Samples
Air samples were collected into evacuated 1-liter Pyrex flasks through
Synflex 1300 tubing after passing through Mg(ClO4)2 to dry the
samples. In Pasadena, samples were collected on alternate afternoons at 14:00
Pacific Standard Time (PST) using an autosampler, whereas at the Palos
Verdes site samples were collected manually once a week (on weekend days)
between 11:00 and 16:00 PST, and typically near 14:00 PST. The mid-afternoon
sampling time was chosen because this is when the planetary boundary layer
tends to be the deepest and most well-mixed during the day. The sampling
path at each location was purged with ambient air before collection.
CO2 was extracted from the air samples cryogenically, following the
methods described in Newman et al. (2008), with the amount of CO2
determined manometrically. Then the δ13C was determined
relative to the Vienna Pee Dee Belemnite (VPDB) standard (Coplen, 1996) by dual-inlet
isotope ratio mass spectrometry (Thermo-Finnigan MAT 252; Bremen, Germany)
on each individual sample. After this analysis, the CO2 was frozen into
a cold finger and combined with 3–7 other individual samples to create a
composite sample characterizing mid-afternoon air over a 2-week (Pasadena)
or 1-month (Palos Verdes) time period for Δ14C analysis. This
differs from the sampling protocol of Affek et al. (2007), who collected on
average two 5 L samples per month, analyzed each sample separately, and
then averaged the results to produce monthly average Δ14C
values for 2004–2005. We found that by combining smaller samples collected
more frequently (alternate days in Pasadena) our results were less scattered
than in the previous report and therefore give interpretable seasonal
variations. Δ14C was analyzed by accelerator mass spectrometer
at the Keck-CCAMS facility at the University of California, Irvine, using
the methods described in Newman et al. (2013) and Xu et
al. (2007). Analyses of air from standard tanks calibrated by NOAA (National
Oceanic and Atmospheric Administration) gave errors for CO2 mole
fractions averaging of ±1.4 ppm (1 ppm = 1 µmol mol-1)
(n= 44) and δ13C of ±0.15 ‰ (n= 30), including extraction, manometry, and mass spectrometry. Although
the uncertainties in the CO2 mole fractions is much higher than by
spectroscopic techniques, it contributes less than half of the total
uncertainty in Cff, which is dominated by the Δ14C average
error of 2 ‰, based on long-term reproducibility of
secondary standards (Xu et al., 2007, 2010; Graven et al, 2013;
Miller et al., 2013).
Schematic diagram showing the use of different data sets for
attribution of the sources of CO2 emissions. Mole fractions of
background (bg) and observations are used to determine Cxs (excess over
background/bg); Δ14C values are used to distinguish Cff
(fossil fuel, ff) and Cbio (biosphere, bio); δ13C
compositions are used to distinguish Cpet (petroleum and/or gasoline, pet)
from Cng (natural gas, ng).
Calculations
A major goal of this study is the attribution of the sources of the Cff
observed. A schematic figure of the flow of data used to calculate the
portion of the total CO2 that is due to biosphere respiration (bio) and
fossil fuel (ff) combustion, including burning of petroleum (pet) and
natural gas (ng), is shown in Fig. 2. Mole fractions of CO2 measured at
the two sites and a background site in La Jolla, CA, were used to calculate
the CO2 excess (xs) over background (bg). The contributions of fossil
fuel combustion and the biosphere to the excess were determined from
radiocarbon measurements, and the fossil fuel component was further broken
down into petroleum and natural gas using δ13C of the CO2.
Details are described below.
Time series of observed CO2 mole fractions for
14CO2 samples (a, b), Δ14C data (c, d), and δ13C (e, f) for Pasadena and Palos Verdes. The solid curves are
backgrounds used in the calculations: δ13C and CO2
backgrounds are from La Jolla, CA and Δ14C from Pt. Barrow, AK. Data are provided in the Supplement to this paper.
Total CO2 emissions and background
CO2 mole fraction
The CO2 excess caused by local emissions at the two sites was
calculated by subtracting an estimate of the background CO2 mole
fraction derived from La Jolla monthly values (Keeling et
al., 2005; Figs. 3 and 4; Supplement). Flask sampling at La Jolla is done so as to
minimize the influence of local CO2 sources by sampling during periods
that simultaneously satisfy three criteria: low variability in CO2
concentration for periods of 3 h or more, wind speed of 2.6 m s-1
or more from a narrow southwesterly to westerly sector, and high
visibility. That these methods successfully minimize influences of local
fossil-fuel emissions is indicated by the consistency of the annual
radiocarbon concentrations at La Jolla compared to clean stations both to
the north and south in the Northern Hemisphere (Graven, 2012). In this
paper, therefore, the La Jolla data presented are screened background data.
