### A-109 

# ANNEX 3 Methodological Descriptions for Additional Source or Sink Categories

3.1. Methodology for Estima ting Emissions of CH 4 

# , N 2 

# O, and Indirect Greenhouse Gases from Stationary Combustion

Estimates of CH 4 

## and N 2 

## O Emissions Methane (CH 4 

### ) and nitrous oxide (N 2 

### O) emissions from stationary combustion were estimated using IPCC emission factors and methods. Estimates were obtained by multiplying emission factors—by sector and fuel type—by fossil fuel and wood consumption data. This “top-down” methodology is characterized by two basic steps, described below. Data are presented in Table A-81 through Table A-85.

Step 1: Determine Energy Consumption by Sector and Fuel Type
Energy consumption from stationary combustion activitie s was grouped by sector: industrial, commercial, residential, electric power, and U.S. territories. For CH 4 

### and N 2 

### O, estimates were based upon consumption of coal, gas, oil, and wood. Energy consumption data for the United States were obtained from EIA’s Monthly Energy Review, January 2010 and Published Supplemental Tables on Petroleum Product detail (EIA 2011). Wood consumption data for the United States was obtained from EIA’s Annual Energy Review (EIA 2010). Because the United States does not include territories in its national energy statistics, fuel cons umption data for territories were collected separately from the EIA from Jacobs (2010). 34 

### Fuel consumption for the industrial sector was adjusted to subtract out construction and agricultural use, which is reported under mobile sources. 35 

### Construction and agricultural fuel use was obtained from EPA (2010). The energy consumption data by sector were then adjusted from higher to lower heating values by multiplying by 0.9 for natural gas and wood and by 0.95 for coal and petroleum fuel. This is a simplified convention used by the International Energy Agency. Table A-81 provides annual energy consumption data for the years 1990 through 2009.

Step 2: Determine the Amount of CH 4 

### and N 2 

### O Emitted
Activity data for each sector and fuel type were then multiplied by emission factors to obtain emission estimates. Emission factors for the residential, commercial, indus trial, and electric power sectors were taken from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006). These N 2 

### O emission factors by fuel type (consistent across sectors) were also assumed for U.S. territories. The CH 4 

### emission factors by fuel ty pe for U.S. territories were estimated based on the emission factor for the primary sect or in which each fuel was combusted. Table A-82 provides emission factors used for each sector and fuel type.



## Estimates of NO x 

## , CO, and NMVOC Emissions

Emissions estimates for NO
x 

### , CO, and NMVOCs were obtained from preliminary data (EPA 2010b, EPA 2009) and disaggregated based on EPA (2003), which, in its final iter ation, will be published on the National Emission Inventory (NEI) Air Pollutant Emission Trends web site.
For indirect greenhouse gases, the major source categories included coal, fuel oil, natural gas, wood, other fuels (i.e., bagasse, liquefied petroleum gases, coke, coke oven gas, and others), and stationary internal combustion, which includes emissions from internal combustion engines not used in transportation. EPA periodically estimates emissions of NO
x 

### , CO, and NMVOCs by sector and fuel type using a "bottom-up" estimating procedure. In other words, the emissions were calculated either for individual sources (e.g., industrial boilers) or for many sources combined, using basic activity data (e.g., fuel consumption or deliveries, etc.) as indicators of emissions. The national activity data used to calculate the individual categories were obtained from various sources. Depending upon the category, these activity data may include

34 U.S. territories data also include combustion from mobile ac tivities because data to allocate territories’ energy use were una vailable. For this reason, CH 4 and N 2 O emissions from combustion by U.S. Territories are only included in the stationary combustion totals. 35 Though emissions from construction and farm use occur due to bot h stationary and mobile sources, detailed data was not availab le to determine the magnitude from each. Currently, these emissions are assumed to be predominantly from mobile sources.

### A-110 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 fuel consumption or deliveries of fuel, tons of refuse burned, raw material processed, etc. Activity data were used in conjunction with emission factors that relate the quantity of emissions to the activity. Table A-83 through Table A-85 present indirect greenhouse gas emission estimates for 1990 through 2008.
The basic calculation procedure for most source cate gories presented in EPA (2003) and EPA (2009) is represented by the following equation:

E p,s 

### = A s 

### × EF p,s 

### × (1 - C p,s 

### /100) Where, E = Emissions p = Pollutant s = Source category A = Activity level EF = Emission factor C = Percent control efficiency

The EPA currently derives the overall emission control efficiency of a category from a variety of sources, including published reports, the 1985 National Acid Precipitation and Assessment Program (NAPAP) emissions inventory, and other EPA databases. The U.S. approach for estimating emissions of NO
x 

### , CO, and NMVOCs from stationary combustion as described above is similar to the methodology recommended by the IPCC (IPCC/UNEP/OECD/IEA 1997).A-111Table A-81: Fuel Consumption by Stationary Combustion for Calculating CH4and N2O Emissions (TBtu)Fuel/End-Use Sector 19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Coal 18,06419,138 20,032 20,517 20,802 20,779 21,682 21,085 21,139 21,562 21,713 22,095 21,758 22,062 21,709 19,283Residential 3117 17 16 12 14 11 12 12 12 11 8 6 8 8 7Commercial 124117 122 129 93 103 92 97 90 82 103 97 65 70 69 61Industrial 1,6401,527 1,455 1,457 1,471 1,373 1,349 1,3581,244 1,249 1,262 1,219 1,188 1,130 1,083 881Electric Power 16,26117,466 18,429 18,905 19,216 19,279 20,220 19,614 19,783 20,185 20,305 20,737 20,462 20,808 20,513 18,296U.S. Territories 710 10 10 11 10 10 4 11 34 32 33 37 47 36 38Petroleum 6,7495,425 5,933 5,879 5,697 5,844 6,047 6,657 5,978 6,630 6,881 6,812 6,413 6,217 5,439 4,994Residential 1,3751,261 1,397 1,334 1,207 1,342 1,427 1,463 1,359 1,466 1,475 1,3691,205 1,225 1,215 1,197Commercial 891694 718 655 609 614 694 719 645 762 767 716 678 681 669 708Industrial 2,8182,254 2,566 2,519 2,129 2,216 2,310 2,567 2,456 2,576 2,774 2,869 3,261 3,103 2,609 2,195Electric Power 1,289755 817 927 1,306 1,211 1,144 1,277961 1,205 1,212 1,235 648 657 468 390U.S. Territories 375462 435 445 445 461 472 632 557 622 654 623 621 552 479 504Natural Gas 18,20221,088 21,492 21,556 21,230 21,254 22,281 21,400 22,129 21,550 21,604 21,243 20,904 22,315 22,436 21,974Residential 4,4914,954 5,354 5,093 4,646 4,835 5,105 4,889 5,014 5,209 4,981 4,9464,476 4,850 4,989 4,852Commercial 2,6823,096 3,226 3,285 3,083 3,115 3,252 3,097 3,225 3,261 3,201 3,0732,902 3,094 3,211 3,168Industrial 7,7218,736 9,049 9,052 8,827 8,402 8,619 7,933 8,100 7,806 7,802 7,1857,125 7,340 7,378 6,887Electric Power 3,3094,302 3,862 4,126 4,675 4,902 5,293 5,458 5,767 5,246 5,595 6,0156,375 7,005 6,829 7,038U.S. Territories 00 0 0 0 0 13 23 23 27 25 24 26 27 29 27Wood 2,2162,370 2,437 2,371 2,184 2,214 2,262 2,006 1,995 2,002 2,121 2,1362,109 2,098 2,044 1,891Residential 580520 540 430 380 390 420 370 380 400 410 430 390 430 450 430Commercial 6672 76 73 64 67 71 67 69 71 70 70 65 69 73 72Industrial 1,4421,652 1,683 1,731 1,603 1,620 1,636 1,443 1,396 1,363 1,476 1,4521,472 1,413 1,344 1,217Electric Power 129125 138 137 137 138 134 126 150 167 165 185 182 186 177 173U.S. Territories NENE NE NE NE NE NE NE NE NE NE NE NE NE NE NENE (Not Estimated)Note: Totals may not sum due to independent rounding.

### A-112 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A-82: CH 4 

### and N 2 

### O Emission Factors by Fuel Type and Sector (g/GJ) 36

Fuel/End-Use Sector CH 4 N 2 O Coal

Residential 300 1.5 Commercial 10 1.5 Industrial 10 1.5 Electric Power 1 1.5 U.S. Territories 1 1.5 Petroleum

Residential 10 0.6 Commercial 10 0.6 Industrial 3 0.6 Electric Power 3 0.6 U.S. Territories 5 0.6 Natural Gas

Residential 5 0.1 Commercial 5 0.1 Industrial 1 0.1 Electric Power 1 0.1 U.S. Territories 1 0.1 Wood

Residential 300 4.0 Commercial 300 4.0 Industrial 30 4.0 Electric Power 30 4.0 U.S. Territories NA NA NA (Not Applicable)

36 GJ (Gigajoule) = 10 9 joules. One joule = 9.486×10 -4 BtuA-113Table A-83: NOx Emissions fromStationary Combustion (Gg)Sector/Fuel Type1990199519961997199819992000200120022003200420052006200720082009Electric Power6,0455,7925,5815,6835,6375,1834,8294,4534,2653,9883,7113,4343,1213,0072,7221,766Coal5,1195,0615,0795,1184,9324,4374,1303,8023,6343,3983,1622,9262,6592,5622,3191,505Fuel Oil200871071312021791471491421331241141041009159Natural gas513510248277329393376325310290270250227219198128WoodNANA56243336373633312926252315Other FuelsaNANANANANANANANANANANANANANANANAInternal Combustion2131341421501491411401401431341241151051019159Industrial2,5592,6502,6662,6142,5702,2832,2782,2961,6991,6381,5771,5151,5201,5251,5301,478Coal530541490487475475484518384370356342343344345334Fuel Oil24022420319619019016615311411010510110210210299Natural gas877999900880869706710711526507488469471472474458WoodNANANANANANANANANANANANANANANANAOther Fuelsa1191111091031041001091168682797677777774Internal Combustion792774965948932813809798591569548527528530532514Commercial671607734539510483507428438456473490486483479501Coal36353032342321211919191919191919Fuel Oil88948688735452525049494949494949Natural gas181210224229220156161165157156156155155155154154WoodNANANANANANANANANANANANANANANANAOther Fuelsa366269394191183249273189213231249267263261257280Residential749813726699651441439446423421420418417417416414CoalbNANANANANANANANANANANANANANANANAFuel OilbNANANANANANANANANANANANANANANANANatural GasbNANANANANANANANANANANANANANANANAWood42442727272521222121212020202020Other Fuelsa707769699671624416417424402400399397397396396394Total10,0239,8629,7079,5349,3698,3908,0537,6236,8256,5036,1815,8585,5455,4325,1484,159NA (Not Applicable)a“Other Fuels” include LPG, wasteoil, coke oven gas, coke, and non-residential wood (EPA 2003, 2009, 2010b).bResidential coal, fuel oil, and natural gas emissions are included in the “Other Fuels” category (EPA 2003, 2009, 2010b).Note: Totals may not sum due to independent rounding.Table A-84: CO Emissions fromStationary Combustion (Gg)Sector/Fuel Type 19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Electric Power 329337 369 385 410 450 439 439 595 590 586 582 598 616 633 631Coal 213227 228 233 220 187 221 220 298 296 293 292 300 308 317 316Fuel Oil 189 11 13 17 36 27 28 38 37 37 37 38 39 40 40Natural gas 4649 72 76 88 150 96 92 125 124 123 122 126 129 133 132Wood NANA NA NA NA NA NA NA NA NA NA NA NA NA NA NAOther FuelsaNANA 7 8 30 24 31 32 44 43 43 43 44 45 46 46InternalCombustion 5252 52 54 55 52 63 67 91 90 89 89 91 94 97 96Industrial 797958 1,078 1,054 1,044 1,100 1,106 1,137 1,149 1,115 1,0801,045 1,064 1,084 1,103 1,030Coal 9588 100 99 96 114 118 125 127 123 119 115 117 119 121 113A-114 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009Fuel Oil 6764 49 47 46 54 48 45 46 44 43 42 42 43 44 41Natural gas 205313 307 307 305 350 355 366 370 359 347 336 342 349 355 331Wood NANA NA NA NA NA NA NA NA NA NA NA NA NA NA NAOther Fuelsa253270 316 302 303 286 300 321 325 315 305 296 301 307 312 291InternalCombustion 177222 305 299 294 296 285 279 282 274 265 257 261 266 271 253Commercial 205211 122 126 122 151 151 154 177 173 169 166 166 167 168 158Coal 1314 13 13 14 16 14 13 15 15 15 14 15 15 15 14Fuel Oil 1617 17 18 15 17 17 17 20 19 19 19 19 19 19 18Natural gas 4049 58 59 57 81 83 84 97 95 93 91 91 92 92 87Wood NANA NA NA NA NA NA NA NA NA NA NA NA NA NA NAOther Fuelsa136132 34 36 36 36 36 38 44 43 42 41 42 42 42 40Residential 3,6683,877 2,364 2,361 2,352 3,323 2,644 2,648 3,044 2,981 2,919 2,8562,867 2,878 2,889 2,725CoalbNANA NA NA NA NA NA NA NA NA NA NA NA NA NA NAFuel OilbNANA NA NA NA NA NA NA NA NA NA NA NA NA NA NANatural GasbNANA NA NA NA NA NA NA NA NA NA NA NA NA NA NAWood 3,4303,629 2,133 2,133 2,133 3,094 2,416 2,424 2,787 2,730 2,672 2,6152,624 2,635 2,645 2,495Other Fuelsa238248 231 229 220 229 228 224 257 252 246 241 242 243 244 230Total 5,0005,383 3,934 3,926 3,928 5,024 4,340 4,377 4,965 4,860 4,753 4,6494,695 4,744 4,792 4,543NA (Not Applicable)a“Other Fuels” include LPG, wasteoil, coke oven gas, coke, and non-residential wood (EPA 2003, 2009, 2010b).bResidential coal, fuel oil, and natural gas emissions are included in the “Other Fuels” category (EPA 2003, 2009, 2010b).Note: Totals may not sum due to independent rounding.Table A-85: NMVOC Emissions from Stationary Combustion (Gg)Sector/Fuel Type 19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Electric Power 4340 44 47 51 49 56 55 44 44 44 44 44 45 45 46Coal 2426 25 26 26 25 27 26 21 21 21 21 21 22 22 22Fuel Oil 52 3 4 5 4 4 4 4 4 4 3 4 4 4 4Natural gas 22 7 7 9 9 12 12 10 10 10 10 10 10 10 10Wood NANA NA NA NA NA NA NA NA NA NA NA NA NA NA NAOther FuelsaNANA + + 1 2 2 2 1 1 1 1 1 1 1 1InternalCombustion 119 9 10 10 10 11 11 8 8 8 8 8 9 9 9Industrial 165187 163 160 159 156 157 160 138 132 126 121 120 119 118 110Coal 75 6 6 6 9 9 10 9 9 8 8 8 8 8 7Fuel Oil 1111 8 7 7 10 9 9 7 7 7 6 6 6 6 6Natural gas 5266 54 54 54 52 53 54 47 45 43 41 41 40 40 37Wood NANA NA NA NA NA NA NA NA NA NA NA NA NA NA NAOther Fuelsa4645 33 31 31 26 27 29 25 24 23 22 22 22 22 20InternalCombustion 4960 63 62 61 60 58 57 49 47 45 43 43 42 42 39Commercial 1821 22 22 21 25 28 29 61 53 45 33 36 38 40 23Coal 11 1 1 1 1 1 1 1 1 1 1 1 1 1 +Fuel Oil 33 3 3 3 3 4 4 6 5 3 2 3 4 5 1A-115Natural gas 710 13 13 12 11 14 14 23 18 14 9 12 16 19 4Wood NANA NA NA NA NA NA NA NA NA NA NA NA NA NA NAOther Fuelsa88 5 5 5 10 9 10 31 29 27 22 19 17 15 18Residential 686725 788 788 786 815 837 837 1,341 1,066 792 519 719 918 1,117 244CoalbNANA NA NA NA NA NA NA NA NA NA NA NA NA NA NAFuel OilbNANA NA NA NA NA NA NA NA NA NA NA NA NA NA NANatural GasbNANA NA NA NA NA NA NA NA NA NA NA NA NA NA NAWood 651688 756 757 756 794 809 809 1,298 1,032 767 502 695 888 1,081 236Other Fuelsa3537 33 32 30 21 27 27 43 35 26 17 23 30 36 8Total 912973 1,018 1,017 1,016 1,045 1,077 1,0811,585 1,296 1,008 716 918 1,120 1,321 424NA (Not Applicable)+ Does not exceed 1 Gg.a“Other Fuels” include LPG, wasteoil, coke oven gas, coke, and non-residential wood (EPA 2003, 2009, 2010b).bResidential coal, fuel oil, and natural gas emissions are included in the “Other Fuels” category (EPA 2003, 2009, 2010b).Note: Totals may not sum due to independent rounding.

### A-116 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 

# 3.2. Methodology for Estimating Emissions of CH 4 

# , N 2 

# O, and Indirect Greenhouse Gases from Mobile Co mbustion and Methodology for and Supplemental Information on Transp ortation-Related GHG Emissions

Estimating CO 2 

## Emissions by Transportation Mode Transportation-related CO 2 

### emissions, as presented in the Carbon Dioxide Emissions from Fossil Fuel Combustion section of the Energy chapter, were calculated using the methodology described in Annex 2.1. This section provides additional information on the data sources and approach used for each transportation fuel type. As noted in Annex 2.1, CO 2 

### emissions estimates for the transportation sector were calculated directly for on-road diesel fuel and motor gasoline based on data sources for individual modes of transportation (considered a “bottom up” approach). For most other fuel and energy types (i.e., jet fuel, aviation gasoline , residual fuel oil, natural gas, LPG, and electricity), CO 2 

### emissions were calculated based on transportation sector-wide fuel consumption estimates from the Energy Information Administration (EIA 2007b and EIA 2007 through 2009) and apportioned to individual modes (considered a “top down” approach).
Based on interagency discussions between EPA, EIA, and FHWA beginning in 2005, it was agreed that use of “bottom up” data would be more accurate for diesel fuel and motor gasoline, based on the availability of reliable transportation-specific data sources. A “bottom up” diesel calculation was implemented in the 1990-2005 Inventory, and a bottom-up gasoline calculation was introduced in the 1990-2006 Inventory for the calculation of emissions from on-road vehicles. Motor gasoline and diesel consumption data for on-road vehicles come from FHWA’s Highway Statistics , Table VM-1 (FHWA 1996 through 2009), and are based on federal and state fuel tax records. These fuel consumption estimates were then combined with estimates of fuel shares by vehicle type from DOE’s Transportation Energy Data Book (DOE 1993 through 2009) to develop an estimate of fuel consumption for each vehicle type (i.e., passenger cars, light-duty trucks, buses, medium- and heavy-duty trucks, motorcycles). Th e on-road gas and diesel fuel consumption estimates by vehicle type were then adjusted for each year so that the sum of gasoline and diesel fuel consumption across all vehicle categories matched the fuel consumption estimates in Highway Statistics’ Tables MF-21 and MF-27 (FHWA 1996 through 2010). Table MF-21 provided fuel consumption estimates for the most current Inventory year; Table MF-27 provided fuel consumption estimates for years 1990-2008. This resulted in a final estimate of motor gasoline and diesel fuel use by vehicle type, consistent with the FHWA total for on-road motor gasoline and diesel fuel use.
Estimates of diesel fuel consumption from rail were taken from the Association of American Railroads (AAR 2008 through 2010) for Class I railroads, the American Publ ic Transportation Association (APTA 2007 through 2010 and APTA 2006) and Gaffney (2007) for commuter rail, the Upper Gr eat Plains Transportation Institute (Benson 2002 through 2004) and Whorton (2006 through 2009) for Class II and III railroads, and DOE’s Transportation Energy Data Book (DOE 1993 through 2010) for passenger rail. Estimates of diesel from ships and boats were taken from EIA’s Fuel Oil and Kerosene Sales (1991 through 2011).
Since EIA’s total fuel consumption estimate for each fuel type is considered to be accurate at the national level, adjustments needed to be made in the estimates for other sectors to equal the EIA total. In the case of motor gasoline, estimates of fuel use by recreational boats come from EPA’s NONROAD Model (EPA 2010b), and these estimates along with those from other sectors (e.g., commercial sector, industrial sector) were adjusted. Similarly, to ensure consistency with EIA’s total diesel estimate for all sectors, the diesel co nsumption totals for the residential, commercial, and industrial sectors were adjusted downward proportionately.
As noted above, for fuels other than motor gasoline and diesel, EIA’s transportation sector total was apportioned to specific transportation sources. For jet fuel, estimate s come from: DOT (1991 through 2010) and FAA (2011, 2006) for commercial aircraft, FAA (2009) for general aviation aircra ft, and DESC (2008) for military aircraft. Estimates for biofuels, including ethanol and biodiesel were discussed sepa rately and were not apportioned to specific transportation sources. Consumption estimates for biofuels were calculated based on data from the Energy Information Administration (EIA2010b).
Table A-86 displays estimated fuel consumption by fuel and vehicle type. Table A-87 displays estimated energy consumption by fuel and vehicle type. The values in both of these tables correspond to the figures used to calculate CO 2 

### emissions from transportation. Except as noted above, they are estimated based on EIA transportation sector energy estimates by fuel type, with activity data used to apportion consumption to the various modes of transport. For motor gasoline, the figures do not include ethanol blended with gasoline; although ethanol is included in FHWA’s totals for reported motor gasoline use. Ethanol is a biofuel and in order to be in line with IPCC methodological guidance and

### A-117 UNFCCC reporting obligations, net carbon fluxes in biogenic carbon reservoirs in croplands are accounted for in the estimates for Land Use, Land-Use Change and Forestry chapter, not in Energy chapter totals. Ethanol and biodiesel consumption estimates are shown separately in Table A-88.
Table A-86. Fuel Consumption by Fuel and Vehicl e Type (million gallons unless otherwise specified) a Fuel/Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009

Motor Gasoline b 110,440.9 118,216.0 129,101.8 137,294.2 132,944.7 132,543.8 127,524.7 127,012.2 Passenger Cars 69,762.4 67,948.0 72,859.7 76,059.6 71,645.8 70,548.0 67,022.7 66,945.0 Light-Duty Trucks 34,698.4 44,369.3 50,773.8 55,336.4 55,459.3 56,117.9 54,889.7 54,826.0 Motorcycles 194.0 200.3 209.8 183.4 212.2 230.9 241.5 241.2 Buses 38.9 41.7 43.6 41.9 41.7 43.6 42.8 38.9Medium- and HeavyDuty Trucks 4,349.8 4,071.6 4,095.5 3,960.1 4,008.3 4, 046.5 3,803.7 3,454.2 Recreational Boats c 1,397.3 1,585.1 1,119.3 1,597.9 1,577.4 1, 556.9 1,524.2 1,506.8 Distillate Fuel Oil (Diesel

Fuel) 25,631.2 31,604.6 39,241.4 44,659.2 45,847.6 46,432.3 43,236.5 39,243.4 Passenger Cars 771.2 764.9 356.1 414.0 403.2 399.7 382.4 380.2 Light-Duty Trucks 1,118.5 1,452.0 1,960.6 2,518.4 2,609.9 2, 658.5 2,618.5 2,603.2 Buses 781.0 851.1 996.6 1,030.1 1,048.5 1, 048.5 1,007.3 910.4Medium- and HeavyDuty Trucks 18,574.2 23,240.7 30,179.9 35,160.0 36,078.5 36,665.4 34,706.1 31,368.6 Recreational Boats 190.4 228.1 265.6 305.4 313.4 321.4 87.8 337.5 Ships and Other Boats 735.3 1,204.3 1,376.8 785.3 729.5 799.8 218.4 118.8 Rail 3,460.6 3,863.5 4,105.9 4,446.1 4,664.5 4, 538.9 4,216.0 3,524.8 Jet Fuel d 18,349.7 17,850.3 20,491.9 19,919.9 17,386.1 17,305.8 15,906.2 14,239.3 Commercial Aircraft 14,103.4 14,795.9 17,352.8 16,532.7 14,065.0 14,163.9 12,533.8 11,425.1 General Aviation Aircraft 662.9 559.8 972.0 1,526.7 1,642.6 1, 485.6 1,705.7 1,364.3 Military Aircraft 3,583.4 2,494.6 2,167.1 1,860.5 1,678.5 1,656.3 1,666.8 1,449.9 Aviation Gasoline d 374.2 329.3 301.9 294.2 278.3 262.8 235.3 221.0 General Aviation Aircraft 374.2 329.3 301.9 294.2 278.3 262.8 235.3 221.0 Residual Fuel Oil d , e 2,006.2 2,587.4 2,962.5 1,713.1 2,046.3 2,579.4 1,770.5 1,064.8 Ships and Other Boats 2,006.2 2,587.4 2,962.5 1,713.1 2,046.3 2, 579.4 1,770.5 1,064.8 Natural Gas d (million cubic feet) 0.7 0.7 0.7 0.6 0.6 0.6 0.7 0.7 Passenger Cars - + - - - - - - Light-Duty Trucks - + - - - - - - Buses - + + + + + + + Pipelines 0.7 0.7 0.6 0.6 0.6 0.6 0.7 0.6 LPG d 265.2 205.5 138.4 308.7 320.0 256.7 459.5 487.5 Buses - 1.6 1.5 1.0 1.0 - - - Light-Duty Trucks 106.1 98.3 88.0 233.2 229.5 184.7 334.0 354.3Medium- and HeavyDuty Trucks 159.1 105.6 48.8 74.6 89.5 72.0 125.5 133.2 Electricity d 4,751.2 4,974.5 5,381.9 7,506.4 7,357.6 8, 172.6 7,699.6 7,688.5 Rail 4,751.2 4,974.5 5,381.9 7,506.4 7,357.6 8,172.6 7,699.6 7,688.5

a FHWA data on vehicle miles traveled from the VM-1 table were not available for 2009 due to a delay caused by changes in data co llection procedures. Based on data from FHWA’s Traffic Volume Trends Program, the overall increase in VMT between 2008 and 2009 was est imated to be 0.2%. Total VMT was distributed am ong vehicle classes based on trends in fuel consumption by fuel type between 2008 and 2009, as described below. Fuel use by vehicle class (also in the VM-1 table) was not av ailable from FHWA for 2009, but ch anges in overall diesel and gaso line consumption were released in Table MF21. Fuel use in vehicle cl asses that were predominantly ga soline were estimated to grow b y the rate of growth for gasoline between 2008 and 2009. Fuel use in vehicle cl asses that were predominantly diesel were estimated to fall b y the same rate that diesel fuel consumption fell overall in 2009. VMT was then distributed to vehicle classes based on these fuel consumption estimates, assuming no relative change in MPG between vehicle classes.

