Emissions of methane (CH4) from offshore oil and gas installations are
poorly ground-truthed, and quantification relies heavily on the use of
emission factors and activity data. As part of the United Nations Climate
& Clean Air Coalition (UN CCAC) objective to study and reduce short-lived
climate pollutants (SLCPs), a Twin Otter aircraft was used to survey CH4
emissions from UK and Dutch offshore oil and gas installations. The aims of
the surveys were to (i) identify installations that are significant CH4
emitters, (ii) separate installation emissions from other emissions using
carbon-isotopic fingerprinting and other chemical proxies, (iii) estimate
CH4 emission rates, and (iv) improve flux estimation (and sampling)
methodologies for rapid quantification of major gas leaks.
In this paper, we detail the instrument and aircraft set-up for two
campaigns flown in the springs of 2018 and 2019 over the southern North Sea
and describe the developments made in both the planning and sampling methodology
to maximise the quality and value of the data collected. We present example
data collected from both campaigns to demonstrate the challenges encountered
during offshore surveys, focussing on the complex meteorology of the marine
boundary layer and sampling discrete plumes from an airborne platform. The
uncertainties of CH4 flux calculations from measurements under varying
boundary layer conditions are considered, as well as recommendations for
attribution of sources through either spot sampling for volatile organic compounds (VOCs) /δ13CCH4 or using in situ instrumental data to determine
C2H6–CH4 ratios. A series of recommendations for both
planning and measurement techniques for future offshore work within marine
boundary layers is provided.
Overview
Methane is a potent greenhouse gas in the atmosphere, with a global warming
potential 84 times that of carbon dioxide when calculated over a 20-year
period (Myhre et al., 2013).
Increases in atmospheric CH4 mixing ratios are expected to have major
influences on Earth's climate, and emission mitigation could go some way
toward achieving goals laid out in the UNFCCC (United Nations Framework Convention on Climate Change) Paris Agreement (Nisbet et al., 2019).
Offshore oil and gas fields make up ∼ 28 % of total global
oil and gas production and are expected to be significant sources of
CH4 to the atmosphere, given that 22 % of global CH4 emissions
are estimated to be from the oil and gas (O&G) sector (Saunois et al.,
2016). Some emissions arise from routine operations or minor engineering
failures (Zavala-Araiza et al., 2017), while others
stem from large unexpected leaks (e.g. Conley et al., 2016; Ryerson et
al., 2012). In some O&G fields, large amounts of non-recoverable
CH4 can be flared or vented due to a number of factors. Thus, the
composition of O&G emissions can be influenced by several variables,
including the targeted hydrocarbon product (oil or gas), extraction
techniques and gas capture infrastructure. O&G installations co-emit
volatile organic compounds (VOCs) such as alkanes, alkenes and aromatics in
addition to CH4. Some of these VOCs are toxic and can have direct
health impacts or, together with NOx, can produce ozone, having an
impact on the regional air quality (Edwards et al., 2013). VOC
and δ13CCH4 measurements can be utilised to fingerprint
the main processes or likely location responsible for associated CH4
emissions (Cardoso-Saldaña et al., 2019; Lee et al., 2018; Yacovitch
et al., 2014a). A recent study has also demonstrated the cost-effectiveness
of airborne measurements for leak detection and repair at O&G facilities
relative to traditional ground-based methods (Schwietzke et
al., 2019).
There is thus a need to develop reliable methodologies to locate emissions,
determine sources in sufficient detail to allow for the quantification of emissions
and validate against publicly reported inventory emissions to enable the
design of suitable mitigation. To date, a number of approaches have been
used. Airborne measurements of both individual and clusters of facilities,
along with production data, have been used to scale up to an inventory of
CH4 emissions for the US Gulf of Mexico (Gorchov
Negron et al., 2020). Ship-based measurements of CH4 and associated
source tracers have been made in both the Gulf of Mexico
(Yacovitch et al., 2020) and in the North Sea (Riddick et al., 2019). The latter
reported fluxes of CH4 from offshore O&G installations in UK waters
that were derived from observations made from small boats at ∼ 2 m above sea level. This approach has advantages in terms of cost, but the
authors recognised a number of key uncertainties in their approach
associated with assumptions around boundary layer conditions and a lack of
3D information (i.e. Gaussian plume modelling and assumptions of constant
wind speed). Measurements from aircraft can provide this 3D spatial
information, enabling better characterisation of both plume morphology and
boundary layer dynamics.
Here we report a project that was designed around the use of a
small-aircraft with flexible instrument payload suitable for agile
deployment. Key objectives were (i) to identify and quantify emissions of
CH4 from a suite of offshore gas fields within a limited geographical
area and (ii) to develop methodologies that can be applied to gas fields
elsewhere to assess emissions at local scales. The project was part of the
United Nations Climate & Clean Air Coalition (UN CCAC) objective to
characterise global CH4 emissions from oil and gas infrastructure.
Targeted observations of atmospheric CH4 and C2H6 plus
sampling for VOC and δ13CCH4 analysis were made from a
Twin Otter aircraft operated by the British Antarctic Survey (BAS). Two campaigns
were conducted, one in April 2018 and one in April–May 2019, with a total of
10 flights (∼ 45 h) over the two campaigns.
The specific aims of the surveys were:
CH4 surveying of facilities with a range of expected (from
inventories) CH4 emissions
resolution of types of emission from installations (such as flaring,
venting, combustion and leaks) using carbon-isotopic fingerprinting and
analysis of co-emitted species (including VOCs).
estimation of total CH4 emissions for the target region
improvement of flux estimation (and sampling) methodologies for rapid
quantification of major gas emissions.
Here, we provide an overview of the measurement platform configuration and
sampling strategy during these campaigns, including instrument comparisons
for hydrocarbon plume detection, spot sampling strategies for VOCs and
δ13CCH4, and flight planning to cope with complex boundary
layer meteorology to allow for the estimation of emission fluxes. Analysis methods
to determine diagnostic hydrocarbon plume characteristics such as
C2H6–CH4 ratios and δ13CCH4 source
attribution are also discussed. A sister publication will present the
estimated facility level emissions in detail and discuss the results in a
regional context.