The La Jolla data were interpolated to determine the appropriate value for
the midpoint of the range of collection dates included in each Δ14C sample, using the algorithm from Thoning et al. (1989), with
two harmonic terms, three polynomial terms, and the smoothed residuals of
the long-term trend (cutoff of 667 days).
Time series of Cxs, Cff, and Cbio calculated from
Δ14C (see text for description of calculations) for Pasadena
(a) and Palos Verdes (b). The errors for Cff are 1 ppm. The negative
Cbio values indicate photosynthetic uptake. The value of Δ14C for fuel for this calculation was taken to be -954 ‰, the average from the summer and winter calculations.
CO2 from fossil fuels, based on
Δ14C
Mass balance calculations were used to calculate the relative contributions
of background air, biosphere respiration and photosynthesis, and fossil fuel
combustion (including natural gas and oil) to the CO2 collected at the
two sites. The following equations quantitatively separate the background
air, biosphere, and fossil fuel combustion contributions to the locally
measured atmospheric CO2 using Δ14C (e.g.,
Levin et al., 2003; Miller et al., 2012; Pataki et al.,
2003; Turnbull et al., 2006; Fig. 4):
Cobs=Cbg+Cff+Cr+CpΔobsCobs=ΔbgCbg+ΔffCff+ΔrCr+ΔpCp,
where subscripts obs, bg, ff, r and p indicate observed, background, fossil
fuels, respiration, and photosynthesis, respectively; C indicates CO2
mole fraction in ppm, and Δ indicates Δ14C in ‰. We assume that Δp is
equivalent to Δbg since natural fractionation
during uptake is corrected in the Δ14C measurement and
therefore substitute Δbg for Δp in Eq. (). Then, after solving Eq. () for Cp and
substituting this for Cp in Eq. (), we solve Eq. () for Cff,
resulting in the following expression for Cff:
Cff=CobsΔobs-ΔbgΔff-ΔbgCrΔr-ΔbgΔff-Δbg.
The value of Δff is -1000 ‰,
since fossil fuels contain no 14C because they have been removed from
the source of this short-lived radionuclide for millions of years.
We use the record from Pt. Barrow, AK (Xiaomei Xu, unpublished data) for the
concurrent background Δ14C values (Δbg), because
this is the most complete record available for the entire time period of
this study. The background Δ14C record at Pt. Barrow, AK is
obtained through the UCI/NOAA ESRL (Earth System Research Laboratory) flask
network program that collects whole air samples using 6 L, one-valve stainless
steel canisters (Silco Can, Restek Co.) that have been pre-evacuated at UCI.
The canisters are pressurized to ∼ 2 atm using an oil-free
pump. Two biweekly samples were collected before 2008, and one weekly
afterwards. For the period from 17 June 2005 to 17 March 2006, some
duplicate samples were collected using 32 L, one-valve stainless steel
canisters. Subsamples were then taken from these samples for 14C
analysis. CO2 is extracted cryogenically at UCI then converted to
graphite by the sealed tube zinc reduction method (Xu et al., 2007). Each
sample is ∼ 2.7 mg C in size. Analysis of Δ14C is
performed at the W M Keck AMS facility at UCI with a total measurement
uncertainty of ±1.3–2.4 ‰. Mass dependent
fractionation is corrected for using “on-line” δ13C
measurements during AMS analysis, which accounts for fractionation that
occurred during graphitization and inside the AMS. Comparison was made of 22
common sample dates spanning 5 years, of measured Δ14C from
Barrow between the UCI and the Scripps Institution of Oceanography's
CO2 Program. It shows that differences in measured Δ14C are
consistent with the reported uncertainties and there is no significant bias
between the programs (Graven et al., 2013). Another inter-comparison is that
of AMS-based atmospheric 14CO2 measurements organized by the NOAA
Earth System Research Laboratory, Boulder, Colorado. The UCI lab is one of the
three groups having inter-laboratory comparability within
1 ‰ for ambient level 14CO2 (Miller et al.