b Figures do not include ethanol blended in motor gasoline. Net carbon fluxes associated with ethanol are accounted for in the L and Use, LandUse Change and Forestry chapter.

c Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA t otal gasoline estimates.

d Estimated based on EIA transportation sector energy estimates by fuel type, with bottom-up activity data used for apportionmen t to modes.

e Fluctuations in reported fuel consump tion may reflect data collection problems. + Less than 0.05 million gallons or 0.05 million cubic feet - Unreported or zero



### Table A-87: Energy Consumption by Fuel and Vehicle Type (Tbtu) Fuel/Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009

### A-118 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Motor Gasoline b 13,813.0 14,678.5 16,014.8 16,729.6 16,516.8 16,470.1 15,843.4 15,779.7 Passenger Cars 8,725.3 8,436.9 9,038.1 9,268.0 8,901.1 8,766.4 8,326.8 8,317.1 Light-Duty Trucks 4,339.8 5,509.2 6,298.4 6,742.8 6,890.2 6,973.3 6,819.4 6,811.5 Motorcycles 24.3 24.9 26.0 22.8 26.4 28.7 30.0 30.0 Buses 4.9 5.2 5.4 5.1 5.2 5.4 5.3 4.8Medium- and HeavyDuty Trucks 544.0 505.6 508.0 492.2 498.0 502.8 472.6 429.1 Recreational Boats c 174.8 196.8 138.8 198.6 196.0 193.5 189.4 187.2 Distillate Fuel Oil (Diesel Fuel) 3,554.8 4,383.3 5,442.4 6,193.8 6,358.6 6,439.7 5,996.5 5,442.7 Passenger Cars 107.0 106.1 49.4 57.4 55.9 55.4 53.0 52.7 Light-Duty Trucks 155.1 201.4 271.9 349.3 362.0 368.7 363.2 361.0 Buses 108.3 118.0 138.2 142.9 145.4 145.4 139.7 126.3Medium- and HeavyDuty Trucks 2,576.1 3,223.3 4,185.7 4,876.4 5,003.7 5,085.1 4,813.4 4,350.5 Recreational Boats 26.4 31.6 36.8 42.4 43.5 44.6 12.2 46.8 Ships and Other Boats 102.0 167.0 190.9 108.9 101.2 110.9 30.3 16.5 Rail 480.0 535.8 569.4 616.6 646.9 629.5 584.7 488.9 Jet Fuel c 2,477.2 2,409.8 2,766.4 2,689.2 2,347.1 2,336.3 2,147.3 1,922.3 Commercial Aircraft 1,904.0 1,997.4 2,342.6 2,231.9 1,898.8 1,912.1 1,692.1 1,542.4 General Aviation

Aircraft 89.5 75.6 131.2 206.1 221.8 200.6 230.3 184.2 Military Aircraft 483.8 336.8 292.6 251.2 226.6 223.6 225.0 195.7 Aviation Gasoline d 45.0 39.6 36.3 35.4 33.4 31.6 28.3 26.6 General Aviation

Aircraft 45.0 39.6 36.3 35.4 33.4 31.6 28.3 26.6 Residual Fuel Oil d , e 300.3 387.3 443.5 256.4 306.3 386.1 265.0 159.4 Ships and Other Boats 300.3 387.3 443.5 256.4 306.3 386.1 265.0 159.4 Natural Gas d 679.9 724.0 672.0 623.9 625.0 665.4 694.5 684.2 Passenger Cars - 1.9 - - - - - - Light-Duty Trucks - 0.1 - - - - - - Buses - 1.0 8.3 15.8 15.5 18.6 20.7 20.8 Pipelines 679.9 721.0 663.7 608.1 609.4 646.8 673.8 663.5 LPG d 22.9 17.7 11.9 28.2 27.5 21.9 39.4 41.2 Buses - 0.1 0.1 0.1 0.1 - - - Light-Duty Trucks 9.2 8.5 7.6 21.3 19.7 15.8 28.6 30.0Medium- and HeavyDuty Trucks 13.7 9.1 4.2 6.8 7.7 6.2 10.8 11.3 Electricity d 16.2 17.0 18.4 25.6 25.1 27.9 26.1 26.1 Rail 16.2 17.0 18.4 25.6 25.1 27.9 26.1 26.1 Total 20,909.3 22,657.1 25,405.6 26,582.1 26,239.8 26,379.1 25,040.5 24,082.3

a FHWA data on vehicle miles traveled from the VM-1 table were not available for 2009 due to a delay caused by changes in data co llection procedures. Based on data from FHWA’s Traffic Volume Trends Program, the overall increase in VMT between 2008 and 2009 was est imated to be 0.2%. Total VMT was distributed am ong vehicle classes based on trends in fuel consumption by fuel type between 2008 and 2009, as described below. Fuel use by vehicle class (also in the VM-1 table) was not av ailable from FHWA for 2009, but ch anges in overall diesel and gaso line consumption were released in Table MF21. Fuel use in vehicle cl asses that were predominantly gasoline were estimated to grow b y the rate of growth for gasoline between 2008 and 2009. Fuel use in vehicle cla sses that were predominantly diesel were estimated to fall b y the same rate that diesel fuel consumption fell overall in 2009. VMT was then distributed to vehicle classes based on these fuel consumption estimates, assuming no relative change in MPG between vehicle classes.

b Figures do not include ethanol blended in motor gasoline. Net carbon fluxes associated with ethanol are accounted for in the L and Use, LandUse Change and Forestry chapter.

c Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA t otal gasoline estimates.

d Estimated based on EIA transportation sector energy estima tes, with bottom-up data used for apportionment to modes

e Fluctuations in reported fuel consump tion may reflect data collection problems. - Unreported or zero



### Table A-88. Biofuel Consumption by Fuel Type (million gallons) Fuel Type 

### 1990 1995 2000 2005 2006 2007 2008 2009 Ethanol 717.5 1,335.8 1,601.7 3,888.0 5,245.0 6,611.6 9,331.5 10,432.2 Biodiesel NA NA NA 137.5 394.6 542.3 484.9 514.0

### A-119 

## Estimates of CH 4 

## and N 2 

## O Emissions Mobile source emissions of greenhouse gases other than CO 2 

### are reported by transport mode (e.g., road, rail, aviation, and waterborne), vehicle type, and fuel type. Emissions estimates of CH 4 

### and N 2 

### O were derived using a methodology similar to that outlined in the Revised 1996 IPCC Guidelines (IPCC/UNEP/OECD/IEA 1997).
Activity data were obtained from a number of U.S. government agencies and other publications. Depending on the category, these basic activity data included fuel consumpt ion and vehicle miles traveled (VMT). These estimates were then multiplied by emission factors, expressed as gram s per unit of fuel consumed or per vehicle mile.

Methodology for On-Road Gasoline and Diesel Vehicles

Step 1: Determine Vehicle Miles Traveled by Vehicle Type, Fuel Type, and Model Year
VMT by vehicle type (e.g., passenger cars, li ght-duty trucks, medium- and heavy-duty trucks, 37 

### buses, and motorcycles) were obtained from the Federal Highway Administration’s (FHWA) Highway Statistics (FHWA 1996 through 2009). As these vehicle categories are not fuel-specific, VMT for each vehicle type was disaggregated by fuel type (gasoline, diesel) so that the appropriate emission factors could be applied. VMT from Highway Statistics Table VM1 (FHWA 1996 through 2009) was allocated to fuel types (gasoline, diesel, other) using historical estimates of fuel shares reported in the Appendix to the Transportation Energy Data Book (DOE 1993 through 2010). These fuel shares are drawn from various sources, including the Vehicle Inventory and Use Survey, the National Vehicle Population Profile, and the American Public Transportation Association. The fuel shares were first adjusted proportionately so that the gasoline and diesel shares for each vehicle type summed to 100 percent in order to develop an interim estimate of VMT for each vehicle/fuel type category that summed to the total national VMT estimate. VMT for alternative fuel vehicles (AFVs) was calculated separately, and the methodology is explained in the following section on AFVs. Estimates of VMT from AFVs were then subtracted from the appropriate interim VMT estimate s to develop the final VMT estimates by vehicle/fuel type category. 38 

### The resulting national VMT estimates for gasoline and diesel on-road vehicles are presented in Table A- 89 and Table A- 90, respectively.
Total VMT for each on-road category (i.e., gasoline passenger cars, light-duty gasoline trucks, heavy-duty gasoline vehicles, diesel passenger cars, light-duty diesel trucks, medium- and heavy-duty diesel vehicles, and motorcycles) were distributed across 31 model years s hown for 2009 in Table A- 93. Distributions for 1990-2007 are presented in the Inventory Docket. This distribution was derive d by weighting the appropriate age distribution of the U.S. vehicle fleet according to vehicle registrations by the aver age annual age-specific vehicle mileage accumulation of U.S. vehicles. Age distribution values were obtained from EPA’s MOBILE6 model for all years before 1999 (EPA 2000) and EPA’s MOVES model for years 1999 forward (EPA 2010a). 39 

### Age-specific vehicle mileage accumulation was obtained from EPA’s MOBILE6 model (EPA 2000).

Step 2: Allocate VMT Data to Control Technology Type
VMT by vehicle type for each model year was distributed across various control technologies as shown in Table A- 97 through Table A- 100. The categories “EPA Tier 0” a nd “EPA Tier 1” were used instead of the early three-way catalyst and advanced three-way catalyst categories, respectively, as defined in the Revised 1996 IPCC Guidelines . EPA Tier 0, EPA Tier 1, EPA Tier 2, and LEV refer to U.S. em ission regulations, rather than control technologies; however, each does correspond to particular combinations of control technologies and engine design. EPA Tier 2 and its predecessors EPA Tier 1 and Tier 0 apply to vehicles equippe d with three-way catalysts. The introduction of “early threeway catalysts,” and “advanced three-way catalysts,” as described in the Revised 1996 IPCC Guidelines , roughly correspond to the introduction of EPA Tier 0 and EPA Tier 1 regulations (EPA 1998). 40 

### EPA Tier 2 regulations affect vehicles produced starting in 2004 and are responsible for a noticeable decrease in N 2 

### O emissions compared EPA Tier 1 emissions technology (EPA 1999b).

37 Medium-duty trucks include vehicles with a gross vehicle weight rating (GVWR) of 8,500 to 14,000 lbs. while heavy-duty trucks include those with a GVWR of over 14,000 lbs. 38 In Inventories through 2002, gasoline-electric hybrid vehicles we re considered part of an “alternative fuel and advanced techn ology” category. However, vehicles are now only separated into gasoline, diesel, or alternative fuel categories, and gas-electric hybrids are no w considered within the gasoline vehicle category. 39 Age distributions were held constant fo r the period 1990-1998, and reflect a 25-year vehicle age span. EPA (2010) provides a v ariable age distribution and 31-year vehicle age span beginning in year 1999. 40 For further description, see “Definitions of Emission Cont rol Technologies and Standards” section of this annex.

### A-120 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Control technology assignments for light and heavy-duty conventional fuel vehicles for model years 1972 (when regulations began to take effect) through 1995 were estimated in EPA (1998). Assignments for 1998 through 2007 were determined using confidential engine family sales data submitted to EPA (EPA 2007b). Vehicle classes and emission standard tiers to which each engine family was certified were taken from annual certification test results and data (EPA 2007a). This information was used to determine the fraction of sales of each class of vehicle that met EPA Tier 0, EPA Tier 1, Tier 2, and LEV standards. Assignments for 1996 and 1997 were estimated based on the fact that EPA Tier 1 standards for light-duty vehicles were fully phase d in by 1996. Tier 2 began initial phase-in by 2004.

Step 3: Determine CH 4 

### and N 2 

### O Emission Factors by Vehicle, Fuel, and Control Technology Type
Emission factors for gasoline and diesel on-road vehicles were developed by ICF (2004). These factors were based on EPA and CARB laboratory test results of different vehicle and cont rol technology types. The EPA and CARB tests were designed following the Federal Test Procedure (FTP), which covers three separate driving segments, since vehicles emit varying amounts of GHGs depending on the driving segment. These driving segments are: (1) a transient driving cycle that includes cold start and running emissions, (2 ) a cycle that represents running emissions only, and (3) a transient driving cycle that includes hot start and running emissi ons. For each test run, a bag was affixed to the tailpipe of the vehicle and the exhaust was collected; the content of this bag was later analyzed to determine quantities of gases present. The emission characteristics of segment 2 was used to define running emissions, and subtracted from the total FTP emissions to determine start emissions. These were then recombined based upon MOBILE6.2’s ratio of start to running emissions for each vehicle class to appr oximate average driving characteristics.

Step 4: Determine the Amount of CH 4 

### and N 2 

### O Emitted by Vehicle, Fuel, and Control Technology Type

Emissions of CH 4 

### and N 2 

### O were then calculated by multiplying total VMT by vehicle, fuel, and control technology type by the emission factors developed in Step 3.

Methodology for Alternative Fuel Vehicles (AFVs)

Step 1: Determine Vehicle Miles Traveled by Vehicle and Fuel Type
VMT for alternative fuel and advanced technology vehicles were calculated from “VMT Projections for Alternative Fueled and Advanced Technology Vehicles through 2025” (Browning 2003). Alternative Fuels include Compressed Natural Gas (CNG), Liquid Natural Gas (LNG), Liquefied Petroleum Gas (LPG), Ethanol, Methanol, and Electric Vehicles (battery powered). Most of the vehicles th at use these fuels run on an Internal Combustion Engine (ICE) powered by the alternative fuel, although many of the vehicles can run on either the alternative fuel or gasoline (or diesel), or some combination. 41 

### The data obtained include vehicle fuel use and total number of vehicles in use from 1992 through 2007. Because AFVs run on different fuel types, their fuel use characteristics are not directly comparable. Accordingly, fuel economy for each vehicle type is expressed in gasoline equivalent terms, i.e., how much gasoline contains the equivalent amount of energy as the alternative fuel. Energy economy ratios (the ratio of the gasoline equivalent fuel economy of a given technology to that of conventional gasoline or diesel vehicles) were taken from full fuel cycle studies done for the California Air Resources Board (Unnasch and Brow ning, Kassoy 2001). These ratios were used to estimate fuel economy in miles per gasoline gallon equivalent for each alternative fuel and vehicle type. Energy use per fuel type was then divided among the various weight categories and vehicle technologies that use that fuel. Total VMT per vehicle type for each calendar year was then determined by dividing the energy usage by the fuel economy. Note that for AFVs capable of running on both/either traditional and alternative fuels, the VMT given reflects only those miles driven that were powered by the alternative fuel, as explained in Browning (2003). VMT estimates for AFVs by vehicle category (passenger car, light-duty truck, heavy-duty vehicles) are shown in Table A- 91, while more detailed estimates of VMT by control technology are shown in Table A- 92.

41 Fuel types used in combination depend on the vehicle class. Fo r light-duty vehicles, gasoline is generally blended with ethano l or methanol; some vehicles are also designed to run on gasoline or an alterna tive fuel – either natural gas or LPG – but not at the same tim e, while other vehicles are designed to run on E85 (85% ethanol) or gasoline, or any mixture of the two. Heavy-duty vehicles are more likely t o run on a combination of diesel fuel and either natural gas, LPG, ethanol, or methanol.

### A-121 Step 2: Determine CH 4 

### and N 2 

### O Emission Factors by Vehicle and Alternative Fuel Type

CH 4 

### and N 2 

### O emission factors for alternative fuel vehicles (AFVs) are calculated according to studies by Argonne National Laboratory (2006) and Lipman & Delucchi ( 2002), and are reported in ICF (2006a). In these studies, N 2 

### O and CH 4 

### emissions for AFVs were expressed as a multiplier corresponding to conventional vehicle counterpart emissions. Emission estimates in these studies represen t the current AFV fleet and were compared against Tier 1 emissions from light-duty gasoline vehicles to develop new multipliers. Alternative fuel heavy-duty vehicles were compared against gasoline heavy-duty vehicles as most alternative fuel heavy-duty vehicles use catalytic after treatment and perform more like gasoline vehicles than diesel vehicles. These emission factors are shown in Table A- 102.

Step 3: Determine the Amount of CH 4 

### and N 2 

### O Emitted by Vehicle and Fuel Type

Emissions of CH 4 

### and N 2 

### O were calculated by multiplying total VMT for each vehicle and fuel type (Step 1) by the appropriate emission factors (Step 2).

Methodology for Non-Road Mobile Sources
CH 4 

### and N 2 

### O emissions from non-road mobile sources were estimated by applying emission factors to the amount of fuel consumed by mode and vehicle type.
Activity data for non-road vehicles include annual fuel consumption statistics by transportation mode and fuel type, as shown in Table A- 96. Consumption data for ship s and other boats (i.e., vessel bunkering) were obtained from DHS (2008) and EIA (1991 through 2011) for distillate fuel, and DHS (2008) and EIA (2007b) for residual fuel; marine transport fuel consumption data for U.S. territories (EIA 2008b, EIA 1991 through 2011) were added to domestic consumption, and this total was reduced by the amount of fuel used for international bunkers. 

### 42 Gasoline consumption by recreational boats was obtained from EPA’s NONROAD model (EPA 2010). Annual diesel consumption for Class I rail was obtained from the Association of American Railroads (AAR) (2008), diesel consumption from commuter rail was obtained from APTA (2007 through 2010) and Gaffney (2007), and consumption by Class II and III rail was provided by Benson (2002 through 2004) and Whorton (2006 through 2009). Diesel consumption by commuter and intercity rail was obtained from DOE (1993 through 2010). Data on the consumption of jet fuel and aviation gasoline in aircraft were obtained from EIA (2007b), as described in Annex 2.1: Methodology for Estimating Emissions of CO 2 

### from Fossil Fuel Combustion, and were reduced by the amount allocated to international bunker fuels. Pipeline fuel consumption was obtained from EIA (2007 through 2010) (note: pipelines are a transportation source but are stationary, not mobile, sources). Data on fuel consumption by all non-transportation mobile sources 

### 43 were obtained from EPA’s NONROAD model (EPA 2010) and from FHWA (1996 through 2010) for gasoline consumption for trucks used off-road.

Emissions of CH 4 

### and N 2 

### O from non-road mobile sources were calculated by multiplying U.S. default emission factors in the Revised 1996 IPCC Guidelines (IPCC/UNEP/OECD/IEA 1997) by activity data for each source type (see Table A- 103).



## Estimates of NO x 

## , CO, and NMVOC Emissions

The emission estimates of NO
x 

### , CO, and NMVOCs from mobile combustion (transportation) were obtained from preliminary data (EPA 2010, EPA 2009), which, in final ite ration, will be published on the EPA's National Emission Inventory (NEI) Air Pollutant Emission Trends web site. Th is EPA report provides emission estimates for these gases by fuel type using a procedure whereby emissions were calculated using basic activity data, such as amount of fuel delivered or miles traveled, as indicators of emissions.
Table A- 104 through Table A- 106 provides complete emission estimates for 1990 through 2009.

Table A- 89: Vehicle Miles Traveled for Gasoline On-Road Vehicles (10 9 

### Miles) a 

### Year Passenger Cars Light-Duty Trucks Heavy-Duty Vehicles Motorcycles

1990 1391.2 554.3 25.4 9.6 1991 1341.7 627.2 25.0 9.2 1992 1354.8 682.9 24.8 9.6

42 See International Bunker Fuels section of the Energy Chapter. 43 “Non-transportation mobile sources” are defined as any vehicle or equipment not used on the traditional road system, but exclu ding aircraft, rail and watercraft. This category includes snowmobiles, golf carts , riding lawn mowers, agricultural equipment, and trucks use d for off-road purposes, among others.

### A-122 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 1993 1356.5 720.5 24.5 9.9 1994 1387.5 738.8 25.0 10.2 1995 1420.6 762.5 24.7 9.8 1996 1454.7 788.0 24.0 9.9 1997 1488.5 820.8 23.6 10.1 1998 1536.6 836.8 23.6 10.3 1999 1559.1 867.4 23.8 10.6 2000 1591.5 886.7 23.6 10.5 2001 1619.3 904.9 23.2 9.6 2002 1649.2 925.8 23.1 9.6 2003 1662.6 943.0 23.5 9.6 2004 1690.2 984.2 23.8 10.1 2005 1698.6 997.5 24.1 10.5 2006 1680.8 1037.2 24.2 12.0 2007 1662.7 1065.6 24.7 13.6 2008 1,606.4 1,062.0 24.7 14.5 2009 1,621.1 1,071.7 22.7 14.6 Source: Derived from FHWA (1996 through 2009).

a FHWA data on vehicle miles traveled from the VM-1 table were not available for 2009 due to a delay caused by changes in data c ollection procedures. Based on data from FHWA’s Tr affic Volume Trends Program, the overall in crease in VMT between 2008 and 2009 was est imated to be 0.2%. Total VMT was distributed among vehicle classes based on tr ends in fuel consumption by fuel type between 2008 and 2009



### Table A- 90: Vehicle Miles Traveled for Diesel On-Road Vehicles (10 9 

### Miles) a 

### Year Passenger Cars Light-Duty Trucks Heavy-Duty Vehicles 1990 16.9 19.7 125.5 1991 16.3 21.6 129.3 1992 16.5 23.4 133.5 1993 17.9 24.7 140.3 1994 18.3 25.3 150.5 1995 17.3 26.9 158.7 1996 14.7 27.8 164.3 1997 13.5 29.0 173.4 1998 12.4 30.5 178.4 1999 9.4 32.6 185.3 2000 8.0 35.2 188.0 2001 8.1 37.0 191.1 2002 8.3 38.9 196.4 2003 8.3 39.6 199.1 2004 8.4 41.3 201.6 2005 8.4 41.9 202.8 2006 8.3 43.5 201.4 2007 8.1 44.6 204.7 2008 7.8 44.4 205.5 2009 7.9 44.8 187.8 Source: Derived from FHWA (1996 through 2009).

a FHWA data on vehicle miles traveled from the VM-1 table were not available for 2009 due to a delay caused by changes in data c ollection procedures. Based on data from FHWA’s Tr affic Volume Trends Program, the overall in crease in VMT between 2008 and 2009 was est imated to be 0.2%. Total VMT was distributed among vehicle classes based on tr ends in fuel consumption by fuel type between 2008 and 2009



### Table A- 91: Vehicle Miles Traveled fo r Alternative Fuel On-Road Vehicles (10 9 

### Miles)

Year Passenger Cars Light-Duty Trucks Heavy-Duty Vehicles 1990 0.2 0.7 1.1 1991 0.2 0.6 1.0 1992 0.3 0.6 0.9 1993 0.3 0.6 1.3 1994 0.3 0.5 1.2 1995 0.4 0.6 1.2 1996 0.5 0.7 1.2 1997 0.6 0.9 1.3

### A-123 1998 0.6 1.0 1.4 1999 0.6 1.0 1.3 2000 0.8 1.2 1.5 2001 0.9 1.2 1.8 2002 1.0 1.3 2.0 2003 1.2 1.4 2.1 2004 1.3 1.6 2.2 2005 1.4 1.6 2.1 2006 1.5 1.8 3.7 2007 1.6 2.0 4.6 2008 1.7 2.2 4.3 2009 1.8 2.3 4.8 Source: Derived from Browning (2003).