Experimental
A DHC6 Twin Otter research aircraft, operated by the British Antarctic
Survey, was equipped with instrumentation to measure atmospheric boundary
layer parameters, including the boundary layer structure and stability, as
well as a number of targeted chemical parameters. These included CH4,
CO2, H2O and C2H6 as well as whole-air sampling for
subsequent analysis of δ13CCH4 and a suite of VOCs.
Here we describe the aircraft capability, aircraft fit and the instruments
deployed.
Aircraft capability
The maximum range of the Twin Otter aircraft during the flight campaigns was
approximately 1000 km. Although the aircraft is capable of flying up to 5000 m altitude, most of the flying was limited to below 2000 m; in regions with
no minimum altitude limit, the aircraft could be flown at the practical
limit of 15 m above sea level. The instrument fit included use of a
turbulence boom, which limited the speed to a maximum of 140 kn
(∼ 70 ms-1); throughout the campaigns, the target
aircraft speed for surveying was 60 ms-1. The aircraft was limited to a
minimum safe separation distance of 200 m from any O&G production
platforms.
The total weight of the aircraft on take-off is limited to 14 000 lb (6350 kg). Allowing for fuel and crew, this left 2086 kg for the instrumentation.
The total power available on the aircraft is 150 A at 28 V, and inverters
were used to provide 220 V to those instruments that required it. Altitude
and air speed were determined by static and dynamic pressure from the
aircraft static ports and heated Pitot tube, logged using Honeywell HPA
sensors at 5 Hz. A radar altimeter recorded the flight height at around 10 Hz. An OxTS (Oxford Technical Solutions) inertial measurement system coupled to a Trimble R7 GPS was used
to determine the aircraft position and altitude. This system gives all three
components of aircraft position, altitude and velocity at a rate of 50 Hz.
The chemistry inlets on the Twin Otter are similar to those fitted to the
FAAM (Facility for Airborne Atmospheric Measurements) BAe (British Aerospace) 146 large atmospheric research aircraft (e.g. O'Shea et al., 2013) and were fitted with the inlet facing to
the rear (Fig. A1). A single line (1/4′′ Synflex tubing) was
taken from the inlet to a high-capacity pump with the instruments branching
from this line. The aircraft was fitted out during the week before each of
the two flight campaigns, allowing for significant changes to be made between
2018 and 2019 based on instrument performance and data from 2018 (Fig. 1).
Instrument schematics for the Twin Otter aircraft as deployed in
2018 and 2019, detailing changes in layout and instrumentation between the
two campaigns. The top panel is the 2018 fit, and the lower panel is the 2019 fit. VP-FAZ is the Twin Otter aircraft ID. (1 in. = 2.54 cm; 1 lb = 0.45 kg.)
Boundary layer physics instrumentation
A fast-response temperature sensor and a nine-hole NOAA BAT “Best Air
Turbulence” probe (Garman et al., 2006) were
mounted on a boom on the front of the aircraft (see photo, Fig. A2). This
instrumental set-up was chosen to reduce flow distortion effects by the
aircraft. These fast-response measurements of wind and temperature
fluctuations were made with a frequency of 50 Hz. Garman et al. (2006) investigated the uncertainty of the wind
measurements by testing a BAT probe in a wind tunnel. They assessed that the
precision of the vertical wind measurements due to instrument noise was
approximately ±0.03 ms-1. Garman et al. (2008) showed that
an additional uncertainty in the wind data occurs when a constant up-wash
correction value is used, as proposed by the model of Crawford et al. (1996). We use the Crawford model, which increases the uncertainty
in the vertical wind component, w, to approximately ±0.05 ms-1.
We assume for the two horizontal wind components, u and v, similar high
uncertainties due to aircraft movement. A detailed description of the Twin
Otter turbulence instrumentation and associated data processing can be found
in Weiss et al. (2011).
Ambient air temperature was observed with Goodrich Rosemount Probes, mounted
on the nose of the aircraft. A non-de-iced model 102E4AL and a de-iced model
102AU1AG logged the temperature at 0.7 Hz. Atmospheric humidity was measured
with a Buck 1011C cooled-mirror hygrometer. The 1011C Aircraft Hygrometer is
a chilled-mirror optical dew point system. The manufacturer stated a reading
accuracy of ±0.1∘C in a temperature range of -40 to +50 ∘C. Chamber pressure and mirror temperature were recorded at 1 Hz.
In situ atmospheric chemistry instrumentation
A Los Gatos Research (LGR) Ultraportable Greenhouse Gas Analyser (uGGA) was
installed to measure CH4, CO2 and H2O. The expected manufacturer
precision for the CH4 measurement was <2 ppb averaged over 5 s
and <0.6 ppb over 100 s. The response time of the LGR uGGA itself
(i.e. the flush time through the measurement cell) was over 10 s. To achieve
higher-temporal-frequency data, a fast Picarro G2311-f was installed to
provide measurements of CH4, CO2 and H2O at ∼ 10 Hz, with 1σ precision of ∼ 1ppb over 1 s for
CH4. A third greenhouse gas analyser, an LGR Ultraportable
CH4/C2H6 Analyser (uMEA) was used to measure CH4 and
C2H6. In-house laboratory measurements suggest C2H6
1σ precision at 1 s is ∼ 17 ppb for the LGR uMEA.
During the 2019 airborne campaign, atmospheric C2H6 was also
monitored by a tuneable infrared laser direct absorption spectrometer
(TILDAS, Aerodyne Research Inc.) (Yacovitch et al., 2014b)
with an expected precision of 50 ppt (parts per trillion) for C2H6 over 10 s. This
instrument utilises a continuous wave laser operating in the mid-infrared
region (at λ=3.3µm). A further description of the TILDAS
instrument set-up and performance is available in the Appendices along with
instrument precisions and response times in Table A1.
