Introduction
Evaluation of the perturbation of atmospheric 14CO2 by nuclear
bomb tests in the middle of the last century has given very useful insight
into carbon cycle dynamics (e.g. Levin and Hesshaimer, 2000). Today this
artificial spike has almost equilibrated with the fast exchanging carbon
reservoirs, and the currently observed global Δ14CO2 trend
(Δ14CO2 being the relative deviation of the 14C/C
ratio in atmospheric carbon dioxide from standard material in per mill (‰);
Stuiver and Polach, 1977) is almost exclusively due to the ongoing input
of 14C-free fossil fuel CO2 into the atmosphere (Levin et al.,
2010; Graven, 2015). This long-term trend can potentially be used to
estimate the global input of fossil fuel CO2 into the atmosphere.
However, the uncertainty in this estimate is still large (ca. 30 %; Levin
et al., 2010) due to the uncertainty in the large 14CO2
disequilibrium fluxes from biosphere and ocean, as well as artificial
14C sources. On the continental scale, however, atmospheric Δ14CO2 measurements provide a powerful and the only direct and
quantitative tool for estimating the regional fossil fuel component. The Δ14CO2 measurements at a polluted station allow separating fossil-fuel-derived
regional CO2 enhancements relative to a clean reference
level from those originating from biospheric fluxes if the Δ14CO2 level at the reference site is also known (Levin et al., 2003;
Turnbull et al., 2009). However, on that local to regional scale (several tens of kilometres),
14CO2 emissions from nuclear facilities, such as boiling
water reactors, can significantly contaminate atmospheric Δ14CO2. The 14C signals from such point sources are well
detectable in their immediate neighbourhood in atmospheric CO2 (and
CH4) (e.g. Levin et al., 1992; Uchrin et al., 1998; Povinec et al.,
2009) and also in plant samples (Levin et al., 1988). The 14CO2
“plumes” from point sources normally quickly disperse at distances of some
tens of kilometres (Pasquill, 1961; Turner, 1970). But if a sampling station
is located in the catchment of such 14CO2 point sources, special
care is required to accurately quantify the Δ14CO2
contamination and correct for it to estimate reliable fossil fuel CO2
values (e.g. Levin et al., 2003).
Nuclear facilities in the surroundings of Heidelberg. Reactor type
(BWR: boiling water reactor, PWR: pressurized water reactor), installed
electrical power, and the average annual 14CO2 emissions during
their respective periods of operation up to 2014, as well as the distance to
the Heidelberg sampling site are given. Different reactor blocks are
separated by a slash. RR are research reactors and RP is the research
reprocessing plant (WAK) of the Karlsruhe Research Center (FZK). After the
operation period, further emissions occur during the decommissioning of the
facilities (data taken from BfS 1986—2015).
Nuclear facility
Installed electric
Type
Operation
Mean 14CO2
Distance from
capacity (MWe)
period
emission (TBq yr-1)
Heidelberg (km)
Philippsburg (KKP) I and II
926/1468
BWR/PWR
1980–2011/1984–2019
0.414/0.055
25
Obrigheim (KWO)
357
PWR
1969–2005
0.008
30
Biblis (KWB) A and B
1225/1300
PWR/PWR
1975–2011
0.025/0.037
37
Karlsruhe FZK/WAK
–
RR/RP
1971–1991
0.036
39
Neckarwestheim (GKN) I and II
840/1400
PWR/PWR
1976–2011/1989–2022
0.008/0.135
55
Here we present results from HYSPLIT dispersion modelling (Draxler and Hess,
1998) of 14CO2 emissions from five nuclear installations in the less than
60 km neighbourhood of our long-term Δ14CO2
monitoring site in Heidelberg. We apply the HYSPLIT model for the period of
1986–2014 with available wind fields of 2.5∘ × 2.5∘,
1∘ × 1∘, and 0.5∘ × 0.5∘ resolution.
Using reported 14CO2 emission rates, these model estimates for the
Heidelberg sampling site allow us to correct for the local 14CO2
contaminations from nuclear facilities (Kuderer, 2016). Our model results,
however, turn out to strongly depend on the resolution of the wind field
used for the calculation. We discuss this important finding and present the
currently most reliable corrections of our long-term Δ14CO2 measurements.
