Atmospheric Environment 74 (2013) 422E431

Atmospheric Environment 74 (2013) 422e431

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Atmospheric Environment

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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 highlights

< 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. article info abstract

Article history: The potent greenhouse gas, methane, CH4, originates from a wide range of anthropogenic and natural Received 24 March 2012 sources. A ground-based, satellite-scale, transcontinental (Florida to California) survey was conducted to Received in revised form understand better emissions from key sources including wetlands, forest fire, and geologic sources, as 12 July 2012 well as to acquire desert background values and lower atmosphere vertical profiling in the San Ber- Accepted 5 February 2013 nardino 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 Keywords: with a cavity ring-down spectrometer system in Southern California in 2012. Significant vibration Methane Gas chromatography reduction efforts allowed near continuous mobile GC measurements. fi Wetlands Nocturnal CH4 measurements tended to be higher compared to daytime values, sometime signi - Greenhouse gas cantly, for similar sources and were concluded due to day/night meteorological differences. The lowest Fire GC observations were 1.80 ppm, observed in the California desert, w60 ppb less than minimum desert Geologic methane CH4 observed in 2012. Thanks to smoke visualization of a brush fire plume, the flux from the fire was 1 Seepage estimated at 0.15 kiloton dayÀ . Geologic CH4 emissions from the La Brea tar pit and surrounding areas La Brea tar pits were surprisingly strong, with peak concentrations of nearly 50 ppm and highly elevated CH4 concen- Southern California trations extending over at least w100 km2, and accounting potentially for a significant fraction of the LA Emission basin CH emissions. Geologic CO emissions also were observed. South US 4 2 Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction concentrations nearly stabilizing in the last decade. These changes may have resulted from anthropogenic emissions (Aydin et al., 1.1. Atmospheric methane 2011), primarily related to Fossil Fuel Industrial emissions mask- ing wetlands increases (Bousquet et al., 2006), although decreased CH4 has a greenhouse warming potential 26 times that of carbon microbial emission have been proposed (Kai et al., 2011). Since dioxide, CO2, on a per molecule basis and century timescale; 2008, strong CH4 growth has resumed (Heimann, 2011), high- however, on a 20-year timescale its radiative contribution is greater lighting the need for better understanding of sources and trends. than that of CO2 (IPCC, 2007), due in part to its shorter lifetime (8e Atmospheric CH4 concentration depends on the balance between 10 years). Since the industrial revolution, CH4 concentrations have sources and sinks, which primarily is governed by hydroxyl radical, nearly tripled, although growth slowed in the 1990s with CH4 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 * Corresponding author. Tel.: 1 805 893 4931; fax: 1 805 893 4927. modeling suggests OH oxidation has increased w5% since 1980, þ þ E-mail addresses: [email protected], [email protected] (I. Leifer). decreasing CH4 lifetimes (Lassey and Ragnauth, 2010).

1 Natural (145e260 Tg yrÀ ) and anthropogenic (264e (Yokota et al., 2009). However, satellite footprints are large, 30 27e 1 1 Â 428 Tg yrÀ )CH sources release a combined w582 87 Tg yrÀ , 30 240 km and 10.5 10.5 km for SCIAMACHY and GOSAT, 4 Æ Â Â but uncertainties are large (Denman et al., 2007). Natural and respectively, limiting direct source interpretation to large-scale anthropogenic microbial sources contribute an estimated w69% to features hundreds of kilometers in size (Bergamaschi et al., 2007). atmospheric budgets (Conrad, 2009). Important natural sources include wetlands, whose emissions are driven by anaerobic mi- 1.3. Study motivation crobial oxidation and contribute w23% of the global CH4 budget. Microbial production underlies most anthropogenic CH4 emissions, Global satellite data are critical to understanding global budgets, such as from landfills, rice, and ruminants. Natural fossil CH4 but current spatial and temporal resolutions are too coarse to eval- emissions generally are estimated at w20% of the global budget uate most sources directly, thus source assessment primarily is from (Etiope et al., 2008), although Lassey et al. (2007) suggest global and continental inversion modeling (Bousquet et al., 2006). 