Calibration of in situ instrumentationCH4 and CO2 calibration
In situ CH4 and CO2 instruments were calibrated in flight using a
manually operated calibration deck, shown in schematic form in Fig. 2. The
calibration gases consisted of a suite of WMO-referenced (World Meteorological Organization) standards with a
“high”, “low” and “target” designation. The high CH4
concentration was ∼ 2600 ppb; low was ∼ 1850 ppb; and target was ∼ 2000 ppb. CO2 concentrations were
high at ∼ 468.5 ppm, low at ∼ 413.9 ppm
and target at ∼ 423.6 ppm. The absolute values of the
cylinders varied between years as they were re-filled and re-certified to
the NOAA WMO-CH4-X2004A and WMO-CO2-X2007 scales. The calibration
deck is designed so that upon the calibration valve opening, the calibration
gas flow rate is sufficient to overflow the inlet. A similar approach to
in-flight calibration is also applied on the NOAA WP-3D aircraft (Warneke
et al., 2016). Full details of the calibration procedure are recorded in the
Appendices. CH4 uncertainty (1σ) is calculated from the
in-flight target gas measurements as 1.24 ppb for the Picarro G2311-f and
1.77 ppb for the uGGA, giving performance comparable with similar
instrumentation on the FAAM aircraft (O'Shea et al.,
2014). The excellent agreement between measured and expected values of
CH4 for the target cylinder (for the Picarro and uGGA) gives us
confidence in being able to operate to high levels of accuracy with a very
limited period of instrument fitting and testing. CO2 uncertainty
(1σ) at 1 Hz is calculated as 0.20 ppm for the Picarro G2311-f and
0.35 ppm for the uGGA. More details on the calibration and associated
uncertainties are shown in the Appendices.
Layout of the plumbing of the calibration system (and inlet system)
for the 2018 campaign.
C2H6 calibration
The calibration cylinders installed on the Twin Otter during both campaigns
did not contain measurable amounts of C2H6, and therefore in-flight
calibrations could not be performed. This represents a limitation on the
accuracy and traceability of the C2H6 measurements during these
campaigns and will be addressed for future studies using the BAS Twin Otter.
The uMEA was calibrated in the laboratory post-campaign for the 2018
campaign and pre- and post-campaign in the laboratory for the 2019 season.
The uMEA instrument cavity is not temperature stabilised, resulting in
significant measurement drift during the course of operation. Corrections
for C2H6 and CH4 measurement drift as a function of cavity
temperature were determined experimentally by analysing two calibration
cylinders alternately over the course of several hours as the cavity
temperature increased. These corrections were then applied to the uMEA
C2H6 and CH4 measurements obtained from both the 2018 and 2019
flight campaigns.
The TILDAS (deployed in 2019) measures a water line, allowing for measurements to
be corrected to dry mole using the TDLWintel software (Nelson
et al., 2004) to account for changes in humidity during the flight (as
discussed in Pitt et al., 2016). The
raw measured data were calibrated pre- and post-flight using two cylinders
of a known concentration, whose mole fractions spanned the measurement range
observed during flights for C2H6. By assuming a linear
relationship, the calibrated mole fraction corresponding to each measured
TILDAS mole fraction was given by interpolating the scale between the pre-
and post-flight calibration reference points. Previous studies have reported
the sensitivity of TILDAS systems to aircraft cabin pressure (Gvakharia
et al., 2018; Kostinek et al., 2019; Pitt et al., 2016). This sensitivity
means that the C2H6 mole fractions measured during the flight
contain a systematic altitude-dependent bias. However, as cabin pressure
only affects the spectroscopic baseline, the zero offset of the measurements
is affected but not the instrument gain factor. Therefore, as long as each
plume measurement is referenced to a measured background at the same
altitude, this cabin pressure sensitivity does not significantly impact the
calculated C2H6 mole fraction enhancements. As stated above,
future deployments will mitigate this issue by employing in-flight
calibration cylinders that are certified for C2H6. The potential
to use a fast, frequent calibration for baseline correction as described by
Gvakharia et al. (2018) and Kostinek et al. (2019) will also be
investigated, although this has payload implications, as it requires an extra
calibration cylinder. Alternatively, the optical bench could be
re-engineered to sit within a hermetically sealed pressure vessel, as
described by Santoni et al. (2014).
Spot sampling
Manually triggered spot sampling provides a cost-effective and relatively
simple sample collection method to allow for analyses which cannot be performed
mid-flight or require specialist laboratory facilities to gain useful levels
of precision. Two discrete air-sampling systems were used during these
flights to enable post-flight analysis for VOCs and δ13CCH4.
Son of Whole Air Sampler (SWAS)
The Son of Whole Air Sampler (SWAS) is a new, updated version of the parent
WAS system fitted to the FAAM BAe 146 large atmospheric research aircraft
(e.g. as used by O'Shea et al., 2014), which it
is designed to supersede. The system comprises a multitude of inert
Silonite-coated (Entech) stainless steel canisters, grouped together
modularly in cases with up to 16 canisters per case. Onboard the Twin
Otter, two cases can be fitted allowing for up to 32 canisters to be carried per
flight. The theory of operation is to capture discrete air samples from
outside of the aircraft and compress the sample either into 1.4 or 2 L canisters at low pressure (40 psi; 275 kPa) via pneumatically actuated bellows valves
(PBVs, Swagelok BNVS4-C). Full details of the operation of SWAS are included
in the Appendices. For the 2019 campaign, SWAS was updated with the addition
of 2 L flow-through canisters, making narrow plumes easier to capture due to
reduced sample line lag and fill times.
SWAS canister sampling was manually triggered during the flights according
to in situ observations made by fast-response instrumentation of CO2,
C2H6 and CH4, with the aim of capturing specific oil and gas
plumes. The samples were analysed at the University of York for VOCs
post-flight using a dual-channel gas chromatograph with flame ionisation
detectors (Hopkins et al., 2003). Firstly, 500 mL aliquots
of air are withdrawn from the sample canister and dried using a condensation
finger held at -30∘C; then they are pre-concentrated onto a multi-bed
carbon adsorbent trap consisting of Carboxen 1000 and Carbotrap B (Supelco)
and transferred to the gas chromatography (GC) columns (Al2O3, NaSO4 deactivated
and open tubular; PLOT – porous layer, open tubular) in a stream of helium. Chromatogram peak
identification was made by reference to a calibration gas standard containing known amounts of 30 VOCs ranging from C2 to C9.
