Assessment of Fugitive Methane Emissions from Near and Far-Field Sources

249 Pages·2015·18.7 MB·English

Mobile Sensors: Assessment of Fugitive Methane
Emissions from Near and Far-Field Sources
by
Tierney Foster-Wittig
DepartmentofCivilandEnvironmentalEngineering
DukeUniversity
Date:
Approved:
JohnD.Albertson,Supervisor
ZbigniewKabala
GabyKatul
AmilcarePorporato
EbenThoma
Dissertationsubmittedinpartialfulfillmentoftherequirementsforthedegreeof
DoctorofPhilosophyintheDepartmentofCivilandEnvironmentalEngineering
intheGraduateSchoolofDukeUniversity
2015
bstract
A
(CivilandEnvironmentalEngineering)
Mobile Sensors: Assessment of Fugitive Methane Emissions
from Near and Far-Field Sources
by
Tierney Foster-Wittig
DepartmentofCivilandEnvironmentalEngineering
DukeUniversity
Date:
Approved:
JohnD.Albertson,Supervisor
ZbigniewKabala
GabyKatul
AmilcarePorporato
EbenThoma
Anabstractofadissertationsubmittedinpartialfulfillmentoftherequirementsfor
thedegreeofDoctorofPhilosophyintheDepartmentofCivilandEnvironmental
Engineering intheGraduateSchoolofDukeUniversity
2015
Copyright(cid:13)c 2015 byTierneyFoster-Wittig
Allrightsreserved
Abstract
The primary focus of this dissertation is on the assessment of fugitive methane emissions
from near and far-field sources. Methane is the second most prevalent greenhouse gas
(GHG) emitted in the United States from anthropogenic activities. Due to measurement
and model limitations, there is not an accurate assessment of how much methane in the
atmosphere is due to anthropogenic sources. This dissertation focuses on measuring the
methane emissions from two of the three largest anthropogenic sources – landfills and
natural gas systems. All measurements are made with a single fixed or single mobile
sensor. Methodsaredevelopedtoassessthesourcestrengthforbothnear(i.e. naturalgas)
andfar-field(i.e. landfill)sourcesusingeitherthefixedormobilesensor.
Forfar-fieldmeasurements,astandardizedversionofamobiletracercorrelationmea-
surement method was developed and used for assessment of methane emissions from 15
landfills in 56 field deployments from 2009 to 2013. A total of 1876 mobile tracer cor-
relation measurement transects were attempted over 131 field sampling days. Transects
were analyzed using signal to noise ratio, plume correlation, and emission rate difference
method quality indicators. The application of the method quality indicators yield 456
transects(33%)thatpassdataacceptancecriteria.
Fornear-fieldsources,techniquesaredevelopedfor1)fixedsensorssamplingthrough
time downwind of a source and 2) mobile sensors passing across plumes downwind of a
source. Forthefixedsensor,thelateralplumegeometryisreconstructedfromthefluctuat-
ing wind direction using a derived relationship between the wind direction and crosswind
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plume position. The crosswind plume spread is estimated with two different methods
(modeled and observed), and subsequently used a Gaussian plume inversion to estimate
the source strengths. For the fixed sensor, the sensor takes measurements for about 20
minutes and we are able to reconstruct the ensemble average of the plume. Two tech-
niques are tested using data from controlled methane release experiments and Wyoming
field studies; these two techniques are 1) least-squares and 2) a probabilistic approach.
For the probabilistic approach, Bayesian inference tools are applied where a constant or
updated prior distribution are utilized to determine the source strength. For the controlled
releaseexperiments,thepercenterrorrangesfrom-66to48%,-84to46%,and-84to46
% for the least-squares approach, Bayesian constant prior approach, and Bayesian prior
approach source strength estimates, respectively. The mean percent error for the least-
squares approach, Bayesian constant prior approach, and Bayesian prior approach are -2
%, -8 %, and -15 % with a standard deviation of 25 %, 26 %, and 24 %, respectively. We
note that the two methods are fairly similar in terms of the expected value of the inferred
source strengths. However, the Bayesian approach offers the power of translating scatter
inthedataintoaquantitativestatementoftheuncertainintheinferredsourcestrength.