30.0 2.3% from fossil sources. Often, satellite-scale data are ground-referenced by airborne (Kort Æ Fires release anthropogenic and natural CH4 and contribute et al., 2008) or ground station measurements (Bergamaschi et al., significantly to inter-annual variability in CH4 growth (NRC, 2010) 2007; Pétron et al., 2012). However, airborne data can be of limited 1 contributing an estimated 14e88 Tg yrÀ (Fletcher et al., 2004). use as high airspeeds prevent resolving finer scale plumes with the Anthropogenic biomass burning in residential settings (open fire- required accuracy, and flight paths may be restricted over urban and 1 places, cooking, etc.) is estimated to contribute 8e12 Tg yrÀ (Piccot industrial areas. Ground measurements from fixed locations near et al., 1996). Wildfires can be significant, for example, 1998 arctic emission sources can investigate nearby individual sources (Bradley 1 wildfires (a very active year) released an estimated 2.9e4.7 Tg yrÀ , et al., 2010; Pétron et al., 2012), as can typical mobile ground mea- or 12% of global fire CH4 emissions (Kasischke and Bruhwiler, surements (Herndon et al., 2005; Pétron et al., 2012; Shorter et al., 2002). Emission rates depend on the fuel type consumed and the 1996); however, such data lack satellite spatial-scales. combustion mode - burning versus smoldering (Lobert and To address this need, a surface expedition was conducted 6e12 Warnatz, 1991). Koppmann et al. (2005) recommended between Oct. 2009 to acquire satellite-scale CH4 data at high spatial reso- 1 3 and 10 g kgÀ biomass for flaming and smoldering combustion, lution to allow source identification and comparison (Fig. 1B) and a respectively, although these values are highly variable. second survey in southern California on 21e22 Feb. 2012. The ex- peditions focused on important CH4 sources with significant 1.2. Atmospheric methane budget estimation growth potential, such as wetlands under warmer climate sce- narios. Data were collected almost entirely while in motion. Herein Source strengths for global CH4 budgets are derived in two we present the detailed approach and application to several natural manners; from top-down estimates based on atmospheric mea- sources including wetlands, a forest fire, a major geologic source, surements and inversion modeling and from bottom-up inventory and the California desert (no source), summarized in Table 1. estimates of individual sources (NRC, 2010). Comparison of top- down and bottom-up estimates can indicate under and over- 2. Methods inventoried sources or un-inventoried CH4 sources, e.g., Hsu et al. (2010) for the Los Angeles Basin. On a global scale, satellite- 2.1. Study area derived, top-down CH4 budgets, such as from SCIAMACHY (Scan- ning Imaging Absorption Spectrometer for Atmospheric Cartog- The 2010 expedition focused on key natural and anthropogenic raphy) have the best global coverage for such budgets (Schneising CH4 sources to characterize better their relative importance and et al., 2011); GOSAT (Greenhouse gases Observing SATellite) being understand better CH4 spatial distribution on a transcontinental the other currently orbiting, tropospheric CH4 observing satellite 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 Type Location Span (km) Starta lengthb Maxc (ppm) Meanc (ppm) California (2010) Sonora Desert 33.42282N,112.60754We33.92035N, 116.54893W 370 11:10 12/10 04:05 2.27 1.91 0.12 Æ California (2012) Mojave Desert 34.99527N,117.54140We35.00681N,117.69483W 15 17:47 21/02 00:15 n/a 1.8639 0.0038 Æ La Brea Geology 34.03500N,118.37500We34.07833N,118.35167W 5.6 03:00 21/02 01:00 47.975 5.0627 4.25 Æ S. Bernadino Mtns. 34.31321N,116.80953We34.33486N,116.83340W 4.0 09:10 21/02 00:14 1.8638 1.8584 0.0016 Æ Florida (2010) Paynes Park Wetland 29.55474N,82.34157We29.58828N,82.36136W 3.8 02:49 10/10 00:07 2.9 2.61 0.2 Æ Panhandle Wetland 30.48551N,85.21087We30.46404N,88.38193W 300 19:20 07/10 05:58 9.06 1.95 0.22 Æ Louisiana (2010) Raceland Fire 29.78582N,90.51951We29.77079N,90.53828W 2.3 18:23 03/10 00:57 4.48 2.58 0.59 Æ a Local Time. b hour:min. c Methane Concentration. were made from St. Petersburg, FL, to Santa Barbara, CA (Fig. 1)by winds not parallel to the roadway (i.e., not east/west), exhaust