### Table A- 92: Detailed Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles (10 6 

### Miles) Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009 Passenger Cars 206.3 400.6 788.1 1,362.2 1,481.2 1,606.3 1,662.3 1,813.0 Methanol-Flex Fuel ICE - 40.9 13.2 - - - - - Ethanol-Flex Fuel ICE + 2.2 120.4 399.1 429.8 504.4 554.2 661.0 CNG ICE 10.6 28.0 68.9 118.8 125.0 129.2 129.7 135.3 CNG Bi-fuel 28.2 75.1 202.9 253.3 275.0 295.3 306.5 330.4 LPG ICE 20.6 40.3 41.9 31.3 24.9 18.5 16.5 11.8 LPG Bi-fuel 146.9 201.7 197.6 175.1 149.1 123.0 112.0 94.4 Biodiesel (BD20) - - 8.2 93.8 186.4 246.1 259.3 295.0
NEVs - 5.2 62.4 135.5 135.7 136.0 136.2 136.5 Electric Vehicle - 7.2 72.6 155.2 155.2 153.8 147.8 148.7 Light-Duty Trucks 660.7 606.8 1,162.0 1,640.0 1,840.3 1,992.0 2,153.0 2,299.7 Ethanol-Flex Fuel ICE - 1.3 122.6 415.0 518.7 657.7 788.5 921.7 CNG ICE 10.9 29.6 145.9 163.8 169.3 175.4 181.8 186.3 CNG Bi-fuel 24.2 71.0 280.1 348.2 365.8 389.2 405.2 432.9 LPG ICE 56.9 48.5 58.4 57.3 54.6 38.4 36.1 32.9 LPG Bi-fuel 568.7 449.4 511.9 502.3 491.4 438.5 422.1 383.6 Biodiesel (BD20) - - 8.2 76.7 163.8 219.2 240.9 263.0 Electric Vehicle - 7.1 35.0 76.4 76.4 73.1 77.8 78.4 Medium-Duty Trucks 508.0 458.4 629.6 831.4 866.4 866.9 921.0 924.0 CNG Bi-fuel 2.3 20.1 117.0 300.7 314.2 337.4 370.8 396.8 LPG ICE 24.3 20.0 29.7 36.8 33.8 31.3 30.9 31.0 LPG Bi-fuel 481.4 418.3 475.9 444.8 362.5 289.1 284.3 223.2 Biodiesel (BD20) - - 7.0 49.1 156.0 209.1 235.0 272.9 Heavy-Duty Trucks 523.9 627.0 712.3 1,084.9 2,495.7 3,337.8 3,024.7 3,435.3 Neat Methanol ICE 3.0 7.8 - - - - - - Neat Ethanol ICE - 2.0 0.1 - - - - - CNG ICE 12.7 32.2 83.7 218.7 252.8 332.1 495.3 516.8 LPG ICE 36.3 46.3 48.3 69.0 65.5 65.3 64.8 64.8 LPG Bi-fuel 471.9 531.9 529.7 569.4 524.1 512.9 505.2 502.2 LNG - 6.9 22.2 117.3 126.5 141.8 158.1 174.2 Biodiesel (BD20) - - 28.3 110.4 1,526.8 2,285.7 1,801.4 2,177.3 Buses 41.4 80.5 111.9 207.4 349.7 406.1 412.3 422.1 Neat Methanol ICE 1.9 3.7 - - - - - - Neat Ethanol ICE 0.1 2.2 - - - - - - CNG ICE 11.2 37.5 53.4 122.9 123.4 131.7 139.2 148.8 LPG ICE 28.2 30.9 35.6 34.6 35.1 33.5 33.7 33.9 LNG - 4.3 13.3 27.8 28.1 25.1 21.1 18.8 Biodiesel (BD20) - - 4.5 15.6 156.8 209.1 210.9 212.5 Electric - 2.0 5.1 6.5 6.2 6.3 6.7 7.3 Total VMT 1,940.3 2,173.4 3,403.9 5,125.9 7,033.3 8,209.0 8,173.3 8,894.0 Source: Derived from Browning (2003). Note: Throughout the rest of this Inventory, medium-duty trucks are grouped with heavy-duty truc ks; they are reported separatel y here because these two categories may run on a sligh tly different range of fuel types. + Less than 0.05 million vehicle miles traveled - Unreported or zero

### A-124 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A- 93 : Age Distribution by Vehicle/Fuel Type for On-Road Vehicles, a 

### 2009

Vehicle Age LDGV LDGT HDGV LDDV b LDDT HDDV MC
0 5.2% 4.0% 5.2% 4.6% 4.1% 5.6% 6.2% 1 5.8% 5.0% 5.7% 5.2% 5.1% 6.2% 12.2% 2 6.4% 7.4% 5.9% 5.8% 6.8% 6.5% 11.0% 3 6.6% 7.5% 6.0% 5.9% 6.5% 7.9% 10.4% 4 6.4% 7.9% 6.0% 5.7% 6.8% 7.6% 9.1%
5 6.2% 7.6% 4.7% 5.5% 10.0% 6.0% 7.7% 6 6.1% 7.1% 3.8% 5.5% 7.2% 4.6% 6.6%
7 6.4% 6.7% 3.3% 5.7% 7.7% 4.2% 5.8% 8 6.4% 6.3% 3.9% 5.7% 8.0% 4.6% 5.0% 9 6.5% 5.8% 4.3% 0.0% 6.6% 5.5% 4.0% 10 6.0% 5.0% 4.3% 0.0% 7.1% 5.8% 3.0% 11 5.3% 4.5% 3.5% 0.0% 1.7% 4.5% 2.5% 12 4.9% 3.8% 2.7% 0.0% 4.3% 3.5% 2.4% 13 4.3% 3.4% 2.7% 0.0% 2.9% 3.3% 2.1% 14 3.7% 2.9% 3.2% 0.0% 2.8% 3.7% 1.6% 15 3.1% 2.6% 3.3% 0.0% 2.4% 3.2% 1.9% 16 2.3% 2.1% 2.5% 0.0% 1.8% 2.4% 1.5% 17 1.8% 1.6% 1.8% 0.0% 1.2% 1.6% 1.3% 18 1.4% 1.3% 1.8% 0.0% 1.0% 1.6% 1.0% 19 1.2% 1.3% 2.3% 0.0% 0.9% 1.8% 0.8% 20 1.0% 1.1% 3.1% 0.0% 0.8% 2.0% 0.6% 21 0.8% 1.1% 2.9% 0.0% 0.6% 1.7% 0.5% 22 0.6% 0.9% 2.5% 0.0% 0.5% 1.3% 0.5% 23 0.5% 0.8% 3.1% 0.0% 0.7% 1.4% 0.4% 24 0.4% 0.7% 2.4% 1.0% 0.6% 1.0% 0.4% 25 0.3% 0.5% 1.5% 1.1% 0.6% 0.6% 0.3% 26 0.2% 0.3% 2.3% 1.2% 0.5% 0.6% 0.2% 27 0.1% 0.2% 1.3% 1.6% 0.5% 0.3% 0.2% 28 0.1% 0.2% 1.2% 1.6% 0.1% 0.3% 0.2% 29 0.1% 0.2% 1.4% 0.8% 0.2% 0.3% 0.2% 30 0.1% 0.2% 1.6% 0.6% 0.1% 0.3% 0.3%
Total 100% 100% 100% 57% 100% 100% 100% Source: EPA (2010).

a The following abbreviations correspond to vehicle types: LDGV (li ght-duty gasoline vehicles), LDGT (light-duty gasoline trucks) , HDGV (heavy-duty gasoline vehicles), LDDV (light- duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel veh icles), and MC (motorcycles).

b According to EPA’s MOVES model, sales of diesel passenger cars in model years 9- 23 was very small compared to total passenger c ar sales, so the calculated fraction of these vehicles in these model years was stored as zero.



### Table A- 94: Annual Average Vehicl e Mileage Accumulation per Vehicle a 

### (miles) Vehicle Age LDGV LDGT HDGV LDDV LDDT HDDV MC b 0 14,910 19,906 20,218 14,910 26,371 28,787 4,786 1 14,174 18,707 18,935 14,174 24,137 26,304 4,475 2 13,475 17,559 17,100 13,475 22,095 24,038 4,164 3 12,810 16,462 16,611 12,810 20,228 21,968 3,853 4 12,178 15,413 15,560 12,178 18,521 20,078 3,543 5 11,577 14,411 14,576 11,577 16,960 18,351 3,232 6 11,006 13,454 13,655 11,006 15,533 16,775 2,921 7 10,463 12,541 12,793 10,463 14,227 15,334 2,611 8 9,947 11,671 11,987 9,947 13,032 14,019 2,300 9 9,456 10,843 11,231 9,456 11,939 12,817 1,989 10 8,989 10,055 10,524 8,989 10,939 11,719 1,678 11 8,546 9,306 9,863 8,546 10,024 10,716 1,368 12 8,124 8,597 9,243 8,124 9,186 9,799 1,368 13 7,723 7,925 8,662 7,723 8,420 8,962 1,368

### A-125 14 7,342 7,290 8,028 7,342 7,718 8,196 1,368 15 6,980 6,690 7,610 6,980 7,075 7,497 1,368 16 6,636 6,127 7,133 6,636 6,487 6,857 1,368 17 6,308 5,598 6,687 6,308 5,948 6,273 1,368 18 5,997 5,103 6,269 5,997 5,454 5,739 1,368 19 5,701 4,642 5,877 5,701 5,002 5,250 1,368 20 5,420 4,214 5,510 5,420 4,588 4,804 1,368 21 5,152 3,818 5,166 5,152 4,209 4,396 1,368 22 4,898 3,455 4,844 4,898 3,861 4,023 1,368 23 4,656 3,123 4,542 4,656 3,542 3,681 1,368 24 4,427 2,822 4,259 4,427 3,250 3,369 1,368 25 4,427 2,822 4,259 4,427 3,250 3,369 1,368 26 4,427 2,822 4,259 4,427 3,250 3,369 1,368 27 4,427 2,822 4,259 4,427 3,250 3,369 1,368 28 4,427 2,822 4,259 4,427 3,250 3,369 1,368 29 4,427 2,822 4,259 4,427 3,250 3,369 1,368 30 4,427 2,822 4,259 4,427 3,250 3,369 1,368 Source: EPA (2000).

a The following abbreviations correspond to vehicle types: LDGV (li ght-duty gasoline vehicles), LDGT (light-duty gasoline trucks) , HDGV (heavy-duty gasoline vehicles), LDDV (light- duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel veh icles), and MC (motorcycles).

b Because of a lack of data, all motorcycles over 12 years old are considered to have the same emissions and travel characteristi cs, and therefore are presented in aggregate.


### Table A- 95: VMT Distribution by Vehicle Age and Vehicle/Fuel Type, a 

### 2009

Vehicle Age LDGV LDGT HDGV LDDV b LDDT HDDV MC 0 7.56% 6.52% 9.50% 10. 78% 7.43% 10.67% 9.53% 1 8.03% 7.67% 9.80% 11. 44% 8.51% 10.80% 17.45% 2 8.49% 10.68% 9.15% 12. 11% 10.26% 10.30% 14.68% 3 8.23% 10.14% 8.97% 11. 74% 9.01% 11.49% 12.87% 4 7.65% 9.95% 8.40% 10. 90% 8.65% 10.06% 10.36% 5 7.00% 8.98% 6.23% 9. 98% 11.59% 7.30% 8.02% 6 6.59% 7.87% 4.64% 9.39% 7.68% 5.15% 6.13% 7 6.51% 6.85% 3.83% 9.28% 7.54% 4.27% 4.88% 8 6.24% 6.07% 4.20% 8.90% 7.14% 4.27% 3.65% 9 6.04% 5.20% 4.34% 0.00% 5.43% 4.69% 2.52% 10 5.25% 4.14% 4.08% 0. 00% 5.32% 4.46% 1.60% 11 4.45% 3.40% 3.14% 0. 00% 1.19% 3.18% 1.12% 12 3.92% 2.65% 2.26% 0. 00% 2.69% 2.27% 1.07% 13 3.28% 2.20% 2.15% 0. 00% 1.69% 1.95% 0.94% 14 2.63% 1.71% 2.33% 0. 00% 1.46% 2.00% 0.71% 15 2.11% 1.42% 2.30% 0. 00% 1.15% 1.57% 0.81% 16 1.52% 1.05% 1.60% 0. 00% 0.80% 1.08% 0.66% 17 1.10% 0.75% 1.09% 0. 00% 0.51% 0.68% 0.55% 18 0.81% 0.54% 1.01% 0. 00% 0.36% 0.60% 0.44% 19 0.67% 0.49% 1.21% 0. 00% 0.32% 0.64% 0.36% 20 0.52% 0.40% 1.56% 0. 00% 0.24% 0.64% 0.26% 21 0.41% 0.33% 1.36% 0. 00% 0.16% 0.50% 0.22% 22 0.29% 0.26% 1.08% 0. 00% 0.13% 0.36% 0.22% 23 0.23% 0.20% 1.29% 0. 00% 0.16% 0.33% 0.18% 24 0.16% 0.15% 0.94% 0. 69% 0.13% 0.22% 0.15% 25 0.11% 0.12% 0.56% 0. 78% 0.13% 0.14% 0.14% 26 0.07% 0.08% 0.87% 0. 86% 0.12% 0.13% 0.10% 27 0.05% 0.05% 0.48% 1. 11% 0.11% 0.07% 0.08% 28 0.04% 0.04% 0.48% 1. 12% 0.03% 0.06% 0.08% 29 0.03% 0.04% 0.53% 0. 54% 0.04% 0.06% 0.09% 30 0.03% 0.05% 0.62% 0. 38% 0.02% 0.06% 0.14% Total 100% 100% 100% 100% 100% 100% 100% Note: Estimated by weighting data in Table A- 93 by data in Table A- 94.

a The following abbreviations correspond to vehicle types: LDGV (li ght-duty gasoline vehicles), LDGT (light-duty gasoline trucks) , HDGV (heavy-duty gasoline vehicles), LDDV (light- duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel veh icles), and MC (motorcycles).

b According to EPA’s MOVES model, sales of diesel passenger cars in model years 9- 23 was very small compared to total passenger c ar sales, so the calculated fraction of these vehicles in these model years was stored as zero.

### A-126 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A- 96: Fuel Consumption for Off-Ro ad Sources by Fuel Type (million gallons)

Vehicle Type/Year 1990 1995 2000 2005 2006 2007 2008 2009 Aircraft a 18,723.9 18,179.6 20,793.7 20,214.1 17,664.4 17,568.6 16,141.6 14,460.2 Aviation Gasoline 374.2 329.3 301.9 294.2 278.3 262.8 235.3 221.0 Jet Fuel 18,349.7 17,850.3 20,491.9 19,919.9 17,386.1 17,305.8 15,906.2 14,239.3 Ships and Other Boats 4,507.2 5,789.1 6,430.6 4,882.7 5,144.6 5,747. 1 4,113.4 3,548.7 Diesel 1,043.1 1,545.7 1,750.1 1,471.4 1,410.5 1, 490.7 675.7 825.7 Gasoline 1,403.4 1,597.3 1,652.9 1,629.9 1,619.6 1, 609.6 1,599.8 1,590.7 Residual 2,060.7 2,646.1 3,027.5 1,781.4 2,114.5 2, 646.8 1,837.9 1,132.3 Construction/ Mining Equipment b 4,160.3 4,834.7 5,439.1 6,520.5 6,656.4 6, 683.7 6,835.0 6,960.0 Diesel 3,674.4 4,386.9 5,094.9 5,823.4 5,968.2 6, 112.9 6,257.7 6,402.5 Gasoline 485.9 447.8 344.2 697.1 688.3 570.8 577.3 557.5 Agricultural Equipment c 3,133.7 3,698.3 3,874.5 4,715.0 4,947.8 4, 861.5 4,516.6 4,640.9 Diesel 2,320.9 2,771.6 3,222.3 3,637.2 3,719.1 3,800.9 3,882.8 3,964.6 Gasoline 812.8 926.7 652.3 1,077.8 1,228.7 1,060.6 633.8 676.3 Rail 3,460.6 3,863.5 4,105.9 4,446.1 4,664.5 4,538.9 4,216.0 3,524.8 Diesel 3,460.6 3,863.5 4,105.9 4,446.1 4,664.5 4,538.9 4,216.0 3,524.8 Other d 5,916.5 6,524.8 6,826.1 8,281.2 8,396.1 8,255.6 8,386.7 8,481.6 Diesel 1,423.3 1,720.0 2,016.0 2,340.3 2,405.4 2,470.6 2,535.8 2,601.0 Gasoline 4,493.2 4,804.7 4,810.2 5,940.9 5,990.7 5,785.0 5,850.9 5,880.7 Total 39,902.2 42,890.0 47,470.0 49,059.5 47,473.8 47,655.5 44,209.2 41,616.4 Sources: AAR (2008 through 2009), APTA (2007 through 2009), BEA (1991 through 2005), Benson (2002 through 2004), DHS (2008), DO C (1991 through 2008), DESC (2008), DOE (1993 through 2009), DOT (1991 through 2009), EIA (2002), EIA (2007b), EIA (2008), EIA (2 007 through 2008), EIA (1991 through 2011), EPA (2007b), FAA (200), FAA (2006), Gaffney (2007), and Whorton (2006 through 2009).

a For aircraft, this is aviation gasoline. For all other categories, this is motor gasoline.

b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consump tion from trucks that are used off-road in construction.

c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agricultur e. d “Other" includes snowmobiles and other recrea tional equipment, logging equipment, lawn and garden equipment, railroad equipment , airport equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes.



### Table A- 97: Control Technology Assignments for Gasoline Passenger Cars (Percent of VMT) Model Years Non-catalyst Oxidation EPA Tier 0 EPA Tier 1 LEV EPA Tier 2 1973-1974 100% - - - - - 1975 20% 80% - - - - 1976-1977 15% 85% - - - - 1978-1979 10% 90% - - - - 1980 5% 88% 7% - - - 1981 - 15% 85% - - - 1982 - 14% 86% - - - 1983 - 12% 88% - - - 1984-1993 - - 100% - - - 1994 - - 60% 40% - - 1995 - - 20% 80% - - 1996 - - 1% 97% 2% - 1997 - - 0.5% 96.5% 3% - 1998 - - <1% 87% 13% - 1999 - - <1% 67% 33% - 2000 - - - 44% 56% - 2001 - - - 3% 97% - 2002 - - - 1% 99% - 2003 - - - <1% 87% 13% 2004 - - - <1% 41% 59% 2005 - - - - 38% 62% 2006 - - - - 18% 82% 2007 - - - - 4% 96% 2008 - - - - 2% 98% 2009 - - - - - 100% Sources: EPA (1998), EPA (2007a), and EPA (2007b). Note: Detailed descriptions of emissions control technologies are provided in the following section of this annex.

### A-127 - Not applicable.


### Table A- 98: Control Technology Assignments fo r Gasoline Light-Duty Trucks (Percent of VMT) a Model Years Non-catalyst Oxidation EPA Tier 0 EPA Tier 1 LEV b EPA Tier 2 1973-1974 100% - - - - - 1975 30% 70% - - - - 1976 20% 80% - - - - 1977-1978 25% 75% - - - - 1979-1980 20% 80% - - - - 1981 - 95% 5% - - - 1982 - 90% 10% - - - 1983 - 80% 20% - - - 1984 - 70% 30% - - - 1985 - 60% 40% - - - 1986 - 50% 50% - - - 1987-1993 - 5% 95% - - - 1994 - - 60% 40% - - 1995 - - 20% 80% - - 1996 - - - 100% - - 1997 - - - 100% - - 1998 - - - 80% 20% - 1999 - - - 57% 43% - 2000 - - - 65% 35% - 2001 - - - 1% 99% - 2002 - - - 10% 90% - 2003 - - - <1% 53% 47% 2004 - - - - 72% 28% 2005 - - - - 38% 62% 2006 - - - - 25% 75% 2007 - - - - 14% 86% 2008 - - - - - 100% 2009 - - - - - 100% Sources: EPA (1998), EPA (2007a), and EPA (2007b).

a Detailed descriptions of emissions control technologies are provided in th e following section of this annex.

b The proportion of LEVs as a whole has decreased since 2001, as carmakers have been able to achieve greater emission reductions with certain types of LEVs, such as ULEVs. Because UL EVs emit about half the emissions of LEVs, a carmaker can reduce the total number of LE Vs they need to build to meet a specified emission average for all of th eir vehicles in a given model year. - Not applicable.


### Table A- 99: Control Technology Assignments fo r Gasoline Heavy-Duty Vehicles (Percent of VMT) a Model Years Uncontrolled Non-catalyst Oxidation EPA Tier 0 EPA Tier 1 LEV b EPA Tier 2
≤ 1981 100% - - - - - - 1982-1984 95% - 5% - - - - 1985-1986 - 95% 5% - - - - 1987 - 70% 15% 15% - - - 1988-1989 - 60% 25% 15% - - - 1990-1995 - 45% 30% 25% - - - 1996 - - 25% 10% 65% - - 1997 - - 10% 5% 85% - - 1998 - - - - 96% 4% - 1999 - - - - 78% 22% - 2000 - - - - 54% 46% - 2001 - - - - 64% 36% - 2002 - - - - 69% 31% - 2003 - - - - 65% 30% 5% 2004 - - - - 5% 37% 59% 2005 - - - - - 23% 77% 2006 - - - - - 22% 78% 2007 - - - - - 21% 79% 2008 - - - - - 20% 80% 2009 - - - - - 19% 81% Sources: EPA (1998), EPA (2007a), and EPA (2007b).

a Detailed descriptions of emissions control technologies are provided in th e following section of this annex.

b The proportion of LEVs as a whole has decreased since 2000, as carmakers have been able to achieve greater emission reductions with certain types of LEVs, such as ULEVs. Because UL EVs emit about half the emissions of LEVs, a manufacturer can reduce the total number o f LEVs they need to build to meet a specified emission average for all of their vehicles in a given model year.

### A-128 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 - Not applicable.


### Table A- 100: Control Technology Assignment s for Diesel On-Road Vehicles and Motorcycles Vehicle Type/Control Technology Model Years Diesel Passenger Cars and Light-Duty Trucks

Uncontrolled 1960-1982 Moderate control 1983-1995 Advanced control 1996-2009 Diesel Medium- and Heavy-Duty Trucks and Buses

Uncontrolled 1960-1990 Moderate control 1991-2003 Advanced control 2004-2006 Aftertreatment 2007-2009 Motorcycles

Uncontrolled 1960-1995 Non-catalyst controls 1996-2009 Source: EPA (1998) and Browning (2005) Note: Detailed descriptions of emissions control technologies are provided in the following section of this annex.



### Table A- 101: Emission Factors for CH 4 

### and N 2 

### O for On-Road Vehicles Vehicle Type/Control Technology N 2 O CH 4 (g/mi) (g/mi) Gasoline Passenger Cars EPA Tier 2 0.0036 0.0173 Low Emission Vehicles 0.0150 0.0105 EPA Tier 1 a 0.0429 0.0271 EPA Tier 0 a 0.0647 0.0704 Oxidation Catalyst 0.0504 0.1355 Non-Catalyst Control 0.0197 0.1696 Uncontrolled 0.0197 0.1780 Gasoline Light-Duty Trucks EPA Tier 2 0.0066 0.0163 Low Emission Vehicles 0.0157 0.0148 EPA Tier 1 a 0.0871 0.0452 EPA Tier 0 a 0.1056 0.0776 Oxidation Catalyst 0.0639 0.1516 Non-Catalyst Control 0.0218 0.1908 Uncontrolled 0.0220 0.2024 Gasoline Heavy-Duty Vehicles EPA Tier 2 0.0134 0.0333 Low Emission Vehicles 0.0320 0.0303 EPA Tier 1 a 0.1750 0.0655 EPA Tier 0 a 0.2135 0.2630 Oxidation Catalyst 0.1317 0.2356 Non-Catalyst Control 0.0473 0.4181 Uncontrolled 0.0497 0.4604 Diesel Passenger Cars Advanced 0.0010 0.0005 Moderate 0.0010 0.0005 Uncontrolled 0.0012 0.0006 Diesel Light-Duty Trucks Advanced 0.0015 0.0010 Moderate 0.0014 0.0009 Uncontrolled 0.0017 0.0011 Diesel Medium- and Heavy-Duty Trucks and Buses Aftertreatment 0.0048 0.0051 Advanced 0.0048 0.0051 Moderate 0.0048 0.0051 Uncontrolled 0.0048 0.0051 Motorcycles Non-Catalyst Control 0.0069 0.0672 Uncontrolled 0.0087 0.0899 Source: ICF (2006b and 2004).

### A-129 a The categories “EPA Tier 0” and “EPA Tier 1” were substituted for the early three-wa y catalyst and advanced three-way catalyst categories, respectively, as defined in the Revised 1996 IPCC Guidelines . Detailed descriptions of emissions contro l technologies are provided at the end of this annex.



### Table A- 102: Emission Factors for CH 4 

### and N 2 

### O for Alternative Fuel Vehicles (g/mi)

N 2 O CH 4 Light Duty Vehicles Methanol 0.067 0.018 CNG 0.050 0.737 LPG 0.067 0.037 Ethanol 0.067 0.055 Biodiesel (BD20) 0.001 0.0005 Medium- and Heavy-Duty Trucks Methanol 0.175 0.066 CNG 0.175 1.966 LNG 0.175 1.966 LPG 0.175 0.066 Ethanol 0.175 0.197 Biodiesel (BD20) 0.005 0.005
Buses Methanol 0.175 0.066 CNG 0.175 1.966 Ethanol 0.175 0.197 Biodiesel (BD20) 0.005 0.005 Source: Developed by ICF (2006a) using ANL (2006) and Lipman and Delucchi (2002).



### Table A- 103: Emission Factors for CH 4 

### and N 2 

### O Emissions from Non-Road Mo bile Combustion (g/kg fuel) Vehicle Type/Fuel Type N 2 O CH 4 Ships and Boats

Residual 0.16 0.03 Gasoline 0.08 0.23 Diesel 0.14 0.02 Rail

Diesel 0.08 0.25 Agricultural Equipment a

Gasoline 0.08 0.45 Diesel 0.08 0.45 Construction/Mining Equipment c

Gasoline 0.08 0.18 Diesel 0.08 0.18 Other Non-Road

All “Other” Categories c 0.08 0.18 Aircraft

Jet Fuel 0.10 0.087 Aviation Gasoline 0.04 2.64 Source: IPCC/UNEP/OECD/IEA (1997) and ICF (2009).

a Includes equipment, such as tractors and combines, as well as fu el consumption from trucks that are used off-ro ad in agricultu re. b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consump tion from trucks that are used off-road in construction.

c “Other" includes snowmobiles and other recr eational equipment, logging equipment, lawn and garden equipment, railroad equipmen t, airport equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes.



