Evaluating methods to estimate methane emissions from oil and gas

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Environmental Modeling

Evaluating methods to estimate methane emissions from oil and gas production facilities using LES simulations Pablo E Saide, Daniel Steinhoff, Branko Kosovic, Jeffrey Weil, Nicole Downey, Doug Blewitt, Steven Hanna, and Luca Delle Monache Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b01767 • Publication Date (Web): 28 Aug 2018 Downloaded from http://pubs.acs.org on September 1, 2018

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Environmental Science & Technology

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Evaluating methods to estimate methane emissions from oil and gas production facilities

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using LES simulations

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Pablo E. Saide (1,*, †), Daniel F. Steinhoff (1), Branko Kosovic (1), Jeffrey Weil (1), Nicole

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Downey (2), Doug Blewitt (2), Steven R. Hanna (3), Luca Delle Monache (1)

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(1) Research Applications Laboratory, National Center for Atmospheric Research, 090 Center Green Drive, Boulder, CO, 80301, United States. (2) Earth System Sciences, LLC, 117 Bryn Mawr Dr. SE Suite 111, Albuquerque, NM, 87106, United States. (3) Hanna Consultants, 7 Crescent Ave., Kennebunkport, ME, 04046, United States.

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KEYWORDS: Source term estimation, methane emissions, transport and dispersion, stochastic

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and variational approaches

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ABSTRACT. Large-eddy simulations (LES) coupled to a model that simulates methane

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emissions from oil and gas production facilities are used to generate realistic distributions of

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meteorological variables and methane concentrations. These are sampled to obtain simulated

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observations used to develop and evaluate source term estimation (STE) methods. A widely used

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EPA STE method (OTM33A) is found to provide emission estimates with little bias when

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averaged over six time-periods and seven well-pads. Sixty-four percent of the emissions

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estimated with OTM33A are within +/-30% of the simulated emissions, showing slightly larger

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spread than the 72% found previously using controlled release experiments. A newly developed

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method adopts the OTM33A sampling strategy and uses a variational or a stochastic STE

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approach coupled to an LES to obtain a better fit to the sampled meteorological conditions and to

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account for multiple sources within the well-pad. This method can considerably reduce the

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spread of the emissions estimates compared to OTM33A (92-95% within +/-30% percent error),

ACS Paragon Plus Environment

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but it is associated to a substantial increase in computational cost due to the LES. It thus provides

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an alternative when the additional costs can be afforded to obtain more precise emission

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estimates.

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Table of Contents (TOC)/Abstract Art.

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INTRODUCTION

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Uncertainties in methane emissions from oil and gas production 1, 2 and their potential influence

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on climate change 3 have driven extensive research using regional and local field experiments

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and theoretical analysis to better constrain estimates of emissions from these sources 4. While

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source term estimation (STE) is used in multiple disciplines 5, there are a wide variety of

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methods that have been proposed and used specifically to estimate methane emissions from oil

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and gas production by using ground-based gas concentrations and meteorological observations 6-

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verification of the ground-based methodologies using controlled releases, it is difficult to

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quantify the accuracy of these methane STE methods for scenarios where factors such as terrain,

, as well as aircraft and remote sensing observations 13-16. Although there has been some


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emissions from multiple sources with different gas compositions within a well pad, and time-

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varying emissions representative of typical operations might influence the results.

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In this work we run a large-eddy simulation (LES) coupled to a model that realistically simulates

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emissions from an oil field (i.e., an emission simulator) to generate synthetic 4-D (space and

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time) methane concentration fields, which we then use to test a variety of STE methods for oil

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and gas production. This technique is known as an Observing System Simulation Experiment

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(OSSE) in the data assimilation field 17, 18. LES simulations are starting to be included in

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methane STE research 19-21, but to our knowledge OSSEs using an LES driven by emission

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simulators have not been applied to test source estimation methodologies at the scale of a single

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well pad (facility level). Although the word “observing” is used in the term OSSE, it is important

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to stress that the observations do not correspond to field measurements of methane and

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meteorology but are in fact simulated by the model, thus we refer to them as “simulated

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observations”.

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The Draft EPA Other Test Method (OTM) 33A 6 is one of the most widely-used STE methods

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for individual facilities 22-24. Assumptions inherent in OTM33A include that methane

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concentrations and wind observations (three velocity components) are available from a vehicle

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that takes measurements downwind of the facility, and that the locations of emissions from major

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sources are known. OTM33A is a simple and easy to employ method that has been evaluated

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using controlled releases 6, and has been well documented with the code being freely available 25.

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In this study we compare and contrast the skill in retrieving emissions of three STE methods:

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OTM33A, a variational-based STE method, and an approach based on Bayesian inference and

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stochastic sampling. The latter two methods use an LES simulation and constitute a novel


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contribution to the field of methane emission estimation at the facility level. Their development,

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advantages, and limitations are also discussed in this study.

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METHODS

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LES model configuration

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The LES configuration is based on idealized simulations of the Weather Research & Forecasting

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(WRF) LES model 26 version 3.8.1, which includes transport and dispersion of passive tracers 27.

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WRF-LES is configured with an outer (coarse) domain with dimensions of 10.3 km by 12.5 km,

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with 30 m horizontal grid-spacing and periodic lateral boundary conditions, and an inner (fine)

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domain of dimensions 6.9 km by 8.3 km (688 x 829 grid-cells) and 10 m horizontal resolution

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(Fig. 1a). This resolution and domain size allows the placement of multiple pads in the domain

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with realistic spacing within each other. It also permits locating observations close-by to the pads

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while still properly resolving turbulence in between pads and monitoring locations, and keeps the

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computational and storage costs at a reasonable level. The inner domain does not feed

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information back to the outer domain (i.e., one-way nesting is adopted in the modeling

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configuration) to avoid the plumes re-entering the outer domain once they reach the boundary as

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this would occur due to the periodicity of the boundary conditions. The simulations have 121

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vertical levels, with grid spacing of ~3 m for the first three layers and ~10 m for the upper layers

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up to the model top at 2 km height. The outer domain is initialized with southerly 5 m s-1

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geostrophic winds and a temperature inversion of 8 K over 150 m to cap the boundary layer

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height at 1 km. Note that due to the LES resolving turbulence and terrain, the winds within the

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inner domain are not constant but exhibit natural variability similar to field observations (Fig.