Methods
Site description
The Heidelberg monitoring site is located on the university campus in the
outskirts of Heidelberg, a medium sized city in the Upper Rhine valley in
southwestern Germany (49∘25′ N, 8∘41′ E, 116 m a.s.l., see Fig. 1).
From 1986–2001, CO2 samples for Δ14C
analysis have been collected from the roof of the former building of the
Institut für Umweltphysik (INF 366), and from 2001 to present from the new building about
500 m to the east (INF 229). At both locations, air was sampled from about
25–30 m a.g.l. The small difference in location of the two sampling sites
is not relevant when estimating the nuclear 14CO2 contamination
with HYSPLIT.
Map of the Heidelberg sampling site in southwest Germany. The locations of
the five nearest nuclear facilities are shown in the enlargement. This
enlargement corresponds to the size of the 2.5∘ × 2.5∘
wind field grid. The 0.5∘ × 0.5∘ wind field resolution
is indicated by the grid in the enlargement.
Five nuclear installations with reported 14CO2 emissions are found
at distances between 25 and 55 km from the Heidelberg station. Figure 1
shows their locations; details of reactor type, installed electrical output,
period of operation, distance from the Heidelberg station, and mean reported
14CO2 emission during their operation up to 2014 are listed in
Table 1. As the prevailing winds in the Upper Rhine valley are from the
southwest, Philippsburg (KKP I and II) is the most important source of
potential 14CO2 contamination in Heidelberg. Philippsburg I is the
only boiling water reactor (BWR) with its major 14C emissions being
14CO2, whereas pressurized water reactors (PWR) emit 14C
mainly as 14CH4 (Kunz, 1985). All other nuclear installations
except for Neckarwestheim II (GKN II) emit less than 15 % of Philippsburg
I. Neckarwestheim, however, is located to the southeast of Heidelberg in the
Neckar valley at a distance of 55 km, so that its relative contribution to
the total 14CO2 contamination is only less than 10 % (see Table 3).
14CO2 sampling and analysis
Two-week and, for limited periods, also one-week integrated large-volume samples
of atmospheric CO2 were collected from the roof of the institute's
buildings by quantitative chemical absorption in basic sodium hydroxide
(NaOH) solution, as described by Levin et al. (1980). Except for the first
few years, samples were collected only during the night (from 19:00 to
07:00 CET, Central European Time)
in order to avoid CO2 contamination from
local traffic. Moving the institute to a new building in the year 2000
required parallel CO2 sampling at both the old and the new sampling
locations on the Heidelberg University campus, in order to quantify possible
differences and then allow combining the data sets from the two locations
about 500 m apart. As the new building is located closer to the Heidelberg
city centre, slightly lower Δ14C values (on average by 0.8 ‰)
were found at the new location over the more than
1-year overlapping period from late 2000 to early 2002. The results
obtained from samples collected until 2002 at INF 366 at about 25 m a.g.l.
were adjusted accordingly, and are now comparable with those obtained at the
current sampling location at INF 229 at about 30 m a.g.l. (for details of
this comparison and correction, see Levin et al., 2008).
14CO2 samples were processed in the Heidelberg 14C laboratory
by acidification of the NaOH solution in a vacuum system. The extracted
CO2 was subsequently purified over charcoal. The 14C/C ratio was
then measured by low-level counting (Kromer and Münnich, 1992). All
results are presented here as 13C-corrected Δ14C
deviations from the international reference standard (oxalic acid) in
per mill. They are corrected for decay to the date of CO2 sampling
(Stuiver and Polach, 1977). Note that Stuiver and Polach (1977) refer to
this 14C notation as Δ not Δ14C; however, in order
to be consistent with other atmospheric radiocarbon literature we stick to
using Δ14C instead of Δ. The precision of Δ14C
values was of the order of 4–5 ‰ in the 1980s and 1990s, 3–4 ‰
in the 2000s, and 2–3 ‰
thereafter.