For the mobile sensor, the vehicle drives through the plume in the crosswind direc-
tion. The measurements show the lateral plume geometry of an instantaneous plume. The
instantaneousplumehasanarrowedGaussianstructure. Themobilesensormeasuredcon-
centrations from 3 controlled release observations and four studies in Colorado. For the
movingsensor,onlyonecontrolledreleasestudywiththreepasseswasestimatedyielding
theresultof-3%error. Althoughthisisapromisingresult,morecontrolledreleasestudies
willneedtobeanalyzed.
The overall goal for estimating the natural gas well source strength is to monitor and
detect leakage at production sites across the United States. The hope is to use single
sensorsattachedtomobileworkvehiclesthatdrivearoundthesiteonadailybasis. These
vehicles collect fixed sensor data when they are stationary downwind of the source as
v
well as mobile sensor data as they drive along the roads near sources. Utilizing the data
collected from these vehicles, the methods presented here will be able to estimate the
source strength with a given confidence in the estimate. The results for both the fixed
and mobile techniques show promise for use with gas sensors on industry work trucks,
opportunisticallyprovidingsurveillanceoveraregionofwellpads.
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Tomylovingsupportivefather. Thankyouforalwaysbelievinginme.
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Contents
Abstract iv
ListofTables vi
ListofFigures vii
ListofSymbolsandAbbreviations xx
Acknowledgements xxii
1 Introduction 1
1.0.1 MobileSensorMeasurements . . . . . . . . . . . . . . . . . . . 2
1.0.2 DispersionModels . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.0.3 DispersionModelEquations . . . . . . . . . . . . . . . . . . . . 7
1.0.4 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 Development of a Mobile Tracer Correlation Method for Assessment of Air
EmissionsfromLandfillsandOtherAreaSources 11
2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Materialandmethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.1 Tracerrelease . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.2 Instrumentation . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.3 Transectexecution . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3 Theory/calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.1 TransectAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . 18
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2.3.2 SourceStrength . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.3 Signal-to-Noiseratio . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.4 Dataassessment . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3.5 Methodqualityindicators . . . . . . . . . . . . . . . . . . . . . 21
2.4 Resultsanddiscussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.5 Summaryandconclusions . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.6 SupplementalInformation . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.6.1 MaterialsandMeasurement . . . . . . . . . . . . . . . . . . . . 31
2.6.2 TracerGas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.6.3 Instumentation . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.6.4 TracerCorrelationAnalysis . . . . . . . . . . . . . . . . . . . . 34
3 Estimation of point source fugitive emission rates from a single sensor time
series: aconditionally-sampledGaussianplumereconstruction 38
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.2 Materialandmethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.3 Theory/calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.4 ResultsandDiscussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.4.1 DataAssessment . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.4.2 SourceStrengthResults . . . . . . . . . . . . . . . . . . . . . . 51
3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.6 AtmosphericDispersionCalculations . . . . . . . . . . . . . . . . . . . 57
4 PointSourceEmissionEstimationfromaSingleFixedSensor: aProbabilistic
Approach 59
4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.2 TechnicalBackground . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.2.1 EnsembleAverageModeling . . . . . . . . . . . . . . . . . . . . 62
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4.2.2 BayesianInference . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.2.3 PriorDistributions . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.2.4 LikelihoodFunctions . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3 ExperimentalData . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.4 ResultsandDiscussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.4.1 SensitivityAnalysis . . . . . . . . . . . . . . . . . . . . . . . . 76
4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.6 SupplementalInformation . . . . . . . . . . . . . . . . . . . . . . . . . 78
5 PointSourceEmissionEstimationfromaSingleMobileSensor: aProbabilis-
ticApproach 81
5.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
5.4.1 SensitivityAnalysis . . . . . . . . . . . . . . . . . . . . . . . . 93
5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5.6 SupplementalData . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6 Conclusion 99
A GaussianPlumePointSourceDispersionEquations 103
B LandfillAnalysisAdditionalFigures 106
B.1 TransectFigures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
B.2 TracerReleaseFigures . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
C GaussianReconstructionAdditionalFigures 118
C.1 Controlledrelease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
C.1.1 GaussianReconstruction . . . . . . . . . . . . . . . . . . . . . . 119
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