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2a,b). The inner domain covers a region with terrain obtained from USGS at 1/3 arc second (~10


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m) resolution (http://ned.usgs.gov/) representative of an area located in the Barnett shale region,

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Texas (33° latitude, -98.2° longitude), where intensive oil and gas operations are active (Fig. 1a).

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The surface roughness is set at 0.1 m uniformly across the domain (which is representative of an

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open field with brush/low vegetation) except for the location of tanks and compressor buildings,

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where a value of 0.6 m is set to model the added turbulence due to these structures, and is chosen

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as the upper range suggested for roughness produced by scattered settlements 28. For a surface

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release, sensitivity analysis showed that mean surface concentrations decreased by less than 15%

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at a distance of 100 m downwind from the source when using 0.6 m versus 0.1 m surface

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roughness at the source location. The reductions were even lower for a source release from the

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second model vertical level showing that even this upper range of surface roughness has a

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limited impact on concentrations relevant for this study. Daytime convective conditions are

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modeled by assuming a 0.2 K m/s sensible heat flux at the surface 29, which are typical

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meteorological conditions found in the Barnett shale region. Background methane is set to 1.98

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ppm and 1.85 ppm in the boundary layer and free-troposphere which correspond to the 2015

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annual mean for the Southern Great Plains (OK site) and Mauna Loa (MLO site) NOAA flask

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network measurements (https://www.esrl.noaa.gov/gmd/ccgg/flask.php), respectively. Three

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hours of simulation are performed, with the first hour used as spin-up, and analysis performed

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for the last two hours. The WRF-LES time-step for the inner domain is 0.1 s and instantaneous

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values are saved every second (i.e., every ten time-steps). Additional details on the WRF-LES

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configuration can be found in the Supplemental Information (SI).

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Emissions

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In the current modeling exercise, we simulate hypothetical oil and gas facilities (i.e., multiple

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well pads) with emission rates and gas compositions based on information from real facilities.


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Seven well pads containing two wells each and their associated equipment are included in the

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model domain. The pads, with dimensions of 100 m x 100 m (i.e., 100 grid cells), are arranged in

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the domain as shown in Figure 1a, which is consistent with the arrangement of actual pads in the

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Barnett shale region based on satellite imagery. An oil field emission simulator is used to

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generate emissions time series for individual components on each well pad. The simulator

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predicts emissions based on operating parameters for the well pad equipment 30. The simulator

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estimates methane emissions for condensate tanks (flashing), dehydrators, compressors,

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pneumatic chemical injection (CI) pumps, pneumatic controllers, and liquids unloading at one

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second temporal resolution. Components are distributed on wells as typically observed for the

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Barnett shale region. The components, when present, are arranged spatially on each pad

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following the distribution shown in Figure 1b. Emissions from tanks (flashing and liquids

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unloading) were assumed to be released from the top of the tank, and thus were put into the

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second model vertical level (~5 m height). Emissions from compressors were assumed to come

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from a compressor building and thus were released into the first two model levels (2 and 5 m),

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distributed in equal parts. The rest of the emissions (CI pump, pneumatic controller and

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dehydrator) were released into the first model level. Emissions are assumed to be at ambient

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temperature and without significant speed when emitted. Future studies should consider cases

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where emissions have significant speed and/or are emitted at high temperature which could

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affect the ability of the STE methods to properly retrieve emissions. Emissions from different

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components and pads are assigned to twenty-two simulated tracers in the WRF-LES framework

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(see assignments in the Table S3) which are then aggregated to build methane concentrations.

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Ten days of emissions are generated with the simulator from which two hours are selected to be

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used as input for the LES simulation. These two hours are selected to contain liquids unloading


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events which can then be included or excluded for analyses of different scenarios as their

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emission rates are several orders of magnitude greater than the other emission sources (200-400

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g/s). With respect to the other sources over the two hours, the most frequent simulated emissions

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come from pneumatic controllers, but their rate is generally below 0.3 g/s. Tank flashing and

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compressor emissions are also frequent and show larger values on the 0.1-3 g/s range. One

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dehydrator is simulated in well-pad #2 assuming constant emissions of ~1.5 g/s. CI pump

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emissions are in the 0.3-3 g/s range and are quite infrequent (less than 5 minutes over 2 hours

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and all pads). Further details including the emission time series (Fig. S1), emission histograms

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(Fig. S2), and detailed descriptions of the emission simulator and emissions from each

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component can be found in the SI text and tables S1-S3.

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Simulated observations

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The simulated observations are obtained by extracting data from the LES model output of the

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inner domain following the OTM33A protocols and then adding a random error representative of

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instrument uncertainties as described later in this section. The OTM33A measurement protocol

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consists of ~20 minutes of meteorological and methane sampling from a vehicle parked

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downwind of the facility at distances ranging from 20 to 200 m. Since turbulence and plume

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dispersion are better resolved at longer distances in the LES, thena distance of 100 m north (i.e.,

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downwind) of the largest emission source on a given well pad is selected. Based on this criterion,

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all potential locations of measurements are shown in Figure 1b with the letter “O”. Although

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winds are forced to be southerly on the LES outer domain, terrain effects could cause deviation

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in the inner domain. All 20-minute sampling intervals for all well-pads were reviewed finding

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that on average winds were southerly for all cases (e.g., see Table S4 and S5 in the SI),

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confirming that the choice of placing observations directly north is appropriate. Also, given the


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dimensions of the pads and the distance selected, the sampling location falls outside of the pad

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area for all pads, where the location may be publicly accessible for measurements. For the

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measurements to be valid for source estimation using the OTM33A approach, the mean wind

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conditions should remain constant over the sampling period, which is why the large-scale

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conditions forcing the LES are kept constant (see LES model configuration section). The LES

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outputs are sampled every second (instantaneous values) at the first vertical layer (~1.8 m height)

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producing six unique 20-minute time series for each pad over the two hours of LES simulations.

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This layer is selected because is the closest to the ~ 3 m height at which instrumentation is

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usually mounted (the second layer is at ~5.4 m height). Sensitivity simulations using

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observations from the second layer were performed showing similar results (see SI section

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“Sensitivity simulations”). Two scenarios are analyzed: (1) including emissions from liquid

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unloading events, and (2) excluding emissions from liquid unloading events. Due to the

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significant emission rate associated with liquids unloading events (more than an order of

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magnitude larger than the sum of other emissions), liquid unloading events confound emission

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estimates from nearby well pads.