Compounds of interest include C2H6, propane, butanes, pentanes,
benzene and toluene; a full list is shown in Table A2.
FlexFoil bag sampling
Spot sampling for δ13CCH4 by collecting whole-air
samples into FlexFoil bags (SKC Ltd) has been in use on both the FAAM BAe
146 research aircraft (e.g. Fisher et al., 2017)
and during ground-based mobile studies (e.g. Lowry et al.,
2020) and provides a relatively cost-effective and rapid methodology for
sample collection. The method does have some limitations, however, as the
FlexFoil sample bags are only stable for a number of compounds (including
CH4). Samples captured in both FlexFoil bags and SWAS were measured at
Royal Holloway using continuous-flow isotope ratio mass spectrometry
(CF-IRMS; Fisher et al., 2006), and each measurement has a
δ13CCH4 uncertainty of ∼ 0.05 ‰. Each sample is also measured for CH4 mole
fraction using cavity ring-down spectroscopy to allow for direct comparison to
in-flight data (Fig. A3). Alternative, continuous in-flight δ13CCH4 instrumentation currently cannot replicate the precision
of laboratory sampling, and the few seconds of enhanced CH4 that would
be encountered during flight is not sufficient for averaging of continuous
δ13CCH4 data to gain a meaningful source δ13CCH4 signature (e.g. Rella et al., 2015).
Overall approach to flight planning
The majority of flights were conducted during good operating conditions, i.e. daytime, no precipitation, clear or broken cloud, winds <10 ms-1, and visibility, to allow for flying at a minimum safe altitude around the
task area. Two approaches were trialled to assess CH4 emissions from
offshore gas installations: (i) regional survey and (ii) specific plume
sampling. The flight modes are demonstrated in Fig. 3, with the dark-grey
pattern showing a flight plan for regional measurements and the orange and
white patterns demonstrating specific plume sampling flight patterns. Flight
plans to sample specific installations were designed to capture a full range
of expected emissions using the UK National Atmospheric Emissions Inventory
(NAEI) as a guide.
Regional survey intentions were twofold: firstly, to offer an
identification process for emitters of interest that could specifically be
targeted for plume sampling modes and, secondly, to build a picture of
aggregate bulk emissions for multiple upwind platforms. This method has been
successfully employed during a Gulf of Mexico airborne study (Gorchov Negron et al., 2020). However, in the work
presented here, regional surveys were poor for identifying plumes (being too
far downwind of platforms or not intercepting thin filament layers
containing CH4 enhancements), and attempts to aggregate bulk emissions
were hindered by the often encountered complex boundary layer structure over
the area, which controlled dispersion of CH4 emissions from rigs. From
the regional flight data derived in 2018 and considering the work in other
offshore studies in this area (e.g. Cain et al., 2017), the regional flight mode was
determined to be of limited scientific value in the context of this project,
and this flight pattern was not used during the 2019 campaign.
Plume sampling flights were conducted in both 2018 and 2019. These flights
involved the use of a box pattern to create both upwind and downwind
transects on either side of the infrastructure of interest. Upwind transects
provided an understanding of other methanogenic sources (such as other
installations, ships or long range transport of air masses from onshore
sources) that could interfere with observed CH4 plumes downwind and
were conducted to be confident that plumes were solely originating from the
targeted infrastructure. Vertically stacked downwind transects at a distance
of 1 to 10 km away from emission sources were conducted to better capture
the vertical extent of the plume in a 2D Lagrangian plane for CH4 flux
quantification using mass balance analysis (e.g. O'Shea et al., 2014). The
vertically stacked transects in profile, as planned from the 2019 field
deployment, are demonstrated in Fig. 3. The separation between vertically
stacked transects was usually 60 m with a minimum absolute height of 45 m
above sea surface up to approximately 260 m to capture the entire extent of
a downwind plume. Plume dispersion was dependent on meteorology and emission
type (venting, fugitive or combustive emissions), and as such, maximal plume
heights varied between individual pieces of infrastructure. Upwind transects were
flown at a median height between the minimum and maximum stacked runs.
Assessing and addressing issues encountered during flights
A number of issues were encountered during the flights that influenced the
measurements made. An initial presentation of these issues is given here,
with recommendations for improvements given in Sect. 6 below.
Complex marine boundary layers
Boundary layer structure proved to be a important influence on observed
CH4 mixing ratios. Figure 4 shows the measured profiles of CH4
(left-hand panel) and potential temperature (right-hand panel) during an
offshore flight in April 2018 along with the corresponding synoptic chart.
Potential temperature was calculated as described by Stull (1988). The
potential temperature profile demonstrates that the boundary layer structure
on this day (and many other days) was partly stable stratified, showing
mostly an increase in potential temperature with height, and the boundary
layer showed complex layering. The prevailing meteorological situation at
that time, illustrated by the synoptic chart in Fig. 4, was of a persistent
anticyclonic ridge, stretching from the south-west over the British Isles
and western Europe, with associated low wind speeds and poorly defined airflow over the southern North Sea sector. The observed layering was partly
also caused by residual boundary layers from previous days and nights which
had not dispersed. The structure of the boundary layer in Fig. 4 clearly had
an important influence on the vertical profile of CH4, which varied and
shows a complex profile with height. Due to the complexity of the boundary
layer structure, it was concluded that it would be inappropriate to use a
particle dispersion model such as the Numerical Atmospheric-dispersion
Modelling Environment (NAME) (Jones et al., 2007) to derive a bulk
regional emission estimate. The impact of the residual layers of CH4
enhancement make in-flight decisions very challenging for two main reasons:
(i) it is difficult to determine which enhancements are from installations
and require further investigation, especially if flying at some distance
downwind from a potential source or on a regional survey pattern, and (ii) emissions being actively released can become trapped in vertically thin
filaments, which can be easily missed when flying stacked legs, depending on
flight altitude. In contrast, on days with a well-mixed boundary layer the
CH4 profile stays relatively constant with height and shows an increase
only near a CH4 source. Figure 5 shows an example of CH4 and
potential temperature profiles, in a well-mixed boundary layer during a
flight in May 2019; the synoptic situation on that day was consistent with a
slow-moving cyclonic south-easterly airflow. It can clearly be seen how the
potential temperature and CH4 profiles stay almost constant with height
and only show structure when intercepting a CH4 emission at 300 to 350 m altitude. The potential temperature profile indicates neutral
stratification of the boundary layer.
