1
1. Airborne in situ methane (CH4) measurements
2
The aircraft (Mooney M20M-TLS) was instrumented with an in situ carbon dioxide
3
(CO2), methane (CH4) and water vapor (H2O) cavity ring-down spectrometer (CRDS,
4
Picarro model 2301-f flight analyzer) [Crosson, 2008] measuring at 0.5 Hz. The analyzer
5
pulled air through a 7.6-m long, 0.00635 m inner-diameter Kynar inlet below the
6
starboard wing. Mole fraction (moles per mole of whole air) measurements from the
7
CRDS analyzer have been corrected for an 11-second time lag, measured prior to flight.
8
The analyzer was calibrated for CH4 between flights with two reference gas tanks of
9
natural air calibrated at NOAA/ESRL on the World Meteorological Organization
10
standard reference scale. Sample air was not dried prior to measurement; the dry mole
11
fraction used in our analysis is derived from an empirical correction to the measured
12
(wet) mole fraction that is a function of ambient water vapor that was measured by the
13
CRDS analyzer, and accounts both for dilution effects and optical interference in the
14
measurement cell. A test of the reported dry mole fraction performed on the ground by
15
injecting water into a stream of gas from a cylinder with a known CH4 mole fraction
16
confirmed that the true dry mole fraction is recovered within 0.5 parts per billion (ppb) of
17
CH4 up to the highest H2O value measured during the flights (2.2%). At this highest H2O
18
value, the correction is approximately 2% for CH4. The stability and consistency of the
19
water correction and calibration for similar models of analyzers has been demonstrated in
20
previous work [Karion et al., 2013; Rella et al., 2012] to be better than 1 ppb of CH4. Dry
21
mole fractions from the CRDS analyzer agree with dry mole fractions measured in flasks
22
that sampled air from a separate identical inlet (described below in Supplementary
23
Methods 2.2) within 2 ppb for CH4 during periods of low atmospheric variability. These
24
values represent the uncertainties associated with both the CRDS and flask measurements
25
and are incorporated in the flux uncertainty analysis. The aircraft was also instrumented
26
for measurement of temperature and atmospheric pressure. Global Positioning System
27
(GPS) location and time were also logged at 1 Hz with the CRDS, flask, and atmospheric
28
measurements.
29
2. Airborne discrete air samples and multi-species analyses
30
Sixty-seven discrete whole air samples were collected in flasks both inside and outside
31
the PBL over the Uintah basin throughout the month of February 2012 and were analyzed
32
for trace gas mole fractions at NOAA in Boulder, Colorado. More than 55 trace gases,
33
including CO2, CH4 and other light alkanes were measured in samples collected with an
34
automated 12-flask sampler (12-pack, High Precision Devices, Inc.). The 12-pack was
35
composed of 0.7 L borosilicate glass flasks, a stainless-steel manifold system, glass
36
valves sealed with Teflon O-rings, and a data logging and control system. Each flask
37
underwent a 10 L flush, and was then filled to 275 kPa over a 10 to 20-second period;
38
there is negligible lag through the 7.6 m line at the high flow rates (~10 L per minute)
39
used to fill the flasks. Samples collected in 12-packs were analyzed at NOAA/ESRL for
40
CH4 on one of two nearly identical automated analytical systems with reproducibility of
41
1.2 ppb. Measurements are reported as dry air mole fractions relative to the same
42
standard scales used for the CRDS calibrations and maintained at NOAA/ESRL
43
[Dlugokencky, 2005; Zhao and Tans, 2006]. The same flask samples were also analyzed
44
for a suite of halocarbons and hydrocarbons using methods documented online
45
(http://www.esrl.noaa.gov/gmd/ccgg/aircraft/analysis.html) and by Montzka et al. [1993]
46
3. High-Resolution Doppler Lidar (HRDL)
47
A High Resolution Doppler Lidar (HRDL) was deployed at a stationary site at Horse
48
Pool, Utah (40.14°N, 109.47°W, elevation 1559 masl), in the Uintah basin gas and oil
49
field [Grund et al., 2001]. This system made range-resolved measurements of line-of-site
50
(LOS) wind speed and aerosol backscatter signal intensity twice per second with 30 m
51
spatial resolution along the beam. Its output beam was directed into the atmosphere using
52
a hemispheric scanner, which performed a repeating 20-minute sequence of scans. Low-
53
elevation-angle azimuthal scans were used to determine the horizontal wind speed and
54
direction from within 12 m of the surface through the top of the planetary boundary layer
55
(PBL). Elevation angle scans were performed along orthogonal azimuthal directions to
56
characterize the spatial variability in the horizontal wind and aerosol in the lower 500 m
57
of the atmosphere. Zenith staring scans were used to characterize the vertical velocity and
58
turbulence from 200 m (the minimum range of the system) through the top of the PBL.