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Noise representing instrument uncertainties is added to the time series extracted from LES

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outputs by assuming a normal distribution with zero mean and standard deviation (SD) based on

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the precision of instrumentation typically used for OTM33A applications (cavity ring down

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methane analyzer and ultrasonic anemometers). A sampling error (SD) of 1.5 ppb is used for

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simulated methane observations, while 2 degrees SD and 1% or 0.05 m s-1 SD (whichever is the

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largest) is used for wind direction and speed, respectively. An example of real25 and simulated

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observations is found in Figures 2a and 2b, respectively, which suggest that the methodology

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produces a similar representation of conditions encountered when sampling according to the


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OTM33A emission estimation method. While the sampling error applied to methane

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concentrations has negligible impact as the typical methane enhancements in this study are 1-10

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ppm (i.e., 3-4 orders of magnitude larger), the errors applied to winds generate some of the noise

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in the data when plotted as a function of wind spend and direction (e.g., compare Fig. 2b to Fig.

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S2). Additional statistics on the mean, maximum, and minimum winds speed and direction can

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be found in Table S4 and S5 of the supplement.

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STE methods

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Three STE methods are evaluated: the standard EPA OTM33A method and two new methods, a

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variational inversion method 31, and a Bayesian inference and stochastic sampling method 32, 33.

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The OTM33A relies upon measured data while the two new methods additionally require the use

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of a LES to estimate emissions sources.

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The OTM33A STE framework, referred to as Point Source Gaussian (PSG) method, is fully

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described by Brantley et al. 6 and EPA 25 and only a brief overview is provided here. First, the

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observations are collected by a stationary vehicle downwind of the source being estimated. Then,

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the observed methane concentrations with the observed background subtracted are binned by

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wind direction, and a Gaussian function is fitted to the resultant data (Fig. 2c). The maximum

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value of the Gaussian fit is then converted from concentration to emissions by performing a two-

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dimensional Gaussian integration that uses the mean wind speed, the distance between the source

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and the stationary vehicle, and local stability classes derived from wind measurements 6. As

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multiple sources are found within a well pad, the location used on the Gaussian integration is that

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of the dominant source (the equipment with the largest emissions) for that time period.


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The new methods use an LES simulation in place of the Gaussian integration to connect

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concentrations to emissions providing additional flexibility to the STE methods. For instance, it

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can facilitate the inclusion of multiple sources (PSG assumes a single source) and deviations

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from the mean winds can be taken into account (PSG assumes constant winds over the whole

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sampling period). The WRF-LES configuration is modified for its use in the STE methods to

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avoid biasing the results due to the use of the same model when generating the simulated

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observations. These modifications include: a different advection scheme and sub-grid turbulence

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parameterization (see details in the SI), and changing the boundary wind speed to 5.5 m s-1 (10%

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increase), and setting the sensible heat flux to 0.16 K m s-1 (20% decrease). Perturbations that

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both change the internal variability and bias the model are introduced, as this would likely be the

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case if this method is applied to field data. These changes in configuration generate large

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differences in simulated wind fields and methane concentrations (Fig. S3 in the SI). These

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differences make these simulations uncorrelated within each other most of the time (Fig. S4 in

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the SI), showing correlation coefficients lower than 0.5 99.97% of the time. Therefore, the two

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simulations are independent, uncorrelated and can be used within an OSSE framework. In

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addition, emissions from all simulated tracers in the modified LES run are set to a constant 1 g s-

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1

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temporal variability in the newly developed STE methods, which is consistent with the PSG

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estimate assumptions. Simulated and estimated emissions are then compared by averaging

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emissions over each of the six 20 minute periods of simulated data collection and for each well

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pad. In the same manner as the PSG method, emissions are estimated for each well pad

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independently, without knowledge of sources or simulated observations upwind of the well pad

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being assessed.

(selected as a unit that could be scaled) representing no a-priori knowledge of the source


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The variational inversion method (simply referred to as variational henceforth) involves

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minimizing a cost-function (J) which can be expressed in the following form for a passive tracer

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:

=

1 1 ∗ − ∗ − + − − 1 2 2

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The right hand side of (1) has two terms, one with the discrepancies between observations (O,

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simulated observations in this work) and model estimates (H*E, with H the sensitivity

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matrix/basis functions) to improve the fit to the observations, and the other with the difference

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between the updated emission estimate (E) and an initial guess (Ep) that contains prior

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knowledge about the source. These two terms are weighted by the confidence (i.e., inverse

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covariance matrices, B-1 and R-1) in the observations errors (R) and emissions errors (B). As the

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variational method requires a first guess on the emissions (Ep), the PSG emissions estimate is

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used as prior information on the total emissions from the pad which then needs to be distributed

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into the sources being estimated within the pad. For this we assume the largest source within a

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pad dominates the emissions (as this is generally the case), assigning it 90% of the total, with the

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remaining 10% distributed among the other sources. Note that this assumption implies that all

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equipment within the well pad could potentially be emitting, while this is often not the case (see

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Fig. S3 in the SI). As the location of the source is known in the PSG method, a similar

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assumption is made for the variational method. That is, we assume that the location of all

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possible emitting sources and the largest source within a pad are known. The square root of the

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diagonal elements of B (standard deviation) represents the errors in the emission priors and are

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set to 100% of the emission guess as most OTM33A estimates are within a factor of two of the

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simulated emissions (see Results section). The diagonal of R represents both the observation


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error (which in the case of the methane sensor is negligible for this study) and the error in the

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model representation of the observations. As the latter is hard to estimate, we assume a value of

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10% of the simulated observation mean and perform sensitivity tests around this value (see

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Results section). This configuration results in a close fit to the simulated observations with little

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information retained about the initial guess. Off-diagonal elements of the R and B covariance

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matrices are set to zero. J is minimized numerically using the algorithm described in Zhu et al. 35,

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bounding the solution to be positive.

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The Bayesian inference (BI) and stochastic sampling method 32 is based on Bayes’s theorem

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(i.e., BI) and uses a Markov Chain Monte Carlo (MCMC) procedure to sample the parameter

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space (i.e., emissions from each source estimated within a pad). Through an iterative procedure,

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the method provides the probability density function (PDF) of the parameters being estimated

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given their prior distributions, which are selected as uniform between zero and three times the

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PSG emissions estimate for the pad. This upper limit is based on the performance of the PSG

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method (see next Results section) so the simulated emissions would be contained in this interval.