Reported 14CO2 emissions from nuclear facilities in the
surroundings of Heidelberg
According to the German Atomic Energy Act (Strahlenschutzverordnung, 2001),
emissions of radioactive substances from nuclear facilities with the exhaust
air must be monitored and reported quarterly to regional and federal
authorities. The Bundesamt für Strahlenschutz (BfS, German Federal
Office for Radiation Protection), releases yearly reports on radioactive
emissions from all German reactors and research facilities; here the
14CO2 emissions are reported separately from other radioactive
substances. These BfS reports are available for the years 1986–2014 (BfS,
1986–2015). For Philippsburg I and II higher resolution, i.e. monthly,
emission data are available (Kernkraftwerk Philippsburg, personal communication, 2013); these
monthly data were used in this work to estimate the 14CO2
contamination in Heidelberg.
14CO2 emissions from nuclear facilities: annual mean emissions
from all facilities (a) and box plots of the distribution of
monthly values from Philippsburg (KKP I and II, b); the boxes
include 50 % of all months of the year with the horizontal bar indicating
the mean and the square indicating the median value of the year. The
whiskers show the minimum and maximum monthly values of the individual
years. The dashed line indicates the shutdown of Philippsburg I shortly
after the Fukushima accident.
Figure 2a shows annual 14CO2 emissions from 1986–2014
for all five facilities listed in Table 1, while Fig. 2b
shows the distribution of monthly emissions from Philippsburg I and II for
the years 1986–2012. Note the huge variability in monthly emissions, which
can differ from month to month by more than a factor of 2. No seasonal
variation nor any relation to particular maintenance activities was
observed. Graven and Gruber (2011) estimated mean emission factors of 0.06 TBq
14CO2 GWa-1 for PWRs and 0.51 TBq 14CO2 GWa-1
for BWRs. However, from our emission data and corresponding power
production reports, we do see large differences from these
emission factors and for PWRs no correlation at all, as displayed in Fig. 3.
Moreover, keeping in mind the huge month-to-month variability in
14CO2 emissions from Philippsburg (Fig. 2b), this
underlines the necessity for reliable high-resolution 14CO2
emission data from nuclear installations if accurate corrections shall be
applied to atmospheric Δ14CO2 observations for fossil fuel
CO2 estimates.
Relationship between annual 14CO2 emissions from Pressurized Water
Reactors (a) and the Boiling Water Reactor Philippsburg I (b)
and their annual electricity supplied. The solid lines show the
specific emission factors reported by Graven and Gruber (2011).
The HYSPLIT model
The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT)
from NOAA offers a variety of services ranging from computing simple air
parcel trajectories up to complex dispersion simulations (Draxler and Hess,
1998). During the simulations, virtual particles are emitted at the source
location and advected to the new particle position, described by the
position vector P, using the input wind velocity vector field
V:
P(t+Δt)_advection=P(t)+0.5⋅V(P,t)+VP′(t+Δt),t+Δt⋅Δt.
The advection equation is solved with a dynamic time step Δt,
demanding that the advective displacement is smaller than the size of a grid
cell (Draxler, 1999). Equation (1) is solved numerically by integrating the
velocity vector over time, making use of the trapezoidal rule, i.e.
averaging the velocity vectors at the initial position
V(P,t) and first-guess position
V(P′(t+Δt), t+Δt)=V{(P(t)+V(P,t)⋅Δt),(t+Δt)} of the particle. To account
for atmospheric dispersion, the particles are displaced stochastically (Eqs. 2a and b):
X_final(t+Δt)=X(t+Δt)_advection+U′_dispersion(t+Δt)⋅Δt
and
Y_final(t+Δt)=Y(t+Δt)_advection+W′_dispersion(t+Δt)⋅Δt,
where the turbulent velocity components U′ and W′ are estimated from the
standard deviations σ of the horizontal or respective vertical
velocity components (Fay et al., 1995). For more details, see Stein et al. (2015)
and references therein.
The HYSPLIT model was run here in the forward mode with an internal spatial
resolution of 0.05∘ × 0.05∘ and an internal time step
fixed by the stability ratio 0.75, i.e. the time step is chosen such that
the maximal advective displacement is smaller than 0.75 times the grid size.
