Atmospheric Environment 74 (2013) 422e431
Contents lists available at SciVerse ScienceDirect
Atmospheric Environment
journal homepage: www.elsevier.com/locate/atmosenv
Transcontinental methane measurements: Part 1. A mobile surface
platform for source investigations
Paige Farrell, Daniel Culling, Ira Leifer*
Marine Science Institute, University of California, Santa Barbara, CA 93106, United States
h i g h l i g h t s
< First-ever cross-country continent scale methane measurements.
< First quantification of methane emissions from the La Brea tar pit/seepage area geologic source.
< Desert background methane measurements.
< Lower atmospheric vertical profiling using a mountain descending road.
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 24 March 2012
Received in revised form
12 July 2012
Accepted 5 February 2013
The potent greenhouse gas, methane, CH4, originates from a wide range of anthropogenic and natural
sources. A ground-based, satellite-scale, transcontinental (Florida to California) survey was conducted to
understand better emissions from key sources including wetlands, forest fire, and geologic sources, as
well as to acquire desert background values and lower atmosphere vertical profiling in the San Bernardino Mountains. A total of 6600 measurements along 7020 km of roadways were made by flame ion
detection, gas chromatography (GC) onboard a recreational vehicle in 2010, and during a second survey
with a cavity ring-down spectrometer system in Southern California in 2012. Significant vibration
reduction efforts allowed near continuous mobile GC measurements.
Nocturnal CH4 measurements tended to be higher compared to daytime values, sometime significantly, for similar sources and were concluded due to day/night meteorological differences. The lowest
GC observations were 1.80 ppm, observed in the California desert, w60 ppb less than minimum desert
CH4 observed in 2012. Thanks to smoke visualization of a brush fire plume, the flux from the fire was
estimated at 0.15 kiloton day!1. Geologic CH4 emissions from the La Brea tar pit and surrounding areas
were surprisingly strong, with peak concentrations of nearly 50 ppm and highly elevated CH4 concentrations extending over at least w100 km2, and accounting potentially for a significant fraction of the LA
basin CH4 emissions. Geologic CO2 emissions also were observed.
! 2013 Elsevier Ltd. All rights reserved.
Keywords:
Methane
Gas chromatography
Wetlands
Greenhouse gas
Fire
Geologic methane
Seepage
La Brea tar pits
Southern California
Emission
South US
1. Introduction
1.1. Atmospheric methane
CH4 has a greenhouse warming potential 26 times that of carbon
dioxide, CO2, on a per molecule basis and century timescale;
however, on a 20-year timescale its radiative contribution is greater
than that of CO2 (IPCC, 2007), due in part to its shorter lifetime (8e
10 years). Since the industrial revolution, CH4 concentrations have
nearly tripled, although growth slowed in the 1990s with CH4
* Corresponding author. Tel.: þ1 805 893 4931; fax: þ1 805 893 4927.
E-mail addresses: [email protected], [email protected] (I. Leifer).
1352-2310/$ e see front matter ! 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.atmosenv.2013.02.014
concentrations nearly stabilizing in the last decade. These changes
may have resulted from anthropogenic emissions (Aydin et al.,
2011), primarily related to Fossil Fuel Industrial emissions masking wetlands increases (Bousquet et al., 2006), although decreased
microbial emission have been proposed (Kai et al., 2011). Since
2008, strong CH4 growth has resumed (Heimann, 2011), highlighting the need for better understanding of sources and trends.
Atmospheric CH4 concentration depends on the balance between
sources and sinks, which primarily is governed by hydroxyl radical,
OH, oxidation. Other losses include escape to the stratosphere, soil,
and shallow sediment microbial degradation, and chlorine oxidation.
The latter three account for w5% each. Atmospheric CH4 isotopic
modeling suggests OH oxidation has increased w5% since 1980,
decreasing CH4 lifetimes (Lassey and Ragnauth, 2010).
P. Farrell et al. / Atmospheric Environment 74 (2013) 422e431
Natural (145e260 Tg yr!1) and anthropogenic (264e
428 Tg yr!1) CH4 sources release a combined w582 # 87 Tg yr!1,
but uncertainties are large (Denman et al., 2007). Natural and
anthropogenic microbial sources contribute an estimated w69% to
atmospheric budgets (Conrad, 2009). Important natural sources
include wetlands, whose emissions are driven by anaerobic microbial oxidation and contribute w23% of the global CH4 budget.
Microbial production underlies most anthropogenic CH4 emissions,
such as from landfills, rice, and ruminants. Natural fossil CH4
emissions generally are estimated at w20% of the global budget
(Etiope et al., 2008), although Lassey et al. (2007) suggest
30.0 # 2.3% from fossil sources.
Fires release anthropogenic and natural CH4 and contribute
significantly to inter-annual variability in CH4 growth (NRC, 2010)
contributing an estimated 14e88 Tg yr!1 (Fletcher et al., 2004).