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The BI-MCMC methods uses a likelihood function to assess the agreement between model and

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observations (simulated observations in this work). Instead of using the logarithm of the

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observations and model estimates as in Delle Monache et al. 32, this study uses the likelihood

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function as the 1st term of the right-hand side of Equation 1 (i.e., no logarithm applied). This

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function is selected to prioritize the fit to larger concentrations rather than to the tails of the

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simulated observations (Fig. 2c). The concentrations in the tails are less robust due to the lower

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number of data points as the tails are associated with less frequent wind directions. Also, the tails

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could be affected to a larger extent by sources outside the pad which are not being accounted for

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in the estimation procedure. Preliminary testing with the log-functions showed the superiority of


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the non-log approach for these reasons. The variational method uses Equation 1 instead of a log-

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normal cost-function 31 for the same reasons. Although it doesn’t apply to the conditions

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simulated on this work, the tails would likely need to be filtered for cases with low wind speeds

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where low probability off-axis trajectories are observed. A range of standard deviations (10-

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40%) of the observation mean used in the likelihood function (i.e., diagonal of R) was tested

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with the final value set to 30 %, which provides more satisfactory results. The output of this

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method is a PDF (see example in Figure S5); thus we chose the distribution mean as a point

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estimate. By doing so the skill in estimating emissions can be compared to the PSG and

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variational methods using the same framework (see next section).

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The variational and BI-MCMC STE methods rely on simulated meteorological fields to link the

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observed concentrations to the sources being estimated (H in Eq. 1). To match these fields to the

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simulated observations, a new procedure for computing the sensitivities is adopted. This

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procedure involves finding the winds from the perturbed simulation (U, V, and W components)

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that have the best fit to the simulated observations over the time-period that it takes for an air

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parcel to travel from the source to the observation location. Thus, the sensitivities used

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correspond to the best representation of the simulated observed winds (and thus the transport) by

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the modified WRF-LES run. The time-period is chosen as 30 seconds because the simulated

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measurements are 100 m from the sources and mean wind speeds are 3-3.5 m s-1 at 1.8 m height

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(lower than the 5 m s-1 geostrophic winds due to surface drag). The best fit of the winds is found

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by minimizing the difference of the squared errors between the 30 second time series of

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simulated wind observations and all possible 30 second time series of the modified WRF-LES

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run at the sampling location (i.e., 7,170 possibilities during the two hours of simulation). An

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empirical scaling factor of 10 is applied to the vertical winds (W) when performing the fitting so


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that they are of the same order of magnitude and thus have similar weight as the horizontal winds

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(U, V). Scaling W by 10 leads to improved results relative to results with no factor or when a

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lower factor is used (not shown). An example of the outcome of the fitting can be found in

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Figure S6. This procedure is performed for each simulated observation, i.e., every second over

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the twenty minutes of simulated observations. The simulated methane observations used in these

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inversion methods correspond to the methane concentrations binned by wind direction after

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subtracting the background (red circles in Fig. 2c) and are used to have a fair comparison with

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the PSG estimates; thus, the time series of sensitivities are aggregated in the same way to

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represent these simulated observations.

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RESULTS AND DISCUSSION

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Performance of the emission estimation methods

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The performance of the three STE methods is assessed in terms of bias and the spread of the

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scatter plots, as shown in Figure 3, where estimated and simulated methane emissions are

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compared. The bias is measured by the average over all times and wells (i.e., ensemble mean) of

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the estimated-to-simulated emission ratio, which is chosen instead of a metric measuring the

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difference between estimated and simulated emissions to provide similar weight to all data

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points. On the other hand, the spread is defined in two ways: by the percentage of estimated-to-

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simulated emission ratios that fall within factor of 2.0 and 1.5, and by the number of data points

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where the percent error defined as in Brantley et al. 23 ([estimated emission – simulated

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emissions]/[estimated emission]) falls within +/- 30%. While all points are included for the

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emission ratio metric, the percent error metric excludes data points where the goodness of fit


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coefficient (R2) for the Gaussian fit computed in the PSG estimate (e.g., Fig. 2c) is below 0.8 to

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assure the validity of the Gaussian assumption of this estimate23.

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Figure 3a shows how the OTM33A emission estimation results compare to the simulated

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methane emissions when liquids unloading events are excluded (Table S4 shows actual values).

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The ensemble mean of the estimated-to-simulated methane emission ratio is 1.08 (1.13 when

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considering estimates with R2>0.8), showing little bias over all time periods and pads, while 93%

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and 76% of the data fall between factors of 2.0 and 1.5, respectively. Additionally, 64% of the

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data have +/- 30% percent error, which is comparable but slightly lower than the 72% found by

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Brantley et al. 23 when assessing the OTM33A performance using controlled outdoor releases.

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However, these studies are not directly comparable as Brantley et al. 23 used a range of

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meteorological conditions and observation distances while in this study large scale

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meteorological conditions and observations distances are kept constant. Despite these

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differences, we hypothesize that the lower performance in this study could be attributed in part to

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the time variation in emissions in contrast to the steady emissions used during controlled

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releases.

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Figure 3b shows the scenario where emissions from liquids unloading are included (Table S5

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shows actual values), showing a large overestimation of emissions on pads #5 and #6 that are

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directly downwind from pads #1 and #4 containing the liquids unloading events (Fig. 1). As pad

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#4 is downwind of pad #1, the emission overestimation is also found for pad #4 during periods

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outside of the liquid unloading event of this pad. The methane background estimated by

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OTM33A is not increased for these pads when they are under the influence of liquid unloading

332

events from other pads (Table S5); thus this outer influence is assigned to the local emissions

333

producing the overestimation. Although some of these estimates can be screened using R2 fit of


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334

the PSG lower than 0.8, some would still be accepted. The OTM33A method includes a mapping

335

survey in its protocol consisting of a vehicle driving around the source before the stationary

336

emission quantification. This survey is performed to potentially identify issues or cases such as

337

the influence of nearby sources. The results here suggest the survey may be useful to eliminate

338

some cases.

339

Figures 3c,d show the performance of the variational and BI-MCMC methods. We find that 98%

340

and 95% of the estimated to simulated emission ratios are within the 1:2/2:1 and 1:1.5/1.5:1

341

lines, respectively, for both methods. Also, after considering estimates with R2 > 0.8 (R2 from the

342

PSG Gaussian fit) we find that 92% and 95% of the data fall within +/- 30% percent error for the

343

variational and BI-MCMC methods, respectively. This shows that the emission estimation

344

methods based on LES simulations contain much lower spread than the OTM33A estimate.