59
HRDL operated continuously for the duration of the February campaign and provided
60
vertical profiles of horizontal wind speed and direction, horizontal and vertical wind
61
speed variance, and uncalibrated aerosol backscatter every 20 minutes. These results
62
were combined to estimate the planetary boundary layer (PBL) depth (the maximum
63
height of the atmosphere that is in contact with the surface through a turbulent process)
64
[Tucker et al., 2009]. These estimates of PBL depth were used to confirm the
65
measurements of the PBL depth from the aircraft. Peak PBL depths on flight days
66
typically ranged from 500 to 1700 magl.
67
4. Details of uncertainty analysis
68
4.1.
69
The mean horizontal wind speed and direction used in equation (1) are the measurements
70
from HRDL, averaged over altitude from the ground to the top of the PBL, and then
71
averaged over the transit time of the air mass across the gas field (approximately 3 hr).
72
Because the mass balance equation applies during conditions of steady horizontal wind
73
speed and direction, variability (both temporal and vertical) in the horizontal wind results
74
in uncertainty in the calculated flux. Variability in the component of the horizontal wind
75
perpendicular to the aircraft heading, V cos , is used to estimate the contribution of wind
76
variability to the total flux uncertainty, to account for correlation that exists between wind
77
speed and direction. Four uncertainty components (measurement uncertainty, vertical
78
variations, temporal variations, and an estimate of spatial variations) are summed in
79
quadrature. Vertical variability is accounted for with a standard error of the mean wind in
80
the vertical dimension (i.e. /√N where N is the number of vertical measurements,
81
typically more than 40). Use of the standard error is justified because there was little
82
evidence of covariance between V cos  and height in the measurements above the first
83
50 magl. Temporal variability is estimated as the standard deviation (1) of the mean
84
wind over time, rather than the standard error, because the temporal variability in the
Wind speed and direction
85
wind showed significant covariance and structure over the transit time period of the air
86
mass over the gas field. We take the spatial (horizontal) variability of the wind in the
87
basin to be approximately 20%, based on wind speed and direction differences between
88
ground-based hourly measurements in Ouray, UT (40.05°N, 109.69°W, 22 km southwest
89
of Horse Pool) [The University of Utah, 2012] and the HRDL wind measurements at 12
90
magl. Although the same constant wind direction is used for the entire plume, the cos 
91
term varies along the path due to changes in the aircraft heading. Supplementary Table 1
92
gives two values and uncertainties for V cos , corresponding to the two main aircraft
93
headings along the path on February 3 (approximately 284 and 331 degrees). The relative
94
uncertainty is 24% in both cases, which is the value used in the flux uncertainty
95
calculation on February 3.
96
4.2.
97
Uncertainty in the plume integration is derived from the measurement uncertainty (2
98
ppb), the variability in background value (5 ppb), and the variability of CH4 in the
99
downwind plume (2 ppb), as described below. Upwind CH4 mole fraction values were
CH4 plume integral
100
obtained by averaging measurements from the endpoint of the air mass back trajectory, in
101
the northeast corner of the domain and within the PBL. CH4 mole fractions in this area
102
ranged between 1926 (early in the flight) and 1916 ppb (late in the flight), and matched
103
mole fractions along a horizontal transect in the north that was upwind of field emissions
104
and the mole fraction at the edges of the downwind plume (Figures 1 and 2). The average
105
of these values, 1921 ppb, was subtracted from the downwind mole fractions, and
106
assigned an uncertainty of 5 ppb in order to encompass the range of observed background
107
values. We note that choosing the background mole fraction in this manner (agreeing
108
with the values at the plume edges) eliminates the need to account for entrainment of
109
cleaner air into the PBL from the free troposphere above the PBL, because air parcels
110
inside and outside the plume will both undergo the same dilution of CH4.