Anthropogenic biomass burning in residential settings (open fireplaces, cooking, etc.) is estimated to contribute 8e12 Tg yr!1 (Piccot
et al., 1996). Wildfires can be significant, for example, 1998 arctic
wildfires (a very active year) released an estimated 2.9e4.7 Tg yr!1,
or 12% of global fire CH4 emissions (Kasischke and Bruhwiler,
2002). Emission rates depend on the fuel type consumed and the
combustion mode - burning versus smoldering (Lobert and
Warnatz, 1991). Koppmann et al. (2005) recommended between
3 and 10 g kg!1 biomass for flaming and smoldering combustion,
respectively, although these values are highly variable.
1.2. Atmospheric methane budget estimation
Source strengths for global CH4 budgets are derived in two
manners; from top-down estimates based on atmospheric measurements and inversion modeling and from bottom-up inventory
estimates of individual sources (NRC, 2010). Comparison of topdown and bottom-up estimates can indicate under and overinventoried sources or un-inventoried CH4 sources, e.g., Hsu et al.
(2010) for the Los Angeles Basin. On a global scale, satellitederived, top-down CH4 budgets, such as from SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography) have the best global coverage for such budgets (Schneising
et al., 2011); GOSAT (Greenhouse gases Observing SATellite) being
the other currently orbiting, tropospheric CH4 observing satellite
423
(Yokota et al., 2009). However, satellite footprints are large, 30 $ 27e
30 $ 240 km and 10.5 $ 10.5 km for SCIAMACHY and GOSAT,
respectively, limiting direct source interpretation to large-scale
features hundreds of kilometers in size (Bergamaschi et al., 2007).
1.3. Study motivation
Global satellite data are critical to understanding global budgets,
but current spatial and temporal resolutions are too coarse to evaluate most sources directly, thus source assessment primarily is from
global and continental inversion modeling (Bousquet et al., 2006).
Often, satellite-scale data are ground-referenced by airborne (Kort
et al., 2008) or ground station measurements (Bergamaschi et al.,
2007; Pétron et al., 2012). However, airborne data can be of limited
use as high airspeeds prevent resolving finer scale plumes with the
required accuracy, and flight paths may be restricted over urban and
industrial areas. Ground measurements from fixed locations near
emission sources can investigate nearby individual sources (Bradley
et al., 2010; Pétron et al., 2012), as can typical mobile ground measurements (Herndon et al., 2005; Pétron et al., 2012; Shorter et al.,
1996); however, such data lack satellite spatial-scales.
To address this need, a surface expedition was conducted 6e12
Oct. 2009 to acquire satellite-scale CH4 data at high spatial resolution to allow source identification and comparison (Fig. 1B) and a
second survey in southern California on 21e22 Feb. 2012. The expeditions focused on important CH4 sources with significant
growth potential, such as wetlands under warmer climate scenarios. Data were collected almost entirely while in motion. Herein
we present the detailed approach and application to several natural
sources including wetlands, a forest fire, a major geologic source,
and the California desert (no source), summarized in Table 1.
2. Methods
2.1. Study area
The 2010 expedition focused on key natural and anthropogenic
CH4 sources to characterize better their relative importance and
understand better CH4 spatial distribution on a transcontinental
scale spanning the south United States. In situ CH4 measurements
Fig. 1. A) Map showing survey path (yellow day, blue night) including major urban centers for 2010 and 2012. City population key on figure. B) Measured survey methane, CH4,
values for 2010. Note truncated color scale to emphasize near background variations. Surface image from GoogleEarth. (For interpretation of the references to colour in this figure
legend, the reader is referred to the web version of this article.)
424
P. Farrell et al. / Atmospheric Environment 74 (2013) 422e431
Table 1
Summary characteristics of survey study areas.
Area
California (2010)
Sonora
California (2012)
Mojave
La Brea
S. Bernadino
Florida (2010)
Paynes Park
Panhandle
Louisiana (2010)
Raceland
a
b
c
Type
Location
Span (km)
Starta
lengthb
Maxc (ppm)
Meanc (ppm)
Desert
33.42282N,112.60754We33.92035N, 116.54893W
370
11:10 12/10
04:05
2.27
Desert
Geology
Mtns.
34.99527N,117.54140We35.00681N,117.69483W
34.03500N,118.37500We34.07833N,118.35167W
34.31321N,116.80953We34.33486N,116.83340W
15
5.6
4.0
17:47 21/02
03:00 21/02
09:10 21/02
00:15
01:00
00:14
n/a
47.975
1.8638
Wetland
Wetland
29.55474N,82.34157We29.58828N,82.36136W
30.48551N,85.21087We30.46404N,88.38193W
3.8
300
02:49 10/10
19:20 07/10
00:07
05:58
2.9
9.06
2.61 # 0.2
1.95 # 0.22
Fire
29.78582N,90.51951We29.77079N,90.53828W
2.3
18:23 03/10
00:57
4.48
2.58 # 0.59
1.91 # 0.12
1.8639 # 0.0038
5.0627 # 4.25
1.8584 # 0.0016
Local Time.
hour:min.