345

Sensitivity analysis was performed by changing the standard deviation of the observation errors

346

around the value originally selected (changed by ±5% and ±10% for the variational and BI-

347

MCMC, respectively) obtaining an increase in the spread with 87% and 92% of data falling

348

within +/-30% percent error for the variational and BI-MCMC methods, respectively. Thus,

349

although results are dependent on these parameters, the skill remains significantly higher than the

350

OTM33A. The variational and BI-MCMC methods also show low overall bias, with ensemble

351

mean of the estimated-to-simulated methane emission ratios of 0.93 and 0.97, respectively.

352

Opposite to the OTM33A results, the overall bias is actually improved for these methods (0.96

353

and 1.0 for variational and BI-MCMC, respectively) when only considering emission estimates

354

with R2 of the Gaussian fit over 0.8.

355

Since the OTM33A method is designed to estimate a single source, a sensitivity test with the

356

LES based methods estimating only the largest source from each well pad was performed. The


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357

variational and BI-MCMC methods showed similar results, with 95% and 81% of the emission

358

ratios within a factor of 2.0 and 1.5, respectively, and 72% of the data with percent errors within

359

+/- 30%. These results are similar to what is obtained with OTM33A, showing that the strategy

360

developed for fitting the winds using the modified LES simulation, as opposed to observed

361

winds in the OTM33A method, was effective. Also, these results show that estimating the

362

emission rate of the multiple sources within a well pad is critical for the reduction on the spread

363

of the total emission estimates from a well pad.

364

Although the variational and BI-MCMC methods use the same basis functions in their

365

algorithms (based on LES simulations), their performances differ, with the BI-MCMC showing

366

slightly better bias and spread. Variational methods target solutions that do not deviate largely

367

from the initial guess, while the BI-MCMC method samples the whole parameter space without

368

penalizing solutions that significantly deviate from the initial estimate. Thus, when the initial

369

guesses are far from the simulated emissions (i.e., there is not a single dominating source), the

370

variational method is at a disadvantage and the BI-MCMC method produces better results. This

371

is reflected in the variational model producing a poorer fit to the observed data with a slightly

372

lower fraction of R2 values above 0.9 (52% variational vs 57% BI-MCMC) and 0.8 (81%

373

variational vs 86% BI-MCMC), and a poorer bias especially for pads #3 (mean ratio 0.74

374

variational vs 0.83 BI-MCMC) and #4 (mean ratio 0.78 variational vs 0.96 BI-MCMC), where

375

different equipment can have comparable emissions within the pad (Fig. S1). A sensitivity test

376

was performed for the variational inversion considering an initial guess with equal distribution of

377

emissions between components, resulting in a similar overall performance (0.97 mean emission

378

ratio, and 90% of emissions within +/- 30% percent error). This shows that the prior distribution

379

of emissions within components plays a small role given the large uncertainty assumed in the


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380

emission errors. As a general conclusion, the use of the BI-MCMC method over the variational

381

method is recommended when there are large a priori uncertainties in the contribution of each

382

source to the total emissions from a well pad.

383

Emission estimates from all methods are biased low for two 20-minute time periods captured at

384

pads #4 and #6 (Fig. 3a, c, d). Analysis of the simulated emissions (Fig. S1) and simulated

385

methane observations (Fig. S7) time series shows that these periods contain tank-flashing events

386

(large emissions over a short period of time). In the case of pad #4 the wind direction has a

387

persistent easterly component for a few minutes and thus the plume from the tank-flashing

388

emissions is not captured in the simulated concentration time series. On the other hand, the tank-

389

flashing plume from pad #6 is captured very weakly (i.e., peak concentrations are not as large

390

and do not last as long compared to similar cases). The LES-based emissions estimates are

391

within a factor of two of the simulated emissions for the pad #6 case which does not occur for

392

OTM33A estimates (Fig. 3), showing that the LES-based methods show better performance than

393

OTM33A for this time period. We hypothesize this is because the fitting of the winds for each

394

data point is able to replicate instantaneous conditions that lead to the emission peak being

395

weakly represented in the simulated observations. On the other hand, the OTM33A method uses

396

average meteorological conditions over the whole observational time period, and thus is not able

397

to resolve the specific time-dependent conditions. Note that tank-flashing emissions occur for

398

these and other pads for other time periods and are well captured by all methods; therefore, the

399

issues mentioned earlier are not related to the constant emission rate assumption of the OTM33A

400

and LES-based methods. Although these issues have little impact on the overall bias (all STE

401

methods show little overall bias), they do increase the spread and thus large temporal variation in

402

the emission time series is a factor that needs to be considered when evaluating STE methods.


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403

The fact that emission spikes and the distribution of sources within a well pad can have an

404

influence on the STE results confirms the value of employing an emission simulator on this

405

study.

406

Applicability of LES-based estimation methods for real-case scenarios

407

The application of the new methods proposed here are based on simulations with an LES model,

408

which requires specific modeling expertise and significant computational resources. For instance,

409

one hour of the simulations performed here requires ~24 hours on 1,296 processors on a high-

410

performance computing platform. However, the computing power needed could be substantially

411

reduced by making the LES domain smaller, containing a small area around the pad being

412

estimated as the presence of nearby pads in the simulations is not required for applying these

413

new STE methods. For instance, we estimate that domain lateral dimension would have to be

414

between 0.5-1 km if a single pad was modeled, which implies a reduction of a factor of 50 to 200

415

in the computational requirements. Also, new developments in LES models, including the

416

portability to a Graphic Processing Unit (GPU) -based system, are making LES simulations

417

faster and computationally more affordable 36. Another requirement of the LES is information on

418

vertical profiles of meteorological variables, which could be obtained from nearby soundings as

419

has been done in previous studies 11. A combination of surface weather measurements performed

420

as part of the STE method (in the case of OTM33A a weather station and an ultrasonic

421

anemometer are required) and reanalysis from numerical weather prediction models to constrain

422

the upper level conditions can be used when the nearest sounding is far away and is not

423

representative of the local weather conditions.