### Table A- 104: NO x 

### Emissions from Mobile Combustion (Gg) Fuel Type/Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009 Gasoline On-Road 5,746 4,560 3,812 3,102 2,897 2,693 2,488 2,212 Passenger Cars 3,847 2,752 2,084 1,692 1,581 1,469 1,357 1,207 Light-Duty Trucks 1,364 1,325 1,303 1,073 1,002 931 860 765Medium- and HeavyDuty Trucks and Buses 515 469 411 326 304 283 261 232 Motorcycles 20 14 13 11 11 10 9 8 Diesel On-Road 2,956 3,493 3,803 2,787 2,603 2,419 2,235 1,987 Passenger Cars 39 19 7 5 5 4 4 3 Light-Duty Trucks 20 12 6 5 4 4 4 3

### A-130 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009Medium- and HeavyDuty Trucks and Buses 2,897 3,462 3,791 2,778 2,594 2,411 2,228 1,981Alternative Fuel OnRoad a IE IE IE IE IE IE IE IE Non-Road 2,160 2,483 2,584 3,122 2,988 2,853 2,718 2,007

Ships and Boats 402 488 506 646 618 590 562 415 Rail 338 433 451 576 551 527 502 371 Aircraft b 25 31 40 47 45 43 41 30 Agricultural Equipment c 437 478 484 565 540 516 491 363 Construction/Mining

Equipment d 641 697 697 811 776 741 706 521 Other e 318 357 407 478 457 437 416 307 Total 10,862 10,536 10,199 9,012 8,488 7,965 7,441 6,206

a NO x emissions from alternative fuel on-road vehicl es are included under gasoline and diesel on-road.

b Aircraft estimates include only emissions related to LTO cy cles, and therefore do not include cruise altitude emissions.

c Includes equipment, such as tractors and combines, as well as fu el consumption from trucks that are used off-ro ad in agricultu re. d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consump tion from trucks that are used off-road in construction.

e “Other" includes snowmobiles and other recrea tional equipment, logging equipment, lawn an d garden equipment, railroad equipment , airport equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes. Note: Totals may not sum due to independent rounding. IE = Included Elsewhere



### Table A- 105: CO Emissions from Mobile Combustion (Gg)

Fuel Type/Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009 Gasoline On-Road 98,328 74,673 60,657 43,374 40,492 37,610 34,727 34,199 Passenger Cars 60,757 42,065 32,867 24,166 22,560 20,954 19,348 19,054 Light-Duty Trucks 29,237 27,048 24,532 17,264 16,117 14,969 13,822 13,612Medium- and HeavyDuty Trucks and Buses 8,093 5,404 3,104 1,844 1,721 1,599 1,476 1,454 Motorcycles 240 155 154 100 94 87 80 79 Diesel On-Road 1,696 1,424 1,088 665 621 576 532 524 Passenger Cars 35 18 7 4 4 4 4 4 Light-Duty Trucks 22 16 6 4 4 3 3 3Medium- and HeavyDuty Trucks and Buses 1,639 1,391 1,075 656 613 569 525 517Alternative Fuel OnRoad a IE IE IE IE IE IE IE IE Non-Road 19,337 21,533 21,814 18,652 17,859 17,067 16,274 8,633

Ships and Boats 1,559 1,781 1,825 1,534 1,469 1,404 1,339 710 Rail 85 93 90 75 72 69 66 35 Aircraft b 217 224 245 196 187 179 171 91 Agricultural Equipment c 581 628 626 520 498 476 454 241 Construction/Mining

Equipment d 1,090 1,132 1,047 872 835 798 761 404 Other e 15,805 17,676 17,981 15,455 14,798 14,141 13,484 7,153 Total 119,360 97,630 83,559 62,692 58,972 55,253 51,533 43,355

a NO x emissions from alternative fuel on-road vehicl es are included under gasoline and diesel on-road.

b Aircraft estimates include only emissions related to LTO cy cles, and therefore do not include cruise altitude emissions.

c Includes equipment, such as tractors and combines, as well as fu el consumption from trucks that are used off-ro ad in agricultu re. d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consump tion from trucks that are used off-road in construction.

e “Other" includes snowmobiles and other recrea tional equipment, logging equipment, lawn an d garden equipment, railroad equipment , airport equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes. Note: Totals may not sum due to independent rounding. IE = Included Elsewhere



### Table A- 106: NMVOCs Emission s from Mobile Combustion (Gg)

Fuel Type/Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009 Gasoline On-Road 8,110 5,819 4,615 3,558 3,358 3,158 2,958 2,878 Passenger Cars 5,120 3,394 2,610 1,987 1,875 1,764 1,652 1,607 Light-Duty Trucks 2,374 2,019 1,750 1,382 1,304 1,226 1,149 1,118Medium- and HeavyDuty Trucks and Buses 575 382 232 171 162 152 142 138

### A-131 Motorcycles 42 24 23 18 17 16 15 15 Diesel On-Road 406 304 216 172 163 153 143 139 Passenger Cars 16 8 3 3 2 2 2 2 Light-Duty Trucks 14 9 4 3 3 3 3 2Medium- and HeavyDuty Trucks and Buses 377 286 209 167 157 148 139 135Alternative Fuel OnRoad a IE IE IE IE IE IE IE IE Non-Road 2,415 2,622 2,399 2,600 2,516 2,430 2,346 1,134

Ships and Boats 608 739 744 798 772 746 720 348 Rail 33 36 35 39 37 36 35 17 Aircraft b 28 28 24 21 20 20 19 9 Agricultural Equipment c 85 86 76 79 77 74 71 35 Construction/Mining

Equipment d 149 152 130 137 132 128 123 60 Other e 1,512 1,580 1,390 1,527 1,477 1,427 1,378 666 Total 10,932 8,745 7,229 6,330 6,037 5,742 5,447 4,151

a NO x emissions from alternative fuel on-road vehicl es are included under gasoline and diesel on-road.

b Aircraft estimates include only emissions related to LTO cy cles, and therefore do not include cruise altitude emissions.

c Includes equipment, such as tractors and combines, as well as fu el consumption from trucks that are used off-ro ad in agricultu re. d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consump tion from trucks that are used off-road in construction.

e “Other" includes snowmobiles and other recrea tional equipment, logging equipment, lawn an d garden equipment, railroad equipment , airport equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes. Note: Totals may not sum due to independent rounding. IE = Included Elsewhere



## Definitions of Emission Control Technologies and Standards

The N 2 

### O and CH 4 

### emission factors used depend on the emission standards in place and the corresponding level of control technology for each vehicle type. Table A- 97 through Table A- 100 show the years in which these technologies or standards were in place and the penetration level for each vehicle type. These categories are defined below.

Uncontrolled
Vehicles manufactured prior to the implementation of pollution control technologies are designated as uncontrolled. Gasoline passenger cars and light-duty trucks (pre-1973), gasoline heavy-duty vehicles (pre-1984), diesel vehicles (pre-1983), and motorcycles (pre-1996) are assumed to have no control technologies in place.

Gasoline Emission Controls
Below are the control technologies and emissions standards applicable to gasoline vehicles.

Non-catalyst
These emission controls were common in gasoline passenger cars and light-duty gasoline trucks during model years (1973-1974) but phased out thereafter, in heavy-duty gasoline vehicles beginning in the mid-1980s, and in motorcycles beginning in 1996. This technology reduces hydrocarbon (HC) and carbon monoxide (CO) emissions through adjustments to ignition timing and air-fuel ratio, air injection into the exhaust manifold, and exhaust gas recirculation (EGR) valves, which also helps meet vehicle NO
x 

### standards.

Oxidation Catalyst
This control technology designation represents the introduction of the catalytic converter, and was the most common technology in gasoline passenger cars and light-duty gasoline trucks made from 1975 to 1980 (cars) and 1975 to 1985 (trucks). This technology was also used in some heavy-duty gasoline vehicles between 1982 and 1997. The two-way catalytic converter oxidizes HC and CO, significantly reducing emissions over 80 percent beyond non-catalyst-system capacity. One reason unleaded gasoline was introduced in 1975 was due to the fact that oxidation catalysts cannot function properly with leaded gasoline.

### A-132 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 EPA Tier 0
This emission standard from the Clean Air Act was met through the implementation of early "three-way" catalysts, therefore this technology was used in gasoline pa ssenger cars and light-duty gasoline trucks sold beginning in the early 1980s, and remained common until 1994. This more sophisticated emission control system improves the efficiency of the catalyst by converting CO and HC to CO 2 

### and H 2 

### O, reducing NO
x 

### to nitrogen and oxygen, and using an on-board diagnostic computer and oxygen sensor. In addition, this type of catalyst includes a fuel metering system (carburetor or fuel injection) with electronic "trim" (als o known as a "closed-loop system"). New cars with three-way catalysts met the Clean Air Act's amended standards (enacted in 1977) of reducing HC to 0.41 g/mile by 1980, CO to 3.4 g/mile by 1981 and NO
x 

### to 1.0 g/mile by 1981.

EPA Tier 1
This emission standard created through the 1990 amendments to the Clean Air Act limited passenger car NO
x 

### emissions to 0.4 g/mi, and HC emissions to 0.25 g/mi. These bounds respectively amounted to a 60 and 40 percent reduction from the EPA Tier 0 standard set in 1981. For light-duty trucks, this standard set emissions at 0.4 to 1.1 g/mi for NO
x 

### , and 0.25 to 0.39 g/mi for HCs, depending on the weight of the truck. Emission reductions were met through the use of more advanced emission control systems, and applied to light-duty gasoline vehicles beginning in 1994. These advanced emission control systems included advanced three-way catalysts, electronically controlled fuel injection and ignition timing, EGR, and air injection.

EPA Tier 2
This emission standard was specified in the 1990 amendments to the Clean Air Act, limiting passenger car NO
x 

### emissions to 0.07 g/mi on average and aligning emissions standards for passenger cars and light-duty trucks. Manufacturers can meet this average emission level by producing vehicles in 11 emission “Bins”, the three highest of which expire in 2006. These new emission levels represent a 77 to 95% reduction in emissions from the EPA Tier 1 standard set in 1994. Emission reductions were met through the use of more advanced emission control systems and lower sulfur fuels and are applied to vehicles beginning in 2004. These advanced emission control systems include improved combustion, advanced three-way catalysts, electronically cont rolled fuel injection and ignition timing, EGR, and air injection.

Low Emission Vehicles (LEV)
This emission standard requires a much higher emission control level than the Tier 1 standard. Applied to lightduty gasoline passenger cars and trucks beginning in small numbers in the mid-1990s, LEV includes multi-port fuel injection with adaptive learning, an advanced computer diagnostics systems and advanced and close coupled catalysts with secondary air injection. LEVs as defined here include tran sitional low-emission vehicles (TLEVs), low emission vehicles, ultra-low emission vehicles (ULEVs) and super ultra-low emission vehicles (SULEVs). In this analysis, all categories of LEVs are treated the same due to the fact that there are very limited CH 4 

### or N 2 

### O emission factor data for LEVs to distinguish among the different types of vehicles. Zero emission vehicles (ZEVs) are incorporated into the alternative fuel and advanced technology vehicle assessments.

Diesel Emission Controls

Below are the two levels of emissions control for diesel vehicles.

Moderate control
Improved injection timing technology and combustion system design for light- and heavy-duty diesel vehicles (generally in place in model years 1983 to 1995) are considered moderate control technologies. These controls were implemented to meet emission standards for diesel trucks and buses adopted by the EPA in 1985 to be met in 1991 and 1994.

### A-133 Advanced control
EGR and modern electronic control of the fuel injection system are designated as advanced control technologies. These technologies provide diesel vehicles with the level of emission control necessary to comply with standards in place from 1996 through 2006.

Aftertreatment
Use of diesel particulate filters (DPFs), oxidation catalys ts and NOx absorbers or selective catalytic reduction (SCR) systems are designated as aftertreatment control. Th ese technologies provide diesel vehicles with a level of emission control necessary to comply with standards in place from 2007 on.



## Supplemental Information on GHG Emissions from Transportation and Other Mobile Sources This section of this Annex includes supplemental info rmation on the contribution of transportation and other mobile sources to U.S. greenhouse gas em issions. In the main body of the I nventory report, emission estimates are generally presented by greenhouse gas, with separate discussions of the methodologies used to estimate CO 2 

### , N 2 

### O, CH 4 

### , and HFC emissions. Although the inventory is not required to provide detail beyond what is contained in the body of this report, the IPCC allows presentation of additional data and de tail on emission sources. The purpose of this sub-annex, within the annex that details the calculation methods and data used for non- CO 2 

### calculations, is to provide all transportation estimates presented throughout the repot in one place.
This section of this Annex reports total greenhouse gas emissions from transportation and other (nontransportation) mobile sources in CO 2 

### equivalents, with information on the contribution by greenhouse gas and by mode, vehicle type, and fuel type. In order to calculate these figures, additional analyses were conducted to develop estimates of CO 2 

### from non-transportation mobile sources (e.g., agricultural equipment, construction/mining equipment, recreational vehicles), and to provide more detailed breakdowns of emissions by source.

Estimation of CO 2 

### from Non-Transportation Mobile Sources
The estimates of N 2 

### O and CH 4 

### from fuel combustion presented in the Energy chapter of the inventory include both transportation sources and other mobile sources. Othe r mobile sources include construction/mining equipment, agricultural equipment, vehicles used off-road, and other sources that have utility associated with their movement but do not have a primary purpose of transporting people or goods (e.g ., snowmobiles, riding lawnmowers, etc.). Estimates of CO 2 

### from non-transportation mobile sources, based on EIA fuel consumption estimates, are included in the agricultural, industrial, and commercial sectors. In order to provide comparable information on transportation and mobile sources, Table A- 107 provides estimates of CO 2 

### from these other mobile sources, developed from EPA’s NONROAD model and FHWA’s Highway Statistics . These other mobile source estimates were developed using the same fuel consumption data utilized in developing the N 2 

### O and CH 4 

### estimates.

Table A- 107: CO 2 

### Emissions from Non-Transportation Mobile Sources (Tg CO 2 

### Eq.) Fuel Type/Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009

Agricultural Equipment a 31.0 36.6 38.8 46.8 49.0 48.4 45.4 46.7 Construction/Mining Equipment b 42.0 48.9 55.3 65.9 67.3 67.8 69.3 70.6 Other Sources c 54.5 59.8 62.8 76.2 77.6 76.7 77.7 78.6 Total 127.6 145.4 156.9 188.9 193.9 193.0 192.4 195.9

a Includes equipment, such as tractors and combines, as well as fu el consumption from trucks that are used off-ro ad in agricultu re. b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consump tion from trucks that are used off-road in construction.

c “Other" includes snowmobiles and other recr eational equipment, logging equipment, lawn and garden equipment, railroad equipmen t, airport equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes. 

### Estimation of HFC Emissions from Transportation Sources
In addition to CO 2 

### , N 2 

### O and CH 4 

### emissions, transportation sources also result in emissions of HFCs. HFCs are emitted to the atmosphere during equipment manufacture and operation (as a result of component failure, leaks, and purges), as well as at servicing and disposal events. There are three categories of transportation-related HFC emissions; Mobile AC represents the emissions from air conditioning units in passenger cars and light-duty trucks, Comfort Cooling represents the emissions from air conditioning units in passen ger trains and buses, and Refrigerated Transport represents the emissions from units used to cool freight during transportation.
Table A- 108 below presents these HFC emissions. Tabl e A- 109 presents all transportation and mobile source greenhouse gas emissions, including HFC emissions.

### A-134 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A- 108: HFC Emissions from Transportation Sources Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009

Mobile AC - 16.7 46.0 59.4 58.3 54.7 50.7 45.9 Passenger Cars - 10.1 24.3 28.4 27.1 24.6 22.1 19.3 Light-Duty Trucks - 6.5 21.7 31.0 31.2 30.1 28.6 26.6 Comfort Cooling for Trains and Buses - + 0.1 0.2 0.3 0.3 0.4 0.4 School and Tour Buses - + 0.1 0.2 0.3 0.3 0.3 0.4 Transit Buses - + + + + + + + Rail - - - + + + + + Refrigerated Transport - 2.3 9.6 13.2 13.6 13.8 13.8 13.9 Medium- and Heavy-Duty

Trucks - 1.7 7.4 11.1 11.4 11.5 11.6 11.6 Rail - 0.5 2.0 2.1 2.2 2.2 2.2 2.2 Ships and Other Boats - + 0.1 + + + + + Total - 19.0 55.7 72.9 72.2 68.8 64.9 60.2 Note: Totals may not sum due to independent rounding. + Less than 0.05 Tg CO 2 Eq. - Unreported or zero



### Contribution of Transportation and Mobile Source s to Greenhouse Gas Emissions, by Mode/Vehicle Type/Fuel Type Table A- 109 presents estimates of greenhouse gas em issions from an expanded analysis including all transportation and additional mobile sources, as well as em issions from electricity generation by the consuming category, in CO 2 

### equivalents. In total, transportation and non-transportation mobile sources emitted 2,014.7 Tg CO 2 

### Eq. in 2009, an increase of 20 percent from 1990. Trans portation sources account for 1,816.9 Tg CO 2 

### Eq. while non-transportation mobile sources account for 197.7 Tg CO 2 

### Eq. These estimates include HFC emissions for mobile AC, comfort cooling for trains and buses, and refrigerated transport. These esti mates were generated using the estimates of CO 2 

### emissions from transportation sources reported in the Carbon Dioxide Em issions from Fossil Fuel Combustion section, and CH 4 

### emissions and N 2 

### O emissions reported in the Mobile Combustion section of the Energy chapter; information on HFCs from mobile air conditioners, comfort cooling for trains and buses, and re frigerated transportation from Chapter 4; and estimates of CO 2 

### emitted from non-transportation mobile sources reported in Table A- 107 above.
Although all emissions reported here are based on estimates reported throughout this inventory, some additional calculations were performed in order to provide a detailed breakdown of emissions by mode and vehicle category. In the case of N 2 

### O and CH 4 

### , additional calculations were performed to develop emissions estimates by type of aircraft and type of heavy-duty vehicle (i.e., medium- and heavy-duty trucks or buses) to match the level of detail for CO 2 

### emissions. N 2 

### O and CH 4 

### estimates were developed for individual aircraft types by multiplying the emissions estimates for aircraft for each fuel type (jet fuel and aviation gasoline) by the portion of fuel used by each aircraft type (from FAA 2010, 2009, and 2006). Similarly, N 2 

### O and CH 4 

### estimates were developed for medium- and heavy-duty trucks and buses by multiplying the emission estimates for heavy-duty vehicles for each fuel ty pe (gasoline, diesel) from the Mobile Combustion section in the Energy chapter, by the portion of fuel used by each ve hicle type (from DOE 1993 through 2010). Otherwise, the table and figure are drawn directly from emission estimates presen ted elsewhere in the inventory, and are dependent on the methodologies presented in Annex 2.1 (for CO 2 

### ), Chapter 4, and Annex 3.8 (for HFCs), and earlier in this Annex (for CH 4 

### and N 2 

### O).
Transportation sources include on-road vehicles, aircraft, boa ts and ships, rail, and pipelines (note: pipelines are a transportation source but are stationary, not mobile sources). In addition, transportation-related greenhouse gas emissions also include HFC released from mobile air conditioners and refrigerated transportation, and the release of CO 2 

### from lubricants (such as motor oil) used in transportation. T ogether, transportation sources were responsible for 1,816.9 Tg CO 2 

### Eq. in 2009.
On-road vehicles were responsible for about 77 percent of all transportation and non-transportation mobile GHG emissions in 2009. Although passenger cars make up the largest component of on-road vehicle greenhouse gas emissions, light-duty and medium- and heavy-duty trucks have been the primary sources of growth in on-road vehicle emissions.Between 1990 and 2009, greenhouse gas emissions from passenger cars decreased 5 percent, while emissions from lightduty trucks increased 64 percent, largely due to the increased use of sport-utility vehicles and other light-duty trucks. Meanwhile, greenhouse gas emissions from medium- and heavy-duty trucks increased 58 percent, reflecting the increased volume of total freight movement and an increasing share transported by trucks.

### A-135 Greenhouse gas emissions from aircraft decreased 22 percent between 1990 and 2009. Emissions from military aircraft decreased 59 percent and commercial aircraft emissions rose 2 percent between 1990 and 2007 then dropped 19 percent from 2007 to 2009.
Non-transportation mobile sources, such as construc tion/mining equipment, agricultural equipment, and industrial/commercial equipment, emitted approximately 197.7 Tg CO 2 

### Eq. in 2009. Together, these sources emitted more greenhouse gases than ships and boats, rail, and pipelines combined. Emissions from non-transportation mobile sources increased rapidly, growing approximately 54 percent between 1990 and 2009. CH 4 

### and N 2 

### O emissions from these sources are included in the “Mobile Combustion” section and CO 2 

### emissions are included in the relevant economic sectors.

Contribution of Transportation and Mobile Sources to Greenhouse Gas Emissions, by Gas Table A- 110 presents estimates of greenhouse gas em issions from transportation and other mobile sources broken down by greenhouse gas. As this table shows, CO 2 

### accounts for the vast majority of transportation greenhouse gas emissions (approximately 96 percent in 2009). Emissions of CO 2 

### from transportation and mobile sources increased by 300.2 Tg CO 2 

### Eq. between 1990 and 2009. In contrast, the combined emissions of CH 4 

### and N 2 

### O decreased by 22.7 Tg CO 2 

### Eq. over the same period, due largely to the introduction of control technologies designed to reduce criteria pollutant
emissions. 44 

### Meanwhile, HFC emissions from mobile air conditioners and refrigerated transport increased from virtually no emissions in 1990 to 60.2 Tg CO 2 

### Eq. in 2009 as these chemicals were phased in as substitutes for ozone depleting substances. It should be noted, however, that the ozone de pleting substances that HFCs replaced are also powerful greenhouse gases, but are not included in national greenhouse gas inventories due to their mandated phase out.

Greenhouse Gas Emissions from Freight and Passenger Transportation Table A- 111 and Table A- 112present greenhouse gas es timates from transportation, broken down into the passenger and freight categories. Passenger modes include li ght-duty vehicles, buses, passenger rail, aircraft (general aviation and commercial aircraft), recreational boats, and mo bile air conditioners, and are illustrated in Table A- 111. Freight modes include medium- and heavy-duty trucks, freight rail, refrigerated transport, waterborne freight vessels, pipelines, and commercial aircraft and are illustrated in Table A- 112. Commercial aircraft do carry some freight, in addition to passengers, and for this Inventory, the emissions ha ve been split between passenger and freight transportation. (In previous Inventories, all commercial aircraft emissions were considered passenger transportation.) The amount of commercial aircraft emissions to allocate to the passenger and freight categories was calculated using BTS data on freight shipped by commercial aircraft, and the total number of passengers enplaned. Each passenger was considered to weigh an average of 150 pounds, with a luggage weight of 50 pounds. The total freight weight and total passenger weight carried were used to determine percent shares which were used to split the total commercial aircraft emissions estimates. The remaining transportation and mobile emissions were from sources not considered to be either freight or passenger modes (e.g., construction/mining and agricultural equipment, lubricants). The estimates in these tables are derived from the estimate s presented in Table A- 109. In addition, estimates of fuel consumption from DOE (1993 through 2010) were used to allocate rail emissions between passenger and freight categories.
In 2009, passenger transportation modes emitted 1,322.1 Tg CO 2 

### Eq., while freight transportation modes emitted 475.3 Tg CO 2 

### Eq. Since 1990, the rate of growth of greenhouse gas emissions from freight sources has been 7 percent higher than emissions from passenger sources, due largely to the rapid increase in emissions associated with medium- and heavy-duty trucks.

Table A- 109: Total U.S. Greenhouse Gas Emissi ons from Transportation and Mobile Sources (Tg CO 2 

### Eq.)

Mode / Vehicle Type / Fuel Type 1990 1995 2000 2005 2006 2007 2008 2009 Percent Change 1990-2009 Transportation

Total a 1,548.3 1,698.5 1,935.8 2,022.2 1,999.0 2,008.9 1,895.4 1,816.9 17% On-Road Vehicles 1,235.2 1,371.3 1,575.1 1,682.9 1,679.7 1,682.1 1,603.8 1,557.4 26% Passenger Cars 657.4 646.0 695.3 709.5 682.9 672.0 632.5 627.4 -5% Gasoline 649.4 627.8 667.3 676.9 651.6 643.3 606.5 604.1 -7% Diesel 7.9 7.9 3.7 4.2 4.1 4.1 3.9 3.9 -51% AFVs + 0.1 + + + + + + 551% HFCs from + 10.1 24.3 28.4 27.1 24.6 22.1 19.3 NA

44 The decline in CFC emissions is not capture d in the official transportation estimates.

### A-136 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Mobile AC Light-Duty

Trucks 336.6 436.6 512.1 551.3 564.0 570.3 553.8 551.0 64% Gasoline 324.5 414.6 469.8 493.0 504.7 511.8 496.5 495.8 53% Diesel 11.5 14.9 20.1 25.9 26.8 27.3 26.9 26.7 133% AFVs 0.6 0.5 0.5 1.4 1.3 1.0 1.8 1.9 227% HFCs from

Mobile AC + 6.5 21.7 31.0 31.2 30.1 28.6 26.6 NA Medium- and

Heavy-Duty Trucks 231.1 277.8 354.6 408.4 418.6 425.2 403.1 365.6 58% Gasoline 39.5 36.8 37.0 35.8 36.3 36.8 34.4 31.1 -21% Diesel 190.7 238.6 309.9 361.0 370.4 376.4 356.3 322.0 69% AFVs 0.9 0.6 0.3 0.5 0.6 0.5 0.8 0.8 -6% HFCs from

Refrigerated Transport + 1.7 7.4 11.1 11.4 11.5 11.6 11.6 NA Buses 8.4 9.2 11.2 12.0 12.3 12.5 12.2 11.2 34% Gasoline 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 -1% Diesel 8.0 8.7 10.2 10.6 10.8 10.8 10.3 9.3 17% AFVs + 0.1 0.5 0.9 0.8 1.0 1.1 1.1 40569% HFCs from

Comfort Cooling + + 0.1 0.2 0.3 0.3 0.4 0.4 NA Motorcycles 1.8 1.8 1.9 1.7 1.9 2.1 2.2 2.2 24% Gasoline 1.8 1.8 1.9 1.7 1.9 2.1 2.2 2.2 24% Aircraft 181.2 175.4 204.4 198.7 173.6 172.7 158.7 142.1 -22% Commercial

Aircraft 136.8 143.1 170.9 162.8 138.5 139.5 123.4 112.5 -18% Jet Fuel 136.8 143.1 170.9 162.8 138.5 139.5 123.4 112.5 -18% General Aviation

Aircraft 9.6 8.2 12.1 17.5 18.5 16.9 18.8 15.3 59% Jet Fuel 6.4 5.4 9.6 15.0 16.2 14.6 16.8 13.4 109% Aviation Gasoline 3.2 2.8 2.6 2.5 2.4 2.2 2.0 1.9 -41% Military Aircraft 34.8 24.1 21.3 18.3 16.5 16.3 16.4 14.3 -59% Jet Fuel 34.8 24.1 21.3 18.3 16.5 16.3 16.4 14.3 -59% Ships and Boats b 45.1 58.6 61.0 45.2 48.4 55.2 37.1 30.5 -32% Gasoline 12.6 14.1 10.0 14.2 14.1 14.0 13.6 13.5 7% Distillate Fuel 9.6 14.9 17.1 11.4 10.9 11.7 3.2 4.8 -50% Residual Fuel 22.9 29.5 33.8 19.6 23.4 29.5 20.2 12.2 -47% HFCs from

Refrigerated Transport - + 0.1 + + + + + NA Rail 39.0 43.7 48.1 53.0 55.1 54.3 50.6 43.3 11% Distillate Fuel 35.8 40.0 42.5 46.0 48.3 47.0 43.6 36.5 2% Electricity 3.1 3.1 3.5 4.8 4.5 5.0 4.7 4.4 45% HFCs from

Comfort Cooling + + + + + + + + NA HFCs from

Refrigerated Transport + 0.5 2.0 2.1 2.2 2.2 2.2 2.2 NA Other Emissions

from Rail Electricity Use 0.1 0.1 + 0.1 0.1 0.1 0.1 0.1 -9% Pipelines c 36.0 38.2 35.2 32.3 32.3 34.3 35.7 35.2 -2% Natural Gas 36.0 38.2 35.2 32.3 32.3 34.3 35.7 35.2 -2% Other

Transportation 11.8 11.3 12.1 10.2 9.9 10.2 9.5 8.5 -28% Lubricants 11.8 11.3 12.1 10.2 9.9 10.2 9.5 8.5 -28%NonTransportation Mobile Total 128.8 146.8 158.3 190.7 195.8 194.8 194.2 197.7 54% Agricultural

Equipment d 31.4 37.0 39.2 47.3 49.6 49.0 45.9 47.2 50%

### A-137 Gasoline 7.3 8.3 5.8 9.6 11.0 9.6 5.7 6.1 -17% Diesel 24.1 28.7 33.4 37.7 38.6 39.4 40.3 41.1 71% Construction/

Mining Equipment e 42.4 49.4 55.8 66.5 67.9 68.4 69.9 71.2 68% Gasoline 4.4 4.0 3.1 6.2 6.2 5.1 5.2 5.0 14% Diesel 38.0 45.4 52.7 60.3 61.8 63.3 64.8 66.3 74% Other Equipment f 55.0 60.4 63.4 76.9 78.3 77.4 78.4 79.3 44% Gasoline 40.3 42.6 42.5 52.7 53.4 51.8 52.2 52.4 30% Diesel 14.7 17.8 20.9 24.2 24.9 25.6 26.2 26.9 83% Transportation

and NonTransportation Mobile Total 1,677.1 1,845.3 2,094.1 2,212.9 2,194.8 2,203.6 2,089.7 2,014.7 20%

a Not including emissions from international bunker fuels.

b Fluctuations in emission estimates reflect data collection problems.

c Includes only CO 2 from natural gas used to power pipelines; does not include emissions from electricity use or non-CO 2 gases. d Includes equipment, such as tractors and combines, as well as fu el consumption from trucks that are used off-ro ad in agricultu re.

e Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consump tion from trucks that are used off-road in construction.

f “Other" includes snowmobiles and other recrea tional equipment, logging equipment, lawn and garden equipment, railroad equipment , airport equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes. + Less than 0.05 Tg CO 2 Eq. - Unreported or zero NA = Not Applicable, as there were no HFC emissions allocated to the transport sector in 1990, and thus a growth rate cannot be calculated.