Methane Concentration.
were made from St. Petersburg, FL, to Santa Barbara, CA (Fig. 1) by
flame ion detection on a gas chromatograph, GC (8610C, SRI Instruments, California), aboard a 9-m recreational vehicle, RV. A total
of w6600 CH4 chromatograms were collected (Fig. 1) over 7020 km
of roadway. Lost GPS affected w8.9% of the data, although gaps
usually were geographically localized and temporally short. Thus,
locations of chromatograms without GPS were known to at worst
10e50 km and general much better. Still, data without GPS information were not used for large-scale investigation (Leifer et al.,
2013), but not for source studies.
Unlike studies that use vans (Herndon et al., 2005; Pétron et al.,
2012; Shorter et al., 1996), RV-based measurements enable
continuous, mobile data collection over extended areas, including
nocturnal measurements through driver rotation. Most urban areas
were traversed nocturnally when vehicular CH4 emissions are
much lower (Shorter et al., 1996) e vehicular emissions are strongly
spatially road-biased. Some daytime measurements were along
heavily trafficked urban and rural interstate corridors; however, for
winds not parallel to the roadway (i.e., not east/west), exhaust from
preceding vehicles should affect data minimally. In some cases,
sources were studied by transecting plumes, in other cases through
slower, convoluted search patterns, necessarily road limited.
2.2. Methodology
Air samples were collected continuously through a roof “air
ram” to prevent exhaust entering the sample line while the RV was
moving (Fig. 2B). Air was pumped through ¼” plastic sample tubing
attached 0.25 m from the air ram’s end into multiple streams that
entered the 4-channel GC, which was configured to achieve the
fastest possible measurement time while yielding adequate CH4
peak/air valley separation. Adequate separation meant that custom
(MatLab, Mathworks, MA) routines could separate the peak from
the valley, despite overlap. Real-time analysis was by conventional
chromatography software (PeakSimple, SRI Instruments, CA),
which is challenged by peaks with baseline drift, noise, and poor
Fig. 2. A) Experimental set-up. B) Photo of recreational vehicle, RV, and “air ram”, C) Photo of gas chromatograph setup in RV including vibration reduction provisions.
P. Farrell et al. / Atmospheric Environment 74 (2013) 422e431
separation. Daily calibration used 1-mL injections, typically ten
repetitions, of a high (10 ppm) and low (1 ppm) standard (See
Supplemental Material).
GC performance was improved by separating heater power from
other GC power usage, with a power conditioner on the latter to
reduce generator and other electrical noise. Significant efforts were
made to reduce vibrations and their resultant noise. Post processing addressed the still significant data noise (see Fig. S-2 for
example chromatogram peaks with low and high noise) through
custom analysis routines. These routines decreased peak area
variability by 27%, reducing the standard deviation from 5.8% to
4.3% in 10 comparison chromatograms. Fire data used a single point
calibration (see Supplemental Material).
A maximum uncertainty of w5% is used for all GC measurements (i.e., w100 ppb for ambient), although in most cases measurement accuracy is half or better, while Picarro measurement
accuracy is better than 1 ppb (0.05%). All anomalies investigated
herein are well above these uncertainties.
Wind speed and direction were not measured and were looked
up from online historical weather archives (weatherunderground.
com, 2010) for Palm Springs, CA, on 10 Oct. 2010. US Census data
provided urban populations (US_Census_Bureau, 2010), except for
Ciudad Juarez (wikipedia.com, 2011).
Due to time constraints and lost GPS signal, 2010 California data,
particularly coastal, were limited. A shorter, RV survey was conducted 21e22 Feb. 2012 with a cavity ring-down spectrometer
(G2301Greenhouse Gas Sensor, Picarro, California), “Picarro.” The
Picarro was mounted on the same vibration padding as the GC and
recorded CH4, CO2, and water vapor at 2 Hz, while GPS was logged
on a separate computer. A 4 m, ¼” ID tube was fed to the roof,
without the air-ram, thus only data collected in motion, or for high
winds was used. The Picarro and GPS computer were synchronized
at the survey start and remained within 1 s after 24 h. The survey
route was planned for night surveys of FFI and urban areas, and day
surveys of the Mojave Desert and San Bernardino Mountains
(w2000 m altitude). A significant improvement was real-time
route planning using a cell phone modem and GoogleEarth (Google, California).
3. Results
3.1. Survey overview
CH4 concentrations decreased from the more urban areas of
Florida into the panhandle with the trend continuing to West
425
Louisiana (Fig. 1B). There was high variability (toward higher concentrations) and a strong increase into eastern Texas, where the
highest overall concentrations were found. Day/night differences
were important, but could explain only part of the observed trends;
east Texas overall CH4 concentrations were elevated relative to all
other surveyed areas, many of which also were surveyed nocturnally. Elevated values here were related to fossil fuel industrial
activities, discussed in Leifer et al. (2013).