from flame ion detection on a gas chromatograph, GC (8610C, SRI In- preceding vehicles should affect data minimally. In some cases, struments, California), aboard a 9-m recreational vehicle, RV. A total sources were studied by transecting plumes, in other cases through of w6600 CH4 chromatograms were collected (Fig. 1)over7020km slower, convoluted search patterns, necessarily road limited. of roadway. Lost GPS affected w8.9% of the data, although gaps usually were geographically localized and temporally short. Thus, 2.2. Methodology locations of chromatograms without GPS were known to at worst 10e50 km and general much better. Still, data without GPS infor- Air samples were collected continuously through a roof “air mation were not used for large-scale investigation (Leifer et al., ram” to prevent exhaust entering the sample line while the RV was 2013), but not for source studies. moving (Fig. 2B). Air was pumped through ¼” plastic sample tubing Unlike studies that use vans (Herndon et al., 2005; Pétron et al., attached 0.25 m from the air ram’s end into multiple streams that 2012; Shorter et al., 1996), RV-based measurements enable entered the 4-channel GC, which was configured to achieve the continuous, mobile data collection over extended areas, including fastest possible measurement time while yielding adequate CH4 nocturnal measurements through driver rotation. Most urban areas peak/air valley separation. Adequate separation meant that custom were traversed nocturnally when vehicular CH4 emissions are (MatLab, Mathworks, MA) routines could separate the peak from much lower (Shorter et al., 1996) e vehicular emissions are strongly the valley, despite overlap. Real-time analysis was by conventional spatially road-biased. Some daytime measurements were along chromatography software (PeakSimple, SRI Instruments, CA), heavily trafficked urban and rural interstate corridors; however, for 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 425 separation. Daily calibration used 1-mL injections, typically ten Louisiana (Fig. 1B). There was high variability (toward higher con- repetitions, of a high (10 ppm) and low (1 ppm) standard (See centrations) and a strong increase into eastern Texas, where the Supplemental Material). highest overall concentrations were found. Day/night differences GC performance was improved by separating heater power from were important, but could explain only part of the observed trends; other GC power usage, with a power conditioner on the latter to east Texas overall CH4 concentrations were elevated relative to all reduce generator and other electrical noise. Significant efforts were other surveyed areas, many of which also were surveyed noctur- made to reduce vibrations and their resultant noise. Post process- nally. Elevated values here were related to fossil fuel industrial ing addressed the still significant data noise (see Fig. S-2 for activities, discussed in Leifer et al. (2013). example chromatogram peaks with low and high noise) through The normalized CH4 probability distribution (integrated custom analysis routines. These routines decreased peak area probability 1.00) was well fit(R2 0.974) with a Gaussian ¼ ¼ variability by 27%, reducing the standard deviation from 5.8% to function for values above 1.966 ppm, the peak of the fit(Fig. 3). For 4.3% in 10 comparison chromatograms. Fire data used a single point concentrations greater than w2.7 ppm CH4, the probability calibration (see Supplemental Material). decreased more slowly than predicted by the Gaussian implying A maximum uncertainty of w5% is used for all GC measure- that higher concentration measurements were more probable than ments (i.e., w100 ppb for ambient), although in most cases mea- if they were Gaussian distributed. For values above 2.7 ppm, surement accuracy is half or better, while Picarro measurement probability decreased with an approximate power law. CH4 values accuracy is better than 1 ppb (0.05%). All anomalies investigated significantly below ambient (w6.5% were less than 1.7 ppm) were herein are well above these uncertainties. due to noise. Regional histograms showed some significant differ- Wind speed and direction were not measured and were looked ences (Fig. 3B). Although Florida and Alabama both exhibited up from online historical weather archives (weatherunderground. Gaussian probability distributions mirroring the overall distribu- com, 