For every nuclear facility location, a separate run has been conducted with
a constant emission rate. Due to the small distance between 14C sources
and the measurement station Heidelberg, simulations were limited to 48 h,
where each run consisted of a 24 h period, with 2500 particles
being emitted every hour, followed by 24 h of sole propagation of the
particles. Thus, for each day, the simulated nuclear 14C activity
included the actual emissions of this day arriving at the sampling site and
the propagated emissions from the day before. This could potentially lead to
loss of particles, which arrive at the measurement site more than 24–48 h
after the release; but for an extended reference period, only a minor
effect has been observed. Note that typical travel times from the nuclear
power plants to Heidelberg are of the order of 6–12 h. The HYSPLIT model
computes for every hour the particle concentration in every grid box, which
gives a dilution factor f (see Eq. 3), describing how much the point source
emissions are diluted over the respective grid. This dilution factor is
strongly depending on the prevailing meteorological conditions. All relevant
control parameters of the different runs are listed in Table 2.
Control parameters of the HYSPLIT runs and used wind field data for
14CO2 contamination estimates for the different nuclear facilities.
Internal spatial resolution
0.05∘ × 0.05∘
Internal temporal resolution
fixed internally by stability criterion (0.75)
Direction of the run
forward
Number of source locations per run
1
Number of runs with different source locations
5
Emission rate (per hour)
1
Hours of emission
24
Total run time (hours)
48
Particles released per cycle
2500
Maximum number of particles
50 000
Wind field resolution:
Philippsburg I and II
1986–2008, 2010: 2.5∘ × 2.5∘*; 2009, 2011–2014: 0.5∘ × 0.5∘
Obrigheim
1986–2014: 2.5∘ × 2.5∘*
Biblis A and B
1986–2014: 2.5∘ × 2.5∘*
Neckarwestheim I and II
1986–2014: 2.5∘ × 2.5∘*
Karlsruhe
1986–2014: 2.5∘ × 2.5∘*
* The HYSPLIT results obtained with 2.5∘ × 2.5∘ wind
fields have been corrected with a factor of 0.43
Relative average Δ14Cnuclear contribution in
Heidelberg from 1986 to spring 2011 (shutdown of Philippsburg I).
Obrigheim
Biblis
Neckarwestheim
Philippsburg
Karlsruhe
A and B
I and II
I and II
%
1.05
1.39
6.80
88.13
2.63
Wind fields
Previous studies have shown that HYSPLIT calculations are sensitive to the
meteorological input data (e.g. Cabello et al., 2008; Lin et al., 2015).
Here we used three different wind velocity fields that have a horizontal
resolution of 2.5∘ × 2.5∘,
1∘ × 1∘, and 0.5∘ × 0.5∘. The GDAS (Global Data Assimilation
System) assimilates meteorological observations in numerical weather
prediction models and archives the results. The one degree fields (GDAS1) are
available since 2005 and the half degree fields (GDAS0p5) since 2008. GDAS1
and GDAS0p5 also differ, besides the horizontal, in the vertical resolution
(Lin et al., 2015).
The NCEP/NCAR (National Centers for Environmental
Prediction/National Center for Atmospheric Research) reanalysis provides
atmospheric analyses with a spatial resolution of 2.5∘ × 2.5∘,
using historical data from 1948 onwards. All three wind
fields are readily available at ftp://arlftp.arlhq.noaa.gov/pub/archives/, last access: 29 March 2016.
Estimation of Δ14Cnuclear
The 14C signal at the sampling site (Δ14Cnuclear)
originating from 14CO2 emissions from each nuclear facility is
calculated by scaling the meteorological dilution factor f (s m-3) at
the measurement station obtained from the HYSPLIT simulation with the
time-varying emission strength Q (Bq s-1) of the source. This specific
14C activity is converted (according to its definition from Stuiver and
Polach, 1977) into Δ14Cnuclear in per mill
according to Eq. (3)
Δ14Cnuclear=f⋅Q⋅XCO2/(MC⋅Vm⋅a)(⋅1000‰),
with the molar volume at standard atmospheric temperature and pressure (STP)
Vm=24.465 mole m-3, molar mass of carbon
MC=12 g mole-1, mole fraction of CO2, XCO2,
and specific activity of the 14C standard a=0.238 Bq gC-1.
(a) Calculated Δ14Cnuclear contributions from
Philippsburg with assumed constant 14CO2 emissions using the three
wind fields with different resolution. (b) Same as (a),
showing the contributions from Neckarwestheim.