### Table A- 110: Transportation and Mobile Source Emissions by Gas (Tg CO 2 

### Eq.)

Gas 1990 1995 2000 2005 2006 2007 2008 2009 Percent Change

1990- 2009 CO 2 1,628.4 1,767.9 1,981.9 2,100.4 2,086.5 2,102.2 1,996.5 1,928.6 18%
N 2 O 43.9 54.0 53.2 36.9 33.6 30.3 26.1 23.9 -46% CH 4 4.7 4.3 3.4 2.5 2.2 2.2 2.0 2.0 -58% HFC - 19.0 55.7 72.9 72.2 68.8 64.9 60.2 NA Total 1,677.0 1,845.2 2,094.1 2,212.8 2,194.7 2,203.5 2,089.6 2,014.6 20% - Unreported or zero NA = Not Applicable, as there were no HFC emissions allocated to the transport sector in 1990, and thus a growth rate cannot be calculated.



### Figure A-4: Domestic Greenhouse Gas Emissions by Mode and Vehicle Type, 1990 to 2009 (Tg CO 2 

### Eq.)

Table A- 111: Greenhouse Gas Emission s from Passenger Transportation (Tg CO 2 

### Eq.)

Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009 Percent Change

1990- 2009 On-Road Vehicles 1,004.1 1,093.5 1,220.5 1,274.5 1,261.1 1,256.9 1,200.8 1,191.7 19% Passenger Cars 657.4 646.0 695.3 709.5 682.9 672.0 632.5 627.4 -5% Light-Duty

Trucks 336.6 436.6 512.1 551.3 564.0 570.3 553.8 551.0 64% Buses 8.4 9.2 11.2 12.0 12.3 12.5 12.2 11.2 34% Motorcycles 1.8 1.8 1.9 1.7 1.9 2.1 2.2 2.2 24% Aircraft 122.7 126.5 153.4 154.4 135.3 136.0 124.3 111.4 -9% General Aviation 9.6 8.2 12.1 17.5 18.5 16.9 18.8 15.3 59% Commercial

Aircraft 113.1 118.3 141.3 136.8 116.7 119.2 105.5 96.1 -15% Recreational Boats 14.5 16.4 12.7 17.4 17.3 17.3 14.5 16.9 17% Passenger Rail 4.4 4.5 5.2 6.2 6.0 6.5 6.2 6.0 37%

### A-138 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Total 1,145.7 1,241.0 1,391.8 1,452.5 1,419.7 1,416.8 1,345.8 1,326.1 16% Note: Data from DOE (1993 through 2010) were used to disaggregate emissions from rail and buses. Emissions from HFCs have been included in these estimates.



### Table A- 112: Greenhouse Gas Emissions fr om Domestic Freight Transportation (Tg CO 2 

### Eq.)

By Mode 1990 1995 2000 2005 2006 2007 2008 2009 Percent Change

1990- 2009 Trucking 231.1 277.8 354.6 408.4 418.6 425.2 403.1 365.6 58% Freight Rail 34.5 39.1 42.8 46.7 49.0 47.8 44.5 37.2 8% Ships and Other Boats 30.6 42.2 48.3 27.9 31.1 37.9 22.6 13.5 -56% Pipelines 36.0 38.2 35.2 32.2 32.3 34.3 35.7 35.2 -2% Commercial Aircraft 23.7 24.8 29.6 26.0 21.8 20.3 18.0 16.4 -31% Total 356.0 422.1 510.5 541.1 552.8 565.5 523.7 467.9 31% Note: Data from DOE (1993 through 2010) were used to disaggregate emissions from rail and buses. Emissions from HFCs have been included in these estimates.

### A-139 

# 3.3. Methodology for Estimating CH 4 

# Emissions from Coal Mining

The methodology for estimating CH 4 

### emissions from coal mining consists of two distinct steps. The first step addresses emissions from underground mines. For these mines, emissions are estimated on a mine-by-mine basis and then are summed to determine total emissions. The second step of the analysis involves estimating CH 4 

### emissions for surface mines and post-mining activities. In contrast to the methodology for underground mines, which uses mine-specific data,the surface mine and post-mining activities analysis consists of multiplying basin-specific coal production by basinspecific emission factors.

Step 1: Estimate CH4 Liberated and CH4 Emitted from Underground Mines
Underground mines generate CH 4 

### from ventilation systems and from degasification systems. Some mines recover and use CH 4 

### generated from degasification systems, thereby reducing emissions to the atmosphere. Total CH 4 

### emitted from underground mines equals the CH 4 

### liberated from ventilation systems, plus the CH 4 

### liberated from degasification systems, minus CH 4 

### recovered and used.

Step 1.1: Estimate CH 4 

### Liberated from Ventilation Systems

All coal mines with detectable CH 4 

### emissions 45 

### use ventilation systems to ensure that CH 4 

### levels remain within safe concentrations. Many coal mines do not have detectable levels of CH 4 

### , while others emit several million cubic feet per day (MMCFD) from their ventilation systems. On a quarter ly basis, the U.S. Mine Safety and Health Administration (MSHA) measures CH 4 

### emissions levels at underground mines. MSHA maintains a database of measurement data from all underground mines with detectable levels of CH 4 

### in their ventilation air. Based on the four quarterly measurements, MSHA estimates average daily CH 4 

### liberated at each of the underground mines with detectable emissions.
For the years 1990 through 1996, 1998 through 2006, 2008 and 2009, MSHA emissions data were obtained for a large but incomplete subset of all mines with detectable em issions. This subset includes mines emitting at least 0.1 MMCFD for some years and at least 0.5 MMCFD for other years, as shown in Table A- 113. Well over 90 percent of all ventilation emissions were concentrated in these subsets. For 1997 and 2007, the complete MSHA databases for all 586 mines (in 1997) and 730 mines (in 2007) with detectable CH 4 

### emissions were obtained. These mines were assumed to account for 100 percent of CH 4 

### liberated from underground mines. Using the complete database from 1997, the proportion of total emissions accounted for by mines emit ting less than 0.1 MMCFD or 0.5 MMCFD was estimated (see Table A- 113). The proportion was then applied to the years 1990 through 2006 to account for the less than 5 percent of ventilation emissions coming from mines without MSHA data.
For 1990 through 1999, average daily CH 4 

### emissions were multiplied by the number of days in the year (i.e., coal mine assumed in operation for all four quarters) to determine the annual emissions for each mine. For 2000 through 2009, MSHA provided quarterly emissions. The average daily CH 4 

### emissions were multiplied by the number of days corresponding to the number of quarters the mine vent was operating. For example, if the mine vent was operational in one out of the four quarters, the average daily CH 4 

### emissions were multiplied by 92 days . Total ventilation emissions for a particular year were estimated by summing emissions from individual mines.

45 MSHA records coal mine methane readings with concentrations of greater than 50 ppm (parts per million) methane. Readings belo w this threshold are considered non-detectable.

### A-140 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A- 113: Mine-Specific Data Used to Estimate Ventilation Emissions
Year Individual Mine Data Used 1990 All Mines Emitting at Least 0.1 MMCFD (A ssumed to Account for 97.8% of Total)* 1991 1990 Emissions Factors Used In stead of Mine-Specific Data 1992 1990 Emissions Factors Used In stead of Mine-Specific Data 1993 All Mines Emitting at Least 0.1 MMCFD (A ssumed to Account for 97.8% of Total)* 1994 All Mines Emitting at Least 0.1 MMCFD (A ssumed to Account for 97.8% of Total)* 1995 All Mines Emitting at Least 0.5 MMCFD (A ssumed to Account for 94.1% of Total)* 1996 All Mines Emitting at Least 0.5 MMCFD (A ssumed to Account for 94.1% of Total)* 1997 All Mines with Detectable Emissions (Assumed to Account for 100% of Total) 1998 All Mines Emitting at Least 0.1 MMCFD (A ssumed to Account for 97.8% of Total)* 1999 All Mines Emitting at Least 0.1 MMCFD (A ssumed to Account for 97.8% of Total)* 2000 All Mines Emitting at Least 0.1 MMCFD (A ssumed to Account for 97.8% of Total)* 2001 All Mines Emitting at Least 0.1 MMCFD (A ssumed to Account for 97.8% of Total)* 2002 All Mines Emitting at Least 0.1 MMCFD (A ssumed to Account for 97.8% of Total)* 2003 All Mines Emitting at Least 0.1 MMCFD (A ssumed to Account for 97.8% of Total)* 2004 All Mines Emitting at Least 0.1 MMCFD (A ssumed to Account for 97.8% of Total)* 2005 All Mines Emitting at Least 0.1 MMCFD (A ssumed to Account for 97.8% of Total)* 2006 2007 2008 2009 All Mines Emitting at Least 0.1 MMCFD (Assu med to Account for 97.8% of Total)* All Mines with Detectable Emissions (A ssumed to Account for 100% of Total) All Mines Emitting at Least 0.1 MMCFD (Assu med to Account for 98.96% of Total)** All Mines Emitting at Least 0.1 MMCFD (Assu med to Account for 98.96% of Total)** * Factor derived from a complete set of individual mine data collected for 1997. ** Factor derived from a complete set of individual mine data collected for 2007.



### Step 1.2: Estimate CH 4 

### Liberated from Degasification Systems
Coal mines use several different types of degasification systems to remove CH 4 

### , including vertical wells and horizontal boreholes to recover CH 4 

### prior to mining of the coal seam. Gob wells and cross-measure boreholes recover CH 4 

### from the overburden (i.e., GOB area) after mining of the seam (primarily in longwall mines).
MSHA collects information about the presence and type of degasification systems in some mines, but does not collect quantitative data on the amount of CH 4 

### liberated. Thus, the methodology estimated degasification emissions on a mine-by-mine basis based on other sources of available data. Many of the coal mines employing degasification systems have provided EPA with information regarding CH 4 

### liberated from their degasification systems. For these mines, this reported information was used as the estimate. In other cases in which mines sell CH 4 

### recovered from degasification systems to a pipeline, gas sales were used to estimate CH 4 

### liberated from degasification systems (see Step 1.3). Finally, for those mines that do not sell CH 4 

### to a pipeline and have not provided information to EPA, CH 4 

### liberated from degasification systems was estimated based on the type of system employed. For example, for coal mines employing gob wells and horizontal boreholes, the methodology assumes that degasification emissions account for 40 percent of total CH 4 

### liberated from the mine.

Step 1.3: Estimate CH 4 

### Recovered from Degasification Syst ems and Utilized (Emissions Avoided)
In 2009, fourteen active coal mines had CH 4 

### recovery and use projects. Thirteen mines sold the recovered CH 4 

### to a pipeline, one used the CH 4 

### on site to heat mine ventilation air, and one of the coal mines used CH

B 4

B 

### to generate electricity. One of the mines that sold gas to a pipeline also used CH

B 4 

### to fuel a thermal coal dryer. In order to calculate emissions avoided from pipeline sales, information was needed regarding the amount of gas recovered and the number of years in advance of mining that wells were drilled. Several state agencies provided gas sales data, which were used to estimate emissions avoided for these projects. Additionally, coal mine operators provided information on gas sales and/or the number of years in advance of mining. Emissions avoided were attributed to the year in which the coal seam was mined. For example, if a coal mine recovered and sold CH 4 

### using a vertical well drilled five years in advance of mining, the emissions avoided associated with those gas sales (cumulative production) were attributed to the well at the time it was mined through (e.g., five years of gas production). Where indi vidual well data is not available, estimated percentages of the operator’s annual gas sales within the field around the coal mine are attributed to emissions avoidance. For some mines, individual well data were used to assign gas sales to the appropriate emissions avoided year. In most cases, coal mine operators provided this information, which was then used to estimate emissions avoided for a particular year. Additionally, several state agencies provided production data for individual wells.

### A-141 Step 2: Estimate CH 4 

### Emitted from Surface Mines and Post-Mining Activities
Mine-specific data were not available for estimating CH 4 

### emissions from surface coal mines or for post-mining activities. For surface mines and post-mining activities, basin-specific coal production was multiplied by a basin-specific emission factor to determine CH 4 

### emissions.

Step 2.1: Define the Geographic Resolution of the Analysis and Collect Coal Production Data
The first step in estimating CH 4 

### emissions from surface mining and post-mining activities was to define the geographic resolution of the analysis and to collect coal production data at that level of resolution. The analysis was conducted by coal basin as defined in Table A- 114, which presents coal basin definitions by basin and by state.
The Energy Information Administration’s (EIA) Annual Coal Report includes state- and county-specific underground and surface coal production by year. To calculate production by basin, the state level data were grouped into coal basins using the basin definitions listed in Table A- 114. For two states

⎯ West Virginia and Kentucky

⎯ county-level production data was used for the basin assignments because coal production occurred from geologically distinct coal basins within these states. Table A- 115 presents the coal production data aggregated by basin.

Step 2.2: Estimate Emissions Factors for Each Emissions Type
Emission factors for surface mined coal were developed from the in situ CH 4 

### content of the surface coal in each basin. Based on an analysis presented in EPA (1993), surf ace mining emission factors were estimated to be from 1 to 3 times the average in situ CH 4 

### content in the basin. For this analysis, the surface mining emission factor was determined to be twice the in situ CH 4 

### content in the basin. Furthermore, the post-mining emission factors used were estimated to be 25 to 40 percent of the average in situ CH 4 

### content in the basin. For this analysis, the post-mining emission factor was determined to be 32.5 percent of the in situ CH 4 

### content in the basin. Table A- 116 presents the average in situ content for each basin, along with the resulting emission factor estimates.

Step 2.3: Estimate CH 4 

### Emitted

The total amount of CH 4 

### emitted was calculated by multiplying the coal production in each basin by the appropriate emission factors.

Total annual CH 4 

### emissions are equal to the sum of underground mine emissions plus surface mine emissions plus post-mining emissions. Table A- 1 17 and Table A- 118 present estimates of CH 4 

### liberated, used, and emitted for 1990 through 2008. Table A- 119 provides emissions by state.
Table A- 114: Coal Basin Definitions by Basin and by State
Basin States Northern Appalachian Basin Maryland, Oh io, Pennsylvania, West Virginia North Central Appalachian Basin Kentucky East, Tennessee, Virginia, West Virginia South Warrior Basin Alabama, Mississippi Illinois Basin Illinois, Indiana, Kentucky West South West and Rockies Basin Arizona, California, Colorado, New Mexico, Utah North Great Plains Basin Mont ana, North Dakota, Wyoming West Interior Basin Arkansas, Iowa, Kansas , Louisiana, Missouri, Oklahoma, Texas Northwest Basin Alaska, Washington State Basin Alabama Warrior Basin Alaska Northwest Basin Arizona South West and Rockies Basin Arkansas West Interior Basin California South West and Rockies Basin Colorado South West and Rockies Basin Illinois Illinois Basin Indiana Illinois Basin Iowa West Interior Basin Kansas West Interior Basin Kentucky East Central Appalachian Basin Kentucky West Illinois Basin Louisiana West Interior Basin Maryland Northern Appalachian Basin

### A-142 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Mississippi Warrior Basin Missouri West Interior Basin Montana North Great Plains Basin New Mexico South West and Rockies Basin North Dakota North Great Plains Basin Ohio Northern Appalachian Basin Oklahoma West Interior Basin Pennsylvania. Northern Appalachian Basin Tennessee Central Appalachian Basin Texas West Interior Basin Utah South West and Rockies Basin Virginia Central Appalachian Basin Washington Northwest Basin West Virginia South Central Appalachian Basin West Virginia North Northern Appalachian Basin Wyoming North Great Plains BasinA-143Table A- 115: Annual Coal Production (Thousand Short Tons)Basin 19901994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Underground CoalProduction 423,556399,102 396,249 409,850 420,657 417,729 391,791 372,766 380,627 357,384 352,785 367,558 368,611 359,020 351,791 357,074 332,061N. Appalachia 103,865100,122 98,103 106,729 112,135 116,718 107,575 105,374 107,025 98,643 98,369 106,915111,151 107,827 106,024 105,228 99,629Cent. Appalachia 198,412170,893 166,495 171,845 177,720 171,279 157,058 150,584 152,457 137,224 130,724 128,559 123,083 117,738 110,103 114,998 98,689Warrior 17,53114,471 17,605 18,217 18,505 17,316 14,799 15,895 15,172 14,916 15,375 16,11413,295 10,737 11,462 12,281 11,505Illinois 69,16769,050 69,009 67,046 64,728 64,463 63,529 53,720 54,364 54,016 51,780 56,32059,180 61,726 61,924 64,609 67,186S. West/Rockies 32,75441,681 42,994 43,088 44,503 45,983 46,957 45,742 51,193 52,121 56,111 59,03960,865 59,670 58,815 55,781 50,416N. Great Plains 1,7222,738 2,018 2,788 2,854 1,723 1,673 1,210 - - 32 201 572 840 2,869 3,669 4,248West Interior 105147 25 137 212 247 200 241 415.623 464 394 410 465 482 594 508 388Northwest----- --- --- - - - - - -Surface CoalProduction 602,753634,401 636,726 654,007 669,271 699,806 708,639 700,608 745,306 735,912 717,869 743,553 762,191 802,975 793,689 813,321 740,175N. Appalachia 60,76144,960 39,372 39,788 40,179 41,043 33,928 34,908 35,334 30,088 27,370 28,17428,873 28,376 26,121 30,413 26,552Cent. Appalachia 94,343106,129 106,250 108,869 113,275 108,345 107,507 110,479 116,983 111,340 99,419 103,968 112,222 118,388 116,226 118,962 97,778Warrior 11,4138,795 7,036 6,420 5,963 5,697 4,723 4,252 4,796 6,320 8,437 9,74211,599 11,889 11,410 11,172 10,731Illinois 72,00051,868 40,376 44,754 46,862 45,715 40,474 33,631 40,894 39,380 36,675 34,01633,702 33,362 33,736 34,266 34,837S. West/Rockies 43,86349,119 46,643 43,814 48,374 49,635 50,349 49,587 52,180 50,006 41,237 42,55842,756 36,798 34,310 34,283 32,167N. Great Plains 249,356308,279 331,367 343,404 349,612 385,438 407,683 407,670 438,367 441,346 444,007 466,224 474,056 518,136 523,695 538,387 496,290West Interior 64,31058,791 59,116 60,912 59,061 57,951 58,309 54,170 50,613 50,459 53,411 51,70652,263 52,021 46,867 44,361 39,960Northwest6,7076,460 6,566 6,046 5,945 5,982 5,666 5,911 6,138 6,973 7,313 7,1656,720 4,005 1,324 1,477 1,860Total CoalProduction 931,068937,594 937,115 965,131 988,783 1,013,828 998,310 973,765 1,021,446 991,838 971,297 1,008,000 1,025,864 1,054,162 1,039,178 1,170,395 1,072,236N. Appalachia 164,626145,082 137,475 146,517 152,314 157,761 141,503 140,282 142,360 128,731 125,739 135,089 140,024 136,203 132,143 135,641 126,181Cent. Appalachia 292,755277,022 272,745 280,714 290,995 279,624 264,565 261,063 269,440 248,564 230,143 232,527 235,305 236,126 226,328 233,960 196,467Warrior 28,94423,266 24,641 24,637 24,468 23,013 19,522 20,147 19,967 21,236 23,812 25,85624,894 22,626 22,872 23,453 22,236Illinois 141,167120,918 109,385 111,800 111,590 110,178 104,003 87,351 95,258 93,396 88,45590,336 92,882 95,088 95,660 98,875 102,023S. West/Rockies 76,61790,800 89,637 86,902 92,877 95,618 97,30695,329 103,373 102,127 97,348 101,597 103,621 96,468 93,125 90,064 82,583N. Great Plains 251,078311,017 333,385 346,192 352,466 387,161 409,356 408,880 438,367 441,346 444,039 466,425 474,628 518,976 526,564 542,056 500,538West Interior 64,41558,938 59,141 61,049 59,273 58,198 58,509 54,411 51,028 50,923 53,805 52,11652,728 52,503 47,462 44,869 40,348Northwest6,7076,460 6,566 6,046 5,945 5,982 5,666 5,911 6,138 6,973 7,313 7,1656,720 4,005 1,324 1,477 1,860Source for 1990-2009 data: EIA (1990 through 2009), Annual Coal Report. U.S. Departmentof Energy, Washington, DC, Table 1.Note: Totals may not sum due to independent rounding.

### A-144 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A- 116: Coal Surface and Post-Mining CH 4 

### Emission Factors (ft 3 

### per Short Ton)

Basin Surface Average in situ Content Underground Average In situ Content Surface Mine Factors Post-Mining Surface Factors Post Mining Underground Northern Appalachia 59.5 138.4 119.0 19.3 45.0 Central Appalachia (WV) 24.9 136.8 49.8 8.1 44.5 Central Appalachia (VA) 24.9 399.1 49.8 8.1 129.7 Central Appalachia (E KY) 24.9 61.4 49.8 8.1 20.0 Warrior 30.7 266.7 61.4 10.0 86.7 Illinois 34.3 64.3 68.6 11.1 20.9 Rockies (Piceance Basin) 33.1 196.4 66.2 10.8 63.8 Rockies (Uinta Basin) 16.0 99.4 32.0 5.2 32.3 Rockies (San Juan Basin) 7.3 104.8 14.6 2.4 34.1 Rockies (Green River Basin) 33.1 247.2 66.2 10.8 80.3 Rockies (Raton Basin) 33.1 127.9 66.2 10.8 41.6 N. Great Plains (WY, MT) 20.0 15.8 40.0 6.5 5.1 N. Great Plains (ND) 5.6 15.8 11.2 1.8 5.1 West Interior (Forest City, Cherokee

Basins) 34.3 64.3 68.6 11.1 20.9

West Interior (Arkoma Basi n) 74.5 331.2 149.0 24.2 107.6 West Interior (Gulf Coast Ba sin) 11.0 127.9 22.0 3.6 41.6 Northwest (AK) 16.0 160.0 32.0 1.8 52.0 Northwest (WA) 16.0 47.3 32.0 5.2 15.4 Sources: 1986 USBM Circular 9067, Results of the Direct Method Determination of the Gas Contents of U.S. Coal Basins , 1983 U.S. DOE Report (DOE/METC/83-76), Methane Recovery from Coalbeds: A Potential Energy Source , 1986-88 Gas Research Institute Topical Reports, A Geologic Assessment of Natural Gas from Coal Seams; Surface Mines Emissions Assessment , U.S. EPA Draft Report, November 2005.A-145Table A- 117: Underground Coal Mining CH4Emissions (Billion Cubic Feet)Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Ventilation Output 112 NA NA 95 96100 90 96 94 92 87 84 79 76 83 75 79 81 100 114Adjustment Factor for MineData*97.8% NA NA 97.8% 97.8% 91.4% 91.4% 100.0% 97.8% 97.8% 97.8% 97.8% 97.8% 97.8% 97.8% 97.8% 97.8% 100.0% 99.0% 99.0%Adjusted Ventilation Output 114 NA NA97 98 109 99 96 96 94 89 86 80 77 84 77 80 81 101 115Degasification SystemLiberated 54 NA NA 45 46 36 52 43 49 40 45 49 51 50 45 48 54 44 49 51Total UndergroundLiberated 168 164 162 142 144 146 150 139 146 134 135 135 131 127 130 124 134 125 150 166Recovered & Used (14) (15) (17) (23) (27) (30) (37) (28) (35) (31) (37) (41) (43) (38)(40) (37) (46) (37) (40) (41)Total 154 150 145 120 117 116 113 111 110 103 98 95 88 89 90 87 88 88 110 125* Refer to Table A- 113.Note: Totals may not sum due to independent rounding.Table A- 118: Total Coal Mining CH4Emissions (Billion Cubic Feet)Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Underground Mining 154 150 145 120 117 116 113 111 110 103 98 95 88 89 90 87 88 88 110 125Surface Mining 30 28 28 28 29 28 29 30 31 31 30 33 32 31 32 33 35 34 35 32Post-Mining (Underground) 19 18 18 16 17 17 18 18 18 17 17 17 16 16 16 16 15 15 15 14Post-Mining (Surface) 5 5 5 4 5 5 5 5 5 5 5 5 55 5 5 6 6 6 5Total 208 201 195 167 168 166 165 164 165 156 149 149 140 141 144 141 144 143 166 176Note: Totals may not sum due to independent rounding.Table A- 119: Total Coal Mining CH4Emissions by State (Million Cubic Feet)State 19901994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 200720082009Alabama 32,27229,630 33,735 29,556 26,426 26,440 25,702 23,342 21,896 18,686 19,288 18,246 15,912 14,699 17,159 21,120 23,975Alaska 6358 63 55 54 50 58 61 56 43 40 56 54 53 49 55 69Arizona 192222 203 177 199 192 200 223 228 217 205 216 205 139 135 136 127Arkansas 78 5 4 3 4 4 2 2 2 1 1 1 4 144 237 119California 1- - - - + + + + + + + + + + - -Colorado 10,3259,192 7,582 5,972 9,189 9,181 9,390 10,808 11,117 12,082 13,216 12,582 13,608 13,102 13,180 12,998 14,100Illinois 10,50210,585 11,563 10,876 8,534 7,847 7,810 8,5427,270 5,972 4,744 5,798 6,5866,954 4,493 7,759 7,322Indiana 2,7952,495 2,025 2,192 2,742 2,878 2,6502,231 3,373 3,496 3,821 3,531 3,702 4,029 4,347 5,452 6,155Iowa 304 - - - + + + + + + + + + + - -Kansas 5723 23 19 29 27 33 16 14 16 12 6 14 34 33 18 15Kentucky 10,95611,259 10,269 8,987 10,451 10,005 9,5619,105 9,363 8,464 8,028 7,926 7,494 9,135 9,278 10,641 12,617Louisiana 8189 95 82 91 82 76 94 95 97 103 97 106 105 80 98 94Maryland 519237 237 259 267 251 225 331 340 401 391 411 421 435 261 325 273Mississippi -- - - - 0 1 57 43 165 264 256 254 271 253 203 246Missouri 21167 44 57 32 30 31 35 29 20 43 46 48 31 19 20 36Montana 1,7491,936 1,834 1,756 1,906 1,992 1,911 1,7831,820 1,738 1,719 1,853 1,8701,931 2,016 2,076 1,804Mew Mexico 451679 586 408 459 489 497 464 6301,280 1,864 2,052 3,001 2,970 2,660 3,479 3,904North Dakota 380420 392 389 385 389 405 407 397 401 401 390 390 396 385 386 390Ohio 5,0654,583 4,189 4,068 4,349 4,350 3,914 3,519 3,619 2,831 2,649 3,183 3,385 3,413 2,672 3,959 4,746Oklahoma 285359 323 286 385 395 469 454 620 660 620 849 877 658 774 970 646A-146 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009Pennsylvania 22,73524,024 25,611 26,440 30,02630,888 24,867 24,830 22,252 19,668 20,28120,020 18,289 18,727 19,519 21,044 23,216Tennessee 296101 112 143 148 116 119 99 142 142 124 136 140 117 120 105 84Texas 1,4261,339 1,347 1,411 1,364 1,345 1,357 1,240 1,152 1,157 1,215 1,173 1,175 1,165 1,073 998 898Utah 3,5872,626 2,570 2,810 3,566 3,859 3,633 2,8162,080 2,709 3,408 5,253 4,7875,445 3,678 5,524 5,449Virginia 46,13726,742 20,059 19,771 16,851 13,978 13,32112,065 11,506 11,227 11,906 11,389 8,790 9,830 10,118 9,334 8,172Washington 186182 181 170 167 173 153 159 172 217 232 210 196 96 + - -West Virginia 49,03930,664 30,552 36,384 33,554 35,566 33,599 30,563 33,985 31,405 28,474 29,46530,612 29,510 29,654 37,406 41,241Wyoming 8,49610,912 12,185 12,838 12,994 14,549 15,607 15,725 17,147 17,352 17,497 18,43518,784 20,752 20,974 21,601 19,903Total 207,844168,433 165,785 165,109 164,171 165,075 155,568 149,371 149,348 140,449 140,544 143,581 140,698 144,004 143,076 165,945 175,598- Zero Cubic Feet+ Does not exceed 0.5 Million Cubic FeetNote: The emission estimates provided above are inclusive ofemissions from underground mines, surface mines and post-mining activities. The following states have neither underground nor surfacemining and thus report no emissions as a result of coal mining: Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho, Maine,Massachusetts, Michigan, Minnesota, Nebraska, Nevada, NewHampshire, New Jersey, New York, North Carolina, Oregon, Rhode Island, South Carolina, South Dakota, Vermont, and Wisconsin.

