In order to examine O2 consumption and CO2
emission in a megacity, continuous observations of atmospheric O2 and
CO2 concentrations, along with CO2 flux, have been carried out
simultaneously since March 2016 at the Yoyogi (YYG) site located in the
middle of Tokyo, Japan. An average O2:CO2 exchange ratio for net
turbulent O2 and CO2 fluxes (ORF) between the urban area and
the overlaying atmosphere was obtained based on an aerodynamic method using
the observed O2 and CO2 concentrations. The yearly mean ORF
was found to be 1.62, falling within the range of the average OR values of
liquid and gas fuels, and the annual average daily mean O2 flux at YYG
was estimated to be -16.3µmol m-2 s-1 based on the ORF
and CO2 flux. By using the observed ORF and CO2 flux, along
with the inventory-based CO2 emission from human respiration, we
estimated the average diurnal cycles of CO2 fluxes from gas and liquid
fuel consumption separately for each season. Both the estimated and
inventory-based CO2 fluxes from gas fuel consumption showed average
diurnal cycles with two peaks, one in the morning and another one in the
evening; however, the evening peak of the inventory-based gas consumption
was much larger than that estimated from the CO2 flux. This can explain
the discrepancy between the observed and inventory-based total CO2
fluxes at YYG. Therefore, simultaneous observations of ORF and CO2
flux are useful in validating CO2 emission inventories from statistical
data.
Introduction
Precise observation of the atmospheric O2 concentration
(O2/N2 ratio) has been carried out since the early 1990s to
elucidate the global CO2 cycle (Keeling and Shertz, 1992). The approach
is based on the –O2:CO2 exchange ratios (oxidative ratio; OR = –ΔO2ΔCO2-1 mol mol-1) for the
terrestrial biospheric activities and fossil fuel combustion. The OR value
of 1.1 has been used widely for the terrestrial biospheric O2 and
CO2 fluxes (Severinghaus, 1995). On the other hand, the ORs of 1.95 for
gaseous fuels, 1.44 for oil and other liquid fuels, and 1.17 for coal or
solid fuels are usually used (Keeling, 1988). Therefore, OR is a useful
indicator of cause(s) of the observed variations in the atmospheric O2
and CO2 concentrations. The atmospheric CO2 concentration has been
observed not only at remote sites such as Mauna Loa (19.5∘ N,
155.6∘ W), Hawaii, USA, to capture a baseline variation in the
background air (e.g., Keeling et al., 2011), but also recently in urban areas
to estimate CO2 emissions locally from fossil fuel combustion (e.g.,
Mitchell et al., 2018; Sargent et al., 2018). For the latter purpose,
simultaneous observations of the atmospheric O2 and CO2
concentrations should provide important insight into validating the
inventory-based CO2 emissions from gaseous, liquid and solid fuels.
Steinbach et al. (2011) estimated a global dataset of spatial and temporal
variations of OR for the fossil fuel combustion using the EDGAR (Emission
Database for Global Atmospheric Research) inventory and fossil fuel
consumption data from the UN energy statistics. The statistically estimated
OR should be validated by observed OR; however, observations of the
atmospheric O2 concentration in urban areas are still limited (e.g., van
der Laan et al., 2014; Goto et al., 2013a). Moreover, simultaneous
observations of the OR and CO2 flux between an urban area and the
overlying atmosphere have never been reported before. Observations of the
CO2 flux have been carried out at various urban stations, such as
London, UK (Ward et al., 2013), Mexico City, Mexico (Velasco et al., 2009),
Beijing, China (Song and Wang, 2012), and Tokyo, Japan (Hirano et al.,
2015), allowing us to observe urban CO2 emission directly in the flux
footprint. Therefore, if the OR for the net turbulent O2 and CO2
fluxes (hereafter referred to as “ORF”) can be observed, then such
information can be used as a useful constraint for evaluating the
contributions of the gaseous, liquid, and solid fuels and the terrestrial
biospheric activities to the observed CO2 flux. From the measurements,
it also becomes possible to observe the urban O2 flux by multiplying
the CO2 flux by ORF.
In this paper, we first present the simultaneous observational results of
the O2 and CO2 concentrations and the CO2 flux in the urban
area of Tokyo, Japan. From a relationship between the vertical gradients of
the observed O2 and CO2 concentrations, we derive ORF based
on an aerodynamic method (Yamamoto et al., 1999). The present paper follows
Ishidoya et al. (2015), who reported ORF for the O2 and CO2
fluxes between a forest canopy and the overlaying atmosphere. We also compare
the observed ORF with the OR value of the overlaying atmosphere above
the urban canopy (hereafter referred to as “ORatm”) to highlight the
characteristics of the O2 and CO2 exchange processes in the urban
canopy air at the YYG site. Finally, we estimate the average diurnal cycles
of CO2 fluxes from gas and liquid fuel consumption separately by using
the ORF, CO2 flux, and inventory-based CO2 emission from
human respiration in order to validate the inventory-based CO2
emissions from gas consumption and traffic.
Experimental proceduresSite description
In order to observe the atmospheric O2 and CO2 concentrations and
CO2 flux between the urban area and the overlaying atmosphere, the
instruments were installed on a roof-top tower of Tokai University (52 m
above ground, 25 m above roof) at Yoyogi (YYG; 35.66∘ N,
139.68∘ E), Tokyo, Japan. The YYG site is a mid-rise residential
area and located in the northern part of Shibuya ward, Tokyo. Figure 1 shows
the location of the YYG site and the flux footprints averaged for summer and
winter runs, calculated by the model of Neftel et al. (2008). The main
land cover around the site is characterized by low- to mid-rise residential
buildings with a mean height of 9 m. The population density in this area is
16 600 persons per square kilometer. At the YYG site, the prevailing wind is from SW in the
summer and NW in the winter. The flux footprint includes a vegetated area of
9 % in the summer and 2 % in the winter, reflecting seasonal changes in
the wind direction.
(a) Location of the Yoyogi site (35.66∘ N,
139.68∘ E, YYG), Tokyo, Japan. (b, c) Aerial photo from the
Geospatial Information Authority of Japan around the study area at YYG.
Ensemble-mean flux footprints in the summer (b) and winter (c)
are also shown by black circles. The contour lines indicate contribution in
measured flux (60 %, 50 %, 40 %, 30 %, 20 % and 10 % from outside to inside). Inside
and outside the red circles indicate the distances of 500 and 1000 m,
respectively, from a roof-top tower of Tokai University where the
observations of O2 and CO2 concentrations and CO2 flux were
carried out.