Results
Δ14Cnuclear estimates using wind fields of different
resolutions
Figure 4a shows 2-weekly (i.e. sampling period) integrated
HYSPLIT-estimated Δ14Cnuclear contributions in Heidelberg
for 2011–2013, originating from assumed constant 14CO2
emissions from Philippsburg of 0.45 TBq yr-1 (corresponding to the
long-term average emission from this facility). The different symbols
distinguish the results when using the three different wind fields, i.e.
with resolution of 2.5∘ × 2.5∘ (black diamonds),
1∘ × 1∘ (blue triangles), and the highest resolution
of 0.5∘ × 0.5∘ (red circles). The 2-week integrated
Δ14Cnuclear signals vary between 0
and 16 ‰ for the coarse resolution wind field, and show
on average lower signals when using the higher resolved wind fields. There
are, however, also situations when we obtain lower contamination signals
with the coarse resolution wind field than with the higher resolved fields.
The 1∘ × 1∘ wind field also yields, on average, slightly
higher Δ14Cnuclear signals from Philippsburg than the
highest resolution (0.5∘ × 0.5∘) wind field, but the
differences between those two are often only marginal. Looking at the
contributions from the Neckarwestheim reactors (GKN I and II; Fig. 4b),
we also estimate the largest Δ14Cnuclear
signals with the low-resolution wind field, while the highest resolution
wind field yields the smallest signals. The mean ratio between the
contamination signals estimated with the highest resolution wind field and
those estimated with the 2.5∘ × 2.5∘ resolution field is
0.43. We consider the results from the higher resolution wind fields more
reliable to calculate Δ14Cnuclear than those with the
coarse resolution field (see discussion below). We can further see that the
contributions from Neckarwestheim 14CO2 emissions on the
Heidelberg Δ14CO2 signal are, on average, about 1 order
of magnitude smaller than those from Philippsburg and, thus, with an average
Δ14Cnuclear of less than 0.2 ‰,
almost negligible.
Estimation of Δ14Cnuclear in Heidelberg from all five
nuclear installations
Owing to its source strength and proximity to Heidelberg, Philippsburg I is
the dominant contributor to the nuclear contamination at our sampling site.
Therefore, and considering the high month-to-month variability in emissions
(Fig. 2b), it is important to use monthly-resolved emission data
to estimate the Δ14Cnuclear signals originating from this
facility. The other four nuclear installations are secondary contributors
permitting the use of annual average 14CO2 emission rates in
absence of higher temporally resolved emission data. For each source
location, the HYSPLIT model was run for every calendar day separately
covering the period 1986–2014.
For the Philippsburg reactor site, the following meteorological data has
been used (Table 2): For 1986–2008 and 2010, we used the 2.5∘ × 2.5∘
fields, for 2009 and 2011–2014 the 0.5∘ × 0.5∘ fields.
For the other four source locations (Obrigheim,
Biblis A and B, Neckarwestheim 1 and 2, and Karlsruhe), the 2.5∘ × 2.5∘
wind field data have been used for the entire period 1986–2014 in order to save computing time.
All coarse grid dilution factors
were then corrected with a factor of 0.43 as an attempt to account for the
effect of underestimating atmospheric dispersion in coarse grid
simulations. This factor was obtained from the comparison made for the
3-year period 2011–2013 at Philippsburg and Neckarwestheim (Fig. 4). The
average relative contributions to the total Δ14Cnuclear
signal for all facilities are listed in Table 3. The largest correction terms
for a 2-week sampling period originating from Philippsburg I and II were
15.2 ‰, from Neckarwestheim I and II it was 3.3 ‰, and from
Biblis A and B it was 1.1 ‰. From the other two facilities, they were always
smaller than 1 ‰.
The individual uncorrected Heidelberg Δ14CO2 data are
displayed in Fig. 5a together with the individual total Δ14Cnuclear corrections Fig. 5b. In the years before the
Philippsburg I shutdown, about 1 % of all corrections were above 10 ‰
and less than 2 % above 5 ‰.
The mean correction was 2.3 ‰ with a standard deviation
of 2.1 ‰. After the shutdown of the BWR Philippsburg I,
the largest 14CO2 source before 2011, Δ14Cnuclear decreased to less than 2 ‰, with
a mean value of 0.44 ± 0.32 ‰ from 2012 to 2014.