111
For uncertainty in the downwind plume due to variability, the CH4 data in the plume
112
(Figure 1, right panel) are smoothed with a 30-point (corresponding to ~5.4 km) moving
113
average and subtracted from the actual CH4 data. The standard error of the resulting
114
variability is calculated by dividing its standard deviation by the square root of the
115
number of uncorrelated measurements in the plume (i.e. /√N). We determined that the
116
autocorrelation length scale of the variability in the plume is approximately 0.7 km, while
117
the width of the plume is approximately 50 km along the flight track, therefore N is
118
approximately 71.
119
4.3.
120
The integration of the downwind plume in the vertical dimension introduces several
121
uncertainties. The integral term includes the molar density of air (nair), derived from on-
122
board pressure and temperature measurements, integrated from ground level (zground)
123
(from the U.S. Geological Survey database at 30 m resolution) to the top of the PBL
124
(zPBL).
125
Uncertainty in this term is dominated by the uncertainty in the height to which the CH4
126
plume mixes, which is composed of two elements. The first is uncertainty in the PBL
127
depth, determined using aircraft vertical profiles (Supplementary Figure 1) and HRDL
Vertical integration inside the PBL
128
measurements. The second is the degree of mixing within the PBL and the extent to
129
which CH4 enhancements from individual point sources were mixed to the top of the PBL
130
at the point of measurement downwind of the basin.
131
Aircraft profiles from February 3 close to Horse Pool in the center of the basin at 15:05
132
LT (black in Supplementary Figure 1) and to the northwest of Horse Pool, upwind of the
133
basin at 15:53 LT (blue in Supplementary Figure 1), show a sharp drop in H2O and CH4
134
at a given altitude, and that altitude (~3250 masl) is determined to be the PBL height for
135
our calculations. Two profiles near Horse Pool earlier in the flight (at 14:27 LT and 14:54
136
LT, not shown) indicate the same PBL depth at those times, implying that the PBL depth
137
was generally constant during the flight. HRDL measurements also indicate that the PBL
138
depth was relatively constant during the 3-hr time of transit of the air mass sampled
139
downwind of the field at ~15:30 LT. The high variability in CH4 mole fractions inside the
140
PBL over the basin is caused by significant horizontal variability from different nearby
141
point sources that are sampled as the plane moves horizontally during the spiraling
142
altitude profile, which is typically 4-6 km in diameter with ascent and descent rates near
143
2.9 m s-1. The upwind profiles (blue) illustrate that CH4 is well mixed in the PBL when
144
there are no nearby sources present. The first component of uncertainty in the vertical
145
integral term, uncertainty in the PBL height, is judged to be approximately 125 m from
146
the profiles at different locations in the basin and at different times of flight and the
147
HRDL measurements at Horse Pool.
148
The second component of uncertainty in the vertical integral is from the possibility of
149
incomplete mixing of surface emissions to the top of the PBL; this mixing is a function of
150
downwind distance from the source to the measurement location. Measurements of
151
vertical turbulent heat flux at the ground station in Horse Pool were used to estimate the
152
minimum distance downwind of a point source at which a plume is well mixed in the
153
PBL.
154
Vertical turbulent heat fluxes and total incoming radiation were measured at Horse Pool
155
at the 19 m level of a 22 m instrumented tower located just west of HRDL
156
(Supplementary Figure 2). Turbulent heat fluxes were calculated using the TOGA
157
COARE Flux algorithm [Fairall et al., 2003]. At the times of the February 3 flight the
158
incoming radiation and turbulent heat flux were within 10% of each other and the mole
159
fraction of water vapor was below 0.4% indicating that most of the net radiation was
160
being converted to sensible heat flux, providing up to 250 W m-2 to drive rapid vertical
161
mixing from the surface to the top of the boundary layer.