### A-147 

# 3.4. Methodology for Estimating CH 4 

# and CO 2 

# Emissions from Natural Gas Systems

The following steps were used to estimate CH 4 

### and non-energy CO 2 

### emissions from natural gas systems.

Step 1: Calculate Emission Estimates for Ba se Year 1992 Using EPA Adjusted GRI/EPA Study
The first step in estimating CH 4 

### and non-energy related (i.e., fugitive, vented and flared) CO 2 

### emissions from natural gas systems was to develop a detailed base year estimate of emissions. The study by EPA/GRI (1996) divides the industry into four stages to construct a detailed emissi on inventory for the year 1992. These stages include: field production, processing, transmissi on and storage (i.e., both underground and liquefied gas storage), and distribution. This study produced emission factors and activity data for over 100 different emission sources within the natural gas system. Emissions for 1992 were estimated by multiplying activity levels by emission factors for each system component and then summing by stage.
Since publication activity data for some of the components in the system have been updated based on publicly available data. For other sources where annual activity data are not available, a set of industry activity factor drivers was developed that can be used to update activity data. Table A-120 through Table A-123 display the 2009 activity levels, CH 4 

### emission factors, and CH 4 

### emissions for each stage. These data are shown to illustrate the kind of data used to calculate CH 4 

### and non-energy CO 2 

### emissions from all stages. Many emission factors determined by EPA/GRI (1996) were assumed to be representative of emissions from each source type over the period 1990 through 2009.
However, several emission factors have been updated since publication of the EPA/GRI 1996 study. Notably, emission factors for gas well cleanups (EPA 2006a, HPDI 2009), condensate storage tanks (EPA 1999, HPDI 2009, TERC 2009), and centrifugal compressors (EPA 2006b, WGC 2009) have been revised. Emissions for gas well completions and workovers (re-completions) with hydraulic fracturing (i.e. unconventional) (EPA 2004, 2007), which were not included in the EPA/GRI study, have also been added. The EPA/GRI study and Inventory did, however, include an estimate for well completions without hydraulic fracturing under the source categ ory Completion Flaring.. The revised 2009 emission factor for centrifugal compressors will be reevaluated in the next Inventory cycle.
For most sources, the CH 4 

### emission factors were adjusted for CO 2 

### content when estimating fugitive and vented non-energy CO 2 

### emissions. In the case of non-energy CO 2 

### emissions from flared sources, acid gas removal units and condensate tanks, specific industry data related to those sour ces was used to derive their respective emission factors.
The activity levels and CH 4 

### emission factors in Table A-120 are arranged into regions designated by the National Energy Modeling System (NEMS) for Oil and Gas Supply. NEMS for Oil and Gas Supply splits the continental United States into 6 regions: Northeast, Midcontinent, Rocky Mountai n, South West, West Coast and Gulf Coast. GRI, however, does not evaluate activity data for each of these regions separately. The GRI national AF estimates were allocated to the NEMS oil and gas supply module regions using the NEMS regional gas well counts to national well count ratios.

Step 2: Collect Aggregate Statistics on Main Driver Variables
As detailed data on each of the over 100 sources were not available for the period 1990 through 2009, activity levels were estimated using aggregate statistics on key dr ivers, including: number of producing wells (EIA 2010a-b, New Mexico 2010a-b, Texas 2010a-b), number of gas plants (A GA 1991-1997; OGJ 1998-2010), number of shallow and deep offshore platforms (BOEMRE 2010a-d), miles of transmission pipeline (OPS 2010a), miles of distribution pipeline (OPS 2010b), miles of distribution services (OPS 2010b), and ener gy consumption (EIA 2010c). Table A-124 provides the activity levels of some of the key drivers in the natural gas analysis.

Step 3: Estimate CH 4 

### Emissions for Each Year and Stage
Emissions from each stage of the natural gas industry were estimated by multiplying the activity factors by the appropriate emission factors, summing all sources for each stage and then accounting for CH 4 

### reductions reported to the Natural Gas STAR Program and CH 4 

### reductions resulting from regulations such as National Emission Standards for Hazardous Air Pollutants (NESHAP) regulations.
Industry partners report CH 4 

### emission reductions by project to the Natural Gas STAR Program. The reductions are estimates using actual measurement data or equipment- specific emission factors. The reductions undergo quality assurance and quality control checks to identi fy errors, inconsistencies, or irregular data before being incorporated into the Inventory. These quality assurance and quality control checks include matching Natural Gas STAR reported reductions to

### A-148 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 specific inventory sources to make sure that a reported reduction for one source is not greater than the emission estimate for that source. Total emissions are estimated by adding th e emission estimates from each stage. The base year of the inventory is 1992; therefore any reductions reported for 1992 or earlier are considered to be already included in the baseyear emission factors and are not subtracted from the inventory estimate. If the reported reduction occurred between 1990 and 1992, then the reduction is added back into the estimate for the appropriate year(s). The reductions are also adjusted to remove the sunsetting time period which is relevant to Natural Gas STAR accounting but not the Inventory. For example, replacing a gas-assisted pump with an electric pump permanently reduces the vented methane emissions from that source, even after the Natural Gas STAR sunsetting period. CH 4 

### emission reductions from the Natural Gas STAR Program beyond the efforts reflected in the 1992 base year are summarized in Table A-125.
The 1990 Clean Air Act (CAA) sets the limits on the amount of hazardous air pollutants (HAPs) that can be emitted in the United States. The NESHAP regulations set the standards to limit emissions of HAPs. The emission sources are required to use the Maximum Achievable Control Technology, giving the operators flexibility to choose the type of control measure(s) to implement. In regards to the oil a nd natural gas industry, the NESHAP regulation addresses HAPs from the oil and natural gas production sectors and the natura l gas transmission and storage sectors of the industry. Though the regulation deals specifically with HAPs reductions, methane emissions are also reduced.
The NESHAP regulation requires that glycol dehydration unit vents and storage tanks that have HAP emissions and exceed a gas throughput and liquids throughput value, respectively, be connected to a closed loop emission control system that reduces emissions by 95 percent. Also, gas pro cessing plants exceeding the threshold natural gas throughput limit are required to routinely implement Leak Detectio n and Survey (LDAR) programs. The emissions reductions achieved as a result of NESHAP regulations were estimate d using data provided in the Federal Register Background Information Document (BID) for this regulation. The BID provides the levels of control measures in place before the enactment of regulation. The emissions reductions were estimated by analyzing the portion of the industry without control measures already in place that would be impacted by the regulation. The reductions are representative of the control measures in both the oil and natural gas industry. CH 4 

### emission reductions from regulations, such as NESHAP, are summarized in Table A-126.
Additionally, some states, such as Wyoming, may require that natural gas produced during well completions not be vented. In these regions emissions from natural gas well completions and re-completions are either recovered for sales or must be flared. The volume of gas recovered by bringing equipment to the wellsite for the treatment and injection of the produced completion gas into the sales pipeline is reported by Natural Gas STAR. The remaining volume of completion gas from states that do not allowing the venting of this gas is flared.

Step 4: Estimate CO 2 

### Emissions for Each Year and Stage
The same procedure for estimating CH 4 

### emissions holds true for estimating non-energy related CO 2 

### emissions, except the emission estimates are not corrected for reductions due to the Natural Gas STAR program or other regulations.
Produced natural gas is composed of primarily CH 4 

### , but as shown in Table A-131, the natural gas contains, in some cases, as much as 8 percent CO 2 

### . The same vented and fugitive emissions of natural gas that led to CH 4 

### emissions also contain a certain volume of CO 2 

### . Accordingly, the CO 2 

### emissions for each sector can be estimated using the same methane activity and emissions factors for these vented and f ugitive sources. The primary difference is that EPA/GRI emission factors are adjusted for CO 2 

### content in each sector.
Using the default CH 4 

### content of produced natural gas from EPA/GRI (1996) of 78.8 percent, the corresponding amount of CO 2 

### emissions from the production sector can be estimated. Each sector of the natural gas system has varying CO 2 

### contents, similar to the way the CH 4 

### content varies among the sectors. Table A-131 shows the CO 2 

### content for the different well types in the production sector of the natural gas system. In the estimation of CO 2 

### emissions from the production sector, the production sector CH 4 

### emission factors were used as a basis to estimate CO 2 

### emissions; however, they were converted to CO 2 

### emission factors by multiplication by a conversion factor. This conversion factor is the ratio of CO 2 

### content in the gas stream (in this case the production concentrations provided in Table A-131) to the corresponding CH 4 

### content of the same gas stream. The three exceptions to this methodology are CO 2 

### emissions from flares, acid gas removal units, and condensate tanks. In the case of flare emissions, a direct CO 2 

### emission factor from EIA (1996) was used. This emission factor was applied to the portion of of fshore gas that is not vented and all of the gas reported as vented and flared onshore by EIA. The amount of CO 2 

### emissions from an acid gas unit in a processing plant is equal to the difference in CO 2 

### concentrations between produced natural gas and pipeline quality gas applied to the throughput of the plant. This methodology was applied to the national gas throughput using national average CO 2 

### concentrations in produced gas (3.45 percent) and transmission quality gas (1 percent). For condensate tanks, a series of E&P Tank (EPA

### A-149 1999) simulations provide the total CO 2 

### vented per barrel of condensate throughput from fixed roof tank flash gas for condensate gravities of API 45 degree and higher. The ratios of emissions to throughput were used to estimate the CO 2 

### emission factor for condensate passing through fixed roof tanks. The detailed source emission estimates for CH 4 

### and CO 2 

### from the production sector are presented in Table A-127 and
Table A-132, respectively.
In the processing sector, the CO 2 

### content of the natural gas remains the same as the CO 2 

### content in the production sector for the equipment upstream of the acid gas removal unit because produced natural gas is usually only minimally treated after being produced and then transported to natural gas processing plants via gathering pipelines. The CO 2 

### content in gas for the remaining equipment that is downstream of the acid gas removal is the same as in pipeline quality gas. The EPA/GRI study estimates the average CH 4 

### content of natural gas in the processing sector to be 87 percent CH 4 

### . Consequently, the processing sector CH 4 

### emission factors were proportioned to reflect the CO 2 

### content of either produced natural gas or pipeline quality gas using the same methodology as the production sector. The detailed source emission estimates for CH 4 

### and CO 2 

### from the processing sector are presented in Table A-128 and Table A-133, respectively.
For the transmission sector, CO 2 

### content in natural gas transmission pipelines was estimated for the top twenty transmission pipeline companies in the United States (separate analyses identified the top twenty companies based on gas throughput and total pipeline miles). The weighted average CO 2 

### content in the transmission pipeline quality gas in both cases—total gas throughput and total miles of pipeline—was estimated to be about 1 percent. To estimate the CO 2 

### emissions for the transmission sector the CH 4 

### emission factors were proportioned from the 93.4 percent CH 4 

### reported in EPA/GRI (1996) to reflect the 1 percent CO 2 

### content found in transmission quality natural gas. The detailed source emissions estimates for CH 4 

### and CO 2 

### for the transmission sector are presented in Table A-129 and Table A-134, respectively.
The natural gas in the distribution sector of the system has the same characteristics as the natural gas in the transmission sector. The CH 4 

### content (93.4 percent) and CO 2 

### content (1 percent) are identical due to the absence of any further treatment between sector boundaries. Thus, the CH 4 

### emissions factors were converted to CO 2 

### emission factors using the same methodology as discussed for the transmission sector. The detailed source emission estimates for CH 4 

### and CO 2 

### for the distribution sector are presented in Table A-130 and Table A-135, respectively.
Because Partners report only CH 4 

### emission reductions to the Natural Gas STAR Program, there was no need to adjust for the Natural Gas STAR program in the CO 2 

### emissions estimates for any of the sectors in the natural gas system. The impact of regulations, such as NESHAP, on CO 2 

### emission reductions are not currently addressed in the CO 2 

### emission estimates.

Table A-120: 2009 Data and CH 4 

### Emissions (Mg) for the Natural Gas Production Stage 2009 EPA Inventory Values Activity Activity Data Emi ssion Factor Emissions (Mg)

North East Gas Wells NE - Associated Gas Wells 1

, 2 46,914 wells NA NA NE - Non-associated Gas Wells (less Unconventional) 183,834 wells 7.59 scfd/well 9,804.22 NE - Unconventional Gas Wells 3 0 wells NE scfd/well 0 Field Separation Equipment Heaters 368 heaters 15.13 scfd/heater 39.10 Separators 130,522 separators 0.96 scfd/sep 877.52 Dehydrators 1,213 dehydrators 23.15 scfd/dehy 197.33 Meters/Piping 9,230 meters 9.59 scfd/meter 622.25 Gathering Compressors Small Reciprocating Compressors 184 compressors 284.95 scfd/comp 368.26 Large Reciprocating Compressors 24 compressors 16,182 scfd/comp 2,730.18 Large Reciprocating Stations 3 s tations 8,776.43 scfd/station 185.09 Pipeline Leaks 87,900 miles 56.57 scfd/mile 34,954.19 Drilling and Well Completion Completion Flaring 266 completions/yr 780 scf/comp 4.00 Unconventional Gas Well Completions 0 completions/yr 7,694,435 scf/comp 0 Well Drilling 8,170 Wells 2,706 scf/well 425.89 Normal Operations Pneumatic Device Vents 89,343 controllers 367 scfd/device 230,608.23 Chemical Injection Pumps 919 active pumps 264 scfd/pump 1,705.46

### A-150 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 2009 EPA Inventory Values Activity Activity Data Emi ssion Factor Emissions (Mg) Kimray Pumps 354,971 MMscf/yr 1,056 scf/MMscf 7,217.80 Dehydrator Vents 398,396 MMscf/yr 293.3 scf/MMscf 2,250.58 Condensate Tank Vents Condensate Tanks without Control Devices 0 MMbbl/yr 21.87 scf/bbl 0 Condensate Tanks with Control Devices 0 MMbbl/yr 4.37 scf/bbl 0 Compressor Exhaust Vented Gas Engines 0 MMHPhr 0.26 scf/HPhr 0 Well Workovers Conventional Gas Wells 7,997 workovers/yr 2,612 scf/w.o. 402.26 Unconventional Gas Wells 0 workovers/yr 7,694,435 scf/w.o. 0 Well Clean Ups (LP Gas Wells) 75,923 LP Gas Wells 1,361,786 scfy/LP well 1,991,319.86 Blowdowns Vessel BD 132,103 vessels 83 scfy/vessel 211.21 Pipeline BD 87,900 miles (gathering) 329 scfy/mile 556.73 Compressor BD 184 compressors 4,016 scfy/comp 14.22 Compressor Starts 184 compressors 8,985 scfy/comp 31.81 Upsets Pressure Relief Valves 352,630 PRV 36 scfy/PRV 245.75 Mishaps 21,975 miles 712 scf/mile 301.34 Midcontinent Gas Wells MC - Associated Gas Wells 1

, 2 72,935 wells NA NA MC - Non-associated Gas Wells 73,913 wells 7.54 scfd/well 3,918.89 MC - Unconventional Gas Wells 13,277 wells 4.71 scfd/well 439.78 Field Separation Equipment Heaters 35,399 heaters 15.08 scfd/heater 3,753.52 Separators 37,928 separators 0.95 scfd/sep 254.26 Dehydrators 12,518 dehydrators 96.70 scfd/dehy 8,510.31 Meters/Piping 119,028 meters 9.56 scfd/meter 8,001.30 Gathering Compressors Small Reciprocating Compressors 9,852 compressors 284.13 scfd/comp 19,679.53 Large Reciprocating Compressors 16 compressors 16,135 scfd/comp 1,814.87 Large Reciprocating Stations 2 s tations 8,751.13 scfd/station 123.04 Pipeline Leaks 68,424 miles 56.40 scfd/mile 27,130.93 Drilling and Well Completion Completion Flaring 126 completions/yr 778 scf/comp 1.89 Unconventional Gas Well Completions 575 completions/yr 7,672,247 scf/comp 84,966.30 Well Drilling 3,875 wells 2,699 scf/well 201.41 Normal Operations Pneumatic Device Vents 135,232 controllers 366 scfd/device 348,046.02 Chemical Injection Pumps 12,381 active pumps 263 scfd/pump 22,905.82 Kimray Pumps 3,664,068 MMscf/yr 1,053 scf/MMscf 74,288.52 Dehydrator Vents 4,112,309 MMscf/yr 292.5 scf/MMscf 23,163.89 Condensate Tank Vents Condensate Tanks without Control Devi ces 13 MMbbl/yr 302.75 scf/bbl 75,803.69 Condensate Tanks with Control Devi ces 13 MMbbl/yr 60.55 scf/bbl 15,160.74 Compressor Exhaust Vented Gas Engines 16,304 MMHPhr 0.25 scf/HPhr 79,973.26 Well Workovers Conventional Gas Wells 3,793 workovers/yr 2,604 scf/w.o. 190.24 Unconventional Gas Wells 1,328 workove rs/yr 7,672,247 scf/w.o. 196,190.87 Well Clean Ups (LP Gas Wells) 30,526 LP Gas Wells 711,385 scfy/LP well 418,245.79 Blowdowns Vessel BD 85,845 vessels 83 scfy/vessel 136.85 Pipeline BD 68,424 miles (gathering) 328 scfy/mile 432.13 Compressor BD 9,852 compressors 4,005 scfy/comp 759.96 Compressor Starts 9,852 compressors 8,960 scfy/comp 1,700.15 Upsets Pressure Relief Valves 167,248 PRV 36 scfy/PRV 116.22 Mishaps 17,106 miles 710 scf/mile 233.90 Rocky Mountain Gas Wells

### A-151 2009 EPA Inventory Values Activity Activity Data Emi ssion Factor Emissions (Mg) RM - Associated Gas Wells 1

, 2 14,948 wells NA NA RM - Non-associated Gas Wells 75,418 wells 35.62 scfd/well 18,882.55 RM - Unconventional Gas Wells 23,417 wells 6.97 scfd/well 1,147.38 Field Separation Equipment Heaters 45,069 heaters 57.43 scfd/heater 18,196.66 Separators 49,319 separators 121.42 scfd/sep 42,097.53 Dehydrators 14,190 dehydrators 90.68 scfd/dehy 9,046.43 Meters/Piping 114,129 meters 52.65 scfd/meter 42,238.56 Gathering Compressors Small Reciprocating Compressors 10,971 compressors 266.44 scfd/comp 20,549.03 Large Reciprocating Compressors 32 compressors 15,131 scfd/comp 3,403.78 Large Reciprocating Stations 4 s tations 8,206.34 scfd/station 230.76 Pipeline Leaks 125,062 miles 52.89 scfd/mile 46,501.51 Drilling and Well Completion Completion Flaring 1 143 completions/yr 729 scf/comp 2.01 Unconventional Gas Well Completions 0 completions/yr 7,194,624 scf/comp 0 Well Drilling 4,393 wells 2,531 scf/well 214.10 Normal Operations Pneumatic Device Vents 144,695 controllers 343 scfd/device 349,219.02 Chemical Injection Pumps 17,593 active pumps 247 scfd/pump 30,521.75 Kimray Pumps 4,153,459 MMscf/yr 987 scf/MMscf 78,968.46 Dehydrator Vents 4,661,570 MMscf/yr 274.3 scf/MMscf 24,623.14 Condensate Tank Vents Condensate Tanks without Control Devi ces 12 MMbbl/yr 21.87 scf/bbl 5,054.59 Condensate Tanks with Control Devi ces 12 MMbbl/yr 4.37 scf/bbl 1,010.92 Compressor Exhaust Vented Gas Engines 18,481 MMHPhr 0.24 scf/HPhr 85,011.32 Well Workovers Conventional Gas Wells 4,299 workovers/yr 2,442 scf/w.o. 202.20 Unconventional Gas Wells 2,342 workove rs/yr 7,194,624 scf/w.o. 324,485.75 Well Clean Ups (LP Gas Wells) 31,148 LP Gas Wells 710,583 scfy/LP well 426,283.58 Blowdowns Vessel BD 108,578 vessels 78 scfy/vessel 162.32 Pipeline BD 125,062 miles (gathering) 307 scfy/mile 740.65 Compressor BD 10,971 compressors 3,756 scfy/comp 793.54 Compressor Starts 10,971 compressors 8,402 scfy/comp 1,775.27 Upsets Pressure Relief Valves 189,586 PRV 34 scfy/PRV 123.54 Mishaps 31,266 miles 666 scf/mile 400.89 Coal Bed Methane

Powder River 28,577,932,572 gal produced water 2.002E-09 Gg/gallon water drainage 57,494.47 South West Gas Wells SW - Associated Gas Wells 1

, 2 56,810 wells NA NA SW - Non-associated Gas Wells 27,802 wells 37.24 scfd/well 7,278.09 SW - Unconventional Gas Wells 13,740 wells 36.52 scfd/well 3,527.07 Field Separation Equipment Heaters 11,258 heaters 58.97 scfd/heater 4,667.26 Separators 23,347 separators 124.68 scfd/sep 20,462.48 Dehydrators 5,964 dehydrators 93.12 scfd/dehy 3,904.30 Meters/Piping 50,678 meters 54.06 scfd/meter 19,258.70 Gathering Compressors Small Reciprocating Compressors 5,650 compressors 273.59 scfd/comp 10,866.09 Large Reciprocating Compressors 16 compressors 15,536 scfd/comp 1,747.51 Large Reciprocating Stations 2 sta tions 8,426.34 scfd/station 118.47 Pipeline Leaks 60,894 miles 54.31 scfd/mile 23,249.07 Drilling and Well Completion Completion Flaring 60 completions/yr 749 scf/comp 0.87 Unconventional Gas Well Completions 3,594 co mpletions/yr 7,387,499 scf/comp 511,365.92 Well Drilling 1,846 wells 2,598 scf/well 92.40 Normal Operations Pneumatic Device Vents 55,168 controllers 353 scfd/device 136,716.45 Chemical Injection Pumps 2,534 active pumps 253 scfd/pump 4,514.24

### A-152 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 2009 EPA Inventory Values Activity Activity Data Emi ssion Factor Emissions (Mg) Kimray Pumps 1,745,768 MMscf/yr 1,014 scf/MMscf 34,081.57 Dehydrator Vents 1,959,335 MMscf/yr 281.6 scf/MMscf 10,626.97 Condensate Tank Vents Condensate Tanks without Control Devi ces 7 MMbbl/yr 302.75 scf/bbl 40,817.37 Condensate Tanks with Control Devi ces 7 MMbbl/yr 60.55 scf/bbl 8,163.47 Compressor Exhaust Vented Gas Engines 7,768 MMHPhr 0.25 scf/HPhr 36,689.57 Well Workovers Conventional Gas Wells 1,807 workovers/yr 2,507 scf/w.o. 87.27 Unconventional Gas Wells 1,374 workove rs/yr 7,387,499 scf/w.o. 195,497.16 Well Clean Ups (LP Gas Wells) 11,482 LP Gas Wells 864,974 scfy/LP well 191,288.32 Blowdowns Vessel BD 40,569 vessels 80 scfy/vessel 62.27 Pipeline BD 60,894 miles (gathering) 316 scfy/mile 370.30 Compressor BD 5,650 compressors 3,856 scfy/comp 419.62 Compressor Starts 5,650 compressors 8,627 scfy/comp 938.74 Upsets Pressure Relief Valves 79,686 PRV 35 scfy/PRV 53.32 Mishaps 15,224 miles 684 scf/mile 200.44 West Coast Gas Wells WC - Associated Gas Wells 1