It is therefore feasible to only apply an average correction of this size to
the Heidelberg measurements of all subsequent years.
(a) Results of Δ14CO2
measurements in Heidelberg
(uncorrected); (b) nuclear contribution from all installations in
Heidelberg (note expanded Δ14C scale).
Uncertainty in estimated Δ14Cnuclear
The uncertainty in our Δ14Cnuclear estimates originates
from uncertainties in emission data and uncertainties in the HYSPLIT model
transport. From comparison with results based on the differently resolved wind
fields (Fig. 4), we find the largest deviations between the 2.5∘ × 2.5∘
and the 1∘ × 1∘ fields while the average
differences between the two finer resolved wind fields are of the order of 30 %,
they can, however, be as large as a factor of 2 for individual 2-week
periods. The uncertainty in the measured monthly emission data is probably
less than 10–20 % and thus small if compared to the uncertainty in the
model transport (although sub-monthly variability in the emissions may also
contribute to the uncertainty in the Δ14Cnuclear
estimates). For the contributions from nuclear installations where only
annual average emission data were available to us, the uncertainty in
emissions is estimated to be 30 %. As the contribution from all four
installations except Philippsburg contribute on average only 12 %
(Table 3), this uncertainty is small compared to the transport uncertainty in the
contributions from Philippsburg. We, therefore, estimate the typical
uncertainty in individual total Δ14Cnuclear signals to
be less than 35 %. It is worth noting from Fig. 4a and b that the
variability of Δ14Cnuclear is larger for the
2.5∘ × 2.5∘ wind field calculations than would be
expected from the mean differences between the fine and the coarse
resolution wind field simulations. Therefore, applying a simple correction factor of
0.43 on all values estimated for the years 1986–2008 and 2010 with the
2.5∘ × 2.5∘ wind field adds variability and
uncertainty to the Δ14Cnuclear corrections, which is,
however, not possible to quantify with the currently available information.
Discussion and conclusions
Our HYSPLIT estimates of 14CO2 contaminations from nuclear
facilities in the catchment area of Heidelberg showed large differences when
using wind fields of different resolution. The calculated mean contamination
was approximately twice as large when using the coarse resolution
2.5∘ × 2.5∘ wind field compared to the two higher
resolution fields. Previous studies have shown that meteorological coarse
grid reanalyses can be well suited to capture synoptic-scale dynamical
processes, but biases in surface wind speeds may be introduced as
reanalysis data are not well adapted to reproduce transient strong wind
events occurring at the mesoscale and generating a large sub-grid scale
variability (Largeron et al., 2015). These can arise in HYSPLIT trajectory
calculations, which are the basis for concentration simulations, when the
air mass passes through areas with complicated topography and meteorological
patterns that are on a smaller scale than the data resolution (Su et al.,
2015). Another and possibly more important factor is that atmospheric
dispersion is included in the model by using the standard deviation of the
interpolated velocity field. Linearly interpolating the coarse wind field to
the internal HYSPLIT grid (here 0.05∘ × 0.05∘) leads to
a less variable velocity field compared to initially starting with a fine
grid. This generates more distinct plume shapes in coarse grid simulations
(Kuderer, 2016). Therefore, using the coarse wind field may underestimate
the effect of atmospheric dispersion, leading to high values when the plume
directly passes the measurement point. We expect this to occur frequently in
the case of the Philippsburg 14CO2 plume, where the source lies in
the main wind direction at rather short distance from the measurement point.
This effect may explain the occasionally high Δ14Cnuclear
values estimated for a number of sampling periods before 2009 (Fig. 5b),
which are not seen in the measured uncorrected data (Fig. 5a). In the case
of Neckarwestheim, this explanation does not hold. However, here we also
consider the results obtained with the finest resolution wind field as more
accurate. Neckarwestheim lies in the hilly Neckar valley with a complex
topography, which is probably better represented by the finer resolution
wind fields. Overall, we expect the HYSPLIT estimates that are based on
higher resolution wind fields to provide more realistic results, in
particular as the topography around Heidelberg is not flat. We therefore
correct the HYSPLIT results obtained with the 2.5∘ × 2.5∘
wind fields for the earlier years when high-resolution wind
fields (0.5∘ × 0.5∘) are not available. Note, however,
that this first rough correction comes with additional uncertainty and
variability (see above).