The normalized CH4 probability distribution (integrated
probability ¼ 1.00) was well fit (R2 ¼ 0.974) with a Gaussian
function for values above 1.966 ppm, the peak of the fit (Fig. 3). For
concentrations greater than w2.7 ppm CH4, the probability
decreased more slowly than predicted by the Gaussian implying
that higher concentration measurements were more probable than
if they were Gaussian distributed. For values above 2.7 ppm,
probability decreased with an approximate power law. CH4 values
significantly below ambient (w6.5% were less than 1.7 ppm) were
due to noise. Regional histograms showed some significant differences (Fig. 3B). Although Florida and Alabama both exhibited
Gaussian probability distributions mirroring the overall distribution (Fig. 3A), they also included very high values (Fig. 3B, light blue
and red (in the web version), respectively) from highly local, very
strong sources. The E. Texas region shows a secondary probability
peak of significantly elevated CH4.
Eastern Louisiana exhibited some anomalously low CH4 concentrations, which resulted from the compressed air supply pressure dropping below regulation and resulting in calibration issues
affecting w6 h of data in the Baton Rouge, LA area (04:30e10:30, 9
Oct.), while waiting to obtain replacement air and replacement
1.0 ppm calibration gas. Based on the baseline trend and calibration
values, these data may be biased low by w0.1e0.2 ppm.
3.2. Desert
The lowest CH4 levels in 2010 were measured in the California
and Arizona deserts with mean California desert CH4 concentrations of 1.80 # 0.074 ppm (Interstate 10 corridor North of the Salton
Sea and east of Los Angeles). Values increased toward the west,
likely due to advection by gentle west-northwest winds transporting CH4 from the Los Angeles basin and Palm Springs, CA,
where peak CH4 was 2.88 ppm. To avoid potential background
signal contamination from Los Angeles air, background CH4 measurements were made in the Mojave desert at 750 m altitude, with
values of 1.8639 # 0.0038 ppm recorded over w15 km transect to
the west of Kramer Junction (34& 59.7160 N, 117& 32.4840 W), and
Fig. 3. A) Normalized methane probability for entire data set, Gaussian least-squares curveefit curve fit for data greater than 1.966 ppm (the peak concentration), and 95%
confidence levels. B) Probability distributions for different regions, as marked on figure. See text for details.
426
P. Farrell et al. / Atmospheric Environment 74 (2013) 422e431
values as low as 1.8443 ppm recorded. However, even small towns
like Hinkley, CA (pop. 1915) showed elevated CH4 concentrations up
to 2.17 ppm (winds were 5 m s!1). Comparison of Picarro and GC
desert values agreed within w50 ppb, although if previous desert
values were somewhat contaminated with LA air, then desert GC
data could have been low by w0.1 ppm.
In the San Bernardino Mountains (Fig. 4), mean CH4 was
1.859 ppm, but as low as 1.851 ppm at 2080 m. Clearly, altitude
plays a role in the variation, which was explored by using altitude
changes in the San Bernardino Mountains (after leaving the LA
Basin) for profiling from 850 to 2200 m (Fig. 4B). Data show a clear
CH4 decrease with altitude. A least squares quadratic polynomial
was fit iteratively to data between 850 and 1850 m, first for all
values less than 1.868 ppm and then for values within 0.0025 ppm
of the fit,
CH4 ¼ 1:883 ! 28:1z ppb=km þ 8098z2 ppb2 km!2
where z is altitude, i.e., a sea level intercept of 1.883 ppm. Elevated
CH4 concentrations at 1900 m were from Bear Lake, a tourism town
next to a same named reservoir. Values for 2100e2200 m were
distant from Bear Lake and clearly represented a different (higher
CH4) air than the lower profile data. This was supported by CO2 data
showing a well-mixed atmosphere whose baseline (not peak), CO2
was w392 ppm from 850 < z < 1850 m, increasing to 393 ppm for
z ¼ 2100e2200 m.
3.3. Wetlands
The Gulf coast features extensive wetlands stretching from the
Florida Keys to south Texas. Florida peninsula wetland CH4 concentrations were higher than for Florida panhandle wetlands,
where the urban population is lower. For example, mean CH4 was
1.95 # 0.22 ppm from 28.25& N to 30.1& N, a distance of 255 km
(Fig. 5A and B), while panhandle CH4 concentrations near Jacksonville, FL were 1.75 # 0.083 ppm. Some of the difference could be
day/night related, the latter data were collected from 17:07 to 19:55
LT, 7 Oct. while peninsula data were collected 23:30 6 Oct to 03:30 7
Oct. However, further Florida panhandle data for an interior
segment along the Interstate 10 corridor (227-km total) showed
very similar CH4 concentrations, 1.71 # 0.1 ppm from 19:55 7 Oct. to
01:20 8 Oct. LT. The highest CH4 concentrations in the survey of
panhandle wetlands were near Jacksonville (w1.9 ppm).
Florida peninsula data showed wide variability (Fig. 5A), largely
due to vibration-induced noise e most Florida data were collected
at 60e100 km h!1, except for two short periods at w28.45& N and
28.8e29& N. Additional vibration reduction decreased noise later
in the expedition for comparable speeds. Applying a runningaverage, 5-point filter to the data halved the standard deviation
to 0.13 ppm (w7%) and eliminated anomalously low values.