2013). Comparison of the Pt. Barrow data with those from La Jolla
(Graven et al., 2012; Fig. 5) shows good agreement for 2004–2007, when
the two data sets overlap. Comparing the calculated values for Cff from
these two backgrounds and propagating through the time series calculations
(Sect. 3.4) results in a difference of approximately 1 % of the signal
we are measuring. We calculate Cbio (the sum of Cr and Cp)
from Eq. (), using the calculated values of Cff and the independent
estimates of Cbg from the La Jolla data, so that we understand the
contribution of the biosphere to total local emissions.
The nuclear power plant contribution, the only other source of 14C, is
small on the west coast of the US (Graven and Gruber, 2011) and therefore
is ignored.
Comparison of possible background records for this study, Pt.
Barrow, AK, (BRW; Xiaomei Xu, unpublished data) and La Jolla, CA (LJO;
Graven et al., 2012). The smoothed brown curve for BRW is the Δ14C background used for this study and was calculated using the
algorithm of Thoning et al. (1989), from the function plus the smoothed
residuals of the long-term trend, using two harmonic and three polynomial terms in
the function and 667 days as the long-term cutoff for the low-pass filter.
Following Turnbull et al. (2006) and Miller et al. (2012), the respiration
terms in the equations above are assumed to reflect contributions due to
heterotrophic respiration. Thus, the second term in Eq. (3) is small in
magnitude and is due to heterotrophic respiration, through which microbes
respire CO2 that was from carbon previously incorporated through
photosynthesis. This term takes into account the isotopic disequilibrium due
to the significant time delay between photosynthetic incorporation and
respiration, assumed to be 10 years on average (Miller et al.,
2012). The magnitude of this correction for our urban Pasadena site is
different relative to sites with smaller anthropogenic CO2 signals,
since the CO2 photosynthesized into the plant a decade ago was not
close to the background air composition of that time but was the local,
“polluted” air. The Δr in Eq. (3) for each sample was
calculated by extrapolating the Pasadena trend back 10 years. Because of the
mild climate in southern California, we used a constant value of Cr= 5 ppm, the same value used for summer by Turnbull et al. (2006). This should
be taken as an upper limit for this urban region. The range of the
correction for the second term in Eq. (3), including the sign, was -0.06 to
-0.11 ppm, generally smaller relative to regions where the biosphere
contribution Cr is large (Miller et al., 2012; Turnbull et
al., 2006). For the data from the Palos Verdes site, we calculated the
heterotrophic correction term using values of Δr calculated by
extrapolating the Pt. Barrow background trend back 10 years and used a
constant value of Cr= 5 ppm, because of the mild climate. The
correction term for the Palos Verdes data ranged from 0.20 to 0.24 ppm. The
small correction for heterotrophic respiration does not affect any of our
conclusions.
In California, there is an added complication when attributing CO2
emissions to fossil fuels using Δ14C. Since 2004, 10 %
ethanol has been added to gasoline. The ethanol contains modern, not fossil,
carbon. For gasoline with 10 % ethanol, 6.7 % of the CO2 emitted
during combustion is from the modern ethanol (EIA, 2015). A correction for
this is made, as discussed in Sect. 2.3.3 below.
δ13C of
CO2
Plots involving the mole fractions and δ13C can be used to
determine δ13C of the local contribution to the observed
CO2 (Fig. 3). Here we use the Miller–Tans approach (Miller–Tans
approach; MT; Miller and Tans, 2003) for this purpose, since it
allows for variations in background composition and we observe a widening
difference between the data for δ13C in Pasadena and the La
Jolla background record in recent years (Fig. 3e). The following mass
balance equations are used in this analysis:
Cobs=Cbg+Csrcδobs×Cobs=δbg×Cbg+δsrc×Csrc
to give
δobs×Cobs-δbg×Cbg=δsrc(Cobs-Cbg),
(Miller and Tans, 2003) where the subscript src represents the local
source of CO2 emissions, δ represents δ13C, and
the appropriate background values are included for each sample. Using this
formulation (Eq. 6), the slope of the correlation (MT slope) gives the
δ13C of this local source. For this analysis, we calculated the
MT slopes for each month and then determined the seasonal averages,
averaging December–January–February as winter, March–April–May as spring,
June–July–August as summer, and September–October–November as autumn. Seven
individual samples, over the 8-year sampling period in Pasadena, were
excluded since they fell more than three times the standard error from their
linear regression best-fit lines. The monthly MT plots for 2011 are shown in
Fig. A1 in the Appendix, as examples. The very high correlation coefficients (R= 0.952–0.999) suggest that δsrc remains constant on timescales of a
month. We assume that this is also the case for the isotopic compositions of
petroleum and natural gas combustion, that we describe below.