2010) for Palm Springs, CA, on 10 Oct. 2010. US Census data tion (Fig. 3A), they also included very high values (Fig. 3B, light blue provided urban populations (US_Census_Bureau, 2010), except for and red (in the web version), respectively) from highly local, very Ciudad Juarez (wikipedia.com, 2011). strong sources. The E. Texas region shows a secondary probability Due to time constraints and lost GPS signal, 2010 California data, peak of significantly elevated CH4. particularly coastal, were limited. A shorter, RV survey was con- Eastern Louisiana exhibited some anomalously low CH4 conducted 21e22 Feb. 2012 with a cavity ring-down spectrometer centrations, which resulted from the compressed air supply pres- (G2301Greenhouse Gas Sensor, Picarro, California), “Picarro.” The sure dropping below regulation and resulting in calibration issues Picarro was mounted on the same vibration padding as the GC and affecting w6 h of data in the Baton Rouge, LA area (04:30e10:30, 9 recorded CH4,CO2, and water vapor at 2 Hz, while GPS was logged Oct.), while waiting to obtain replacement air and replacement on a separate computer. A 4 m, ¼” ID tube was fed to the roof, 1.0 ppm calibration gas. Based on the baseline trend and calibration without the air-ram, thus only data collected in motion, or for high values, these data may be biased low by w0.1e0.2 ppm. winds was used. The Picarro and GPS computer were synchronized at the survey start and remained within 1 s after 24 h. The survey 3.2. Desert route was planned for night surveys of FFI and urban areas, and day surveys of the Mojave Desert and San Bernardino Mountains The lowest CH4 levels in 2010 were measured in the California fi (w2000 m altitude). A signi cant improvement was real-time and Arizona deserts with mean California desert CH4 concentra- route planning using a cell phone modem and GoogleEarth (Goo- tions of 1.80 0.074 ppm (Interstate 10 corridor North of the Salton Æ gle, California). Sea and east of Los Angeles). Values increased toward the west, likely due to advection by gentle west-northwest winds trans- 3. Results porting CH4 from the Los Angeles basin and Palm Springs, CA, where peak CH4 was 2.88 ppm. To avoid potential background 3.1. Survey overview signal contamination from Los Angeles air, background CH4 measurements were made in the Mojave desert at 750 m altitude, with CH concentrations decreased from the more urban areas of values of 1.8639 0.0038 ppm recorded over w15 km transect to 4 Æ Florida into the panhandle with the trend continuing to West the west of Kramer Junction (34 59.7160N, 117 32.4840W), 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 1.95 0.22 ppm from 28.25 N to 30.1 N, a distance of 255 km Æ like Hinkley, CA (pop.1915) showed elevated CH4 concentrations up (Fig. 5A and B), while panhandle CH4 concentrations near Jack- 1 to 2.17 ppm (winds were 5 m sÀ ). Comparison of Picarro and GC sonville, FL were 1.75 0.083 ppm. Some of the difference could be Æ desert values agreed within w50 ppb, although if previous desert day/night related, the latter data were collected from 17:07 to 19:55 values were somewhat contaminated with LA air, then desert GC LT, 7 Oct. while peninsula data were collected 23:30 6 Oct to 03:30 7 data could have been low by w0.1 ppm. Oct. However, further Florida panhandle data for an interior In the San Bernardino Mountains (Fig. 4), mean CH4 was segment along the Interstate 10 corridor (227-km total) showed 1.859 ppm, but as low as 1.851 ppm at 2080 m. Clearly, altitude very similar CH concentrations, 1.71 0.1 ppm from 19:55 7 Oct. to 4 Æ plays a role in the variation, which was explored by using altitude 01:20 8 Oct. LT. The highest CH4 concentrations in the survey of changes in the San Bernardino Mountains (after leaving the LA panhandle wetlands were near Jacksonville (w1.9 ppm). Basin) for profiling from 850 to 2200 m (Fig. 4B). Data show a clear Florida peninsula data showed wide variability (Fig. 5A), largely CH4 decrease with altitude. A least squares quadratic polynomial due to vibration-induced noise e most Florida data were collected 1 was fit iteratively to data between 850 and 1850 m, first for all at 60e100 km hÀ , except for two short periods at w28.45N and values less than 1.868 ppm and then for values within 0.0025 ppm 28.8e29N. Additional vibration reduction decreased noise later of the fit, in the expedition for comparable speeds. Applying a running- average, 5-point filter to the data halved the standard deviation 2 2 2 CH 1:883 28:1z ppb=km 8098z ppb kmÀ to 0.13 ppm (w7%) and eliminated anomalously low values. 