Example of CH4 and potential-temperature profiles showing the
large amount of structure arising from residual boundary layers. The
increase of the potential temperature with height shows stable
stratification of the boundary layer. The synoptic chart over the eastern
North Atlantic and north-western Europe shows contoured sea level pressure
(hPa), 2 m temperature (∘C, right-hand side colour scale) and
wind for 20 April 2018 12:00 UT and reveals relatively low wind speeds and
poorly defined airflow over the southern North Sea sector, allowing for the
build-up of residual boundary layers. Synoptic chart image produced by the
UK National Centre for Atmospheric Science (NCAS) using Weather Research and
Forecasting model WFR-ARW (Advanced Research WRF) version 3.7.1, with a 20 km grid spacing and 51
vertical levels initialised using the NOAA Global Forecast System. NCAS (National Centre for Atmospheric Science)
Weather Research Catalogue (https://sci.ncas.ac.uk/nwr/pages/home, last access: 6 November 2020). The black
rectangle approximates the survey region.
Example of CH4 and potential-temperature profiles in a
well-mixed boundary layer under neutral conditions. The potential
temperature and CH4 profiles stay relatively constant and CH4
shows only an increase in the surface layer and when intercepting an
enhancement at 300 to 350 m height. The synoptic chart for 6 May 2019 12:00 UT shows a cyclonic south-easterly airflow over the southern North Sea
sector originating from the Benelux region. The black rectangle approximates
the survey region over open water.
Instrument response times
The role of the continuous in-flight measurements is to provide the backbone
of the dataset and ensure that, at a bare minimum, the flights are able to
identify areas of CH4 enhancement and inform on the likely sources of
the CH4 enhancement, hence the decision to run redundancy measurements
of CH4 utilising an LGR uGGA. Figure 6 shows typical instrument
responses to a CH4 plume, and it is clear that the cell turnover time of
the uGGA is not sufficient to capture the fine detail of the plume. Whilst
the uGGA and uMEA are capable of determining the whole infrastructure mass
balance and average infrastructure ethane–methane ratios, the refined
understanding of the true plume is lost in these slower response
instruments. This is important, as the combined Picarro G2311-f and TILDAS
data can detect several sources from the same installation (Fig. 6) because
of their rapid measurement cell turnover. This information can be used to
infer either cold venting (CH4 and C2H6) or combustion from
flares or generators (CO2, CH4 and C2H6), which could
then be used to determine CH4 emission factors from identified flares (Gvakharia et al., 2017).
A cross section of CH4, CO2 and C2H6
measurement response during one plume sample as recorded by Picarro G2311-f
in pink and green (10 Hz as dashed lines and downsampled to 1 Hz as solid
lines), TILDAS 1 Hz in cyan and Los Gatos uGGA 1 Hz in brown. The difference
between the uGGA and Picarro at 1 Hz arises from the slower uGGA response
time is due to the slower cell turnover. The blue-shaded area shows enhancement
in C2H6 and CH4, indicating cold venting; the orange-shaded
area shows enhancement in C2H6, CH4 and a small amount of
CO2potentially indicating a co-located combustion source.
There are a number of other implications that arise from slow measurement
response. For example, in-flight spot sampling requires guidance from fast-response instruments that can indicate the optimum timing to collect samples
that span the plume and thereby capture the representative chemical nature
of the plume. Further, in-flight calibrations must be matched to the slowest-response instrument to ensure stabilisation of the measurement of
calibration gases across all instruments. Although useful from a
cross-checking purpose, use of slower-response instruments can introduce
additional, unwanted loss of measurement time and excessive use of
calibration gases, and the benefits of instrument redundancy should be
carefully considered.
Spot sampling improvements between the 2018 and 2019 campaigns
In-flight spot sample collection was carried out during both the 2018 and
2019 campaigns. Such sampling is challenging and requires fast-response
instruments to be viewable to the operator to give the best chance of
collecting samples at appropriate points across the plumes. For 2019, a
number of simple adaptations were introduced that significantly increased
the success of capturing plumes (Fig. A3). The improvements included
modified flight planning, with an increased number of passes through
discovered plumes. This approach resulted in increased fuel consumption per
plume but contributed to the higher success rate of plume capture. The
comprehensive update to the SWAS system, which included continuous sample
throughflow allowed for more precise spot sampling to be achieved.
Creation of data productsMethane fluxes
A methane flux can be calculated from the CH4 mixing ratio data using
mass balance techniques (e.g. O'Shea et al., 2014; Pitt et al., 2019) in
which a vertical 2D plane is defined at a fixed distance downwind of the
infrastructure of interest, and sampling is conducted across the stacked
transects at this distance if a plume is identified in the downwind plane.
Fluxes were derived using Eq. ():
Flux=Xplume-Xbackground×nair×V×Δx×Δz,
where Flux is the bulk net flux passing through the x-z plane per unit time,
nair is the molar density of air (mol m-3), Xplume is the
average CH4 mole fraction measured within the plume and
Xbackground is the CH4 mole fraction of the background. V is the
wind component perpendicular to the flight track; Δx is the plume
width perpendicular to the upwind–downwind direction; and Δz relates to the
vertical extent of the plume.
The CH4 and CO2 measurements from the 10 Hz response instruments
were used to provide the highest accuracy in the (i) lateral plume width and
(ii) number of unique plumes identified from each individual platform.
Slower-response instruments would allow for flux calculations but would not
be able to identify individual plumes from the same platform. This could be
useful to distinguish, for example, multiple plumes from different emission
processes that are spatially distinct within the same platform (e.g. a
fugitive source versus a flare). A background mixing ratio was selected to best
represent the conditions observed during the flight at the specific time of
survey. An average of 30 s of data from either side of the plume on each run were
used if this was deemed appropriate with a clean upwind sampling leg. When
the upwind sampling was contaminated, more caution should be taken when
selecting an appropriate background so that the background value is not
distorted by extraneous far-field sources.