Elevated CH4 was observed w20 km south of Gainesville, FL
(pop. 53,000). The peak corresponds with Paynes Prairie Park
(85 km2), which straddles the highway but primarily lies to the
east. The CH4 peak exhibits comparable size scale to the park, which
comprises savannah and a number of lakes and ponds. Winds were
near zero at the time but had been light (<1.5 m s!1) and from the
west earlier in the evening, i.e., not from Gainesville, FL.
3.4. Fire
Elevated CH4 concentrations (to 4.88 ppm), were observed
during three transects through a smoke plume to the northeast of
Raceland, LA on 3 Oct. 2010 between 18:50 and 19:40 LT (Fig. 6).
Smoke was observed originating from brush and/or agricultural
land w2 km northwest of Highway 90, and allowed visualization of
where elevated CH4 concentrations were expected and detected.
Fig. 4. A) Methane, CH4, data for San Bernardino mountains, with highly accentuated color scale, data was 20 point running-average smoothed. B) All data versus altitude for San
Bernardino mountain area (not LA Basin slope) and quadratic curve fit. See text for details. Surface image from GoogleEarth. (For interpretation of the references to colour in this
figure legend, the reader is referred to the web version of this article.)
P. Farrell et al. / Atmospheric Environment 74 (2013) 422e431
427
Fig. 5. A) Florida peninsula methane, CH4, concentrations, and 5-point running-average smoothed data, see B for locations. B) Location of data in A. Blue arrow on B shows location
of focused area on C. C) CH4 concentration near Gainesville, FL. Data key and length scale on figure. Times are local time. Surface image from GoogleEarth. (For interpretation of the
references to colour in this figure legend, the reader is referred to the web version of this article.)
Based on visual observations, the upper extent of the plume at the
highway was estimated at about 100e150 m. Winds were from the
northwest at 3.6 m s!1, which was consistent with the observed
plume advection being approximately orthogonal to the highway.
Wind speed data was from Houma, LA, w26 km southwest of the
fire (www.weatherundergrund.com),
An effort was made to model the plumes by curve fitting a
Gaussian to each transect after a running-average 5-point
smoothing function was applied, and by simulation of observed
CH4 with a Gaussian plume model (Hanna et al., 1982). Unfortunately, these approaches completely failed to reproduce the
observed spatial pattern. Specifically, simulating a 7-km half-width
plume at 2-km distance from a point source requires unrealistically
slow winds (<0.01 m s!1), yet the plume advection as visualized by
the smoke clearly was consistent with higher wind speeds. Most
probably, the source was a line rather than a point, which would
produce a broad plume at a short distance from the source. This
scenario is feasible because fires often burn along fronts.
Transverse transects through a line-source plume (along the
road) would produce a broad plateau of maximum values with
lateral boundaries being marked by a steep concentration drop off,
a pattern observed for two of the transects (Fig. 6B and C). Ambient
concentrations were calculated for both sides of the plateau. One
transect (Fig. 6D) showed a distinct and asymmetric pattern that
could be explained by wind shifting from approximately perpendicular to the road to an oblique angle.
Based on the well-mixed geometric plume model described
above and estimated plume height, source fluxes were estimated
for the two symmetric plume transects. The asymmetric plume
appeared inconsistent with the model and was not analyzed. The
flux was calculated by:
F ¼
Z
huðcðxÞ ! cA Þdx
integrated (trapezoidal) over lateral distance, x, along a straight,
northeastesouthwest section of Highway 90, and where F is the
total CH4 flux, h is height, u is wind speed, c(x) is the CH4 concentration, and cA is the ambient concentration. The origin for x was set
to where the peak was at a maximum. The lower cA was used for
calculating the plume enhancement.
For the Raceland, LA wind speed of 3.6 m s!1, a 125-m plume
height and orthogonal winds to the road, a best guess flux was
w0.13 kiloton day!1. Significant uncertainties arise because winds
were from a significant distance from the plume, plume height was
determined from a visual estimate, and ambient CH4 was characterized poorly. However, observations constrain these values. From
smoke plume visualization, winds are improbable to have been
faster than 5 m s!1, and could not have been slower than 2 m s!1.
Ambient CH4 could not be lower than 1.95 ppm, and based on the
standard deviation (0.06 ppm), unlikely to be higher than 2.12 ppm.
The upper limit is based on an average and standard deviation of
the ambient values recorded before and after the first transect. The
most ambiguous variable is the plume height.
A 2.5 million point Monte Carlo simulation for an uncertainty
(half-width) for u of 1.5 m s!1, truncated at 2 and 5 m s!1, an uncertainty of h of 50 m, truncated at 75 and 200 m, and ambient of
with an uncertainty of 0.06 ppm, showed a most probable values of
Fig. 6. A) Map of methane, CH4, fire related measurements. Length scale on panel. B)eD) CH4 concentration with respect to along road distance, x, during three transects of a plume
from a brush fire. Data were collected at 1840, 1910, and 1855 LT, for BeD, respectively. Each data point’s uncertainty is circa 50 ppb. The origin for x was set at each profiles’ peak
concentration. Surface image from GoogleEarth.