Continuous measurements of the atmospheric O2 and CO2
concentrations and CO2 flux
Observations of the atmospheric O2 and CO2 concentrations have
been carried out at the YYG site using a continuous measurement system
employing a paramagnetic O2 analyzer (POM-6E, Japan Air Liquid) and a
non-dispersive infrared CO2 analyzer (NDIR; Li-820, LI-COR) since March 2016. The O2 concentration is reported as the O2/N2 ratio in
per meg:
δO2/N2=O2/N2sampleO2/N2standard-1×106,
where the subscripts “sample” and “standard” indicate the sample air and the
standard gas, respectively. Because O2 is about 20.94 % of air by
volume (Tohjima et al., 2005a), the addition of 1 µmol of O2 to 1 mol of dry air increases δ(O2/N2) by 4.8 per meg
(=1/0.2094). If CO2 were to be converted one-for-one into O2,
this would cause an increase of 4.8 per meg of δ(O2/N2),
equivalent to an increase of 1 µmol mol-1 in O2 for each 1 µmol mol-1 decrease in CO2. Therefore, the ratio of 4.8 per
meg µmol mol-1 was used to convert the observed δ(O2/N2) to O2 concentration relative to an arbitrary
reference point. In this study, δ(O2/N2) values of each
air sample were measured with the paramagnetic analyzer using working
standard air that was measured against our primary standard air (cylinder
no. CRC00045; AIST scale) using a mass spectrometer (Thermo Scientific
Delta-V) (Ishidoya and Murayama, 2014).
Sample air was taken at the tower heights of 52 and 37 m using a diaphragm
pump at a flow rate higher than 10 L min-1 to prevent
thermally diffusive fractionation of air molecules at the air intake (Blaine
et al., 2006). Then, a large portion of the air is exhausted from the
buffer, with the remaining air allowed to flow into the analyzers from the
center of the buffer. It is then sent to an electric cooling unit with a
water trap cooled to -80∘C at a flow rate of 100 mL min-1,
with the pressure stabilized to 0.1 Pa and measured for 10 min at each
height (one-cycle measurements). The method to sample a small subset of air
from a high flow rate is similar to those used in Goto at el. (2013b), and we
have confirmed that the atmospheric δ(O2/N2) values
observed by the measurement system agree well with those obtained from
independent continuous measurements of δ(O2/N2) using the
mass spectrometer (see Fig. 4 in Ishidoya et al., 2017). After nine cycles of
measurements (five and four cycles for 37 and 52 m, respectively), high-span
standard gas, prepared by adding appropriate amounts of pure O2 or
N2 to industrially prepared CO2 standard air, was introduced into
the analyzers with the same flow rate and pressure as the sample air and
measured for 5 min, and then low-span standard gas was measured by the
same procedure. The dilution effects on the O2 mole fraction measured
by the paramagnetic analyzer were corrected experimentally, not only for the
changes in CO2 of the sample air or standard gas measured by the NDIR,
but also for the changes in Ar of the standard gas measured by the mass
spectrometer as δ(Ar/N2). The analytical reproducibility of the
δ(O2/N2) and CO2 concentration achieved by the system
was about 5 per meg and 0.06 µmol mol-1, respectively, for
2 min average values. Details of the continuous measurement system used
are given in Ishidoya et al. (2017).
It should be noted that we used the gravimetrically prepared air-based
CO2 standard gas system with uncertainties of ±0.13µmol mol-1 on the TU-10 scale (Nakazawa et al., 1991) to determine CO2
concentration in this study. The highest concentration of the
gravimetrically prepared standard gas was about 450 µmol mol-1,
while CO2 concentrations of more than 600 µmol mol-1 were
observed in this study. Therefore, we compared the NDIR-based CO2
concentrations observed in this study with those observed by using cavity
ring-down spectroscopy (CRDS; G2401, Picarro) on the NIES-09 scale (Machida et
al., 2011) at the YYG site (our unpublished data). Although the highest
CO2 concentration of the gravimetrically prepared standard of the
NIES-09 scale is similar to that of the TU-10 scale, a slope of 0.974 ppm ppm-1 is derived from a least-squares regression line fitted to the
relationship between the CO2 concentrations observed by NDIR on the
TU-10 scale and those by CRDS on the NIES-09 scale with a correlation
coefficient (r) of 0.978. On the other hand, we obtained a slope of 1.002
per meg per meg-1 (r=0.999) from the regression line fitted to the
relationship between the O2 concentrations of gravimetrically prepared
standard gases (Aoki et al., 2019) measured by the mass spectrometer on the
AIST scale and the gravimetric values of the standard gases covering a much
wider range than the atmospheric variations in the O2 concentration.
Therefore, the uncertainty in OR due to the span uncertainties of O2
and CO2 concentrations is expected to be within 3 %.
In order to observe the CO2 flux at the YYG site, the turbulence and
the turbulent fluctuation of CO2 were observed at 52 m with a high time
resolution of 10 Hz by using a sonic anemometer (WindMasterPro, Gill) and an
open-path infrared gas analyzer (LI-7500, LI-COR) starting November 2012. The
sensors were located at more than 5 times the mean building height (9 m), and
then it was above the urban roughness sublayer. Turbulent flux of CO2
was calculated by the eddy correlation method using EddyPro®
(Licor) for every 30 min period. Correlations were applied in the
calculation for water-vapor density fluctuation (Webb et al., 1980) and mean
vertical wind by using the double rotation algorithm (Wilczak et al., 2001).
The calculated flux was filtered for data quality based on the steady test
and the integral turbulence characteristics in Aubinet et al. (2012). We
used the flag 0–2 data in EddyPro® software based on Mauder
and Foken (2006).
Results and discussionVariations in the atmospheric O2 and CO2 concentrations
We show the 10 min average values of the atmospheric O2 and CO2
concentrations observed at the height of 52 m at YYG in Fig. 2. As seen in
the figure, O2 and CO2 concentrations vary in opposite phase with
each other on timescales ranging from several hours to a seasonal cycle. In
general, opposite phase variations of atmospheric O2 and CO2 are
driven by fossil fuel combustion and terrestrial biospheric activities. In
contrast, the atmospheric O2 variation in µmol mol-1 due to
the air–sea exchange of O2 is much larger than that of CO2 on
timescales shorter than 1 year (e.g., Goto et al., 2017; Hoshina et al.,
2018); this is because the equilibration time for O2 between the
atmosphere and the surface ocean is much shorter than that for CO2 due
to the influence of the carbonate dissociation effect on the air–sea
exchange of CO2 (Keeling et al., 1993). Therefore, we attribute the
opposite phase variations in O2 and CO2 observed in this study
mainly to fossil fuel combustion and terrestrial biospheric activities.
Figure 2 also shows that ΔO2, obtained by subtracting O2
at 41 m from that at 52 m on the tower, varies in opposite phase with the
corresponding ΔCO2. High ΔO2 values are more
frequently observed in the winter than in the summer, and short-term
(several hours to days) decreases in the O2 concentration are intense
in the winter.
Variations in O2 and CO2 concentrations observed at the
tower height of 52 m at Yoyogi, Tokyo, Japan, for the period March 2016–September 2017. The O2 concentrations are expressed as deviations from
the value observed at 09:58 local time on 9 March 2016. ΔO2, representing
the differences calculated by subtracting the observed O2
concentrations at 37 m from that at 52 m, are also shown. ΔCO2
are the same as ΔO2 but for CO2 concentration. Daily mean
CO2 fluxes observed using the eddy correlation method are also shown,
and the flux takes on a positive value when the urban area emits CO2 to
the overlaying atmosphere.
