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Appendix A GLOSSARY OF STATISTICAL AND GHG INVENTORY TERMS, continued TERM STATISTICAL DEFINITION GHG INVENTORY APPLICATION COVARIANCE A measure of the mutual dependence between two variables. DEGREES OF FREEDOM DIRECT MEASUREMENT DISTRIBUTION FUNCTION EMISSION FACTOR ERROR ESTIMATION EXPERT JUDGEMENT INDEPENDENCE INDIRECT MEASUREMENT KEY SOURCE CATEGORY The number of independent results used in estimating the standard deviation. A distribution function, or cumulative distribution function, F(x), for a random variable X, specifies the probability Pr(X ≤ x) that X is less than or equal to x. The difference between true and observed values. The assessment of the value of a quantity or its uncertainty through the assignment of numerical observation values in an estimation formula. Two random variables are independent if there is a complete absence of association between how their sample values vary. Correlation coefficients are commonly used to document lack of independence between two random variables. A measurement that produces a final result directly from the scale on an instrument. A coefficient that relates the activity data to the amount of chemical compound that is emitted. Emission factors are often based on a sample of measurement data, averaged to develop a representative rate of emission for a given activity level under a given set of operating conditions. A carefully considered, welldocumented qualitative or quantitative judgment made in the absence of unequivocal observational evidence by a person or persons who have expertise in the given field. A measurement that produces a final result by calculation using results from one or more direct measurements. A key source category is one whose estimate has a significant influence on the total direct GHGs in terms of their absolute level of emissions, their emission, or both. Pilot Version, September 2009 A-2
Appendix A GLOSSARY OF STATISTICAL AND GHG INVENTORY TERMS, continued TERM STATISTICAL DEFINITION GHG INVENTORY APPLICATION LINEAR REGRESSION LOGNORMAL DISTRIBUTION MEAN MEASUREMENT MEDIAN MONTE CARLO METHOD NORMAL DISTRIBUTION OUTLIER POPULATION PRECISION Linear regression provides a way of fitting a straight line to a set of observed data points, taking into account the effects of observational variability. This is an asymmetric distribution, which starts from zero, rises to a maximum and then tails off more slowly to infinity. The average of two or more observed values. The sample mean, or arithmetic average, is an estimator for the mean. The median, or population median, is the 50 th population percentile. For symmetric distributions it equals the mean. The principle of Monte Carlo analysis is to perform the inventory calculation many times, where each time the uncertain emission factors or model parameters and activity data are chosen randomly (by the computer) from within the distribution of uncertainties specified initially by the user. The normal (or Gaussian) distribution has a probability density function that can be defined by two parameters (the mean and the standard deviation). The population is the totality of items under consideration. In the case of a random variable, the probability distribution is considered to define the population of that variable. The degree to which data within a set cluster together. For example, if emissions observations are plotted against corresponding activity levels, the slope of the line fitted by a linear regression provides an estimate of the appropriate emission factor. Procedure for determining a value for a physical variable. Uncertainties in emission factors and/or activity data are often large and may not have normal distributions. In this case the conventional statistical rules for combining uncertainties become very approximate. Monte Carlo analysis can deal with this situation by generating an uncertainty distribution for the inventory estimate. A result that differs considerably from the main body of results in a set. Precision is the inverse of uncertainty in the sense that the more precise something is, the less uncertain it is. Pilot Version, September 2009 A-3
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Prepared by the LEVON Group, LLC an
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Table of Contents SECTION Page FORE
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List of Tables SECTION Page 2-1 Ove
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FOREWORD The global oil and natural
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DOCUMENT AT A GLANCE This document
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1.0 INTRODUCTION Policymakers use e
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The Intergovernmental Panel on Clim
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2.0 SOURCES OF UNCERTAINTY There ar
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Table 2-1. Overview of Methods Used
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the uncertainty associated with cur
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d. Data processing uncertainty Typi
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d. Material Balance For some emissi