4 ¼ À þ Elevated CH4 was observed w20 km south of Gainesville, FL where z is altitude, i.e., a sea level intercept of 1.883 ppm. Elevated (pop. 53,000). The peak corresponds with Paynes Prairie Park 2 CH4 concentrations at 1900 m were from Bear Lake, a tourism town (85 km ), which straddles the highway but primarily lies to the next to a same named reservoir. Values for 2100e2200 m were east. The CH4 peak exhibits comparable size scale to the park, which distant from Bear Lake and clearly represented a different (higher comprises savannah and a number of lakes and ponds. Winds were fi 1 CH4) air than the lower pro le data. This was supported by CO2 data near zero at the time but had been light (<1.5 m sÀ ) and from the showing a well-mixed atmosphere whose baseline (not peak), CO2 west earlier in the evening, i.e., not from Gainesville, FL. was w392 ppm from 850 < z < 1850 m, increasing to 393 ppm for z 2100e2200 m. 3.4. Fire ¼

3.3. Wetlands Elevated CH4 concentrations (to 4.88 ppm), were observed during three transects through a smoke plume to the northeast of The Gulf coast features extensive wetlands stretching from the Raceland, LA on 3 Oct. 2010 between 18:50 and 19:40 LT (Fig. 6). Florida Keys to south Texas. Florida peninsula wetland CH4 con- Smoke was observed originating from brush and/or agricultural centrations were higher than for Florida panhandle wetlands, land w2 km northwest of Highway 90, and allowed visualization of where the urban population is lower. For example, mean CH4 was 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 appeared inconsistent with the model and was not analyzed. The highway was estimated at about 100e150 m. Winds were from the flux was calculated by: 1 northwest at 3.6 m sÀ , which was consistent with the observed plume advection being approximately orthogonal to the highway. F hu c x c dx Wind speed data was from Houma, LA, w26 km southwest of the ¼ ð ð Þ À AÞ fire (www.weatherundergrund.com), Z An effort was made to model the plumes by curve fitting a integrated (trapezoidal) over lateral distance, x, along a straight, Gaussian to each transect after a running-average 5-point northeastesouthwest section of Highway 90, and where F is the smoothing function was applied, and by simulation of observed total CH4 flux, h is height, u is wind speed, c(x) is the CH4 concen- CH4 with a Gaussian plume model (Hanna et al., 1982). Unfortu- tration, and cA is the ambient concentration. The origin for x was set nately, these approaches completely failed to reproduce the to where the peak was at a maximum. The lower cA was used for observed spatial pattern. Specifically, simulating a 7-km half-width calculating the plume enhancement. 1 plume at 2-km distance from a point source requires unrealistically For the Raceland, LA wind speed of 3.6 m sÀ , a 125-m plume 1 slow winds (<0.01 m sÀ ), yet the plume advection as visualized by height and orthogonal winds to the road, a best guess flux was 1 the smoke clearly was consistent with higher wind speeds. Most w0.13 kiloton dayÀ . Significant uncertainties arise because winds probably, the source was a line rather than a point, which would were from a significant distance from the plume, plume height was produce a broad plume at a short distance from the source. This determined from a visual estimate, and ambient CH4 was charac- scenario is feasible because fires often burn along fronts. terized poorly. However, observations constrain these values. From Transverse transects through a line-source plume (along the smoke plume visualization, winds are improbable to have been 1 1 road) would produce a broad plateau of maximum values with faster than 5 m sÀ , and could not have been slower than 2 m sÀ . lateral boundaries being marked by a steep concentration drop off, Ambient CH4 could not be lower than 1.95 ppm, and based on the a pattern observed for two of the transects (Fig. 6B and C). Ambient standard deviation (0.06 ppm), unlikely to be higher than 2.12 ppm. concentrations were calculated for both sides of the plateau. One The upper limit is based on an average and standard deviation of transect (Fig. 6D) showed a distinct and asymmetric pattern that the ambient values recorded before and after the first transect. The could be explained by wind shifting from approximately perpen- most ambiguous variable is the plume height. dicular to the road to an oblique angle. A 2.5 million point Monte Carlo simulation for an uncertainty 1 1 Based on the well-mixed geometric plume model described (half-width) for u of 1.5 m sÀ , truncated at 2 and 5 m sÀ , an un- above and estimated plume height, source fluxes were estimated certainty of h of 50 m, truncated at 75 and 200 m, and ambient of for the two symmetric plume transects. The asymmetric plume 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