To examine a relationship between the appearances of high ΔO2
and O2 concentration decrease, detail variations in the O2 and
CO2 concentrations, ΔO2 and ΔCO2 for the
periods 16–23 December and 1–9 July 2016, are shown in Fig. 3. As seen
in the figure, increases in ΔO2 coincide with decreases in
O2 concentration in December, especially in the nighttime. Such
a coincidence is also seen in July; however, the increases in ΔO2
are much smaller than those in December. Therefore, it is highly likely that
O2 is consumed within the urban canopy at YYG, more so in the winter
due to an increased usage of gas and/or liquid fuels for heating and to a
temperature inversion near the surface. The daily mean CO2 flux from
the urban area to the overlaying atmosphere shown in Fig. 2 shows a seasonal
cycle with a wintertime maximum, consistent with the enhancement of O2
consumption in the urban canopy.
Same as in Fig. 2 but for O2 and CO2 concentrations,
ΔO2 and ΔCO2 for the periods 16–23 December and
1–9 July 2016.
In this study, we focus on the short-term variations of O2 and CO2
for periods of several hours to days, to elucidate the O2 and CO2
exchange processes between the urban area and the atmosphere by examining
two types of OR: one is ORatm calculated from a relationship between
the O2 and CO2 concentration values observed at 52 or 37 m and
the other one is ORF, for the O2 and CO2 fluxes between the
urban area and the overlaying atmosphere, calculated from a relationship
between ΔO2 and ΔCO2. The relationships of the
O2 and CO2 fluxes with ORF are based on the aerodynamic
method of Yamamoto et al. (1999):
2FO=-KΔO2Δz,3FC=-KΔCO2Δz,4ORF=-FOFC=-ΔO2ΔCO2.
Here, FO (FC) (µmol m-2 s-1) represents the O2
(CO2) flux from the urban area to the overlaying atmosphere, K is the
vertical diffusion coefficient, and ΔO2Δz-1
(ΔCO2Δz-1) is the vertical concentration gradient
of O2 (CO2). The vertical diffusion is a sum of mass-independent
eddy and mass-dependent molecular diffusion; however, the effect of molecular
diffusion on the observed variations of O2 and CO2 concentrations
is generally negligible in the troposphere. It is significant in the
stratosphere (e.g., Ishidoya et al., 2013a). Therefore, we used the same
diffusion coefficient K for O2 and CO2 in Eqs. (2) and (3), which
enabled us to estimate FO by using the observed ΔO2,
ΔCO2 and FC as in Eq. (4). In general, ORatm reflects
wider footprints of O2 and CO2 than ORF due to horizontal
atmospheric transport (Schmid, 1994). We note that the definitions of
ORF and ORatm are similar to those of ERF and ERatm,
respectively, reported by Ishidoya et al. (2013b, 2015).
In order to calculate ORatm for short-term variations, (1) we applied a
best-fit curve consisting of the fundamental and its first harmonics
(periods of 12 and 6 months) and a linear trend to the maximum (minimum)
values of O2 (CO2) observed at 52 m during the successive 1-week
periods and regarded the best-fit curve as its baseline variation; (2) then,
the baseline variation of O2 (CO2) concentration was subtracted
from the respective O2 (CO2) concentrations observed at 52 m.
Figure 4 shows the baseline variations and the variations in the O2 and
CO2 concentrations observed at Minamitorishima (MNM; 24.28∘ N, 153.98∘ E), Japan (updated from Ishidoya et al., 2017). MNM is
a small and isolated coral island located 1850 km southeast of Tokyo,
Japan, and the observation site was operated by the Japan Meteorological
Agency (JMA) under the Global Atmosphere Watch program of the World
Meteorological Organization (WMO/GAW). The baseline variations of O2
and CO2 at YYG show clear seasonal cycles with peak-to-peak amplitudes
of 28 and 16 µmol mol-1, respectively, with a corresponding seasonal
maximum and minimum appearing in mid August. The amplitude of the seasonal
O2 (CO2) cycle and the appearance of a seasonal maximum (minimum)
were found to be larger and earlier, respectively, than those observed at
MNM, while the annual average values of the baseline concentration
variations of O2 and CO2 at YYG did not differ significantly from
those at MNM. These characteristics of the seasonal cycles and the annual
average values of the baseline variations at YYG and their comparison with
those at MNM are generally consistent with those observed at similar
latitude over the western Pacific region (Tohjima et al., 2005b). Therefore,
in spite of the fact that the YYG site is located in a megacity, the
baseline variations of O2 and CO2 concentrations are similar to
those in the background air.
Baseline variations of O2 and CO2 concentrations at the
tower height of 52 m at Yoyogi, Tokyo, Japan, represented by their best-fit
curves (black solid lines) to the respective maximum and minimum values during
the successive 1-week periods (black dashed lines). Variations of 24 h averaged O2 and CO2 concentrations at Minamitorishima, Japan
(blue dashed line), and their best-fit curves (blue solid lines), are also
shown (updated from Ishidoya et al., 2017).
O2:CO2 exchange ratio between the urban area and the overlaying
atmosphere
Figure 5a shows the relationship between all the ΔO2 and
ΔCO2 values to obtain the average ORF throughout the
observation period in this study. When errors in both species are
non-negligible, a standard least-squares linear regression will give a
biased and erroneous slope. Therefore, we apply an unweighted Deming
regression analysis to the data (e.g., Linnet, 1993), assuming the ratio
between the squared analytical standard deviations to be 0.062/5×0.20942 (ppm ppm-1) to take into account the measurement uncertainties of CO2
and O2 concentrations. We consider the slope obtained by Deming
regression to be ORF, but we use a standard deviation obtained from a
standard least-squares regression to indicate the uncertainty of the slope.
The jackknife method (Linnet, 1990) could be used to derive a standard error for
Deming regression; however, by using a short dataset extracted from the
observed data used in the present study, we confirmed that the standard
deviations obtained from an ordinary regression are larger than the errors
from the jackknife method. Therefore, using a standard deviation from
ordinary regression is reasonable to ensure larger uncertainty for the
ORF. The average ORF value was calculated to be 1.620±0.004
(±1σ). This value falls within the range of the average OR
values of 1.44 for liquid fuels and 1.95 for gas fuels, which suggests that
the O2 and CO2 fluxes at the YYG site were driven mainly by a
consumption of liquid and gas fuels rather than terrestrial biospheric
activities of which OR is about 1.1 (Severinghaus, 1995). The relationship
between the O2 and CO2 concentration anomalies, calculated by
subtracting the respective baseline variations shown in Fig. 4 from the
observed O2 and CO2 concentrations, is also shown in Fig. 5b.
By applying the Deming regression analysis to the data, we obtained an
average ORatm value of 1.541±0.002 (±1σ)
throughout the observation period. The ORatm value also falls within
the range of the average OR values for liquid fuels and gas fuels. However,
the ORatm in this figure is not appropriate in representing the OR for
the O2 and CO2 fluxes around the YYG site since it was determined
by using the entire 18 months of collected observations that the site is
influenced by various trajectories of air masses with a much wider regional
signature than the flux footprints. Therefore, we compare below the ORF
and ORatm values by changing the aggregation periods to calculate the
ORs and examine the validity of using ORF rather than ORatm to
evaluate the relationship between the local O2 and CO2 fluxes.