In an earlier study by Levin et al. (2003), Philippsburg I and II were
considered as the sole sources for the nuclear contamination at the
Heidelberg sampling site. A Gaussian plume model (Turner, 1970) with a
constant mean dispersion factor had been applied there to calculate Δ14Cnuclear as a first approximation, but using the same monthly
14CO2 emissions as in the present study. The mean nuclear signal
estimated by Levin et al. (2003) was Δ14Cnuclear =4.8±2.0 ‰ ranging from 0.2 to
10 ‰ for monthly mean values. This earlier estimate of
14CO2 contamination is approximately twice the value obtained with
the HYSPLIT model and the high-resolution wind fields. Graven and Gruber
(2011) used the TM3 model with a spatial resolution of 1.8∘ × 1.8∘
and estimated for 2005 a total Δ14Cnuclear
of 2.1 (1.1–3.7) ‰ for the Heidelberg grid cell. Their
estimate is in agreement with our results for that year (2.1 ± 1.6 ‰)
obtained with the 2.5∘ × 2.5∘
resolution wind field corrected by the factor of 0.43. As in the present
study, Graven and Gruber (2011) also included 14C contributions from
other nuclear installations in their estimates. However, their assumed
emissions from the Philippsburg I reactor were estimated with the average
emission factor for BWR, which is about 20 % smaller than the measured
value for 2005 used in our estimate. They also mention that their Eulerian
model may have underestimated the true contamination due to its coarse
resolution, which would dilute point source emissions over a large grid in
an Eulerian approach.
These comparisons with earlier studies indicate that more work and higher
resolution models and wind fields are needed to reduce the uncertainty in
the 14CO2 contamination estimates from nuclear installations at
measurement sites where Δ14CO2 observations shall be used
to precisely determine the regional fossil fuel CO2 component.
Currently, we have to take into account a model transport uncertainty of
about 1–2 ‰ in the estimated Δ14Cnuclear contamination, if the measurement site is located
closer than about 30 km downwind from a nuclear facility, which has a
14CO2 emission rate of about 0.5 TBq yr-1 similar to the
Philippsburg I boiling water reactor with 1 MWe power production. Other
reactor types, such as the Canadian CANDU reactors may have significantly
larger emission rates (Graven and Gruber, 2011; Vogel et al., 2013); the
uncertainty in corresponding Δ14Cnuclear estimates in
their close neighbourhood may then be considerably larger.
The limited temporal resolution of 14CO2 emission rates from
nuclear installations cause additional uncertainty in the Δ14Cnuclear
estimates, as generally only annual mean emissions are
reported. Graven and Gruber (2011) assume that 14CO2 emissions are
proportional to the annual power production. However, the present study on
the influence from German reactors on the Heidelberg measurement site does
not fully support this finding. Figure 3 does not show significant
correlations between annual 14CO2 emissions and corresponding
electricity supply. Therefore, assuming emission factors as suggested by
Graven and Gruber (2011) will add considerable uncertainty to the Δ14Cnuclear
estimates, which may be as large as the uncertainties
estimated here for the wind-field-based model transport error.
Overall, we conclude that careful investigation of potential 14CO2
emissions in the catchment of sampling sites is required when using Δ14CO2 observations for fossil fuel CO2 estimates. The
differences in our HYSPLIT modelling results, when based on differently
resolved wind fields together with the findings from earlier studies,
suggest that current Δ14Cnuclear estimates may be wrong by
a factor of 2. Therefore, careful investigations with high-resolution
models must be performed at all stations where 14C-based fossil fuel
CO2 measurements are conducted. Based on our simulations, the shutdown
of Philippsburg I in 2011, if not accounted for in the Δ14Cnuclear correction, would have masked a fossil fuel CO2
signal of 1 ppm, corresponding to 10 % of the average total fossil fuel
CO2 signal in Heidelberg. Therefore, we plan similar studies for the
European ICOS atmospheric station network
(https://www.icos-ri.eu/icos-stations-network, last access: 5 June 2018). The basis must be
high-resolution 14CO2 emissions data from nuclear facilities,
which need to be made available for these investigations if contamination
estimates are to be accurate.