For the flux analysis, a flux across each individual stacked horizontal run
downwind of a plume was calculated before scaling in the vertical component.
The flux was then integrated across potential minimum and maximum plume
depths. Figure 7 (upper panel) represents a reduced vertical resolution of
the plume where transects at intermediate altitudes through the plume were
not conducted. In this case, the minimal plume depth is the narrow span
captured by observation in the 45.9–51.9 m altitude window. The maximal
plume depth is taken as the height difference between the highest and lowest
transects without CH4 enhancements, which are above and below the
plume, respectively; this value has to be used as the maximum due to
incomplete sampling of the void area seen in the upper panel of Fig. 7. In
cases where the base and top of the plume were not sampled (e.g. during 2018
sampling), the lower limit was selected as the sea surface, and the upper
limit of the plume was selected as the atmospheric marine boundary layer.
The greatest uncertainty in bulk flux arises when the vertical extent of the
plume is not fully captured. For the 2019 campaign, the flux uncertainty
related to plume depth was reduced by a factor of 10 compared to the 2018
campaign (as seen in Table 1) by completing a rigorous set of stacked
transects at multiple heights throughout the plume. The fluxes presented
here serve to demonstrate the approach and the impact of sampling strategy
and meteorological conditions on the calculation. Flux estimates for all
sampled platforms will be presented in a future study, including a full
treatment of component uncertainties.
Plumes measured from separate installations to demonstrate the
differences in strategies between 2018 and 2019. (a) Plume sampled
downwind with poorer vertical spatial resolution in the 2D plane during the
2018 portion of the campaign. CH4 measured values are much higher due
to platform activities during the survey time. (b) Plume sampled
downwind in 2019 with intermediate transects enabling higher vertical
spatial resolution. Note that the colour scale across each plot signifies
different measured CH4; the scales on the upper and lower plots are
different.
A comparison of flux lower and upper bounds for two individual
example plumes across each year of survey as scaled by the vertical
resolution available. The plumes themselves are not comparable, but the
method changes demonstrate the increased certainty in the final results.
SurveyCH4 flux lowerCH4 flux upperyearbound (kT yr-1)bound (kT yr-1)20181.8317.920190.671.04Ethane–methane ratios (C2:C1) as a source tracer
It has already been well established that continuous C2H6
measurements can be an excellent diagnostic tool for ascribing enhancements
of co-located CH4 and C2H6 to natural gas emissions in both
urban areas (e.g. Plant et al., 2019), semi-rural areas (e.g.
Lowry et al., 2020) and during large-scale evaluations of oil and gas
fields from aerial studies in the USA (e.g. Peischl et al., 2018), Canada (Johnson et al., 2017) and the Netherlands (Yacovitch et al., 2018). During this work, two
methods were used to establish C2H6–CH4 ratios (hereafter,
described as C2:C1). In 2018 the LGR uMEA was used to measure
C2H6–CH4 ratios. The benefits of such instrumentation are in
its simplicity of operation and that few considerations are required for
corrections or variable lags, as both species are measured at the same rate
and within the same optical cavity. C2:C1 can therefore be readily
determined as the gradient of a linear regression between the C2H6
and CH4 measurements. However, the low sensitivity to C2H6
(standard deviation of >10 ppb in C2H6 over 10 s of
background flying) only allowed emissions from two platforms to be
characterised for C2:C1 ratios during the whole of the 2018 campaign and
none during 2019 using the LGR uMEA method.
In 2019 the addition of the TILDAS 1 Hz C2H6 instrument allowed
for better precision of C2H6 (<1 ppb) with a faster flush
time in the measurement cell. The C2H6 data are time-matched with
the 1 Hz Picarro CH4 dataset to allow C2:C1 derivation. As the
instruments do not have the exact same flow rate and different cell
residence times, the C2:C1 ratios were determined using the integral of each
CH4 and C2H6 enhancement using Gaussian peak fitting. A
comparison between the 2018 flight, 2019 flight and published data derived
from the same geographical area is shown in Table 2. Although both
instruments have been operated for this work without in-flight calibration
or engineering solutions to address cabin-pressure-sensitivity issues (Gvakharia et al., 2018) due to weight and time
constraints, the agreement between years and with published expected values
is highly reassuring. The added value in high-precision C2:C1 demonstrates
that C2H6 is not just a tracer for matching emissions to natural
gas; it can give information as to proportions of emissions from mixed
sources (as previously used by Peischl et al., 2018) or can be used to identify a likely
emission point in a process chain depending upon enrichment or depletion of
C2H6 relative to CH4. The inclusion of a continuous
instrument with a level below parts per billion (sub-ppb) of detection for C2H6 is
considered vital for future work with thermogenic sources of CH4 to
allow for more precise source attribution of emissions where no spot sampling
has occurred.
Reported data for C2:C1 for a single installation surveyed during
both the 2018 and 2019 surveys. Well data from UK oil and gas authority report are available at
https://dataogauthority.blob.core.windows.net/external/DataReleases/ShellExxonMobil/GeochemSNS.zip (last access: 7 January 2020)
alongside measured C2:C1 for CH4 enhancements measured during flights
in the same geographic area.
Instrument(s)MethodC2:C1Uncertainty2018 flightLos Gatos ultraportable CH4/C2H6Linear regression0.029±0.0142019 flightTILDAS C2H6 and Picarro G2311-f CH4Plume area integration0.029±0.003Published well data0.031±0.009δ13CCH4 for CH4 source attribution
The principal method of δ13CCH4 source characterisation
utilises the principles outlined by Keeling (1961) and Pataki et
al. (2003) and has been well utilised since to
create δ13CCH4 databases for a plethora of known
CH4 sources (e.g. Sherwood et al., 2017). In
order for a Keeling plot to give useful results to determine a δ13CCH4 source signature of a CH4 emission, the emission must
have been successfully captured multiple times and with a range of CH4
mixing ratios (which could be achieved by passes at different distances or
heights downwind of a point source). This sampling process takes time
(especially on an aircraft), where the emission plume is only intercepted
once per transect and time in the plume is limited so that only one spot
sample can be taken whilst “in-plume”. Beyond the time limitations,
sampling of a range of CH4 mixing ratios from emissions and appropriate
background samples is not straightforward. Background sampling must capture
the air into which emissions are released, but during flights the
meteorological conditions often resulted in significant variation of
CH4 mixing ratios and δ13CCH4 with altitude, in
addition to horizontal variations. Where repeat transects were conducted at
different altitudes, this made selection of appropriate background samples
for Keeling plots challenging, since the background CH4 mixing ratio
and δ13C varied over the different altitudes. This becomes
particularly detrimental to Keeling plot validity where the range in sampled
emission mixing ratios is small, since uncertainty in the background samples
then becomes more important.