(a) Relationship between the ΔO2 and ΔCO2 shown in Fig. 2. The average ORF (see text) for the observation
period, derived from the Deming regression fitted to the data, is also shown.
(b) Same as in (a) but for the deviations of O2 and CO2
concentrations from their baseline variations shown in Fig. 3 and the
average ORatm (see text). OR values expected from the consumptions of
gas and liquid fuels are also shown.
Figure 6 shows examples of the ORF calculated by applying Deming
regression fitted to ΔO2 and ΔCO2 values during
the successive 12 h periods observed in January 2017 and July 2016. The
corresponding ORatm and wind direction observed for the periods are
also shown in the figure. As seen in the figure, variabilities in the
ORF and ORatm are larger in July than in December. The average
ORF, calculated using the OR values within a range of 0.5 to 2.5, were
1.65±0.20 and 1.52±0.32 in the winter (December to February)
and summer (July to September), respectively. The corresponding average
ORatm values were 1.61±0.15 in the winter and 1.45±0.27
in the summer. To examine the dependency of the OR on the wind direction, we
also calculated ORF and ORatm for the periods when the prevailing
wind directions were observed to be from 320 to 360∘
(NW) and from 180 to 220∘ (SW) in the winter and summer,
respectively. The number of measurements taken during the time of these
prevailing winds constituted 30 % (winter) and 8 % (summer) of the
total number of measurements. The calculated ORF, ORatm and
prevailing winds are shown by blue dots in Fig. 6. The average ORF
(ORatm) values, calculated using the OR values within a range of 0.5 to
2.5, were 1.65±0.25 (1.58±0.19) in the winter and 1.58±0.40 (1.42±0.33) in the summer, respectively. Therefore, the average
ORF and ORatm calculated using all the values obtained from the
12 h aggregation periods did not differ significantly from those that
were calculated using only the data that were associated with the
above-mentioned prevailing wind directions. The average ORF seems to be
slightly higher than ORatm; however, their uncertainties are too large
to discuss the significance of the slight difference. Taking these facts
into consideration, we use all the O2 and CO2 concentration data
without filtering by the wind direction to increase the number of data
points for calculating ORF and ORatm; this is consistent with the
purpose of this study to derive representative OR values at the YYG site in
order to validate the CO2 emission inventory (Hirano et al., 2015). For
analyses of specific events, we have reported analytical results of
ORatm and simultaneously measured PM2.5 aerosol composition for a
week long pollution event at the YYG site (Kaneyasu et al., 2020).
(a) ORF (black dots, top) calculated by applying Deming
regression fitted to ΔO2 and ΔCO2 values during
the successive 12 h periods observed in January 2017. The corresponding
ORatm (black dots, middle) obtained from the deviations of O2 and
CO2 concentrations from their baseline variations shown in Fig. 4, and
the wind directions (black dots, bottom), are also shown. Angles of
90, 180, 270 and 360∘ for the
wind direction denote winds from east, south, west and north, respectively.
The ORF and ORatm obtained from the data observed during the
period with the prevailing wind direction (blue dots, bottom) are also shown
by blue dots. (b) Same as in (a) but for July 2016.
To examine the seasonal difference between the ORF and ORatm
values, we show the ORF values calculated by applying regression lines
to 1 d and 1-week successive ΔO2 and ΔCO2 values
in Fig. 7. The corresponding ORatm values, obtained by applying Deming
regression fitted to successive O2 and CO2 concentration
anomalies in Fig. 5b, are also shown. Since there is no statistically
significant difference between the two (based on the uncertainties shown in
the figure (±1σ)), we focus our discussion on the OR values
obtained from the 1-week successive data. Clear seasonal cycles with
wintertime maxima are found in both the ORF and ORatm values at
YYG. Larger ORatm values in the winter than in the summer in urban
areas have been reported by some past studies (e.g., van der Laan et al.,
2014; Ishidoya and Murayama, 2014; Goto et al., 2013a) and generally
interpreted as a result of the wintertime increase and decrease in fossil
fuel combustion and terrestrial biospheric activities, respectively.
Biospheric activities included in the summertime and wintertime flux
footprints at YYG were 9 % and 2 %, respectively (Hirano et al., 2015), and
there was no significant solid fuel consumption, such as a coal-fired power-generation plant of which OR is expected to be 1.17 (Keeling, 1988),
detected in the footprints. At YYG, the effect of emissions from coal
combustion is evaluated simultaneously by the use of aerosol composition
monitored every 4 h (Kaneyasu et al., 2020). From these measurements,
emission contribution from coal combustion can be detected under a limited
meteorological condition, such as a stagnant condition under a weak
south-southwesterly wind. This condition occurred only several times a year,
mostly from spring to fall. Therefore, the wintertime ORF was
determined mainly by gas and liquid fuel consumption around the YYG site,
given that few vegetation and weak terrestrial biospheric activities
took place in the wintertime. If we assume the wintertime ORF is
determined only by gas and liquid fuel consumption, with OR values of 1.95
and 1.44, respectively, then 45 % of the CO2 flux during the December
to February (DJF) period was driven by gas fuel consumption, with the rest
attributed to liquid fuel consumption. It should be noted that the
contributions of gas and liquid fuels are expected to be underestimated and
overestimated since we have ignored the contribution from human respiration
with OR values in the range of 1.0 to 1.4. The respiration quotients (the
reciprocal of OR) for carbohydrates, lipid and protein are known to be about
1.0, 0.7 and 0.8, respectively. We also conducted detail analyses to
separate out the contributions from the consumption of gas and liquid fuels
and human respiration by using the observed CO2 flux and ORF and
comparing the results with the CO2 emission inventory in Sect. 3.3.
Figure 7 also shows that the ORF values were systematically larger than
ORatm throughout the year, except for October 2016 and July 2017. The
average ORF and ORatm during DJF were 1.67±0.03 and
1.63±0.02, respectively, both of which agree with the OR value of
1.65 calculated using the statistical data of fossil fuel consumption in
Tokyo reported by the Agency of Natural Resources and Energy
(http://www.enecho.meti.go.jp/en/, last access: 18 December 2018), assuming OR values of 1.95, 1.44 and 1.17
for gas, liquid and solid fuel consumption, respectively (hereafter
referred to as “ORff”). By using the same procedure as above, the
average ORff was calculated to be 1.52±0.1 for the Kanto area of
about 17 000 km2 that includes Tokyo. Therefore, it is suggested that not
only ORF, but also ORatm at YYG, mainly reflected an influence of
the fossil fuel consumption in Tokyo rather than that in the wider Kanto
area in the wintertime. Both the ORF and ORatm values in the
summer were lower than ORff in Tokyo (1.65), but ORatm was also
found to be lower than ORff for the Kanto area (1.52). These lower
ORF and ORatm values, compared to those of the ORff, suggest
that the ratio of fossil fuel combustion to terrestrial biospheric
activities and human respiration is lower in the summer than that in the
winter. The slightly lower ORatm than ORF at YYG throughout the
year is probably due to the higher contribution of the air mass from the Kanto
area to ORatm than ORF, since the Kanto area as a whole has lower
ORff than for Tokyo; in addition, the southern Kanto area (including
Tokyo) has a larger vegetation coverage of about 50 % than that in the
area around the YYG site. From the comparison results of the ORF with
ORatm in Figs. 5–7, it is suggested that the ORatm reflects
wider footprints of O2 and CO2 than ORF for the aggregation
periods at least longer than 12 h to calculate the ORatm.