428
P. Farrell et al. / Atmospheric Environment 74 (2013) 422e431
0.21 and 0.09 kiloton dy!1 for the two passes, shown in Fig. 6B and
C, respectively (values were within e!1 from 0.1 to 0.49 and 0.024e
0.158 kiloton dy!1, respectively.
3.5. Geological source
The highest CH4 concentrations from both surveys, almost
50 ppm, were near the La Brea tar pits in the Los Angeles Basin.
Note, these values likely were underestimates due to the Picarro
plus sample path’s response time; lab tests suggest combined flow
path and instrumentation characteristics yields an effective w1 s
response time. La Brea was surveyed between 0300 and 0400 LT
when mobility is greater due to negligible traffic, a well-defined
boundary layer trapping CH4 closer to the surface, while the low
winds keep the emitted CH4 closer to the source, all of which
leading to stronger signatures. The coastal boundary layer was
w100 m based on visualization of coastal refinery steam plumes
south of LAX airport. The strongest plumes were found at the
Northwest corner of the La Brea tar pit (Fig. 7D) and were found at a
similar location during repeat circuits of the park. The plumes were
narrow, w50 m across and thus of similar size scale as the ponds
(Fig. 7D). CH4 concentrations were over 5 ppm in neighborhoods
for several kilometers although there were other strong sources
nearby (Fig. 8). Levels decreased from La Brea in both travel directions (to the north and to the west), extending further to the
west (downslope winds from mountains to the north likely blocked
advection in a northerly direction). The gentle and continuous
decrease toward the west suggests a similar geologic source
responsible for most of the elevated CH4 concentrations
toward LAX airport. Widespread seepage in the La Brea area is well
documented (Gamache and Frost, 2003). Observed CH4
concentrations were much larger than observed elsewhere in the
surveyed regions of the Los Angeles Basin.
A comparison of CH4 and CO2 concentrations near La Brea
(Fig. 7C) showed a strong correlation for the very high CH4 levels
associated with the direct tar pit emissions, 1.1 ppm CO2 ppm!1 CH4
which was highly distinct from the correlation from traffic (Supp.
Figs. S-3 & S-4). This suggests seepage of both CO2 and CH4. A
much stronger relationship between CO2 and CH4 was observed for
low concentrations (Supp. Fig. S-3), which could have resulted from
earlier traffic.
Winds were not calm earlier in the evening, blowing from the
east until about 2000 or 2100 LT. This allows a crude flushing model
emission estimation, where we assume the entire area was “swept
out” or “flushed” of daytime CH4 emissions by winds which ceased
w6 h prior to data collection. A spatial plume extent of w95 km2
was estimated assuming that elevated CH4 concentrations were
symmetric along a HollywoodeLAX axis, covering an area w14 km
long by w7 km wide (Supp. Fig. S-5). Although the coastal
boundary layer was observed to be w100 m, tall buildings in the
vicinity of La Brea and in downtown Los Angeles likely imply a
thicker boundary layer height in the La Brea (Wilshire), approximated as 300 m. The average CH4 concentration measured over this
region was 4.95 ppm, with Hollywood data suggesting ambient was
w2.1 ppm. Combining these factors yields a CH4 emission rate of a
quarter kiloton day!1; although more data were collected in the
vicinity of the seeps. Binning by distance from La Brea (Fig. 8B) and
averaging over the area yields a mean CH4 concentration elevation
of 3.8 ppm, implying wa sixth of a kiloton day!1 CH4 emissions.
Clearly a number of hypotheses could have been tested by a more
targeted survey pattern, for example, the plume could have been
more extensive. In any case, the La Brea flux is a significant fraction
Fig. 7. A) Methane, CH4, in the Los Angeles Basin, data reduced to 10 s average to minimize clutter. Blue surface track is for nocturnal data, yellow is for daytime data. B) Inset shows
La Brea tar pit area detail; data reduced to 2 s averages. C) CH4 versus carbon dioxide, CO2, scatter plot for the La Brea area (See Table 1 for spatial definition). D) CH4 concentration in
the immediate vicinity of the La Brea tar pits. Surface image from GoogleEarth. Color scale for all panels is limited to 5 ppm for increased detail on closer to ambient concentrations
shown on A. Symbol size (and height) increases approximately with concentration, but varies with perspective. Approximate symbol size scale on panel B for panel B. (For
interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
P. Farrell et al. / Atmospheric Environment 74 (2013) 422e431
429
Fig. 8. A) Methane, CH4, concentrations, in the La Brea area binned into 100 $ 100 m bins, and filtered with a 100-m low pass filter. Data key on figure. Also shown are unsmoothed
CH4 concentrations. B) Mean radial CH4 concentration with distance from La Brea tar pit.
of the south LA County emission CH4 inventory of 1800 tons day!1
(Hsu et al., 2010). Other geologic contributions arise from known
coastal geologic seepage areas like Playa del Rey, where CH4 enhancements also were observed (Fig. 7A), thus these represent only
a portion of overall geologic contribution to the LA Basin likely.
