 3a). During F2, winds were weaker and more variable both in magnitude and direction, coming from S-E at the low level close to the surface and from N-E at the two upper levels, with magnitudes ranging from ∼2 m s−1 at the lowest level to ∼7 m s−1 in the free troposphere (Table 1). Rome Urbe weather station reported clear sky conditions. Aircraft measured winds revealed some variability within the same transects at the bottom and medium flight levels (Fig. 3b), with air coming from the
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Fig. 2 Vertical profiles of CO2 concentration (left) and potential temperature (right), at the four profiling locations (blue, PRF1; red, PRF2; black, PRF3; and green, PRF4) during the three flights
F1 (a), F2 (b), and F3 (c). High-frequency 50 Hz data are averaged at 1 Hz, and error bars represent associated 95 % confidence intervals
W at the western part of the flight path, and a stable wind field from the N-W at the top. CO2 plume signal is noticeable on the southern portion of the flight path, with a difference of about 40–50 ppm between northern and southern flight sections.
During F3, Rome Urbe weather station reported clear sky conditions and low winds (Table 1). Aircraft observed winds were from N-E in the two low flight levels, turning to S-W in the free troposphere. Higher CO2 concentration air masses were detected downwind of
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Environ Monit Assess (2014) 186:2053–2066 the top-surface level and associated 95 % confidence interval (CO2_top), and derived vertical velocity to satisfy mass conservation over the computational domain (Wtop)
Table 2 Flight-based summary of top-surface variables: aircraft observations of PBL depth (zi) for each sounding location (PRF1, PRF2, PRF3, and PRF4) respectively, prescribed top-surface elevation of the computational domain (ztop), CO2 concentration at Flight
zi (m)
ztop (m)
CO2_top (ppm)
Wtop (cm s−1)
F1
581, 480, 480, and 603
600
371±4
3.2
F2
280, 290, n.a., and n.a.
510
377±1
1.2
F3
300, 200, 300, and 300
320
382±1
0.3
the city in the S-W and eastern portions of the flight path, at the lowest flight level (Fig. 3c). Mass budget computations By combining interpolated wind speed and direction at the lateral faces of the flight path polygon with air density data, actual air flow rates across each lateral face have been computed for the three flights (Fig. 4), giving insight
a
LEVEL1: 200 m ASL
on where air masses are entering and exiting the control volume. Overall air masses entering (negative sign) and exiting (positive sign) the control volume have been computed as −8.1e+07 and 6.8e+07 kg s−1 for F1, −3.3e+07 and 2.8e+07 kg s−1 for F2, and −1.8e+07 and 1.5e+ 07 kg s−1 for F3. The mass flux at the top surface has been computed as a residual, to achieve mass conservation over the whole control volume, and always resulted as a positive term (i.e., air exiting the domain, Fig. 4).
LEVEL2: 314 m ASL
LEVEL3: 774 m ASL
42
42
42
41.95
41.95
41.95
41.9
41.9
41.9
41.85
41.85
41.85
b
41.8
41.8
41.8 41.75 12.3
12.4
12.5
12.6
41.75 12.3
LEVEL1: 179 m ASL
41.95
12.4
12.5
12.6
12.7
41.75 12.3
LEVEL2: 364 m ASL 42
42
41.95
41.95
41.9
41.9
41.9
41.85
41.85
41.8
41.8
c
12.4
12.5
12.6
41.75 12.3
LEVEL1: 162 m ASL
12.4
12.5
12.6
12.7
LEVEL2: 365 m ASL 42
42
41.95
41.95
41.9
41.9
41.9
41.85
41.85
41.85
41.8
41.8
41.8
12.5
12.6
41.75 12.3
12.7
12.4
12.5
Fig. 3 Wind 2-D vector (blue arrows) and CO2 concentration (colored circles) measured along the aircraft track during the three flights F1 (a), F2 (b), and F3 (c) at three altitudes: low (LEVEL1 boxes), medium (LEVEL 2 boxes), and high (LEVEL 3 boxes).
12.6
12.4
12.5
12.6
12.7
LEVEL3: 754 m ASL
42
12.4
12.6
41.8
41.75 12.3
41.95
41.75 12.3
12.5
LEVEL3: 862 m ASL
41.85
41.75 12.3
12.4
12.7
41.75 12.3
12.4
12.5
12.6
12.7
Exact average flight altitude is reported on top of each figure. Orthogonal axes represent longitude and latitude; data are averaged with a 10-s time window
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amounts entering and exiting the control volume through lateral and top surfaces, have been calculated as 14.7±4.5, 2.5±1.2, and 10.3±1.2 μmol m−2 s−1 (Fig. 5). Total estimated uncertainties are the result of propagation of uncertainties related to the horizontal CO2 flow rates, with those related to the top surface CO2 flux estimation. Partitioning total figures between these two factors, revealed top surface contributing 81 % of total uncertainty for F1 and 28 and 8 % for F2 and F3, respectively. Inventorial data Average inventorial annual CO2 emission data were 3.2 μmol m −2 s −1 for the entire city area and 9.1 μmol m−2 s−1 for the study area encompassed by the measurement flights. For a direct comparison with aircraft measurements, which are per-se sporadic in time, the hourly resolution daily course for nonweekend days in December and January was spatially averaged across the area encompassed by flights. Inventorial fluxes at the time of flights was therefore calculated as 10.9 ± 2.5, 9.6 ± 1.3, and 17.4 ± 9.6 μmol m−2 s−1 for F1, F2, and F3, respectively (Fig. 5). The complete daily course of emissions features very low fluxes (