Therefore, to use ORF rather than ORatm is more appropriate to
validate inventory-based CO2 emissions from gas, liquid and solid fuels
in the flux footprint.
ORF calculated by applying Deming regression fitted to 1 d (gray open circles) and 1-week (black closed circles) successive ΔO2 and ΔCO2 values. Also plotted are ORatm calculated by applying Deming regression fitted to 1 d (light red open
circles) and 1-week (dark red closed circles) successive O2 and
CO2 deviations from their baseline variations shown in Fig. 3. OR
values expected from the consumptions of gas, liquid and solid fuels and
land biospheric activities are also shown.
Consumption of gas and liquid fuels estimated from the observed CO2 flux and O2:CO2 exchange ratio for net turbulent flux
In this section, we derive average diurnal cycles of ORF, CO2 and
O2 fluxes and estimate the CO2 fluxes from gas and liquid fuel
consumption separately. Figure 8 shows the average diurnal cycles of ΔO2 and ΔCO2 for each season. To derive the average
diurnal cycles, the observed ΔO2 and ΔCO2 values
of each day in a season were overlain on top of the values of other days,
added up and divided by the number of days in the season. The error bars
shown in Fig. 8 indicate ±1 standard error (σ/n).
The ΔO2 and ΔCO2 values vary systematically in
opposite phase and take positive and negative values, respectively,
indicating transport of O2 uptake and CO2 emission signals from
the urban area to the overlaying atmosphere throughout the year. Daily maxima
of ΔO2 shown in Fig. 8 are higher in the winter than in the
summer and occur in the nighttime. These characteristics would be
attributable to an enhancement of the anthropogenic O2 consumption in
the winter, while the nighttime decrease in O2 concentration would be
due to the O2 consumption near the surface and temperature inversion
near the surface. It must be noted that the ΔCO2 values in the
daytime are nearly zero, while the ΔO2 values are not. The
intercepts of the regression lines fitted to the relationship between
ΔO2 and ΔCO2 in Fig. 8 are 0.27, 0.41, 0.45 and
0.44 µmol mol-1 in DJF, MAM, JJA and SON, respectively.
Unfortunately, we did not fix the cause(s) of such biases yet, although it (they)
may be related, to some extent, to natural exchange processes between the
urban area and the overlaying atmosphere. Therefore, because of these issues,
the use of ORF, calculated by applying a Deming regression fitted to
2 h period values of ΔO2 and ΔCO2 of the
climatological diurnal cycle (the number of data included in each 2 h
periods were 400–800, depending on the season), to determine the
relationship between the O2 and CO2 fluxes, is preferable. The
ORF values plotted in Fig. 8 show diurnal cycles with daytime minima in
DJF, MAM and SON, while no clear cycle is found in JJA. From 10:00 to 16:00
local time, the ORF values are in the range of 1.44–1.59 for all
seasons. On the other hand, the ORF values from 18:00 to 09:00 local
time are more variable, in the range of 1.39–1.74, and are clearly larger
in the winter than in the summer.
Plots of average diurnal cycles of ΔO2 (filled
circles) and ΔCO2 (open circles) for each season: December to
February (back), March to May (green), June to August (blue) and September
to November (red). Average diurnal cycles of ORF, calculated by
applying Deming regression fitted to the 2 h period values of ΔO2 and ΔCO2, are also plotted seasonally (see text).
Average diurnal cycles of the CO2 flux observed using the eddy
correlation method, and those of the O2 flux calculated from the
CO2 flux and ORF values, are also plotted seasonally. Error bars
indicate ±1 standard error.
The observed CO2 flux and the estimated O2 flux for each season
are shown in Fig. 8. The CO2 flux shows clear diurnal cycles with two
peaks for all seasons, one in the morning and the other in the evening. The
shape of the diurnal CO2 flux cycle, with larger flux in the winter
than in the summer, was also found in our previous study at YYG for the
period 2012–2013 (Hirano et al., 2015). On the other hand, the O2 flux
shows similar diurnal cycles but in opposite phase with the CO2 flux.
The daily mean CO2 fluxes were 15.6±0.2, 11.2±0.1, 9.3±0.1 and 11.5±0.1µmol m-2 s-1 in DJF, MAM, JJA
and SON, respectively, while the respective daily mean O2 fluxes were
-25.4±0.3, -17.8±0.2, -14.1±0.2 and -17.7±0.2µmol m-2 s-1. The annual average daily mean O2 flux was
-16.3µmol m-2 s-1. Steinbach et al. (2011) reported a global
dataset of CO2 emissions and O2 uptake associated with fossil fuel
combustion using the EDGAR inventory with country-level information on OR,
based on the fossil fuel consumption data from the UN energy statistics
database. The O2 uptake around Tokyo for the year 2006 has been shown
to be about e16–e17 kgO2 km-2 yr-1 (Fig. 2 in
Steinbach et al., 2011), which corresponds to -9 to -24µmol m-2 s-1 of O2 flux and is consistent with those observed in
this study. In this regard, the atmospheric O2 concentration decreased
secularly due mainly to fossil fuel combustion at a rate of change of about
-4µmol yr-1 (e.g., Keeling and Manning, 2014), corresponding to
-0.04µmol m-2 s-1 of O2 flux, assuming 5.1×1014 m2 for the surface area of the earth, 5.124×1021 g for the total
mass of dry air (Trenberth, 1981) and 28.97 g mol-1 for the mean
molecular weight of dry air. Therefore, the consumption rate of atmospheric
O2 in an urban area of Tokyo is several hundred times larger than the
global mean surface consumption rate.
The CO2 emission inventory was developed based on Hirano et al. (2015)
with some modifications. We added human respiration based on the hourly
population data (Regional Economy Society Analyzing System,
https://resas.go.jp/, last access: 11 March 2020). Respiration amount per person was referred from
Moriwaki and Kanda (2004). We also added CO2 emission due to gas
consumption by restaurants to the Hirano et al. (2015) inventory, which only
accounted for household emission. Monthly gas consumption in restaurants was
acquired from the statistical data published by the local government
(http://www.toukei.metro.tokyo.jp/tnenkan/2015/tn15q3i006.htm, last access: 11 March 2020). Diurnal
variation in the gas consumption by the restaurants was obtained from
Takahashi et al. (2006) and Takata et al. (2007). We also modified the
household gas consumption using the study by Etsuki (2010). As for the
traffic, we used traffic load data (http://www.jartic.or.jp/, last access: 11 March 2020) which
recorded the number of vehicles on the road every hour every day, whereas
Hirano et al. (2015) used traffic data for a single day in 2010.