162
Using measurements of buoyancy flux and PBL height, we estimate a mean mixing time,
163
tm = zPBL/w*, for a tracer to mix from the surface to the top of the boundary layer, where
164
zPBL is the PBL height and w* is the convective velocity scale, following Stull [1991]:
1
165
𝑔𝑧𝑃𝐵𝐿 ̅̅̅̅̅̅̅ 3
𝑤∗ = [
(𝑤′𝜃𝑣 ′)]
̅̅̅
𝜃𝑣
166
In the above relationship, w*, the convective velocity scale, is a function of the mean
167
virtual potential temperature (v), the boundary layer depth (zPBL), and the measured
168
̅̅̅̅̅̅̅
buoyancy flux at the surface (𝑤′𝜃
𝑣 ′); g is the gravitational constant, and the overbar
169
denotes time averages of these quantities. For February 3 the mean mixing time is 16
170
minutes, calculated using the average turbulent heat flux measured at Horse Pool
171
throughout the flight (Supplementary Figure 2). This mean mixing time (tm) is the
172
minimum time needed for a surface plume to reach the top of the PBL, but there are
173
likely to be some heterogeneities in vertical mixing as the point source plume is first
174
lifted vertically as a coherent plume. Within approximately 3 mixing times (3tm) a plume
175
is fully mixed from the top of the PBL to the ground [Stull, 1991; Weil et al., 2004]. In
176
reality, we expect that the mixing profile is also affected by mechanical mixing due to
177
shear caused by variable terrain in sustained winds > 5 m s-1, which the above estimates
178
do not take into account.
179
Using the calculated mixing times (tm) and the mean winds during February 3 2012, we
180
estimate that a downwind distance of 5.3 km is required before a point source plume can
181
be assumed to reach the top of the boundary layer. In our analysis, uncertainty due to
182
vertical heterogeneities and possible incomplete mixing is incorporated in the uncertainty
183
assigned to the PBL height by considering the fractions of point sources within one
184
length scale and between one and three length scales upwind of our measurements. For
185
the fraction within one length scale (the 1% of the portion of Uintah County’s producing
186
gas wells that were upwind of the downwind transect on February 3), we assume a 100%
187
uncertainty and between one and three length scales (the 18% of the producing gas wells
188
that were upwind of the downwind transect on February 3) we assume 20% uncertainty,
189
based on likely vertical mixing profiles at this downwind range [Weil et al., 2004]. This
190
uncertainty would not be a bias necessarily because of the complex evolution of the
191
vertical distribution of the plume with time with respect to the height of the aircraft (400
192
– 600 magl). The PBL height uncertainty for February 3 (8.3%) incorporates an estimate
193
of incomplete mixing for this fraction of sources of 3.7% (100% on 1% of the sources
194
(1%) in quadrature with 20% on 18% of the sources (3.6%)). Gas well locations were
195
obtained from the State of Utah [State of Utah Department of Natural Resources Division
196
of Oil Gas and Mining, 2012b].
197
5. Fraction of total production
198
To relate our CH4 emissions estimate to leakage of natural gas we have determined other
199
possible CH4 sources within the study region bracketed by upwind and downwind
200
measurements. While there is no evidence for landfills [US Department of Agriculture,
201
2009] or coal mining [State of Utah Department of Natural Resources Division of Oil
202
Gas and Mining, 2012c] in this region, we must account for cattle emissions and natural
203
CH4 seepage. An estimate of 44,000 head of cattle in Uintah County, based on the 2007
204
US Census of Agriculture [US Department of Agriculture, 2009], with an average CH4
205
emission rate of 342 g d-1 head-1 [Griffith et al., 2008] leads to an estimate of 630±340 kg
206
hr-1, or approximately 1.1% of the total emissions estimated on February 3, 2012. This
207
per head emission rate was measured for free-ranging cattle, which emit more CH4 than
208
feedlot or dairy cows; Uintah County contains all three types so we use the higher rate as
209
a conservative estimate here. A 54% uncertainty is assigned to this value based on a 20%
210
uncertainty in the total number of cows (derived from the difference in the number of
211
heads of cattle between the 2002 and 2007 census reports), and a 50% uncertainty in the
212
emission rate, based on ranges found in other literature, as cited in Griffith et al. [2008],
213
and summing the two uncertainty estimates in quadrature.
214
Natural seepage CH4 fluxes measured in nearby Rangely, CO (an oil field 57 km from
215
Horse Pool) during 200-2002 showed a wide range of values, with seasonal means from
216
0.10 to 17.8 mg m-2 day-1 [Klusman, 2003]. We scale the mean of these estimates
217
(~9.0±8.8 mg m-2 day-1) to the approximate area of our measurement footprint (~2000
218
km2) to estimate CH4 seepage of 750±733 kg hr-1, or approximately 1.3% of the February
219
3 CH4 flux. Given possible emission of cattle and natural CH4 seepage, the best CH4
220
emissions estimate from the mass balance approach is reduced by 1.4±1.1x103 kg hr-1 to
221
give 54.6±15.4x103 kg hr-1 as the CH4 emissions from natural gas and oil production and
222
related operations in the field.