, 2 24,548 wells NA NA WC - Non-associated Gas Wells 1,786 wells 42.49 scfd/well 533.50 WC - Unconventional Gas Wells 3 0 wells NE 0 Field Separation Equipment Heaters 1,786 heaters 67.29 scfd/heater 844.89 Separators 1,304 separators 142.27 scfd/sep 1,303.93 Dehydrators 256 dehydrators 106.25 scfd/dehy 191.54 Meters/Piping 3,364 meters 61.68 scfd/meter 1,458.59 Gathering Compressors Small Reciprocating Compressors 2,074 compressors 312.19 scfd/comp 4,550.68 Large Reciprocating Compressors 8 compressors 17,728 scfd/comp 997.03 Large Reciprocating Stations 1 s tations 9,615.15 scfd/station 67.59 Pipeline Leaks 16,367 miles 61.97 scfd/mile 7,130.46 Drilling and Well Completion Completion Flaring 3 completions/yr 855 scf/comp 0.04 Unconventional Gas Well Completions 0 completions/yr 8,429,754 scf/comp 0 Well Drilling 79 wells 2,965 scf/well 4.53 Normal Operations Pneumatic Device Vents 1,790 controllers 402 scfd/device 5,060.58 Chemical Injection Pumps 1,213 active pumps 289 scfd/pump 2,465.10 Kimray Pumps 75,055 MMscf/yr 1,157 scf/MMscf 1,671.97 Dehydrator Vents 84,237 MMscf/yr 321.3 scf/MMscf 521.34 Condensate Tank Vents Condensate Tanks without Control Devices 0 MMbbl/yr 21.87 scf/bbl 0 Condensate Tanks with Control Devices 0 MMbbl/yr 4.37 scf/bbl 0 Compressor Exhaust Vented Gas Engines 334 MMHPhr 0.28 scf/HPhr 1,799.92 Well Workovers Conventional Gas Wells 78 workovers/yr 2,861 scf/w.o. 4.30 Unconventional Gas Wells 0 workovers/yr 8,429,754 scf/w.o. 0 Well Clean Ups (LP Gas Wells) 738 LP Gas Wells 1,491,925 scfy/LP well 21,195.07 Blowdowns Vessel BD 3,346 vessels 91 scfy/vessel 5.86 Pipeline BD 16,367 miles (gathering) 360 scfy/mile 113.57 Compressor BD 2,074 compressors 4,400 scfy/comp 175.73 Compressor Starts 2,074 compressors 9,844 scfy/comp 393.14 Upsets Pressure Relief Valves 3,426 PRV 40 scfy/PRV 2.62 Mishaps 4,092 miles 780 scf/mile 61.48 Gulf Coast Gas Wells

### A-153 2009 EPA Inventory Values Activity Activity Data Emi ssion Factor Emissions (Mg) GC - Associated Gas Wells 1

, 2 27,134 wells NA NA GC - Non-associated Gas Wells 75,046 wells 40.97 scfd/well 21,613.40 GC - Unconventional Gas Wells 3 0 wells NE scfd/well 0 Field Separation Equipment Heaters 16,810 heaters 64.88 scfd/heater 7,667.69 Separators 49,381 separators 137.17 scfd/sep 47,618.06 Dehydrators 10,775 dehydrators 102.45 scfd/dehy 7,760.09 Meters/Piping 87,505 meters 59.48 scfd/meter 36,586.28 Gathering Compressors Small Reciprocating Compressors 6,079 compressors 301.01 scfd/comp 12,863.02 Large Reciprocating Compressors 24 compressors 17,094 scfd/comp 2,884.00 Large Reciprocating Stations 3 s tations 9,270.90 scfd/station 195.52 Pipeline Leaks 98,419 miles 59.75 scfd/mile 41,342.16 Drilling and Well Completion Completion Flaring 109 completions/yr 824 scf/comp 1.73 Unconventional Gas Well Completions 0 completions/yr 8,127,942 scf/comp 0 Well Drilling 3,335 wells 2,859 scf/well 183.66 Normal Operations Pneumatic Device Vents 52,157 controllers 388 scfd/device 142,210.39 Chemical Injection Pumps 2,477 active pumps 279 scfd/pump 4,853.92 Kimray Pumps 3,153,749 MMscf/yr 1,115 scf/MMscf 67,739.71 Dehydrator Vents 3,539,561 MMscf/yr 309.8 scf/MMscf 21,121.91 Condensate Tank Vents Condensate Tanks without Control Devices 33 MMbbl/yr 21.87 scf/bbl 13,900.13 Condensate Tanks with Control Devices 33 MMbbl/yr 4. 37 scf/bbl 2,780.03 Compressor Exhaust Vented Gas Engines 14,033 MMHPhr 0.27 scf/HPhr 72,923.33 Well Workovers Conventional Gas Wells 3,265 workovers/yr 2,759 scf/w.o. 173.49 Unconventional Gas Wells 0 workovers/yr 8,127,942 scf/w.o. 0 Well Clean Ups (LP Gas Wells) 30,994 LP Ga s Wells 2,522,975 scfy/LP well 1,506,084 Blowdowns Vessel BD 76,966 vessels 88 scfy/vessel 129.99 Pipeline BD 98,419 miles (gathering) 347 scfy/mile 658.48 Compressor BD 6,079 compressors 4,243 scfy/comp 496.73 Compressor Starts 6,079 compressors 9,492 scfy/comp 1,111.26 Upsets Pressure Relief Valves 143,954 PRV 38 scfy/PRV 105.98 Mishaps 24,605 miles 752 scf/mile 356.41 Coal Bed Methane Black Warrior 5,026 wells 0.0023 Gg/well 11,693.03 Offshore Platforms Shallow water Gas Platforms (GoM and Pacific) 2,072 Shallow water gas platforms 19,178 scfd/platform 279,356 Deepwater Gas Platforms (GoM and Pacific) 40 Deepwater gas platforms 79,452 scfd/platform 22,389

1 Emissions from oil wells that produce associated gas are estimated in the Petroleum Systems mode l. Here the oil wells count is used as a driver only.

2 NA = not applicable; i.e. this data is not applicable for the Natural Gas systems model.

3 NE = not estimate; some emission factors were not estimat ed because there is no correspond ing activity for that region.



### Table A-121: 2009 Data and CH 4 

### Emissions (Mg) for the Natural Gas Processing Stage
2009 EPA Inventory Values Activity Activity Data Emi ssion Factor Emissions(Mg) Plants 578 plants 7,906 scfd/plant 32,124 Recip. Compressors 4,876 compressors 11,196 scfd/comp 383,767 Centrifugal Compressors (wet seals) 646 compressors 51,370 scfd/comp 233,371 Centrifugal Compressors (dry seals) 140 compressors 25,189 scfd/comp 24,725 Compressor Exhaust Gas Engines 35,030 MMHPhr 0.24 scf/HPhr 161,923 Gas Turbines 41,535 MMHPhr 0.01 scf/HPhr 4,560

### A-154 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 2009 EPA Inventory Values Activity Activity Data Emi ssion Factor Emissions(Mg) AGR Vents 293 AGR units 6,083 scfd/AGR 12,527 Kimray Pumps 1,269,018 MMscf/yr 178 scf/MMscf 4,344 Dehydrator Vents 11,432,591 MMscf/yr 122 scf/MMscf 26,764 Pneumatic Devices 578 gas plants 164,721 scfy/plant 1,834 Blowdowns/Venting 578 gas plants 4,060 Mscfy/plant 45,197



### Table A-122: 2009 Data and CH 4 

### Emissions (Mg) for the Na tural Gas Transmission Stage 2009 EPA Inventory Values Activity Activity Data Emi ssion Factor Emissions(Mg) Pipeline Leaks 301,546 miles 1.55 Scfd/ mile 3,294 Compressor Stations

(Transmission) Station 1,790 Stations 8,778 Scfd/station 110,458 Recip Compressor 7,197 Compressors 15,205 Scfd/ comp 769,264 Centrifugal Compressor (wet

seals) 667 Compressors 50,222 Scfd/ comp 235,445 Centrifugal Compressor (dry seals) 55 Compressors 32,208 Scfd/ comp 12,436 Compressor Stations (Storage) Station 392 Stations 21,507 Scfd/ comp 59,230 Recip Compressor 1,152 Compressors 21,116 Scfd/ comp 171,007 Centrifugal Compressor (wet

seals) 84 Compressors 45,441 Scfd/ comp 26,819 Centrifugal Compressor (dry seals) 29 Compressors 31,989 Scfd/ comp 6,532 Wells (Storage) 18,267 Wells 115 Scfd/ comp 14,704 M&R (Trans. Co. Interconnect) 2,684 Stations 3,984 scfd/station 75,180 M& R (Farm Taps + Direct Sales) 79,592 Stations 31 scfd/station 17,457 Dehydrator vents (Transmission) 1,141 ,012 MMscf/Year 94 scf/MMscf 2,060 Dehydrator vents (Storage) 2,029,824 MMscf/Year 117 scf/MMscf 4,581 Compressor Exhaust Engines (Transmission) 45,546 MMH Phr 0.24 scf/HPhr 210,531 Turbines (Transmission) 10,868 MMHPhr 0.01 scf/HPhr 1,193 Engines (Storage) 4,995 MMHPhr 0.24 scf/HPhr 23,091 Turbines (Storage) 1,755 MMHPhr 0.01 scf/HPhr 193 Generators (Engines) 2,229 MMHPhr 0.24 scf/HPhr 10,302 Generators (Turbines) 26 MMHPhr 0.01 scf/HPhr 3 Pneumatic Devices Trans + Stor Pneumatic Devices Trans 70,458 Devices 162,197 Scfy/device 220,104 Pneumatic Devices Storage 15,421 Devices 162,197 Scfy/device 48,174 Routine Maintenance/Upsets Pipeline venting 301,546 Miles 32 Mscfy/mile 183,816 Station Venting Trans + Storage

Station Venting Transmission 1,790 Compressor Stations 4,359 Mscfy/station 150,278

Station Venting Storage 392 Compressor Stations 4,359 Mscfy/station 32,890 LNG Storage LNG Stations 70 Stations 21,507 scfd/station 10,623 LNG Reciprocating Compressors 270 Compressors 21,116 scfd/comp 40,147 LNG Centrifugal Compressors 64 Compressors 30,573 scfd/comp 13,766 LNG Compressor Exhaust LNG Engines 718 MMHPhr 0.24 scf/HPhr 3,320 LNG Turbines 113 MMHPhr 0.01 scf/HPhr 12 LNG Station Venting 70 Stations 4,359 Mscfy/station 5,899 LNG Import Terminals LNG Stations 9 Stations 21,507 scfd/station 1,376 LNG Reciprocating Compressors 44 Compressors 21,116 scfd/comp 6,561 LNG Centrifugal Compressors 8 Compressors 30,573 scfd/comp 1,676 LNG Compressor Exhaust LNG Engines 1,904 MMHPhr 0.24 scf/HPhr 8,802 LNG Turbines 461 MMHPhr 0.01 scf/HPhr 51

### A-155 2009 EPA Inventory Values Activity Activity Data Emi ssion Factor Emissions(Mg) LNG Station Venting 9 Stations 4,359 Mscfy/station 764



### Table A-123: 2009 Data and CH 4 

### Emissions (Mg) for the Natural Gas Distribution Stage 2009 EPA Inventory Values Activity Activity Data Emission Factor Emissions(Mg) Pipeline Leaks Mains—Cast Iron 35,429 miles 239 Mscf/mile-yr 162,880 Mains—Unprotected steel 67,331 miles 110 Mscf/mile-yr 142,893 Mains—Protected steel 484,337 miles 3 Mscf/mile-yr 28,609 Mains—Plastic 621,404 miles 10 Mscf/mile-yr 118,605 Services—Unprotected steel 5,218,497 services 2 Mscf/service 170,941 Services Protected steel 15,389,666 services 0.2 Mscf/service 52,314 Services—Plastic 42,601,520 services 0.01 Mscf/service 7,629 Services—Copper 1,083,539 serv ices 0.3 Mscf/service 5,308 Meter/Regulator (City Gates) M&R >300 3,945 stations 180 scfh/station 119,685 M&R 100-300 14,398 stations 96 scfh/station 232,233 M&R <100 7,696 stations 4 scfh/station 5,596 Reg >300 4,314 stations 162 scfh/station 117,825 R-Vault >300 2,533 stations 1 scfh/station 556 Reg 100-300 13,049 stations 41 scfh/station 89,166 R-Vault 100-300 5,863 stations 0.2 scfh/station 178 Reg 40-100 39,159 stations 1 scfh/station 6,871 R-Vault 40-100 34,726 stations 0.1 scfh/station 507 Reg <40 16,604 stations 0.1 scfh/station 373 Customer Meters Residential 40,646,493 outdr meters 143 scfy/meter 112,160 Commercial/Industry 4,121,452 meters 48 scfy/meter 3,802 Routine Maintenance Pressure Relief Valve

Releases 1,208,501 mile main 0.1 Mscf/mile 1,164 Pipeline Blowdown 1,316,917 miles 0.1 Mscfy/mile 2,587 Upsets Mishaps (Dig-ins) 1,316,917 miles 2 Mscfy/mile 40,328A-156 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009Table A-124: Key Activity Data DriversVariable Units 19901992199520002005 2006 2007 2008 2009Transmission Pipelines Lengthmiles291,990291,468296,947298,957300,663300,458301,180303,388301,546WellsNE—Associated Gas Wells*# wells68,26167,48966,10258,67146,47147,03446,64647,08846,914NE—Non-associated Gas Wells*# wells124,241129,157129,789143,922158,238164,322172,493174,682183,834MC—Associated Gas Wells*# wells64,37970,64072,48367,88065,65265,90369,23472,62272,935MC—Non-associated Gas Wells*# wells53,94059,35865,58551,21771,37973,91480,65082,70587,190RM—Associated Gas Wells*# wells13,74914,14213,74512,32813,32713,43712,02114,76314,948RM—Non-associated Gas Wells*# wells24,33926,32332,66864,53972,43875,17070,53291,39598,835SW—Associated Gas Wells*# wells69,33968,13059,95454,83055,50254,55055,25156,78756,810SW—Non-associated Gas Wells*# wells24,21722,60927,39232,34634,19435,41738,04940,17641,542WC—Associated Gas Wells*# wells20,67219,81919,10920,49421,56222,18922,11024,27124,548WC—Non-associated Gas Wells*# wells1,2921,2541,1141,3381,4241,5031,5061,6931,786GC—Associated Gas Wells*# wells36,27935,37634,79232,49728,09027,31926,23427,66127,134GC—Non-associated Gas Wells*# wells41,75337,30741,97848,31657,60060,71568,18872,04775,046PlatformsGulf of Mexico and Pacific OCS Offshore Platforms# platforms3,9433,9663,9814,0203,9113,9143,8393,7613,583GoM and Pacific OCS Deep WaterPlatforms# platforms171923385967636568Gas Plants# gas plants761732675585566571574577578Distribution Services# of services47,883,08349,142,00854,644,03356,761,04258,556,33562,255,43563,524,38863,559,29664,293,222Steel—Unprotected# of services7,633,5267,138,5636,151,6535,675,5205,308,3755,642,4705,448,8045,388,6235,218,497Steel—Protected# of services19,781,58119,742,08621,002,45517,855,56015,883,42315,732,03715,756,04815,456,86615,389,666Plastic# of services18,879,86520,692,67426,044,54531,795,87136,152,27739,632,31341,092,51541,573,06942,601,520Copper# of services1,588,1111,568,6851,445,3801,434,0911,212,2601,248,6151,227,0211,140,7381,083,539Distribution Mainsmiles944,157888,9251,001,7061,048,4851,093,9091,209,4191,198,5851,188,7141,208,501Cast Ironmiles58,29252,91750,62544,75037,37136,97737,66936,46235,429Steel—Unprotectedmiles108,94199,61994,05882,80069,29171,73869,52569,37467,331Steel—Protectedmiles465,538469,106503,288471,510461,459481,811489,815479,502484,337Plasticmiles311,386267,283353,735449,425525,788618,893601,575603,377621,404* NEMS (National Energy Modeling System) projects the production, imports, conversion, consumption, and prices of energy, subject to assumptions on macroeconomicand financial factors, worldenergy markets, resource availability and costs, behavioral and technological choice criteria, cost and performance characteristics of energy technologies, and demographics

### A-157 Table A-125: CH 4 

### Reductions Derived from the Natural Gas STAR Program (Gg)
Process 1992* 1995 2000 2005 2006 2007 2008 2009 Production 0 75 318 1,425 1,471 1,863 2,239 1,993 Processing 0 5 23 129 140 139 145 83 Transmission and Storage 0 121 258 500 480 454 440 367 Distribution 0 19 27 35 48 35 28 41 Total 0 220 626 2,090 2,138 2,492 2,852 2,484 *Reductions are relative to 1992; therefore, there are zero reductions in 1992. Note: These reductions will not match the Natural Gas STAR program reductions. These numbers are adjusted for reductions prior to the 1992 base year, and do not include a s unsetting period. Totals may not sum due to independent rounding.



### Table A-126: CH 4 

### Reductions Derived from Regulations (Gg)
Process 1990 1995 2000 2005 2006 2007 2008 2009

Production 9.8 24.1 303.6 278.4 1,572 783.5 966.5 733.7 Processing NA NA 12.9 12.1 12.4 13.0 13.7 14.2 Transmission and Storage NA NA NA NA NA NA NA NA Distribution NA NA NA NA NA NA NA NA Total 9.8 24.1 316.5 290.5 1,584 796.5 980.2 747.9 NA Not applicable Note: NESHAP regulations went into effect in 1999. Totals may not sum due to independent rounding.



### Table A-127: CH 4 

### Emission Estimates from the Natural Gas Production Stage Excluding Reductions from the Natural Gas STAR Program and Regulations (Gg) Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009 Normal Fugitives Associated Gas Wells Non-Associated Gas

Wells (less conventional) 30.82 31.68 37.42 48.05 52.88 50.86 52.48 58.63 62.03 Unconventional Gas Wells 0.01 0.02 0.04 0.26 0.53 1.32 1.53 1.67 1.59 Field Separation Equipment Heaters 12.57 13.28 16.48 23.56 26.90 28.01 28.36 33.09 35.17 Separators 43.83 44.16 54.79 76.51 86.43 90.24 94.09 106.73 112.61 Dehydrators 14.75 15.76 17.89 20.77 23.95 24.93 26.17 28.07 29.61 Meter/ Piping 43.61 44.95 54.89 73.80 83.74 87.18 90.23 102.50 108.17 Gathering Compressors Small Reciprocating Comp. 30.22 32.15 38.27 45.57 53.85 56.10 58.49 65.19 68.88 Large Reciprocating Comp. 7.50 8.56 9.80 11.70 12.60 12.59 12.71 13.58 13.58 Large Reciprocating Stations 0.51 0.58 0.66 0.79 0.85 0.85 0.86 0.92 0.92 Pipeline Leaks 98.03 102.22 114.49 133.58 148.45 152.97 160.02 172.60 180.31 Vented and Combusted Drilling and Well Completion Completion Flaring 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 Unconventional Gas Well Completions 16.48 23.53 31.66 422.38 203.69 2,502.62 855.93 1,089.76 596.33 Well Drilling 0.74 0.54 0.53 1.01 1.63 1.85 1.90 1.93 1.12 Coal Bed Methane Powder River 0.04 0.47 1.48 28.76 46.38 56.38 54.60 57.58 57.49 Black Warrior 2.72 4.84 6.25 6.82 9.88 10.45 11.09 11.69 11.69 Normal Operations Pneumatic Device Vents 569.76 607.88 697.61 810.83 954.88 991.35 1,043.32 1,145.65 1,211.86 Chemical Injection Pumps 26.89 29.38 35.04 43.44 51.88 53.93 54.93 62.93 66.97 Kimray Pumps 131.82 140.65 159.45 185.01 213.32 222.04 233.36 250.28 263.97 Dehydrator Vents 41.10 43.86 49.72 57.69 66.52 69.24 72.76 78.04 82.31 Condensate Tank Vents

### A-158 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009 Condensate Tanks without Control Device 77.69 59.56 58.09 67.47 99.33 109.76 114.96 135.58 135.58 Condensate Tanks with Control Device 15.54 11.91 11.62 13.49 19.87 21.95 22.99 27.12 27.12 Compressor Exhaust Vented Gas Engines 119.06 125.48 151.43 180.03 215.10 224.16 235.19 261.65 276.40 Well Workovers Conventional Gas Wells 0.56 0.58 0.64 0.73 0.84 0.87 0.94 1.00 1.06 Unconventional Gas Wells 2.92 7.16 16.07 98.40 241.50 492.00 574.10 685.67 716.17 Well Clean Ups (LP Gas Wells) 2,651.65 2,699.26 2,931.40 3,330.18 3,755.97 3,804.83 4,134.53 4,333.13 4,554.42 Blowdowns Vessel BD 0.36 0.38 0.42 0.49 0.56 0.58 0.62 0.67 0.71 Pipeline BD 1.56 1.63 1.82 2.13 2.36 2.44 2.55 2.75 2.87 Compressor BD 1.17 1.24 1.48 1.76 2.08 2.17 2.26 2.52 2.66 Compressor Starts 2.61 2.78 3.31 3.94 4.65 4.85 5.05 5.63 5.95 Upsets Pressure Relief Valves 0.34 0.35 0.39 0.45 0.51 0.53 0.57 0.61 0.65 Mishaps 0.85 0.88 0.99 1.15 1.28 1.32 1.38 1.49 1.55 Offshore Offshore water Gas Platforms (GoM & Pacific) 290.64 298.00 307.56 324.06 321.98 321.51 305.25 298.98 279.36 Deepwater Gas Platforms (GoM & Pacific) 5.21 5.94 7.40 12.81 20.43 23.20 21.10 21.78 22.39 Total 4,242 4,360 4,819 6,028 6,725 9,423 8,274 9,059 8,931 Note: Totals may not sum due to independent rounding.



### Table A-128: CH 4 

### Emission Estimates from the Natu ral Gas Processing Plants Excluding Reductions from the Natural Gas STAR Program and Regulations (Gg)

Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009 Normal Fugitives Plants 42.30 40.68 37.52 32.51 31.46 31.74 31.90 32.07 32.12 Reciprocating Compressors 324.94 324.74 338.42 349.51 327.87 337.12 351.55 371.25 383.77 Centrifugal Compressors (wet seals) 240.29 240.15 248.60 251.32 229.24 229.92 230.99 232.45 233.37 Centrifugal Compressors (dry seals) 0.00 0.00 0.81 3.50 6.48 9.50 14.21 20.64 24.73 Vented and Combusted Normal Operations Compressor Exhaust Gas Engines 137.10 137.02 142.79 147.47 138.34 142.24 148.33 156.64 161.92 Gas Turbines 3.86 3.86 4.02 4.15 3.90 4.01 4.18 4.41 4.56 AGR Vents 16.49 15.87 14.63 12.68 12.27 12.38 12.44 12.51 12.53 Kimray Pumps 3.68 3.68 3.83 3.96 3.71 3.82 3.98 4.20 4.34 Dehydrator Vents 22.66 22.65 23.60 24.38 22.87 23.51 24.52 25.89 26.76 Pneumatic Devices 2.41 2.32 2.14 1.86 1.80 1.81 1.82 1.83 1.83 Routine Maintenance

Blowdowns/Venting 59.51 57.24 52.78 45.74 44.26 44.65 44.88 45.12 45.20 Total 853.24 848.20 869.16 877.08 822.18 840.69 868.81 907.01 931.14

### A-159 Note: Totals may not sum due to independent rounding.



### Table A-129: CH 4 

### Emission Estimates from the Natural Gas Transmi ssion and Storage Excluding Reductions from the Natural Gas STAR Program and Regulations (Gg) Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009 Fugitives Pipelines Leaks 3.19 3.18 3.24 3.27 3.28 3.28 3.29 3.31 3.29 Compressor Stations

(Transmission) Station 106.96 106.77 108.77 109.51 110.13 110.06 110.32 111.13 110.46 Recip Compressor 744.89 743.55 757.53 762.66 767.01 766.49 768.33 773.96 769.26 Centrifugal Compressor (wet seals) 246.76 246.32 249.68 245.59 236.93 236.75 236.81 237.00 235.44 Centrifugal Compressor (dry seals) 0.00 0.00 0.81 4.53 11.01 11.01 11.36 12.44 12.44 Compressor Stations (Storage) Station 54.64 58.36 60.35 62.17 60.06 54.36 58.76 60.62 59.23 Recip Compressor 157.80 168.48 174.27 179.62 173.53 157.05 169.67 175.16 171.01 Centrifugal Compressor (wet seals) 33.22 35.46 36.55 34.41 30.91 27.39 27.65 27.78 26.82 Centrifugal Compressor (dry seals) 0.00 0.00 0.13 2.54 4.10 4.10 5.72 6.53 6.53 Wells (Storage) 13.56 14.49 14.98 15.43 14.91 13.49 14.59 15.05 14.70 M&R (Trans. Co. Interconnect) 72.80 72.67 74.03 74.53 74.96 74.91 75.09 75.64 75.18 M&R (Farm Taps + Direct Sales) 16.90 16.87 17.19 17.31 17.41 17.39 17.44 17.56 17.46 Vented and Combusted

Normal Operation

Dehydrator Vents

(Transmission) 1.99 1.99 2.03 2.04 2.05 2.05 2.06 2.07 2.06 Dehydrator Vents

(Storage) 4.23 4.51 4.67 4.81 4.65 4.20 4.54 4.69 4.58 Compressor Exhaust

Engines (Transmission) 176.92 186.65 204.91 215.30 203.10 200.09 213.12 214.32 210.53 Turbines (Transmission) 1.00 1.06 1.16 1.22 1.15 1.13 1.21 1.21 1.19 Engines (Storage) 21.30 22.75 23.53 24.24 23.41 21.19 22.91 23.63 23.09 Turbines (Storage) 0.18 0.19 0.20 0.20 0.20 0.18 0.19 0.20 0.19 Generators (Engines) 8.66 9.13 10.03 10.54 9.94 9.79 10.43 10.49 10.30 Generators (Turbines) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Pneumatic Devices Trans+Stor

Pneumatic Devices Trans 213.13 212.75 216.75 218.21 219.46 219.31 219.84 221.45 220.10 Pneumatic Devices Storage 44.44 47.46 49.09 50.56 48.85 44.21 47.79 49.31 48.17 Routine Maintenance/Upsets

Pipeline Venting 177.99 177.67 181.01 182.24 183.28 183.15 183.59 184.94 183.82 Station venting Trans+Storage

Station Venting Transmission 145.52 145.2 6 147.99 148.99 149.84 149.74 150.10 151.20 150.28 Station Venting Storage 30.34 32.41 33.51 34.52 33.35 30.18 32.63 33.66 32.89 LNG Storage

LNG Stations 9.24 9.45 9.77 10.30 10.62 10.62 10.62 10.62 10.62 LNG Reciprocating

Compressors 34.50 35.37 36.67 38.84 40.15 40.15 40.15 40.15 40.15 LNG Centrifugal

Compressors 11.78 12.09 12.55 13.31 13.77 13.77 13.77 13.77 13.77 LNG Compressor Exhaust

LNG Engines 3.21 3.23 3.26 3.30 3.32 3.32 3.32 3.32 3.32 LNG Turbines 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 LNG Station Venting 5.13 5.25 5.43 5.72 5.90 5.90 5.90 5.90 5.90

### A-160 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009 LNG Import Terminals

LNG Stations 0.21 0.21 0.21 0.21 0.42 0.42 0.42 1.06 1.38 LNG Reciprocating

Compressors 1.01 1.01 1.01 1.01 2.02 2.02 2.02 5.05 6.56 LNG Centrifugal

Compressors 0.26 0.26 0.26 0.26 0.52 0.52 0.52 1.29 1.68 LNG Compressor Exhaust

LNG Engines 1.74 0.96 0.49 4.41 12.18 11.28 14.81 6.91 8.80 LNG Turbines 1 0.01 0.00 0.00 0.03 0.07 0.07 0.09 0.04 0.05 LNG Station Venting 0.12 0.12 0.12 0.12 0.24 0.24 0.24 0.59 0.76 Total 2,344 2,376 2,442 2,482 2,473 2,430 2,479 2,502 2,482

1 Emissions are not actually 0, but too sm all to show at this level of precision. Note: Totals may not sum due to independent rounding.