The ORF is determined as a ratio of net turbulent fluxes of O2 and
CO2 from mixed consumption of gas, liquid and solid fuels and
terrestrial biospheric activities and human respiration. In this study, the
total net turbulent CO2 flux from the urban area to the overlaying
atmosphere is calculated using the eddy correlation method. The CO2
emission inventories from gas consumption, traffic and human respiration
have also been updated from the original data published by Hirano et al. (2015). We can then proceed to separate out the CO2 flux from gas and
liquid fuel consumption by using Eq. (4), followed by Eqs. (5)–(6):
5FO=-ORG×FG+ORL×FL+ORR×FR,6FC=FG+FL+FR,
where FG, FL and FR (µmol m-2 s-1) represent
the CO2 fluxes from gas and liquid fuel consumption and human
respiration from the urban area to the overlaying atmosphere, and ORG,
ORL and ORR are the OR values for gas and liquid fuel consumption
and human respiration, respectively. We use 1.95, 1.44 and 1.2 for ORG,
ORL and ORR, respectively. For this analysis, it is assumed that
the contributions from solid fuel consumption and terrestrial biospheric
activities are negligible, given the fact that in the flux footprint area,
significant solid fuel consumption is absent and the vegetated area is
relatively small. We also assume an ORR value of 1.2 as an intermediate
value of the reciprocal of respiration quotients for carbohydrates, lipid
and protein. We use the FC observed by the eddy correlation method and
the FR obtained from the CO2 emission inventory to estimate
FG and FL.
Figure 9 shows average diurnal cycles of the observed total CO2 flux
and the CO2 flux from gas and liquid fuel consumption estimated from
Eqs. (4) to (6) for each season. The average diurnal cycles of the
inventory-based total, gas, traffic and human respiration CO2 fluxes
are also shown in the figure. As seen in Fig. 9, similar diurnal cycles with
two peaks are found in both the observed and inventory-based total CO2
fluxes for all seasons. Two peaks of the diurnal cycles are also found in
the diurnal cycles of the estimated and inventory-based CO2 fluxes from
gas consumption; however, the evening peaks of the inventory-based flux in
MAM, JJA and SON are clearly larger than the estimated values. It is also
seen from the figure that the diurnal cycles of inventory-based traffic
CO2 flux do not change significantly throughout the year, while those
of the estimated CO2 flux from liquid fuel consumption show large
variabilities, especially in the morning. Such variability may be caused by
the smaller ΔO2 and ΔCO2 values observed during
the daytime, compared to those in the nighttime, as well as a rapid
change in the atmospheric stability after the daybreak. The actual diurnal
cycles of liquid fuel consumption do not seem to change significantly
throughout the year, considering the results of the inventory-based traffic
CO2 flux. We therefore consider the standard deviations of the seasonal
diurnal cycles of the estimated CO2 flux from liquid fuel consumption
from the annual average diurnal cycle to be the uncertainties for the annual
average cycle.
Average diurnal cycles of the total CO2 flux observed using
the eddy correlation method (black filled circles) and the estimated CO2
flux from gas (blue filled circles) and liquid (red filled circles)
fuel consumption by using the total CO2 flux and ORF for each season:
December to February (a), March to May (b), June to August (c) and September
to November (d). Average diurnal cycles of the CO2 emission inventory
of gas consumption (blue open circles), traffic (red open circles), human
respiration (green open circles) and their total (black open circles) around
YYG are also shown for each season. See text in detail.
Figure 10 shows the same diurnal cycles of the observed, estimated, and
inventory-based CO2 fluxes as in Fig. 9 but for the annual average
cycle. The observed total CO2 flux is found to be significantly smaller
than the inventory-based flux in the evening. Similar discrepancy was also
seen in our previous study (Hirano et al., 2015). The main cause of this
discrepancy in the evening is likely the much larger inventory-based
CO2 flux from gas consumption than the estimated flux. The estimated
CO2 flux from liquid fuel consumption is somewhat larger than the
inventory-based traffic CO2 flux in the evening, thus contributing to
the above-mentioned discrepancy to some extent. Although the uncertainty in
the estimated CO2 flux is large in the morning, the observed peak of
the estimated CO2 flux from gas fuel consumption early in the morning
and the gradual increase in the estimated CO2 flux from liquid fuel
consumption over the same time period can be distinguishable. Such temporal
variations of the estimated CO2 flux are reasonable since gas fuel
consumption for domestic heating and cooking should increase early in the
morning and liquid fuel consumption from the traffic should increase during
the morning commute. Consequently, it is confirmed that the simultaneous
observations of the ORF and CO2 flux are useful in validating the
CO2 emission inventories developed based on statistical data. However,
as shown in Figs. 8–10, a large number of ΔO2 and ΔCO2 measurement data are needed to derive reliable ORF based on an
aerodynamic method. If we measure O2 concentration with high
time resolution to determine net turbulent O2 flux by an eddy
correlation method, then it will be possible to derive high-time-resolution
ORF as a ratio of the observed O2-to-CO2 fluxes. Such an
innovative technique will enhance the value of the ORF observations
significantly for an evaluation of the urban CO2 emissions.
Same as in Fig. 9 but for the annual average diurnal cycles. The
error bars for the estimated CO2 flux from liquid fuel consumption are
the standard deviations of the diurnal cycles of the flux for respective
seasons from the annual average cycle, assuming that the actual diurnal
cycles of liquid fuel consumption do not change significantly throughout
the year (see text).
Conclusions
Continuous simultaneous observations of atmospheric O2 and CO2 and
CO2 flux have been carried out at the YYG site, Tokyo, Japan, since March 2016. Sample air was taken from air intakes set at heights of 52 and 37 m
of the YYG tower, allowing us to apply an aerodynamic method by using the
vertical gradients of the O2 and CO2 concentration measurements.
We compared ORF obtained from the aerodynamic method with ORatm,
representing OR of the overlaying atmosphere above the urban canopy. We found
clear seasonal variations with wintertime maxima for both ORF and
ORatm as well as slightly higher ORF than ORatm throughout
the year. The annual mean ORF and ORatm were observed to be 1.62
and 1.54, respectively, falling within the range of the respective average
OR values of 1.44 and 1.95 of liquid and gas fuels. The slightly lower
ORatm than ORF throughout the year was probably due to an
influence of the air mass from the wider Kanto area to ORatm at YYG
since the OR value of 1.1 for the terrestrial biospheric activities is lower
than those for liquid and gas fuel consumption; in addition, the influence
of the vegetation included in the flux footprints at YYG was much smaller
than that in the surrounding Kanto area. Therefore, we prefer to use
ORF rather than ORatm to validate the inventory-based CO2
emissions from gas, liquid and solid fuels in the YYG flux footprint region.