1 0.21 and 0.09 kiloton dyÀ for the two passes, shown in Fig. 6B and concentrations were much larger than observed elsewhere in the 1 C, respectively (values were within eÀ from 0.1 to 0.49 and 0.024e surveyed regions of the Los Angeles Basin. 1 0.158 kiloton dyÀ , respectively. A comparison of CH4 and CO2 concentrations near La Brea (Fig. 7C) showed a strong correlation for the very high CH4 levels 1 3.5. Geological source associated with the direct tar pit emissions, 1.1 ppm CO2 ppmÀ CH4 which was highly distinct from the correlation from traffic(Supp. The highest CH4 concentrations from both surveys, almost Figs. S-3 & S-4). This suggests seepage of both CO2 and CH4.A 50 ppm, were near the La Brea tar pits in the Los Angeles Basin. much stronger relationship between CO2 and CH4 was observed for Note, these values likely were underestimates due to the Picarro low concentrations (Supp. Fig. S-3), which could have resulted from plus sample path’s response time; lab tests suggest combined flow earlier traffic. path and instrumentation characteristics yields an effective w1s Winds were not calm earlier in the evening, blowing from the response time. La Brea was surveyed between 0300 and 0400 LT east until about 2000 or 2100 LT. This allows a crude flushing model when mobility is greater due to negligible traffic, a well-defined emission estimation, where we assume the entire area was “swept boundary layer trapping CH4 closer to the surface, while the low out” or “flushed” of daytime CH4 emissions by winds which ceased 2 winds keep the emitted CH4 closer to the source, all of which w6 h prior to data collection. A spatial plume extent of w95 km leading to stronger signatures. The coastal boundary layer was was estimated assuming that elevated CH4 concentrations were w100 m based on visualization of coastal refinery steam plumes symmetric along a HollywoodeLAX axis, covering an area w14 km south of LAX airport. The strongest plumes were found at the long by w7 km wide (Supp. Fig. S-5). Although the coastal Northwest corner of the La Brea tar pit (Fig. 7D) and were found at a boundary layer was observed to be w100 m, tall buildings in the similar location during repeat circuits of the park. The plumes were vicinity of La Brea and in downtown Los Angeles likely imply a narrow, w50 m across and thus of similar size scale as the ponds thicker boundary layer height in the La Brea (Wilshire), approxi- (Fig. 7D). CH4 concentrations were over 5 ppm in neighborhoods mated as 300 m. The average CH4 concentration measured over this for several kilometers although there were other strong sources region was 4.95 ppm, with Hollywood data suggesting ambient was nearby (Fig. 8). Levels decreased from La Brea in both travel di- w2.1 ppm. Combining these factors yields a CH4 emission rate of a 1 rections (to the north and to the west), extending further to the quarter kiloton dayÀ ; although more data were collected in the west (downslope winds from mountains to the north likely blocked vicinity of the seeps. Binning by distance from La Brea (Fig. 8B) and advection in a northerly direction). The gentle and continuous averaging over the area yields a mean CH4 concentration elevation 1 decrease toward the west suggests a similar geologic source of 3.8 ppm, implying wa sixth of a kiloton dayÀ CH4 emissions. responsible for most of the elevated CH4 concentrations Clearly a number of hypotheses could have been tested by a more toward LAX airport. Widespread seepage in the La Brea area is well targeted survey pattern, for example, the plume could have been documented (Gamache and Frost, 2003). Observed CH4 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, CH , 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 4 Â CH4 concentrations. B) Mean radial CH4 concentration with distance from La Brea tar pit.