4. Discussion
4.1. Measurement approach
During the 2010 survey, the lowest measurements were observed
in the California desert, 1.80 ppm, but measurements were along the
Interstate 10 corridor to the west of Palm Springs and Los Angeles. In
the 2012 survey, measurements were taken on a largely nontrafficked highway in the Mojave desert and recorded slightly
higher concentrations of 1.8639, suggesting an underestimate of GC
CH4 concentrations that day of w100 ppb (desert seasonal variations
should be small). However, the influence of even small population
centers was apparent in the 2012 desert data, and thus values likely
were slightly elevated due to human activity. Measurements at
somewhat higher elevation (1437 m) in a Nevada dry lakebed found
1.781e1.908 ppm (Yates et al., 2011). Even here, a wind speed and
direction relationship was found that suggested a local source.
4.2. Day/night meteorology
Day/night variations can cause significant concentration
changes for the same flux, potentially overwhelming dayenight
emission differences. Depending on wind speed, boundary layer
height, and atmospheric stability, ground concentrations downwind of a discrete source can vary dramatically (Bradley et al., 2010;
Hanna et al., 1982). Atmospheric stability affects mixing and thus
ground concentrations; assuming of course that the CH4 measurements are downwind of the source under consideration.
Day/night atmospheric conditions are significantly different, for
example, daytime surface winds tend to be significantly higher
than nighttime winds, which were light to calm during the survey.
This leads to greater daytime dilution and lower daytime concentrations (Hanna et al., 1982). Perhaps more important is atmospheric stability and the implications for boundary layer thickness
and mixing (diffusion), both of which are significantly less at night
(Shorter et al., 1996). A shallow boundary layer “traps” CH4 closer to
the ground, allowing buildup of higher surface concentrations
(Sasakawa et al., 2010). A stable atmosphere also leads to slower
plume dispersion and therefore higher surface concentrations near
the source. Thus, nighttime boundary layer thickness enhances
near-surface CH4 concentrations, enabling easier source attribution
than daytime measurements. For example, a diurnal trend was
reported for Siberian wetlands with a variation of 0.1e0.2 ppm
during summer months and with peak concentrations around
dawn following nighttime buildup (Sasakawa et al., 2010). The
diurnal cycle corresponded to the morning development of the
convective mixed layer and dilution, followed by collapse of the
layer after sunset leading to overnight accumulation.
4.3. Wetlands
CH4 concentrations in the Florida peninsula wetlands were
elevated compared to lower levels in Florida panhandle wetlands
and the Mississippi delta. These lower concentrations are unrelated
to the change in system performance (Fig. 4), which occurred while
surveying the Baton Rouge, LA area. The most likely explanation is
the greater urbanization of the Florida peninsula - note the Florida
panhandle economy is tourist dominated, which is slow on October
weekdays (as observed during the survey by low relative traffic
levels and few cars in hotel parking lots). Also, day/night CH4 differences were not observed for the Florida panhandle wetlands.
An effort was made to compare coastal (brackish) and inshore
(fresh) wetlands in the eastern Florida panhandle, but no notable
difference was found (coastal ¼ 1.73 ppm, inland ¼ 1.74 ppm).
Surveys of these two regions were from 20:00 to 23:00 LT during
which time atmospheric conditions did not change significantly.
DeLaune et al. (1983) found an inverse relationship between CH4
emissions and salinity in the Barrataria Bay, LA area. Thus, these
430
P. Farrell et al. / Atmospheric Environment 74 (2013) 422e431
coastal CH4 measurements likely reflected CH4 transport rather
than a strong coastal source.
A broad peak of significantly elevated CH4 concentrations was
observed during a nighttime transit of Paynes Prairie Park, FL with
peak CH4 concentration of 2.6 ppm. Although a pipeline leak cannot
be ruled out, the plume and park’s size-scales were similar. Part of
the explanation likely lies in boundary layer shallowness when
traversed at 02:51 LT, when winds were calm. Another possible
contribution could be nearby lake bubble emissions e CH4 concentrations up to 4 ppm were reported for a Swiss reservoir, primarily due to bubbles (Eugster et al., 2011). A nearby landfill also
was a potential source.
4.4. Fire
Efforts to estimate the fire source strength based on a Gaussian
plume model were unsuccessful, implying a non-point, source
plume. Unfortunately, although the GC data resolved the plume, it
could not resolve the plume edges, preventing further plume model.
Instead, a simplistic “sheet’ model was used for well-mixed CH4 in
the plume. Plume height was estimated thanks to the visibility of
the smoke, yielding an emission rate of 0.15 kiloton CH4 day!1.