In Fig. 8, a sensitivity analysis is presented from one of the flights
investigating the effect of reducing the number of samples on the
uncertainty in the δ13CCH4 source signature determined for
a plume. In this case nine samples were collected, but this took place over eight downwind
transects and one upwind transect of a cluster of installations, which is
not feasible to repeat for sampling large numbers of installations. As shown
in Fig. 8, the uncertainty in the δ13CCH4 source
signatures increases only slightly with a reduction in number of sampling
points, with the exception of one n=3 run where the source signature is
poorly defined. A minimum of three data points can therefore be sufficient
for classifying a source of CH4 emissions (such as thermogenic,
microbial or pyrogenic sources), providing that the background and point
samples are captured with a large enough range of CH4 concentration
and providing that there is no mixing of sources. This will typically
require collection of more than three samples, given some may miss the
targeted plumes or potentially be lost during storage or processing as
aforementioned. Although a two-point Keeling plot is technically possible,
it is impossible to gauge the quality of the regression to be sure that only
a single source has been captured.
(a) Keeling plot determined using nine samples collected around
one installation, assumed to be the single source of excess CH4. (b) An
illustration of the variation in δ13CCH4 source signature
and its uncertainty determined by Keeling plot analyses for reduced sample
sizes. Each analysis represents a single Monte Carlo experiment with the
original data, reducing the number of data points to the sample size
indicated at random; the δ13CCH4 source signature is then
calculated with the remaining sample points. Error bars are 2 times the
standard error.
Conclusions
Given the restrictions and time constraints on the science flights,
important lessons for offshore oil and gas airborne measurement campaigns
have been learned for rapid instrument re-fitting and agile deployment of a
small aircraft for future campaigns. A key finding from this study is that
offshore meteorological conditions define the ability of the flights to
produce valuable data and suitable meteorology with a well-mixed (neutral)
boundary layer is critical to deriving a regional emission estimate through
regional modelling. Flying in conditions with multiple residual boundary
layers makes interpretation difficult and pin-pointing emissions especially
challenging, as emission plumes can easily be missed when they are trapped in
thin filaments, increasing the uncertainties of measurement-based emission
flux calculations. Although not possible for this work given aircraft
scheduling, it is recommended that offshore observations are scheduled with
a long window of opportunity to ensure optimal flying conditions.
Predictions of the likelihood of a residual boundary layer over a coastal
area could be achieved through high-spatial-resolution forecast models such
as the UK Met Office forecast model (Milan et al., 2020).
Information on the temperature structure over the previous few days using
all the assimilated information, such as tephigrams and synoptic charts,
would help determine the likelihood of residual boundary layers versus a
simpler stratified, well-mixed layer. For methods using alternative
platforms such as ships or drones, coincidental measurements of vertical
profiles must be made to capture the true nature of the emission plume in
the current meteorology.
Due to the size of the aircraft, payload restrictions and power limitations
demand challenging decisions for instrument selection. We recommend
deploying at least one instrument measuring CH4 (and CO2) at 10
Hz, allowing several plumes emitted from a single installation to be
resolved (Fig. 6). Priority should next be given to a C2H6
instrument capable of a sub-ppb limit of detection at 1 Hz (or higher) in
order to give certainty to the source of the CH4 emission. Using C2:C1
appears to be the simplest method for source attribution and is robust for
distinguishing natural gas emissions, where the gas has an C2H6 component (Lowry et al., 2020; Plant et al., 2019). Spot sampling is
challenging, payload heavy and time consuming, as several passes are needed
to collect enough samples (especially for δ13CCH4 source
attribution). However, results can be very informative, such as the ability
to distinguish between a gas leak and a geological reservoir from depth or a near-surface reservoir (Lee et
al., 2018). The improvements to SWAS, allowing for continuous throughflow,
has increased the success rate of peak sampling but still relies on
accurate user triggering.
For mass balance flux calculations, an emission plume and the surrounding
background variation in the species of interest, alongside local
meteorology, must be fully resolved during the observation stage. This
includes instruments with appropriate response times to fully capture the
plume and identify any internal structure that may suggest a mixed source.
An upwind leg must be conducted to ensure the plume and background are not
contaminated by extraneous far-field sources, and the plume must be
significantly distinct from this background for meaningful flux
calculations. The plume must be laterally and vertically resolved in the 2D
plane as much as possible at a fixed distance downwind of the source.
Straight and level runs must extend to either side of the plume, and the
vertical resolution must include multiple stacked transects with an
identification of the top and bottom of the plume (where feasible) to reduce
uncertainty in the plume bulk net flux. Full understanding of the
meteorology with meteorological measurement instrumentation and a complete
profile to determine characteristics of the marine boundary layer from the top
to the surface, including determination of inversion heights, must be
conducted during the flight day when appropriate radiosonde soundings are
not available. The observed impact of complex boundary layer dynamics on
plume dispersion also highlights an important limitation of ship-based plume
measurements, which are unable to resolve the vertical structure of the
plume and therefore rely on the assumption of idealised models of plume
dispersion.
Response rates and precision for the instrument set-up on the BAS
Twin Otter. All measurements were time-shifted to match the Picarro G2311-f
for analysis.
T90MeasurementResponsePrecision of primaryInstrumentspeciesratespecies of interestLGR uGGACH4, CO217 s(CH4) 1 ppb over 10 sPicarro G2311-FCH4, CO20.4 s(CH4) 1.2 ppb over 1 sLGR uMEAC2H6, CH417 sa (C2H6) 17 ppb over 1 sTILDASC2H6<2 sb (C2H6) 50 ppt over 10 s
a Measured in laboratory. b Manufacturer's expected precision.