223
To relate this CH4 emissions estimate to natural gas production we use a volume fraction
224
of CH4 in natural gas of 0.89 (based on an estimate of the raw gas composition in the
225
Uintah basin from A. Bar-Ilan, personal communication, 2012). We assign an uncertainty
226
of 0.1 to the volume fraction, based on a realistic range of average CH4 content of the
227
leaked gas of 0.89±0.10 (i.e. the top value of 0.99 represents the extreme case of a leak of
228
99% CH4). We convert our estimate of gas leakage from mass to volume using industry
229
standard conditions of 288.7 K (60°F) and 101.35 kPa (14.7 psia). We calculate average
230
hourly production from the total natural gas production from oil and gas wells in Uintah
231
County for February 2012 (7.06x108 m-3) [State of Utah Department of Natural
232
Resources Division of Oil Gas and Mining, 2012a]. Our leakage estimate includes a 5%
233
uncertainty on the production amount, estimated from the change in average daily
234
production from January to February, 2012 and from February to March, 2012.
235
6. Representativeness of emissions on February 3 2012
236
During the time of the study the Uintah oil and gas basin was an actively growing field,
237
with an average growth rate in production of 2% month-1 between December 2011 and
238
April 2012 (Supplementary Figure 5). While February production jumped by 4% from
239
January production, daily activity (in terms of new wells spudded or starting production,
240
Supplementary Figure 6) was not significantly different in the week surrounding
241
February 3 than most in January, February and March of 2012 (average number of
242
spudded wells, January through March = 2.0 per day and average number of wells
243
starting production = 1.7 per day, during the entire time period from January through
244
March). From this perspective, we have little reason to suspect potential sources of
245
emissions were significantly greater or fewer on the day of the measurements than in the
246
surrounding days and have no reason to discount them as unrepresentative of emissions
247
in the basin during February 2012. However, we also note that we have no specific
248
knowledge (via emissions estimates from other days) as to the extent of day-to-day
249
variability of emissions in this basin and would caution against any extrapolation of this
250
data.
251
Supplementary Table 1. Summary of measurements and parameters used to calculate
252
the total CH4 molar flux, February 3, 2012 (numbers may not sum up due to rounding).
253
Two values of the wind component perpendicular to the aircraft track are given, one for
254
each mean heading of the aircraft; they have the same relative uncertainty (24%) when
255
we account for correlation between V and cos .
Parameter
wind speed
V
wind direction
256
257
258
Mean Value
Variability
(one-sigma)
Relative
Uncertainty
5.2 m s-1
1.2 m s-1
24%
55.2°
7.2°
wind component
perpendicular to
aircraft track
V cos 𝜃
3.8/5.1 m s-1
0.7/1.0 m s-1
24%
methane
enhancement
∆XCH4
56.3 ppb
5.6 ppb
10%
boundary layer
height
zPBL
3250 masl (~1700
magl)
125 m
7*%
flux on Feb 3
fluxCH4
3.5x106 mol hr-1
1.0x106 mol hr-1
27%
* Does not account for a 3.7% error due to incomplete mixing.
259
260
Supplementary References
261
Crosson, E. R. (2008), A cavity ring-down analyzer for measuring atmospheric levels of
262
methane, carbon dioxide, and water vapor, Appl. Phys. B-Lasers Opt., 92(3), 403-408.
263
Dlugokencky, E. J. (2005), Conversion of NOAA atmospheric dry air CH4 mole
264
fractions to a gravimetrically prepared standard scale, Journal of Geophysical Research,
265
110(D18), 8.
266
Fairall, C. W., E. F. Bradley, J. E. Hare, A. A. Grachev, and J. B. Edson (2003), Bulk
267
Parameterization of Air–Sea Fluxes: Updates and Verification for the COARE
268
Algorithm, Journal of Climate, 16(4), 571-591.
269
Griffith, D. W. T., G. R. Bryant, D. Hsu, and A. R. Reisinger (2008), Methane emissions
270
from free-ranging cattle: Comparison of tracer and integrated horizontal flux techniques,
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Grund, C. J., R. M. Banta, J. L. George, J. N. Howell, M. J. Post, R. A. Richter, and A.
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279
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280
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288
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289
df.
290
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291
Well Information Query,
292
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293
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294
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295
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298
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309
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310
311
312
313