19


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424

The results show a large reduction in spread of the emissions estimates when using multiple

425

sources and their potential locations on the well pads as opposed to assuming only a single

426

source as in the PSG formula used by OTM33A. This requires knowledge of the potential

427

emitting sources on the well pad, which can be obtained by multiple methods including visual

428

inspection of the equipment on a specific site, use of infrared cameras, and use of aerial

429

imagery25. The OTM33A method already requires an evaluation of the source location using

430

these methods, thus little additional work is required to perform this assessment for the LES-

431

based methods. A limitation is that locations of unexpected leaks will not be included, but this is

432

a limitation of most STE methods including the OTM 33A as the knowledge of the source

433

location is needed. However, if the leak happens near-by equipment locations then the

434

implications of unexpected leaks could be minimized as those emissions would be assigned to

435

the nearby equipment.

436

A disadvantage of the LES-based STE methods is that the simulations would have to be

437

performed for every pad configuration and meteorological condition, requiring a large number of

438

simulations unless the pads have the same layout and the meteorological conditions do not vary

439

significantly across samples. A way to deal with this limitation is to build a database of LES

440

simulations. The LES would be run for multiple meteorological conditions that generate different

441

mean wind speeds and stability conditions but keeping the wind direction constant as the

442

measurements can be rotated. Terrain would have to be simplified to flat surfaces in the LES

443

database. An idealized circular pad (to account for a rotating squared pad) would be placed in the

444

middle of the domain (to account for LES spin-up upwind of the pad and leave room for placing

445

observations downwind) and tracers would be tagged to each grid-cell within the pad so they can

446

be activated when the potential sources are identified. The observed wind conditions would be


20

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447

matched by scanning the different simulations by applying the procedure of fitting the winds

448

explained in this work. Such a database would only need to be generated once and could be

449

sampled for multiple well locations and campaigns without much extra cost.

450

The OTM33A method has the advantage that it is straightforward to apply with little

451

computational requirements once the concentration and wind data have been collected on site.

452

Here we show that OTM33A can produce nearly unbiased results, but with substantial spread.

453

The methods based on LES simulations developed here can help reduce the spread but to a

454

substantial additional cost, thus they can be used when the more precise emissions are needed

455

and the extra costs can be afforded.

456

The LES-based STE methods presented here use simulated methane observations extracted from

457

the WRF-LES output using the OTM33A protocols. These methods can also be applied to other

458

measurement protocols that have been developed to estimate emissions using mobile in-situ

459

measurements 7, 9, 10, or even with in-situ or remote sensing data collected from aircraft 37, 38, and

460

can even be applied to other type of point sources where different measurement protocols and

461

emission estimation methods have been developed 39. The results presented in this work

462

represent the first such application of a high-resolution synthetic emission data set to evaluate

463

measurement and STE methods at a single well pad. Also, these methods could be evaluated and

464

compared with each other using a similar framework as the one presented here to better assess

465

their skill and limitations.


21


466

Page 22 of 29

FIGURES

467 468

Figure 1. a) Terrain used in the inner domain including the location of the seven well pads. Axis

469

shows latitude and longitude in degrees while contours show elevation in meters. The scale is

470

shown on the upper right corner. b) Hypothetical layout of the possible components of a pad

471

including tank with flashing emissions (TF), tank with liquids unloading emissions (TL),

472

chemical injection pump (CI), dehydrator (D), pneumatics (P) and compressor (C). The index

473

indicates to which well the components belong to as each pad contains two wells. The letter O

474

shows the potential location of the simulated observations. The solid black line represents the

475

pad outline (100 m by 100 m) and the solid blue lines the LES grid (10 m resolution).

476


22

Page 23 of 29


477 478

Figure 2. Methane concentrations (ppm) as a function of wind speed and wind direction

479

corresponding to a) field observations from the OTM 33A test files

25

, and b) simulated


23


Page 24 of 29

480

observations for pad #1 for the first 20-minute period without liquids unloading emissions. c)

481

Methane concentration binned by wind direction and Gaussian fit used in the PSG estimate for

482

the data in b). a), b) and c) are produced using code found in the OTM33A Appendix F1.

483 484

Figure 3. Scatter plots of total methane emissions from a well pad comparing estimates versus

485

simulated emissions. a) shows OTM33A estimates for the no-liquids unloading emission

486

scenario, and b) shows OTM33A estimates when all emissions are considered. c) shows

487

variational inversion and d) BI-MCMC estimates, both for the no-liquids unloading emission


24

Page 25 of 29


488

scenario. Note the change in scale on b). Colors represent the R2 of the Gaussian fit for OTM33A

489

(a and b) and R2 of the fit to the observations after variational and BI-MCMC estimates (c and

490

d). Black, magenta, and red show R2 over 0.9, in between 0.9 and 0.8, and lower than 0.8,

491

respectively.

492

Supporting Information.

493

Supplemental figures, tables and text are provided in a single PDF

494 495

AUTHOR INFORMATION

496

Corresponding Author and Present Addresses

497

* †Now at UCLA Department of Atmospheric & Oceanic Sciences, 520 Portola Plaza, 7127

498

Math Sciences Building, MS 156505, Los Angeles, CA 90095. Email: [email protected]

499

ACKNOWLEDGMENTS

500

The National Center for Atmospheric Research is supported by the National Science Foundation.

501

This work was funded by the ExxonMobil Upstream Research Company. We acknowledge the

502

contributions from four anonymous reviewers which helped to improve an early version of this

503

manuscript considerably.

504

REFERENCES

505 506 507 508

1. Brandt, A. R.; Heath, G. A.; Kort, E. A.; O'Sullivan, F.; Pétron, G.; Jordaan, S. M.; Tans, P.; Wilcox, J.; Gopstein, A. M.; Arent, D.; Wofsy, S.; Brown, N. J.; Bradley, R.; Stucky, G. D.; Eardley, D.; Harriss, R., Methane Leaks from North American Natural Gas Systems. Science 2014, 343, (6172), 733-735.


25


Page 26 of 29

509 510 511

2. Alvarez, R. A.; Pacala, S. W.; Winebrake, J. J.; Chameides, W. L.; Hamburg, S. P., Greater focus needed on methane leakage from natural gas infrastructure. Proceedings of the National Academy of Sciences 2012, 109, (17), 6435-6440.

512 513 514 515 516 517 518

3. Stocker, T. F.; Qin, D.; Plattner, G.-K.; Tignor, M.; Allen, S. K.; Boschung, J.; Nauels, A.; Xia, Y.; Bex, V.; Midgley, P. M. Climate Change 2013. The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change-Abstract for decision-makers; Groupe d'experts intergouvernemental sur l'evolution du climat/Intergovernmental Panel on Climate Change-IPCC, C/O World Meteorological Organization, 7bis Avenue de la Paix, CP 2300 CH-1211 Geneva 2 (Switzerland): 2013.