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3.0 OVERVIEW OF MEASUREMENT PRACTIC
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3.1 Flow Measurement Practices Cont
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All these factors should be assesse
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Table 3-1. Example of FFMS Combined
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3.2 Uncertainties of Flow Measureme
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Table 3-3. Compilation of Specifica
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EXHIBIT 3-3: FACTORS TO CONSIDER WH
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3.3.2 Quantifying Sampling Precisio
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Table 3-4. Summary of Selected Carb
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3.5 Heat Content Determination The
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3.5.2 Computational Methods Heating
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using standard methods, non-standar
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0.6 0.4 0.2 Error 0 -0.2 -0.4 -0.6
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s ± t × (Equation 4-1) n where s
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4.2.1 Propagation Equations There a
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For two terms that might be correla
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EXHIBIT 4-1: Uncertainty Example fo
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EXHIBIT 4-2: Uncertainty Example fo
Page 61 and 62: Further guidance for assigning unce
Page 63 and 64: 4.4 Quantifying Measurement Uncerta
Page 65 and 66: EXHIBIT 4-4: Uncertainty Example fo
Page 69 and 70: Are the data based on a single poin
Page 71 and 72: Two methods are compared. The first
Page 73 and 74: . EXHIBIT 4-6: Uncertainty Example
Page 77 and 78: Table 4-13 shows the uncertainty in
Page 79 and 80: Table 4-13. Comparison of Annual Em
Page 81 and 82: • The evolution of activity and e
Page 83 and 84: These guidelines concentrate solely
Page 85 and 86: (Reprinted from the API Compendium)
Page 87 and 88: Table 5-2. Onshore Oil Field (High
Page 89 and 90: associated with them are assigned b
Page 91 and 92: Table 5-4. Onshore Oil Field (High
Page 93 and 94: 5.2.4 Propagating Assymetric Uncert
Page 95 and 96: U( rel) = U( rel) + U( rel) + U( re
Page 97 and 98: Coke burn rate approach (Equation 5
Page 99 and 100: Wt% C ∑ lbmole x lbmole C 12 lb C
Page 101 and 102: The CO 2 emissions are calculated u
Page 103 and 104: GHG Emissions, tonnes/yr 60,000 50,
Page 105 and 106: vast majority of the uncertainty fo
Page 107 and 108: 6.0 REFERENCES AGA, 2008, Greenhous
Page 109 and 110: The Alberta Energy and Utilities Bo
Page 111: Appendix A GLOSSARY OF STATISTICAL
Page 115 and 116: Appendix A GLOSSARY OF STATISTICAL
Page 117 and 118: APPENDIX B LIST OF INDUSTRY MEASURE
Page 119 and 120: Chapter 2.8B Establishment of the L
Page 121 and 122: Chapter 11.5.3 Chapter 12.1.1 Chapt
Page 123 and 124: Chapter 19.4 Chapter 20.1 RP 85 RP
Page 125 and 126: ISO 8222:2002 ISO 8697:1999 ISO 902
Page 127 and 128: ISO 7194:2008 Measurement of fluid
Page 129 and 130: ASTM D4292 ASTM D4892 ASTM D5002 AS
Page 131 and 132: APPENDIX C OPERATING CONDITIONS, IN
Page 133 and 134: Appendix C OPERATING CONDITIONS, IN
Page 135 and 136: Appendix D SELECT MEASUREMENT METHO
Page 137 and 138: c. ASTM UOP539-97, Refinery Gas Ana
Page 139 and 140: interference between known componen
Page 141 and 142: maintained over the room temperatur
Page 143 and 144: APPENDIX E UNITS CONVERSION
Page 145 and 146: − Gasoline density (average) = 0.
Page 147 and 148: Appendix F UNCERTAINTY ESTIMATION D
Page 149 and 150: The uncertainty for the heaters/reb
Page 151 and 152: ∑ 2 U ( abs) = U( abs) ∑(Wt%C)
Page 153 and 154: Combining the fuel usage for the tw
Page 155 and 156: Uncertainty Estimate of CO 2 e Emis
Page 157 and 158: U ( abs) = ( U ( abs) + U ( abs) +
Page 159 and 160: E E CO2e 219 tonne CO ⎛ 2 0.0108
Page 161 and 162: E CO 2 6 500× 10 scf gas lbmole ga
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• Fuel Economy (miles per gallon)
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Upper: U ( abs) = (U( abs) +U( abs)
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default CH 4 content is 5.53% from
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Uncertainty Estimate of CO 2 e Emis
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( ) Urel ( ) = Urel ( ) = Urel ( )
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Uncertainty Estimate for CH 4 and C
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specific mole % for CH 4 and CO 2 i
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E CO2 0.07239 tonne CH 4 tonne mole
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U ( rel) = U( rel) +U( rel) +U( rel
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Table F-14. Fugitive Emission and U
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U ( abs) = (U( abs) +U( abs) +U( ab
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Table F-15. Onshore Oil Field (High
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