We use the results from the 14CO2 calculations for the fraction of
Cxs from the biosphere (Fbio=1-Fff) together with the
MT slopes to attribute the CO2 derived from petroleum and natural gas
combustion (Cpet and Cng) by mass balance, first by calculating
the δ13C of the fossil fuel component, using
δff=δxs-δbio×1-FffFff,
where Fff is the fraction of Cxs due to emissions from fossil fuel
combustion, as calculated from the 14CO2 data. The values for
δxs are the seasonal δ13C values from the MT
analyses and δbio is taken to be -26.6 ‰, the average δ13C of the ambient air plus the discrimination
of -16.8 ‰ for the biosphere (Bakwin et al., 1998).
This value represents data from temperate northern latitudes (28–55∘ N), dominated by C3 plants with some C4 grasses present (Bakwin
et al., 1998). Indeed, grasses in southern California are mostly C3 ryes,
fescue, and bluegrass, with some C4 grasses such as St. Augustine
(www.cropsreview.com/c3-plants.html, last access: 25 January
2016). The proportions of CO2 emitted by petroleum and natural gas
combustion are calculated using the δ13C values:
δff=Fpetff×δpet+(1-Fpetff)×δngFpetff=δff-δngδpet-δng,
with an analogous equation for Fngff, where Fpetff and
Fngff are the fractions of petroleum and natural gas combustion
contributions in Cff, respectively. The values of δng and
δpet used were -40.2 ± 0.5 ‰ for
natural gas (Newman et al., 2008; covering measurements in 1972–1973
and 1999) and -25.5 ± 0.5 ‰ for petroleum
combustion (average of measurements in Newman et al. (2008);
measurements in 2005), and -26.0, -25.1, and -25.5 ‰
measured in 2007, 2012, and 2014, respectively). The Cff, Cpet,
and Cbio components were corrected for the presence of 10 % ethanol
in California gasoline by multiplying Cpet by 0.067 (the fraction of
CO2 emitted by burning the ethanol portion of the ethanol-gasoline
mixture; EIA, 2015) to give the amount, in ppm, of CO2 that was
included in Cbio but should have been attributed to Cpet. The same
amount was deducted from Cbio. The magnitude of this correction is 0.5–1.2 ppm, averaging 0.84 ppm, which represents approximately a quarter of
the Cbio, but the latter is very small, averaging 3–4 ppm and the
correction does not affect our results with respect to Cpet and
Cng.
Time series analysis
We used the algorithm of Jiang et al. (2008) to study details of the
average annual patterns of the total CO2 and Cff in Pasadena, in
order to compare with patterns at sites with less contribution from regional
fossil fuel combustion, such as Palos Verdes and La Jolla background. This
method uses the first three Legendre polynomials and harmonic terms to
decompose the signal (Prinn et al., 2000). The harmonic terms define the
seasonal and semi-annual cycles, which we compared to results of the same
analysis for flask data from La Jolla, CA (Keeling et al., 2005).
To determine trends in the Cff time series, derived from the
radiocarbon data, we used the empirical mode decomposition (EMD) method
(Huang et al., 1998; Kobayashi-Kirschvink et al., 2012). Using this
method, nonlinear and nonstationary time series can be broken down into
intrinsic mode functions (IMFs) with increasing period lengths and, finally,
to a long-term trend with at most only one minimum or maximum with slope of
zero. The algorithm involves using cubic splines to calculate maximum and
minimum envelopes for the data series. The average of these envelopes for
each time is subtracted from the original or the previous iteration. This
process is repeated until the average is a horizontal line, giving the first
IMF. This IMF is subtracted from the raw time series (or previous starting
point) and then repeated until the resulting IMF has only one maximum or
minimum in the series, the long-term trend. High-frequency modes are removed
first, with the earliest representing noise. The later modes are interpreted
in terms of known processes, such as annual cycles (e.g., IMFs 3 and 4).
Following Wu and Huang (2009), we added random noise equivalent to the error
in the measurements to create 300 time series, for which the ensemble EMD (EEMD) analyses were averaged. The EEMD technique is data adaptive, not
assuming any shape for the IMFs.