519 520 521 522 523

4. Zavala-Araiza, D.; Lyon, D. R.; Alvarez, R. A.; Davis, K. J.; Harriss, R.; Herndon, S. C.; Karion, A.; Kort, E. A.; Lamb, B. K.; Lan, X.; Marchese, A. J.; Pacala, S. W.; Robinson, A. L.; Shepson, P. B.; Sweeney, C.; Talbot, R.; Townsend-Small, A.; Yacovitch, T. I.; Zimmerle, D. J.; Hamburg, S. P., Reconciling divergent estimates of oil and gas methane emissions. Proceedings of the National Academy of Sciences 2015, 112, (51), 15597-15602.

524 525 526

5. Hanna, S. R.; Young, G. S., The need for harmonization of methods for finding locations and magnitudes of air pollution sources using observations of concentrations and wind fields. 2017, 148, (Supplement C), 361-363.

527 528 529

6. Brantley, H. L.; Thoma, E. D.; Squier, W. C.; Guven, B. B.; Lyon, D., Assessment of Methane Emissions from Oil and Gas Production Pads using Mobile Measurements. Environmental Science & Technology 2014, 48, (24), 14508-14515.

530 531 532

7. Rella, C. W.; Tsai, T. R.; Botkin, C. G.; Crosson, E. R.; Steele, D., Measuring Emissions from Oil and Natural Gas Well Pads Using the Mobile Flux Plane Technique. Environmental Science & Technology 2015, 49, (7), 4742-4748.

533 534 535 536 537

8. Lamb, B. K.; McManus, J. B.; Shorter, J. H.; Kolb, C. E.; Mosher, B.; Harriss, R. C.; Allwine, E.; Blaha, D.; Howard, T.; Guenther, A.; Lott, R. A.; Siverson, R.; Westburg, H.; Zimmerman, P., Development of Atmospheric Tracer Methods To Measure Methane Emissions from Natural Gas Facilities and Urban Areas. Environmental Science & Technology 1995, 29, (6), 1468-1479.

538 539 540

9. Day, S.; Dell’Amico, M.; Fry, R.; Tousi, H. J., Field measurements of fugitive emissions from equipment and well casings in Australian coal seam gas production facilities. CSIRO: Australia, 2014.

541 542 543

10. Yacovitch, T. I.; Herndon, S. C.; Pétron, G.; Kofler, J.; Lyon, D.; Zahniser, M. S.; Kolb, C. E., Mobile Laboratory Observations of Methane Emissions in the Barnett Shale Region. Environmental Science & Technology 2015, 49, (13), 7889-7895.

544 545 546

11. Lan, X.; Talbot, R.; Laine, P.; Torres, A., Characterizing Fugitive Methane Emissions in the Barnett Shale Area Using a Mobile Laboratory. Environmental Science & Technology 2015, 49, (13), 8139-8146.


26

Page 27 of 29


547 548 549

12. Omara, M.; Sullivan, M. R.; Li, X.; Subramanian, R.; Robinson, A. L.; Presto, A. A., Methane emissions from conventional and unconventional natural gas production sites in the Marcellus Shale Basin. 2016, 50, (4), 2099-2107.

550 551 552 553 554

13. Karion, A.; Sweeney, C.; Kort, E. A.; Shepson, P. B.; Brewer, A.; Cambaliza, M.; Conley, S. A.; Davis, K.; Deng, A.; Hardesty, M.; Herndon, S. C.; Lauvaux, T.; Lavoie, T.; Lyon, D.; Newberger, T.; Pétron, G.; Rella, C.; Smith, M.; Wolter, S.; Yacovitch, T. I.; Tans, P., Aircraft-Based Estimate of Total Methane Emissions from the Barnett Shale Region. Environmental Science & Technology 2015, 49, (13), 8124-8131.

555 556 557 558

14. Nathan, B. J.; Golston, L. M.; O’Brien, A. S.; Ross, K.; Harrison, W. A.; Tao, L.; Lary, D. J.; Johnson, D. R.; Covington, A. N.; Clark, N. N.; Zondlo, M. A., Near-Field Characterization of Methane Emission Variability from a Compressor Station Using a Model Aircraft. Environmental Science & Technology 2015, 49, (13), 7896-7903.

559 560 561 562

15. Lavoie, T. N.; Shepson, P. B.; Cambaliza, M. O. L.; Stirm, B. H.; Karion, A.; Sweeney, C.; Yacovitch, T. I.; Herndon, S. C.; Lan, X.; Lyon, D., Aircraft-Based Measurements of Point Source Methane Emissions in the Barnett Shale Basin. Environmental Science & Technology 2015, 49, (13), 7904-7913.

563 564 565

16. Hirst, B.; Jonathan, P.; González del Cueto, F.; Randell, D.; Kosut, O., Locating and quantifying gas emission sources using remotely obtained concentration data. 2013, 74, (Supplement C), 141-158.

566 567

17. Arnold, C. P.; Dey, C. H., Observing-Systems Simulation Experiments: Past, Present, and Future. Bulletin of the American Meteorological Society 1986, 67, (6), 687-695.

568 569

18. Hoffman, R. N.; Atlas, R., Future Observing System Simulation Experiments. Bulletin of the American Meteorological Society 2015, 97, (9), 1601-1616.

570 571 572 573

19. Conley, S.; Faloona, I.; Mehrotra, S.; Suard, M.; Lenschow, D. H.; Sweeney, C.; Herndon, S.; Schwietzke, S.; Pétron, G.; Pifer, J.; Kort, E. A.; Schnell, R., Application of Gauss's theorem to quantify localized surface emissions from airborne measurements of wind and trace gases. Atmos. Meas. Tech. 2017, 10, (9), 3345-3358.

574 575 576 577

20. Viatte, C.; Lauvaux, T.; Hedelius, J. K.; Parker, H.; Chen, J.; Jones, T.; Franklin, J. E.; Deng, A. J.; Gaudet, B.; Verhulst, K.; Duren, R.; Wunch, D.; Roehl, C.; Dubey, M. K.; Wofsy, S.; Wennberg, P. O., Methane emissions from dairies in the Los Angeles Basin. Atmos. Chem. Phys. 2017, 17, (12), 7509-7528.