Photo of the rear-facing chemistry inlets on the BAS Twin Otter
aircraft.
Photo of the BAS Twin Otter showing the turbulence boom
protruding from the front of the aircraft superstructure.
CO2 and CH4 calibration
The three cylinders were sampled periodically in flight to determine the
instrument gain factor (slope) and zero offset for each analyser. These
parameters were linearly interpolated between calibrations and used to
rescale the raw measured data (for further details see Pitt et al., 2016).
The uncertainties associated with instrument drift and any instrument
non-linearity were assessed by sampling the target cylinder midway
between high–low calibrations. The raw target cylinder measurements were
rescaled as per the sample data; the mean offset of these target
measurements from the WMO-traceable cylinder value (and associated standard
deviations) are given for the LGR uGGA and Picarro instrument and are
plotted in Fig. A4.
The typical duration of calibration cylinder measurements during the 2018
campaign was 45 s. The Picarro G2311-f analyser had a high flow rate of
∼ 5 SLPM (standard litre per minute), resulting in rapid flushing of both the inlet tubing
and sample cavity. The measured value for each calibration was taken as the
average over 15 s prior to the calibration end, as this allowed sufficient
time for the measured value to reach equilibrium. The uGGA and uMEA both had
much lower flow rates of ∼ 0.5 SLPM, resulting in a much
longer equilibration time. Consequently, the calibration duration was not of
sufficient length for the uGGA and uMEA measurements to reach equilibrium,
and their calibration routine was compromised. For these instruments each
calibration run was fitted to an offset exponential function in an attempt
to predict the mixing ratio at which equilibration would have occurred,
given an infinite amount of calibrating time. In order to improve the data
quality and to reduce the post processing time, the calibration periods were
run for 75 s per cylinder during the 2019 campaign to ensure that all
instruments reached equilibrium. Target cylinders were run approximately
every 1 h of flight.
Examples from a 2018 flight (a) and a 2019 flight (b) with attempted capture of CH4 plumes in spot samples (both SWAS
and FlexFoil bags). Note the improved ability to sample at the correct
period to capture short-lived enhancement in both SWAS and FlexFoil samples
for 2019 compared to 2018 thanks to flight planning and SWAS development
improvements.
SWAS operation
Each sample is compressed into the canisters using a modified metal bellows
pump (Senior Aerospace 28823-7) capable of 150 SLPM open flow but filling
the canisters at ∼ 50 SLPM measured average integrated for
∼ 6 and 9 s for the 1.4 and 2 L canisters,
respectively. Canister fill pressure is controlled electronically using a
back-pressure controller (Alicat PCR3; BPC). The BPC can maintain flow at
any set point pressure (in general 40 psi; 275 kPa), including the final fill
pressure set point. This allows the 2 L flow through canisters to be filled,
even before the operator activates the sampling, enabling air masses to be
sampled through which the aircraft has already flown seconds earlier.
Target gas data from flights during 2018 for the Picarro G2311-f
and Los Gatos uGGA instruments for both CO2 and CH4.
Summary of VOCs measured from SWAS samples at the University of York.
Volatile organic compounds identified and quantified from SWAS samples DetectionCompoundlimit (ppt)Ethane4Ethene4Propane6Propene2iso-Butane1n-Butane1Acetylene1trans-2-Butene2but-1-Ene2cis-2-Butene2Cyclopentane2iso-Butene2iso-Pentane1n-Pentane11,3-Butadiene2trans-2-Pentene2pent-1-Ene22,3-Methylpentanes2n-Hexane2Isoprene1n-Heptane2Benzene12,2,4-Trimethylpentane2n-Octane2Toluene1Ethylbenzene2m+p-Xylenes2o-Xylene2
Bespoke software was created to allow control of the SWAS system wirelessly
from any position in the aircraft using the Ethernet network. Bespoke
software was also created for the analysis of the canisters once in the
laboratory. The SWAS flown on the 2018 campaign (V1) was a prototype and was
updated to the current final version (V2) to fulfil the requirements of the
FAAM BAe 146 and to address potential issues experienced with the prototype.
V2 uses the same canisters and valves as V1 but differs slightly in the size
of each case and the plumbing of gas lines. In V2, the canister and valve
geometry was optimised to allow an elbow compression fitting between the
valve and the canisters to be eliminated, with the valve mounted directly to
the canister. This reduces the risk of leaks by 66 %. The geometry also
allowed for the reduction in size by 1U rack unit, allowing for more canisters to be
fitted in the same space, improved control electronics and sample logging to
ensure canister fill times were captured accurately and stored securely. V2
also saw the addition of 2 L flow-through canister cases to complement the
1.4 L to-vacuum canister cases. These allowed sample air to be flushed
through the canister at a user-defined pressure and makes capturing narrow
plumes easier due to reduced sample line lag and fill time.
Code and data availability
The data for this work will be available via request at the British Antarctic Survey Polar Data Centre.
Author contributions
The paper was written and figures were prepared by JF, PB and PD with
assistance from AEJ, MC, JP, SB and JS. The experimental design and flight
planning were performed by GA, JP, JDL, TLC and DL. Aircraft set-up and
in-flight measurements were performed by JP, PB, PD, SA, SY, AW, TLC, JF, SW, JW
and SB. Laboratory measurements were made by REF, RP, SW and SB. Data processing
and calibrations were performed by JF, LH, PB, JS, PD, MC and SB. Modelling
work was done by NW, JP, PB and LH.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work was funded under the Climate & Clean Air Coalition (CCAC) Oil
and Gas Methane Science Studies (MSS) programme, hosted by the United Nations
Environment Programme. Funding was provided by the Environmental Defense
Fund, Oil and Gas Climate Initiative, European Commission, and CCAC.
Financial support
This research has been supported by the Climate & Clean Air Coalition (CCAC) (grant no. DTIE18-EN018).
Review statement
This paper was edited by Pierre Herckes and reviewed by two anonymous referees.
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