1 of the south LA County emission CH4 inventory of 1800 tons dayÀ This leads to greater daytime dilution and lower daytime concen- (Hsu et al., 2010). Other geologic contributions arise from known trations (Hanna et al., 1982). Perhaps more important is atmo- coastal geologic seepage areas like Playa del Rey, where CH4 en- spheric stability and the implications for boundary layer thickness hancements also were observed (Fig. 7A), thus these represent only and mixing (diffusion), both of which are significantly less at night a portion of overall geologic contribution to the LA Basin likely. (Shorter et al., 1996). A shallow boundary layer “traps” CH4 closer to the ground, allowing buildup of higher surface concentrations 4. Discussion (Sasakawa et al., 2010). A stable atmosphere also leads to slower plume dispersion and therefore higher surface concentrations near 4.1. Measurement approach the source. Thus, nighttime boundary layer thickness enhances near-surface CH4 concentrations, enabling easier source attribution During the 2010 survey, the lowest measurements were observed than daytime measurements. For example, a diurnal trend was in the California desert, 1.80 ppm, but measurements were along the reported for Siberian wetlands with a variation of 0.1e0.2 ppm Interstate 10 corridor to the west of Palm Springs and Los Angeles. In during summer months and with peak concentrations around the 2012 survey, measurements were taken on a largely non- dawn following nighttime buildup (Sasakawa et al., 2010). The trafficked highway in the Mojave desert and recorded slightly diurnal cycle corresponded to the morning development of the higher concentrations of 1.8639, suggesting an underestimate of GC convective mixed layer and dilution, followed by collapse of the CH4 concentrations that day of w100 ppb (desert seasonal variations layer after sunset leading to overnight accumulation. should be small). However, the influence of even small population centers was apparent in the 2012 desert data, and thus values likely 4.3. Wetlands were slightly elevated due to human activity. Measurements at somewhat higher elevation (1437 m) in a Nevada dry lakebed found CH4 concentrations in the Florida peninsula wetlands were 1.781e1.908 ppm (Yates et al., 2011). Even here, a wind speed and elevated compared to lower levels in Florida panhandle wetlands direction relationship was found that suggested a local source. and the Mississippi delta. These lower concentrations are unrelated to the change in system performance (Fig. 4), which occurred while 4.2. Day/night meteorology surveying the Baton Rouge, LA area. The most likely explanation is the greater urbanization of the Florida peninsula - note the Florida Day/night variations can cause significant concentration panhandle economy is tourist dominated, which is slow on October changes for the same flux, potentially overwhelming dayenight weekdays (as observed during the survey by low relative traffic emission differences. Depending on wind speed, boundary layer levels and few cars in hotel parking lots). Also, day/night CH4 dif- height, and atmospheric stability, ground concentrations down- ferences were not observed for the Florida panhandle wetlands. wind of a discrete source can vary dramatically (Bradley et al., 2010; An effort was made to compare coastal (brackish) and inshore Hanna et al., 1982). Atmospheric stability affects mixing and thus (fresh) wetlands in the eastern Florida panhandle, but no notable ground concentrations; assuming of course that the CH mea- difference was found (coastal 1.73 ppm, inland 1.74 ppm). 4 ¼ ¼ surements are downwind of the source under consideration. Surveys of these two regions were from 20:00 to 23:00 LT during Day/night atmospheric conditions are significantly different, for which time atmospheric conditions did not change significantly. example, daytime surface winds tend to be significantly higher DeLaune et al. (1983) found an inverse relationship between CH4 than nighttime winds, which were light to calm during the survey. 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 platform. A key improvement implemented in the 2012 expedition than a strong coastal source. was real-time data analysis and route planning through cell phone A broad peak of significantly elevated CH4 concentrations was modem technology. Further improvement would allow visualiza- observed during a nighttime transit of Paynes Prairie Park, FL with tion of data within a GoogleEarth-type environment. Such data peak CH4 concentration of 2.6 ppm. Although a pipeline leak cannot enables active survey path modification to constrain better sources be ruled out, the plume and park’s size-scales were similar. Part of and characterize plume transport processes. Effective application of the explanation likely lies in boundary layer shallowness when adaptive survey techniques requires real-time meteorology data, traversed at 02:51 LT, when winds were calm. Another possible which can be collected during regular survey pauses or downloaded. contribution could be nearby lake bubble emissions e CH4 con- Measurement of several species, including CO2, would help centrations up to 4 ppm were reported for a Swiss reservoir, pri- discriminate between different anthropogenic and natural sources. marily due to bubbles (Eugster et al., 2011). A nearby landfill also Cavity ring-down spectrometers provide the higher data density was a potential source. necessary to resolve plume processes, as was not possible for the fire data. These sensors also have dramatically lower vibration 4.4. Fire sensitivity than GCs and orders