578 579 580 581

21. Caulton, D. R.; Li, Q.; Bou-Zeid, E.; Lu, J.; Lane, H. M.; Fitts, J. P.; Buchholz, B.; Golston, L. M.; Guo, X.; McSpiritt, J.; Pan, D.; Wendt, L.; Zondlo, M. A., Improving Mobile Platform Gaussian-Derived Emission Estimates Using Hierarchical Sampling and Large Eddy Simulation. 2017, 2017, 1-39.

582 583 584

22. Foster-Wittig, T. A.; Thoma, E. D.; Albertson, J. D., Estimation of point source fugitive emission rates from a single sensor time series: A conditionally-sampled Gaussian plume reconstruction. Atmospheric Environment 2015, 115, (Supplement C), 101-109.


27


Page 28 of 29

585 586 587 588

23. Brantley, H. L.; Thoma, E. D.; Eisele, A. P., Assessment of volatile organic compound and hazardous air pollutant emissions from oil and natural gas well pads using mobile remote and on-site direct measurements. Journal of the Air & Waste Management Association 2015, 65, (9), 1072-1082.

589 590 591 592

24. Robertson, A. M.; Edie, R.; Snare, D.; Soltis, J.; Field, R. A.; Burkhart, M. D.; Bell, C. S.; Zimmerle, D.; Murphy, S. M., Variation in Methane Emission Rates from Well Pads in Four Oil and Gas Basins with Contrasting Production Volumes and Compositions. Environmental Science & Technology 2017, 51, (15), 8832-8840.

593 594 595

25. EPA, Other Test Method (OTM) 33 and 33A Geospatial Measurement of Air PollutionRemote Emissions Quantiﬁcation-Direct Assessment (GMAP-REQ-DA). https://www.epa.gov/emc/emc-other-test-methods, 2016.

596 597 598

26. Moeng, C.-H.; Dudhia, J.; Klemp, J.; Sullivan, P., Examining Two-Way Grid Nesting for Large Eddy Simulation of the PBL Using the WRF Model. Monthly Weather Review 2007, 135, (6), 2295-2311.

599 600 601

27. Nunalee, C. G.; Kosović, B.; Bieringer, P. E., Eulerian dispersion modeling with WRFLES of plume impingement in neutrally and stably stratified turbulent boundary layers. Atmospheric Environment 2014, 99, 571-581.

602

28.

603 604

29. Ruiz-Columbie, A., Atmospheric boundary layer evening transitions over West Texas. 2008.

605 606 607

30. Smith, N.; Blewitt, D.; Hebert, L. B. In Impact of routine episodic emissions on the expected frequency distribution of emissions from oil and gas production sources, AGU Fall Meeting, San Francisco, USA, 2015, 2015; San Francisco, USA, 2015.

608 609 610 611 612 613

31. Saide, P. E.; Peterson, D.; da Silva, A.; Anderson, B.; Ziemba, L. D.; Diskin, G.; Sachse, G.; Hair, J.; Butler, C.; Fenn, M.; Jimenez, J. L.; Campuzano-Jost, P.; Perring, A. E.; Schwarz, J. P.; Markovic, M. Z.; Russell, P.; Redemann, J.; Shinozuka, Y.; Streets, D. G.; Yan, F.; Dibb, J.; Yokelson, R.; Toon, O. B.; Hyer, E.; Carmichael, G. R., Revealing important nocturnal and dayto-day variations in fire smoke emissions through a multiplatform inversion. Geophysical research letters 2015, 2015GL063737.

614 615 616 617 618

32. Delle Monache, L.; Lundquist, J. K.; Kosović, B.; Johannesson, G.; Dyer, K. M.; Aines, R. D.; Chow, F. K.; Belles, R. D.; Hanley, W. G.; Larsen, S. C.; Loosmore, G. A.; Nitao, J. J.; Sugiyama, G. A.; Vogt, P. J., Bayesian Inference and Markov Chain Monte Carlo Sampling to Reconstruct a Contaminant Source on a Continental Scale. Journal of Applied Meteorology and Climatology 2008, 47, (10), 2600-2613.

619 620 621

33. Chow, F. K.; Kosović, B.; Chan, S., Source Inversion for Contaminant Plume Dispersion in Urban Environments Using Building-Resolving Simulations. Journal of Applied Meteorology and Climatology 2008, 47, (6), 1553-1572.

Oke, T., Boundary layer climates. 2nd. Methuen, 289p 1987.


28

Page 29 of 29


622 623 624

34. Saide, P. E.; Bocquet, M.; Osses, A.; Gallardo, L., Constraining surface emissions of air pollutants using inverse modelling: method intercomparison and a new two-step two-scale regularization approach. Tellus B 2011, 63, (3), 360-370.

625 626 627

35. Zhu, C.; Byrd, R. H.; Lu, P.; Nocedal, J., Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Transactions on Mathematical Software (TOMS) 1997, 23, (4), 550-560.

628 629 630

36. Schalkwijk, J.; Jonker, H. J. J.; Siebesma, A. P.; Meijgaard, E. V., Weather Forecasting Using GPU-Based Large-Eddy Simulations. Bulletin of the American Meteorological Society 2015, 96, (5), 715-723.

631 632 633 634

37. Caulton, D. R.; Shepson, P. B.; Santoro, R. L.; Sparks, J. P.; Howarth, R. W.; Ingraffea, A. R.; Cambaliza, M. O. L.; Sweeney, C.; Karion, A.; Davis, K. J.; Stirm, B. H.; Montzka, S. A.; Miller, B. R., Toward a better understanding and quantification of methane emissions from shale gas development. Proceedings of the National Academy of Sciences 2014, 111, (17), 6237-6242.

635 636 637 638 639

38. Frankenberg, C.; Thorpe, A. K.; Thompson, D. R.; Hulley, G.; Kort, E. A.; Vance, N.; Borchardt, J.; Krings, T.; Gerilowski, K.; Sweeney, C.; Conley, S.; Bue, B. D.; Aubrey, A. D.; Hook, S.; Green, R. O., Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners region. Proceedings of the National Academy of Sciences 2016, 113, (35), 9734-9739.

640 641 642

39. Weitkamp, E. A.; Lipsky, E. M.; Pancras, P. J.; Ondov, J. M.; Polidori, A.; Turpin, B. J.; Robinson, A. L., Fine particle emission profile for a large coke production facility based on highly time-resolved fence line measurements. 2005, 39, (36), 6719-6733.

643


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Evaluating methods to estimate methane emissions from oil and gas

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