By comparison, a boreal Siberian forest fire in Siberia released
CH4 at a rate of 3.5 ton CH4 km!2 (Kajii et al., 2002) Thus, if biomass
density was the same, the Raceland fire would have required a
combustion rate of 30 km2 per day, 3-km progression along a 10km front, which is within reasonable rates and dimensions based
on observations. Total area that could have burned based on locations of water bodies was w110 km2, or significantly greater than
the estimated daily combustion rate.
4.5. Geologic emissions
Surprisingly large and extensive elevated methane concentrations were found in the northern Los Angeles area. The highest
concentrations were found in the immediate vicinity of the La Brea
tar pits. Migration is through faults from a reservoir w1300 m
directly below (Gurevich et al., 1993) and has been documented
nearby, for example gas and oil surface in a nearby underground
parking structure. Aside from natural migration along faults, which
have been active singe at least the late Pliocene seepage is increased
by the presence of many abandoned well bores. For example, an
explosion that destroyed a shopping store on 24 March 1985, had
migrated up the “third street fault and certain wellbores,” with
evidence suggesting widespread gas migration along faults and
abandoned oil wells (Gurevich et al., 1993).
This geologic, fossil source appears relatively large compared to
other strong sources in the Los Angeles Basin that were surveyed,
such as the Port of Long Beach. Thus, these data are consistent with
the missing CH4 in the inventory assessment of Hsu et al. (2010)
arising from a geologic source. Daily wind patterns likely transport the CH4 westward during the day, with a morning “pulse”
likely. Other areas of elevated CH4 were observed downwind from
Playa del Rey and Marina del Rey, two areas that overlie the Playa
del Rey oil field (Chilingar and Endres, 2005). This field was used as
a storage field since 1942, and leaks into the adjacent Venice oil
field. Several hundred abandoned wells are in the area, providing
potential migration pathways.
4.6. Future study improvements
This study demonstrated advantages in terms of spatial coverage
and continuous data collection for trace atmospheric gas measurements by a mobile laboratory/accommodation platform. Further
vibration reduction would be beneficial on the vehicle and on the GC
platform. A key improvement implemented in the 2012 expedition
was real-time data analysis and route planning through cell phone
modem technology. Further improvement would allow visualization of data within a GoogleEarth-type environment. Such data
enables active survey path modification to constrain better sources
and characterize plume transport processes. Effective application of
adaptive survey techniques requires real-time meteorology data,
which can be collected during regular survey pauses or downloaded.
Measurement of several species, including CO2, would help
discriminate between different anthropogenic and natural sources.
Cavity ring-down spectrometers provide the higher data density
necessary to resolve plume processes, as was not possible for the
fire data. These sensors also have dramatically lower vibration
sensitivity than GCs and orders of magnitude higher data rates
(0.1 s). Furthermore, simultaneous measurement of CH4 and CO2
can identify situations where vehicular emissions are biasing data
(See Supplemental Material), which release w2 Tg yr!1 (Piccot
et al., 1996), Because major traffic corridors typically transect urban areas, which were associated with elevated CH4, secondary
roads should be used in urban areas. Both daytime and nighttime
data collection are needed for urban areas and other sources as well
as an effort to characterize boundary layer thickness. Future surveys should incorporate north and south transits, rather than
largely covering a very narrow latitudinal band, and multiple season measurements.
5. Conclusions
This study showed that trace atmospheric gas measurement
from a mobile laboratory/accommodation platform has advantages
in terms of spatial coverage on fine and large spatial scales. Measurements likely were affected strongly by day/night meteorological differences. Urban areas generally were associated with
elevated CH4 concentrations with an urban contribution likely
explaining elevated CH4 concentrations in the Florida peninsula
compared to the Florida panhandle despite extensive wetlands
being present in both. Even very low population centers in the
Mojave Desert had detectable CH4 signatures. Data collected during
a descent from the San Bernardino Mountains provided a unique
approach to atmospheric profiling. Smoke visualization from a
brush fire allowed an estimation of the fire CH4 source strength of
w0.15 kilotons day!1, although uncertainties were large. Natural
and quasi-natural (abandoned well pipes) CH4 emissions from
North Los Angeles were focused on the La Brea tar pits and appear
to be significant to overall Los Angeles county inventories.
Acknowledgments
Support from the National Science Found, ATM Rapid Response
program, Award No 1042899, NASA award No NNX12AQ16G and
the Gulf of Mexico Hydrates Research Consortium administered by
the Center for Marine Resources and Environmental Technology at
the University of Mississippi through the Department of Energy’s
Cooperative Agreement Award No. DE-FC26-06NT42877. The
participation of Monica Leifer in 2012 data collection is gratefully
acknowledged. Views and conclusions in this article are the authors
and do not necessarily represent the view of the University of
California University of Mississippi or the U.S. Government. Any use
of trade, product, or company names is solely descriptive and does
not imply endorsement by the U.S. Government.
Appendix A. Supplementary data
Supplementary data related to this article can be found at http://
dx.doi.org/10.1016/j.atmosenv.2013.02.014.
P. Farrell et al. / Atmospheric Environment 74 (2013) 422e431
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