Seasonal variations were seen in the average diurnal ORF cycles,
showing daytime minima in DJF, MAM and SON, while no clear diurnal cycle was
distinguishable in JJA. The daily mean O2 flux at YYG, calculated from
the ORF and CO2 flux, was about -25 and -14µmol m-2 s-1 in the winter and the summer, respectively, which means the
consumption rate of atmospheric O2 in an urban area of Tokyo is several
hundred times larger than the global mean surface consumption rate. We
estimated the average diurnal cycles of CO2 flux from the consumption
of gas and liquid fuels for each season, based on the average diurnal cycles
of ORF and CO2 flux and the CO2 emission inventory of human
respiration around the YYG site. Discrepancy between the estimated and
inventory-based CO2 fluxes from gas fuel consumption was found to be
the main cause of the significantly smaller evening peak of the observed
total CO2 flux than that of the inventory-based total flux. Along with
the peak in the estimated CO2 flux from the gas fuel consumption, the
gradual increase in the estimated CO2 flux from the liquid fuel
consumption found in the morning is consistent with the fact that the gas
fuel consumption for domestic heating and cooking, and liquid fuel
consumption from traffic during commuting, occur in the morning. Therefore,
we can use simultaneous observations of ORF and CO2 flux as a
powerful tool to validate CO2 emission inventories obtained from
statistical data.
Data availability
The data at the YYG site presented in this study can be accessed by contacting
the corresponding author.
Author contributions
SI designed the study and drafted the manuscript. Measurements of O2
concentrations, CO2 concentrations, and CO2 flux were conducted by
SI, SI and YT, and HS, respectively. HS prepared CO2 emission inventory
data. NA prepared standard gas for the O2 measurements. SI and KT
conducted O2 observations at MNM. HS, NK and HK examined the results
and provided feedback on the manuscript. All the authors approved the final
manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank Takashi Nakajima at
Tokai University, Shohei Murayama at the National Institute of Advanced Industrial Science and
Technology (AIST), and JANS Co. Ltd. for supporting the observation.
Financial support
This study was partly supported by
the JSPS KAKENHI (grant nos. 24241008, 15H02814 and 18K01129), the
Environment Research and Technology Development Fund (grant no. 1-1909), and the Global Environment Research
Coordination System from the Ministry of the Environment, Japan.
Review statement
This paper was edited by Thomas Karl and reviewed by three anonymous referees.
ReferencesAoki, N., Ishidoya, S., Matsumoto, N., Watanabe, T., Shimosaka, T., and Murayama, S.: Preparation of primary standard mixtures for atmospheric oxygen measurements with less than 1 µmol mol-1 uncertainty for oxygen molar fractions, Atmos. Meas. Tech., 12, 2631–2646, 10.5194/amt-12-2631-2019, 2019.
Aubinet, M., Vesala, T., and Papale, D. (Eds.): Eddy Covariance: A
Practical Guide to Measurement and Data Analysis, Springer, the Netherlands, 2012.Blaine, T. W., Keeling, R. F., and Paplawsky, W. J.: An improved inlet for precisely measuring the atmospheric Ar/N2 ratio, Atmos. Chem. Phys., 6, 1181–1184, 10.5194/acp-6-1181-2006, 2006.
Etsuki, R.: SCADA system of Tokyo Gas for wide-area city gas
distribution, in: Proceedings of the 53rd Japan Joint Automatic Control Conference, Kochi, Japan, November 2010, 46, 1099–1102,
2010.Goto, D., Morimoto, S., Ishidoya, S., Ogi, A., Aoki, S., and Nakazawa, T.:
Development of a high precision continuous measurement system for the
atmospheric O2/N2 ratio and its application at Aobayama, Sendai,
Japan, J. Meteorol. Soc. Jpn., 91, 179–192, 2013a.Goto, D., Morimoto, S., Aoki, S., and Nakazawa, T.: High precision continuous
measurement system for the atmospheric O2/N2 ratio at
Ny-Ålesund, Svalbard and preliminary observational results,
Nankyoku Shiryo (Antarct. Rec.), 57, 17–27, 2013b.Goto, D., Morimoto, S., Aoki, S., Patra, P. K., and Nakazawa, T.: Seasonal
and short-term variations in atmospheric potential oxygen at Ny-Ålesund, Svalbard, Tellus B, 69, 1311767, 10.1080/16000889.2017.1311767,
2017.Hirano, T., Sugawara, H., Murayama, S., and Kondo, H.: Diurnal variation of
CO2 flux in an urban area of Tokyo, SOLA, 11, 100–103, 2015.Hoshina, Y., Tohjima, Y., Katsumata, K., Machida, T., and Nakaoka, S.: In situ observation of atmospheric oxygen and carbon dioxide in the North Pacific using a cargo ship, Atmos. Chem. Phys., 18, 9283–9295, 10.5194/acp-18-9283-2018, 2018.Ishidoya, S. and Murayama, S.: Development of high precision continuous
measuring system of the atmospheric O2/N2 and Ar/N2 ratios
and its application to the observation in Tsukuba, Japan, Tellus B, 66, 22574, 10.3402/tellusb.v66.22574, 2014.Ishidoya, S., Sugawara, S., Morimoto, S., Aoki, S., Nakazawa, T., Honda, H., and Murayama, S.: Gravitational separation in the stratosphere – a new indicator of atmospheric circulation, Atmos. Chem. Phys., 13, 8787–8796, 10.5194/acp-13-8787-2013, 2013a.Ishidoya, S., Murayama, S., Takamura, C., Kondo, H., Saigusa, N., Goto, D.,
Morimoto, S., Aoki, N., Aoki, S., and Nakazawa, T.: O2:CO2
exchange ratios observed in a cool temperate deciduous forest ecosystem of
central Japan, Tellus B, 65,
21120, 10.3402/tellusb.v65i0.21120, 2013b.Ishidoya, S., Murayama, S., Kondo, H., Saigusa, N., Kishimoto-Mo, A. W., and
Yamamoto, S.: Observation of O2:CO2 exchange ratio for net
turbulent fluxes and its application to forest carbon cycles, Ecol. Res., 30,
225–234, 2015.Ishidoya, S., Tsuboi, K., Murayama, S., Matsueda, H., Aoki, N., Shimosaka,
T., Kondo, H., and Saito, K.: Development of a continuous measurement system
for atmospheric O2/N2 ratio using a paramagnetic analyzer and its
application in Minamitorishima Island, Japan, SOLA, 13, 230–234, 2017.Kaneyasu, N., Ishidoya, S., Terao, Y., Mizuno, Y., and Sugawara, H.:
Estimation of PM2.5 Emission Sources in the Tokyo Metropolitan Area by
Simultaneous Measurements of Particle Elements and Oxidative Ratio in Air,
ACS Earth Space Chem., 4, 297–304, 2020.Keeling, R. F. and Manning, A. C.: Studies of recent changes in atmospheric O2 content, in Treatise on Geochemistry, Vol. 5, 2nd Edn., Elsevier, Amsterdam, 385–404, 2014.Keeling, C. D., Piper, S. C., Whorf, T. P., and Keeling, R. F.: Evolution of
natural and anthropogenic fluxes of atmospheric CO2 from 1957 to 2003,
Tellus B, 63, 1–22, 2011.Keeling, R. F.: Development of an Interferometric Oxygen Analyzer for
Precise Measurement of the Atmospheric O2 Mole Fraction, PhD thesis,
Harvard University, Cambridge, 1988.