of magnitude higher data rates (0.1 s). Furthermore, simultaneous measurement of CH4 and CO2 Efforts to estimate the fire source strength based on a Gaussian can identify situations where vehicular emissions are biasing data 1 plume model were unsuccessful, implying a non-point, source (See Supplemental Material), which release w2TgyrÀ (Piccot plume. Unfortunately, although the GC data resolved the plume, it et al., 1996), Because major traffic corridors typically transect ur- could not resolve the plume edges, preventing further plume model. ban areas, which were associated with elevated CH4, secondary Instead, a simplistic “sheet’ model was used for well-mixed CH4 in roads should be used in urban areas. Both daytime and nighttime the plume. Plume height was estimated thanks to the visibility of data collection are needed for urban areas and other sources as well 1 the smoke, yielding an emission rate of 0.15 kiloton CH4 dayÀ . as an effort to characterize boundary layer thickness. Future sur- By comparison, a boreal Siberian forest fire in Siberia released veys should incorporate north and south transits, rather than 2 CH4 at a rate of 3.5 ton CH4 kmÀ (Kajii et al., 2002) Thus, if biomass largely covering a very narrow latitudinal band, and multiple sea- density was the same, the Raceland fire would have required a son measurements. combustion rate of 30 km2 per day, 3-km progression along a 10- km front, which is within reasonable rates and dimensions based 5. Conclusions on observations. Total area that could have burned based on locations of water bodies was w110 km2, or significantly greater than This study showed that trace atmospheric gas measurement the estimated daily combustion rate. from a mobile laboratory/accommodation platform has advantages in terms of spatial coverage on fine and large spatial scales. Mea- 4.5. Geologic emissions surements likely were affected strongly by day/night meteorological differences. Urban areas generally were associated with Surprisingly large and extensive elevated methane concentra- elevated CH4 concentrations with an urban contribution likely tions were found in the northern Los Angeles area. The highest explaining elevated CH4 concentrations in the Florida peninsula concentrations were found in the immediate vicinity of the La Brea compared to the Florida panhandle despite extensive wetlands tar pits. Migration is through faults from a reservoir w1300 m being present in both. Even very low population centers in the directly below (Gurevich et al., 1993) and has been documented Mojave Desert had detectable CH4 signatures. Data collected during nearby, for example gas and oil surface in a nearby underground a descent from the San Bernardino Mountains provided a unique parking structure. Aside from natural migration along faults, which approach to atmospheric profiling. Smoke visualization from a have been active singe at least the late Pliocene seepage is increased brush fire allowed an estimation of the fire CH4 source strength of 1 by the presence of many abandoned well bores. For example, an w0.15 kilotons dayÀ , although uncertainties were large. Natural explosion that destroyed a shopping store on 24 March 1985, had and quasi-natural (abandoned well pipes) CH4 emissions from migrated up the “third street fault and certain wellbores,” with North Los Angeles were focused on the La Brea tar pits and appear evidence suggesting widespread gas migration along faults and to be significant to overall Los Angeles county inventories. abandoned oil wells (Gurevich et al., 1993). This geologic, fossil source appears relatively large compared to Acknowledgments other strong sources in the Los Angeles Basin that were surveyed, such as the Port of Long Beach. Thus, these data are consistent with Support from the National Science Found, ATM Rapid Response the missing CH4 in the inventory assessment of Hsu et al. (2010) program, Award No 1042899, NASA award No NNX12AQ16G and arising from a geologic source. Daily wind patterns likely trans- the Gulf of Mexico Hydrates Research Consortium administered by port the CH4 westward during the day, with a morning “pulse” the Center for Marine Resources and Environmental Technology at likely. Other areas of elevated CH4 were observed downwind from the University of Mississippi through the Department of Energy’s Playa del Rey and Marina del Rey, two areas that overlie the Playa Cooperative Agreement Award No. DE-FC26-06NT42877. The del Rey oil field (Chilingar and Endres, 2005). This field was used as participation of Monica Leifer in 2012 data collection is gratefully a storage field since 1942, and leaks into the adjacent Venice oil acknowledged. Views and conclusions in this article are the authors field. Several hundred abandoned wells are in the area, providing and do not necessarily represent the view of the University of potential migration pathways. California University of Mississippi or the U.S. Government. Any use of trade, product, or company names is solely descriptive and does 4.6. Future study improvements not imply endorsement by the U.S. Government.

This study demonstrated advantages in terms of spatial coverage Appendix A. Supplementary data and continuous data collection for trace atmospheric gas measurements by a mobile laboratory/accommodation platform. Further Supplementary data related to this article can be found at http:// vibration reduction would be beneﬁcial on the vehicle and on the GC dx.doi.org/10.1016/j.atmosenv.2013.02.014. P. Farrell et al. / Atmospheric Environment 74 (2013) 422e431 431

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