Keeling, R. F. and Shertz, S. R.: Seasonal and interannual variations in
atmospheric oxygen and implications for the global carbon cycle, Nature,
358, 723–727, 1992.
Keeling, R. F., Bender, M. L., and Tans, P. P.: What atmospheric oxygen
measurements can tell us about the global carbon cycle, Global Biogeochem.
Cy., 7, 37–67, 1993.
Linnet, K.: Estimation of the linear relationship between the measurements
of two methods with proportional errors, Stat. Med., 9, 1463–1473, 1990.
Linnet, K.: Performance of Deming regression analysis in case of
misspecified analytical error ratio in method comparison studies, Clin.
Chem., 44, 1024–1031, 1998.Machida, T., Tohjima, Y., Katsumata, K., and Mukai, H.: A new CO2
calibration scale based on gravimetric one-step dilution cylinders in
National Institute for Environmental Studies-NIES 09 CO2 scale, Paper
presented at: Report of the 15th WMO Meeting of Experts on Carbon Dioxide
Concentration and Related Tracer Measurement Techniques, September 2009,
Jena, Germany, WMO/GAW Rep. 194, edited by: Brand, W., 165–169, WMO,
Geneva, Switzerland, 2011.
Mauder, M. and Foken, T.: Impact of post-field data processing on eddy
covariance flux estimates and energy balance closure, Meteorol.
Z., 15, 597–609, 2006.Mitchell, L. E., Lin, J. C., Bowling, D. R., Pataki, D. E., Strong, C.,
Schauer, A. J., Bares, R., Bush, S. E., Stephens, B. B., Mendoza, D.,
Mallia, D., Holland, L., Gurney, K. R., and Ehleringer, J. R.: Long-term urban
carbon dioxide observations reveal spatial and temporal dynamics related to
urban characteristics and growth, P. Natl. Acad.
Sci. USA, 115, 2912–2917, 10.1073/pnas.1702393115, 2018.
Moriwaki, R. and Kanda, M.: Seasonal and Diurnal Fluxes of Radiation, Heat,
Water Vapor, and Carbon Dioxide over a Suburban Area, J. Appl. Meteorol.,
43, 1700–1710, 2004.
Nakazawa, T., Aoki, S., Murayama, S., Fukabori, M., Yamanouchi, T., and
Murayama, H.: The concentration of atmospheric carbon dioxide at Japanese
Antarctic station, Syowa, Tellus B, 43, 126–135, 1991.
Neftel, A., Spirig, C., and Ammann, C.: Application and test of a simple
tool for operational footprint evaluations, Environ. Pollut., 152,
644–652, 2008.Sargent, M., Barrera, Y., Nehrkorn, T., Hutyra, L. R., Gately C. K., Jones
T., McKain, K., Sweeney, C., Hegarty, J., Hardiman, B., Wang, J. A., and Wofsy,
S. C.: Anthropogenic and biogenic CO2 fluxes in the Boston urban
region, P. Natl. Acad. Sci. USA, 115,
7491–7496, 10.1073/pnas.1803715115, 2018.
Schmid, H. P.: Source areas for scalars and scalar fluxes, Bound.-Lay.
Meteorol., 67, 293–318, 1994.Severinghaus, J.: Studies of the terrestrial O2 and carbon cycles in
sand dune gases and in biosphere 2, PhD thesis, Columbia University, New
York, 1995.
Song, T. and Wang, Y.: Carbon dioxide fluxes from an urban area in Beijing,
Atmos. Res., 106, 139–149, 2012.Steinbach, J., Gerbig, C., Rödenbeck, C., Karstens, U., Minejima, C., and Mukai, H.: The CO2 release and Oxygen uptake from Fossil Fuel Emission Estimate (COFFEE) dataset: effects from varying oxidative ratios, Atmos. Chem. Phys., 11, 6855–6870, 10.5194/acp-11-6855-2011, 2011.
Takahashi, M., Imamura, E., Urabe, W., and Miyanaga, T.: A
Measurement Survey of Electricity, Gas and Hot Water Demand at a Restaurant and Analysis of Time
Variation in the End-use Energy Demand, Socio-economic Research Center, Rep. No. Y05024,
Central Research Institute of Electric Power Industry, Tokyo, 2006.
Takata, H., Murakawa, S., and Takana, A.: Analisis on the loads of hot water
supply demands in restaurants, J. Environ. Eng. (Transactions of AIJ), 616,
59–65, 2007.Tohjima, Y., Machida, T., Watai, T., Akama, I., Amari, T., and Moriwaki, Y.:
Preparation of gravimetric standards for measurements of atmospheric oxygen
and re-evaluation of atmospheric oxygen concentration, J. Geophys. Res.,
110, D11302, 10.1029/2004JD005595, 2005a.Tohjima, Y., Mukai, H., Machida, T., Nojiri, Y., and Gloor, M.: First
measurements of the latitudinal atmospheric O2 and CO2
distributions across the western Pacific, Geophys. Res. Lett., 32, L17805,
10.1029/2005GL023311, 2005b.
Trenberth, K. E.: Seasonal variations in global sea level pressure and the
total mass of the atmosphere, J. Geophys. Res., 86, 5238–5246, 1981.Velasco, E., Pressley, S., Grivicke, R., Allwine, E., Coons, T., Foster, W., Jobson, B. T., Westberg, H., Ramos, R., Hernández, F., Molina, L. T., and Lamb, B.: Eddy covariance flux measurements of pollutant gases in urban Mexico City, Atmos. Chem. Phys., 9, 7325–7342, 10.5194/acp-9-7325-2009, 2009.van der Laan, S., van der Laan-Luijkx, I. T., Zimmermann, L., Conen, F., and
Leuenberger, M.: Net CO2 surface emissions at Bern, Switzerland
inferred from ambient observations of CO2, δ(O2/N2),
and 222Rn using a customized radon tracer inversion, J. Geophys. Res.-Atmos., 119, 1580–1591, 10.1002/2013JD020307, 2014.Ward, H. C., Evans, J. G., and Grimmond, C. S. B.: Multi-season eddy covariance observations of energy, water and carbon fluxes over a suburban area in Swindon, UK, Atmos. Chem. Phys., 13, 4645–4666, 10.5194/acp-13-4645-2013, 2013.
Webb, E. K., Pearman, G. I., and Leuning, R.: Correction of flux
measurements for density effects due to heat and water vapor transfer, Q. J.
Roy. Meteor. Soc., 106, 85–100, 1980.
Wilczak, J. M., Oncley, S. P., and Stage, S. A.: Sonic anemometer tilt
correction algorithms, Bound.-Lay. Meteorol., 99, 127–150, 2001.Yamamoto, S., Murayama, S., Saigusa, N., and Kondo, H.: Seasonal and
inter-annual variation of CO2 flux between a temperate forest and the
atmosphere in Japan, Tellus B, 51, 402–413, 1999.