### Table A-130: CH 4 

### Emission Estimates from the Natura l Gas Distribution Stage Excluding Re ductions from the Natural Gas STAR Program (Gg) Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009 Pipeline Leaks Mains—Cast Iron 267.99 243.28 232.74 205.73 171.81 170.00 173.18 167.63 162.88 Mains—Unprotected steel 231.20 211.42 199.62 175.72 147.05 152.25 147.55 147.23 142.89 Mains—Protected steel 27.50 27.71 29.73 27.85 27.26 28.46 28.93 28.32 28.61 Mains—Plastic 59.43 51.02 67.52 85.78 100.36 118.13 114.82 115.16 118.61 Services—Unprotected steel 250.05 233.84 201.51 185.91 173.89 184.83 178.49 176.51 170.94 Services Protected steel 67.24 67.11 71.39 60.70 53.99 53.48 53.56 52.54 52.31 Services—Plastic 3.38 3.71 4.66 5.69 6.47 7.10 7.36 7.44 7.63 Services—Copper 7.78 7.68 7.08 7.02 5.94 6.12 6.01 5.59 5.31 Meter/Regulator (City Gates) M&R >300 110.41 117.93 121.96 125.62 121.36 109.84 118.73 122.50 119.68 M&R 100-300 214.25 228.82 236.64 243.76 235.49 213.13 230.38 237.70 232.23 M&R <100 5.16 5.51 5.70 5.87 5.67 5.14 5.55 5.73 5.60 Reg >300 108.70 116.09 120.06 123.67 119.48 108.13 116.88 120.60 117.83 R-Vault >300 0.51 0.55 0.57 0.58 0.56 0.51 0.55 0.57 0.56 Reg 100-300 82.26 87.86 90.86 93.59 90.42 81.83 88.45 91.26 89.17 R-Vault 100-300 0.16 0.18 0.18 0.19 0.18 0.16 0.18 0.18 0.18 Reg 40-100 6.34 6.77 7.00 7.21 6.97 6.31 6.82 7.03 6.87 R-Vault 40-100 0.47 0.50 0.52 0.53 0.51 0.47 0.50 0.52 0.51 Reg <40 0.34 0.37 0.38 0.39 0.38 0.34 0.37 0.38 0.37 Customer Meters Residential 103.47 110.51 114.29 117.72 113.73 102.93 111.26 114.80 112.16 Commercial/Industry 3.97 4.25 4.78 4.66 3.95 3.85 3.98 4.03 3.80 Routine Maintenance Pressure Relief Valve Releases 0.91 0.86 0.96 1.01 1.05 1.16 1.15 1.14 1.16 Pipeline Blowdown 2.39 2.55 2.64 2.72 2.62 2.37 2.57 2.65 2.59 Upsets Mishaps (Dig-ins) 37.20 39.74 41.09 42.33 40.89 37.01 40.01 41.28 40.33 Total 1,591 1,568 1,562 1,524 1,430 1,394 1,437 1,451 1,422 Note: Totals may not sum due to independent rounding.



### Table A-131: U.S. Production Sector CO 2 

### Content in Natural Gas by NEMS Region and Natural Gas Well type U.S. Region

Well Types North East Midcontinen t Gulf Coast South West Rocky Mountain West Coast Lower-48 States Conventional 0.92% 0.79% 2.17% 3.81% 7.95% 0.16% 3.41% Un-conventional 7.42% 0.31% 0.23% NA 0.64% NA 4.83% All types 3.04% 0.79% 2.17% 3.81% 7.58% 0.16% 3.45%

### A-161 Table A-132: CO 2 

### Emission Estimates from the Na tural Gas Production Stage (Gg) Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009 Normal Fugitives Gas Wells Non-Associated Gas Wells 3.56 3.57 4.25 6.46 7.00 6.18 5.86 7.33 8.00 Unconventional Gas Wells 2 0.00 0.00 0.00 0.01 0.01 0.03 0.03 0.03 0.03 Field Separation Equipment Heaters 1.86 1.91 2.32 3.95 4.44 4.61 4.48 5.54 5.95 Separators 6.04 5.97 7.17 11.29 12.68 13.20 13.16 15.78 16.82 Dehydrators 1.50 1.54 1.73 2.58 2.83 2.94 2.93 3.38 3.60 Meter/ Piping 5.85 5.85 6.95 10.90 12.21 12.69 12.57 15.14 16.15 Gathering Compressors Small Reciprocating Comp. 2.95 2.98 3.57 5.46 6.17 6.41 6.38 7.63 8.15 Large Reciprocating Comp. 0.69 0.91 1.02 1.33 1.40 1.40 1.40 1.62 1.62 Large Reciprocating

Stations 0.05 0.06 0.07 0.09 0.09 0.09 0.09 0.11 0.11
Pipeline Leaks 10.75 10.85 11.99 16.04 17.64 18.21 18.25 20.80 21.94 Vented and Combusted Drilling and Well Completion Completion Flaring 2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Unconventional Gas Well

Completions 2.19 1.58 5.27 122.20 21.29 748.86 166.74 108.05 85.45
Well Drilling 0.07 0.05 0.05 0.12 0.18 0.21 0.20 0.22 0.13 Coal Bed Methane Powder River 1 NE NE NE NE NE NE NE NE NE Black Warrior 1 NE NE NE NE NE NE NE NE NE Normal Operations Pneumatic Device Vents 58.49 60.24 69.38 101.86 114.87 119.29 118.75 139.81 149.18 Chemical Injection Pumps 2.95 3.11 3.75 6.33 7.18 7.45 7.20 8.94 9.61 Kimray Pumps 13.41 13.71 15.43 22.91 25.15 26.12 26.05 30.04 31.99 Dehydrator Vents 4.18 4.28 4.81 7.14 7.84 8.15 8.12 9.37 9.98 Condensate Tank Vents Condensate Tanks without Control Device 10.25 9.43 8.79 9.34 10.25 11.26 11.17 11.90 11.90

Condensate Tanks with Control Device 2.05 1.89 1.76 1.87 2.05 2.25 2.23 2.38 2.38
Compressor Exhaust Vented Gas Engines 1 NE NE NE NE NE NE NE NE NE Well Workovers Gas Wells 0.06 0.06 0.06 0.08 0.09 0.10 0.10 0.11 0.12 Unconventional Gas Wells 0.47 0.84 2.35 22.89 43.99 118.88 135.55 146.36 145.24 Well Clean Ups (LP Gas

Wells) 251.41 252.88 273.66 339.56 375.89 367.69 377.12 413.61 439.94

Blowdowns Vessel BD 0.04 0.04 0.04 0.06 0.06 0.07 0.07 0.08 0.08 Pipeline BD 0.17 0.17 0.19 0.26 0.28 0.29 0.29 0.33 0.35 Compressor BD 0.11 0.12 0.14 0.21 0.24 0.25 0.25 0.29 0.31 Compressor Starts 0.25 0.26 0.31 0.47 0.53 0.55 0.55 0.66 0.70 Upsets Pressure Relief Valves 0.03 0.03 0.04 0.05 0.06 0.06 0.06 0.07 0.07 Mishaps 0.09 0.09 0.10 0.14 0.15 0.16 0.16 0.18 0.19 Flaring Emissions - Onshore 9,092.72 10,172.49 17,167.79 5,525.04 7,193.00 7,812.35 8,664.25 10,024.82 9,545.39 Offshore Offshore water Gas Platforms (GoM & Pacific) 1.48 1.51 1.56 1.64 1.63 1.63 1.55 1.52 1.42 Deepwater Gas Platforms (GoM & Pacific) 0.03 0.03 0.04 0.07 0.10 0.12 0.11 0.11 0.11

Flaring Emissions - Offshore 230.37 163.13 197.22 204.31 180.66 146.48 160.05 360.00 360.00

Total 9,704 10,720 17,792 6,425 8,050 9,438 9,746 11,336 10,877

1 Energy use CO 2 emissions not estimated to avoid doubl e counting. NE = not estimated.

2 Emissions are not actually 0, but too sm all to show at this level of precision.

Note: Totals may not sum due to independent rounding.

### A-162 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A-133: CO 2 

### Emission Estimates from the Na tural Gas Processing Plants (Gg) Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009 Normal Fugitives Plants – Before CO 2 removal 2.56 2.46 2.27 1.97 1.90 1.92 1.93 1.94 1.94 Plants – After CO 2 removal 0.57 0.55 0.50 0.44 0.42 0.43 0.43 0.43 0.43 Reciprocating Compressors – Before CO 2 removal 19.67 19.66 20.49 21.16 19.85 20.41 21.28 22.47 23.23 Reciprocating Compressors – After CO 2 removal 4.37 4.36 4.55 4.70 4.41 4.53 4.72 4.99 5.16 Centrifugal Compressors (wet seals) – Before CO 2 removal 14.55 14.54 15.05 15.21 13.88 13.92 13.98 14.07 14.13 Centrifugal Compressors (wet seals) – After CO 2 removal 3.23 3.23 3.34 3.38 3.08 3.09 3.10 3.12 3.14 Centrifugal Compressors (dry seals) – Before CO 2 removal 0 0 0.05 0.21 0.39 0.58 0.86 1.25 1.50 Centrifugal Compressors (dry seals) – After CO 2 removal 0 0 0.01 0.05 0.09. 0.13 0.19 0.28 0.33 Vented and Combusted Normal Operations Compressor Exhaust Gas Engines 1 NE NE NE NE NE NE NE NE NE Gas Turbines 1 NE NE NE NE NE NE NE NE NE AGR Vents 27,708 26,652 24,577 23,288 21,694 21,161 21,144 21,328 21,130 Kimray Pumps 0.39 0.39 0.41 0.42 0.40 0.41 0.43 0.45 0.46 Dehydrator Vents 2.42 2.42 2.52 2.61 2.45 2.51 2.62 2.77 2.86 Pneumatic Devices 0.29 0.27 0.25 0.22 0.21 0.21 0.22 0.22 0.22 Routine Maintenance Blowdowns/Venting 6.36 6.12 5.64 4.89 4.73 4.78 4.80 4.83 4.83 Total 27,763 26,706 24,632 23,343 21,746 21,214 21,199 21,385 21,189

1 Energy use CO 2 emissions not estimated to avoid double counting. NE = not estimated. Note: Totals may not sum due to independent rounding.



### Table A-134: CO 2 

### Emission Estimates from the Natura l Gas Transmission and Storage (Gg) Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009 Fugitives Pipelines Leaks 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.10 0.10 Compressor Stations (Transmission) Station 3.09 3.08 3.14 3.16 3.18 3.17 3.18 3.21 3.19 Recip Compressor 21.49 21.45 21.85 22.00 22.12 22.11 22.16 22.33 22.19 Centrifugal Compressor (wet seals) 7.12 7.11 7.20 7.08 6.83 6.83 6.83 6.84 6.79 Centrifugal Compressor (dry seals) 0 0 0.02 0.13 0.32 0.32 0.33 0.36 0.36 Compressor Stations (Storage) Station 1.58 1.68 1.74 1.79 1.73 1.57 1.69 1.75 1.71 Recip Compressor 4.55 4.86 5.03 5.18 5.01 4.53 4.89 5.05 4.93 Centrifugal Compressor (wet seals) 0.96 1.02 1.05 0.99 0.89 0.79 0.80 0.80 0.77 Centrifugal Compressor (dry seals) 0 0 0.00 0.07 0.12 0.12 0.17 0.19 0.19 Wells (Storage) 0.39 0.42 0.43 0.45 0.43 0.39 0.42 0.43 0.42 M&R (Trans. Co. Interconnect) 2.10 2.10 2.14 2.15 2.16 2.16 2.17 2.18 2.17 M&R (Farm Taps + Direct Sales) 0.49 0.49 0.50 0.50 0.50 0.50 0.50 0.51 0.50 Vented and Combusted Normal Operation Dehydrator Vents (Transmission) 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 Dehydrator Vents (Storage) 0.12 0.13 0.13 0.14 0.13 0.12 0.13 0.14 0.13 Compressor Exhaust Engines (Transmission) 1 NE NE NE NE NE NE NE NE NE Turbines (Transmission) 1 NE NE NE NE NE NE NE NE NE

### A-163 Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009 Engines (Storage) 1 NE NE NE NE NE NE NE NE NE Turbines (Storage) 1 NE NE NE NE NE NE NE NE NE Generators (Engines) 1 NE NE NE NE NE NE NE NE NE Generators (Turbines) 1 NE NE NE NE NE NE NE NE NE Pneumatic Devices Trans+Stor Pneumatic Devices Trans 6.15 6.14 6.25 6.29 6.33 6.33 6.34 6.39 6.35 Pneumatic Devices Storage 1.28 1.37 1.42 1.46 1.41 1.28 1.38 1.42 1.39 Routine Maintenance/Upsets Pipeline Venting 5.13 5.13 5.22 5.26 5.29 5.28 5.30 5.33 5.30 Station venting Trans+Storage Station Venting Transmission 4.20 4.19 4.27 4.30 4.32 4.32 4.33 4.36 4.33 Station Venting Storage 0.88 0.93 0.97 1.00 0.96 0.87 0.94 0.97 0.95 LNG Storage LNG Stations 0.31 0.32 0.33 0.35 0.36 0.36 0.36 0.36 0.36 LNG Reciprocating Compressors 1.16 1.18 1.23 1.30 1.34 1.34 1.34 1.34 1.34 LNG Centrifugal Compressors 0.39 0.41 0.42 0.45 0.46 0.46 0.46 0.46 0.46 LNG Compressor Exhaust LNG Engines 1 NE NE NE NE NE NE NE NE NE LNG Turbines 1 NE NE NE NE NE NE NE NE NE LNG Station Venting 0.17 0.18 0.18 0.19 0.20 0.20 0.20 0.20 0.20 LNG Import Terminals LNG Stations 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.04 0.05 LNG Reciprocating Compressors 0.03 0.03 0.03 0.03 0.07 0.07 0.07 0.17 0.22 LNG Centrifugal Compressors 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.04 0.06 LNG Compressor Exhaust LNG Engines 1 NE NE NE NE NE NE NE NE NE LNG Turbines 1 NE NE NE NE NE NE NE NE NE LNG Station Venting 2 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.02 0.03 Total 61.75 62.37 63.73 64.44 64.36 63.31 64.18 65.04 64.55

1 Energy use CO 2 emissions not estimated to avoid doub le counting. NE = not estimated. 2 Emissions are not actually 0, but too sm all to show at this level of precision. Note: Totals may not sum due to independent rounding.



### Table A-135: CO 2 

### Emission Estimates from the Nat ural Gas Distribution Stage (Gg) Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009 Pipeline Leaks Mains—Cast Iron 7.73 7.02 6.71 5.93 4.96 4.90 5.00 4.84 4.70 Mains—Unprotected steel 6.67 6.10 5.76 5.07 4.24 4.39 4.26 4.25 4.12 Mains—Protected steel 0.79 0.80 0.86 0.80 0.79 0.82 0.83 0.82 0.83 Mains—Plastic 1.71 1.47 1.95 2.47 2.89 3.41 3.31 3.32 3.42 Total Pipeline Miles Services—Unprotected steel 7.21 6.75 5.81 5.36 5.02 5.33 5.15 5.09 4.93 Services Protected steel 1.94 1.94 2.06 1.75 1.56 1.54 1.54 1.52 1.51 Services—Plastic 0.10 0.11 0.13 0.16 0.19 0.20 0.21 0.21 0.22 Services—Copper 0.22 0.22 0.20 0.20 0.17 0.18 0.17 0.16 0.15 Meter/Regulator (City Gates) M&R >300 3.18 3.40 3.52 3.62 3.50 3.17 3.42 3.53 3.45 M&R 100-300 6.18 6.60 6.83 7.03 6.79 6.15 6.65 6.86 6.70 M&R <100 0.15 0.16 0.16 0.17 0.16 0.15 0.16 0.17 0.16 Reg >300 3.14 3.35 3.46 3.57 3.45 3.12 3.37 3.48 3.40 R-Vault >300 0.01 0.02 0.02 0.02 0.02 0.01 0.02 0.02 0.02 Reg 100-300 2.37 2.53 2.62 2.70 2.61 2.36 2.55 2.63 2.57 R-Vault 100-300 0.00 0.01 0.01 0.01 0.01 0.00 0.01 0.01 0.01 Reg 40-100 0.18 0.20 0.20 0.21 0.20 0.18 0.20 0.20 0.20 R-Vault 40-100 0.01 0.01 0.01 0.02 0.01 0.01 0.01 0.01 0.01 Reg <40 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 Customer Meters Residential 2.98 3.19 3.30 3.40 3.28 2.97 3.21 3.31 3.24 Commercial/Industry 0.11 0.12 0.14 0.13 0.11 0.11 0.11 0.12 0.11 Routine Maintenance

### A-164 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009
Pressure Relief Valve Releases 0.03 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.03
Pipeline Blowdown 0.07 0.07 0.08 0.08 0.08 0.07 0.07 0.08 0.07 Upsets Mishaps (Dig-ins) 1.07 1.15 1.19 1.22 1.18 1.07 1.15 1.19 1.16
Total 45.90 45.24 45.05 43.97 41.25 40.20 41.46 41.85 41.03

1 Emissions are not actually 0, but too sm all to show at this level of precision. Note: Totals may not sum due to independent rounding.

### A-165 

# 3.5. Methodology for Estimating CH 4 

# and CO 2 

# Emissions from Petroleum Systems

The methodology for estimating CH 4 

### and non-combustion CO 2 

### emissions from petroleum systems is based on the 1999 EPA draft report, Estimates of Methane Emissions from the U.S. Oil Industry (EPA 1999) and the study, Methane Emissions from the U.S. Petroleum Industry (EPA/GRI 1996). Sixty-four activities that emit CH 4 

### and thirty activities that emit non-combustion CO 2 

### from petroleum systems were examined from these reports. Most of the activities analyzed involve crude oil production field operations, which accounted for about 98 percent of total oil industry CH 4 

### emissions. Crude transportation and refining accounted for the remaining CH 4 

### emissions of less than one half and about one and a half percent, respectively. Non-combustion CO 2 

### emissions were analyzed for production operations and asphalt blowing in refining operations. Non-combustion CO 2 

### emissions from transportation operations are not included because they are negligible. The following steps were taken to estimate CH 4 

### and CO 2 

### emissions from petroleum systems.

Step 1: Determine Emission Factors for all Activities

The CH 4 

### emission factors for the majority of the activitie s for 1995 are taken from the 1999 EPA draft report, which contained the most recent and comprehensive determination of CH 4 

### emission factors for the 64 CH 4 

### -emitting activities in the oil industry at that time. Emission factors for pneumatic devices in the production sector were recalculated in 2002 using emissions data in the EPA/GRI 1996c study. The gas engine emission factor is taken from the EPA/GRI 1996b study. The oil tank venting emission factor is taken from the API E&P Tank Calc weighted average for API gravity less than 45 API degrees with the distribution of gravities taken from a sample of production data from the HPDI database. Offshore emissions from shallow water and deep water oil platforms are taken from analysis of the Gulf-wide Offshore Activity Data System (GOADS) report (EPA 2005, BOEMRE 2004). The emission factors determined for 1995 were assumed to be representative of emissions from each sour ce type over the period 1990 through 2009. Therefore, the same emission factors are used for each year throughout this period.

The CO 2 

### emission factors were derived from the corresponding source CH 4 

### emission factors. The amount of CO 2 

### in the crude oil stream changes as it passes through various equipment in petroleum production operations. As a result, four distinct stages/streams with varying CO 2 

### contents exist. The four streams that are used to estimate the emissions factors are the associated gas stream separated from crude oil, hydrocarbons flashed out from crude oil (such as in storage tanks), whole crude oil itself when it leaks downstream, a nd gas emissions from offshore oil platforms. The standard approach used to estimate CO 2 

### emission factors was to use the existing CH 4 

### emissions factors and multiply them by a conversion factor, which is the ratio of CO 2 

### content to methane content for the particular stream. Ratios of CO 2 

### to CH 4 

### volume in emissions are presented in Table A- 5. The two ex ceptions are the emissions factor for storage tanks, which is estimated using API E&P Tank Calc simulation runs of tank em issions for crude oil of different gravities less than 45 API degrees; and the emissions factor for uncontrolled asphalt blowing, which is estimated using the data and methods provided by API (2009).

Step 2: Determine Activity Levels for Each Year
Activity levels change from year to year. Some factors change in proportion to crude oil rates: production, transportation, refinery runs. Some change in proportion to th e number of facilities: oil wells, petroleum refineries. Some factors change proportional to both the rate and number of facilities.
For most sources, activity levels found in the EPA/GRI 1996 for the 1995 base year are extrapolated to other years using publicly-available data sources. For the rema ining sources, the activity levels are obtained directly from publicly available data and are not extrapolated from the 1995 base year.
For both sets of data, a determination was made on a case-by-case basis as to which measure of petroleum industry activity best reflects the change in annual activity. Publicly-reported data from the Bureau of Ocean Energy Management, Regulation and Enforcement (BOEMRE), Energy Information Administration (EIA), American Petroleum Institute (API), the Oil & Gas Journal (O&GJ), the Inters tate Oil and Gas Compact Commission (IOGCC), and the U.S Army Corps of Engineers (USACE) were used to extrapolate the activity levels from the base year to each year between 1990 and 2009. Data used include total domestic crude oil production, number of domestic crude oil wells, total imports and exports of crude oil, total petroleum refinery crude runs , and number of oil-producing offshore platforms. The activity data for the total crude transported in the transportation sector is not available. In this case, all the crude oil that was transported was assumed to go to refineries. Therefore, the activity data for the refining sector was used also for the transportation sector. In the few cases where no data was located, oil industry data based on expert judgment was used. In the case of non-combustion CO 2 

### emission sources, the activity factors remain the same as for CH 4 

### emission sources.

### A-166 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Step 3: Estimate Methane and Carbon Dioxide Emissions for Each Activity for Each Year

Annual CH 4 

### emissions from each of the 64 petroleum system activities and CO 2 

### emissions from the 30 petroleum system activities were estimated by multiplying the activity data for each year by the corresponding emission factor. These annual emissions for each activity were then summed to estimate the total annual CH 4 

### and CO 2 

### emissions, respectively. Table A- 140 provides the CO2 content in natural gas for equipment in different crude streams to estimate CO2 emission factors using CH4 emission 

### factors.
Table A-136, Table A-137, Table A-138, and Table A-141 pr ovide 2009 activity factors, emission factors, and emission estimates and Table A-139 and Table A-142 provide a summary of emission estimates for the years 1990, 1995, 2000, and 2005 through 2009. Table A- 140 provides the CO 2 

### content in natural gas for equipment in different crude streams to estimate CO 2 

### emission factors using CH 4 

### emission factors.

Table A-136: 2009 CH 4 

### Emissions from Petroleum Prod uction Field Operations

Activity/Equipment 2009 EPA Inventory Values
Emission Factor Activity Factor Emissions (Bcf/yr) Emissions (Gg/yr) Vented Emissions 67.625 1,300

Oil Tanks 7.39 scf of CH 4 /bbl crude 1,490 MMbbl/yr (non stripper wells) 11.016 211.8

Pneumatic Devices, High Bleed 330 scfd CH 4 /device 139,797 No. of high-bleed devices 16.859 324.2

Pneumatic Devices, Low Bleed 52 scfd CH 4 /device 259,624 No. of low-bleed devices 4.928 94.76

Chemical Injection Pumps 248 scfd CH 4 /pump 28,166 No. of pumps 2.550 49.04 Vessel Blowdowns 78 scfy CH 4 /vessel 182,451 No. of vessels 0.014 0.274 Compressor Blowdowns 3,775 scf/yr of CH 4 /compressor 2,473 No. of compressors 0.009 0.179 Compressor Starts 8,443 scf/yr. of CH 4 /compressor 2,473 No. of compressors 0.021 0.401 Stripper wells 2,345 scf/yr of CH 4 /stripper well 317,230 No. of stripper wells vented 0.744 14.31

Well Completion Venting 733 scf/completion 11,804 Oil well completions 0.009 0.166 Well Workovers 96 scf CH 4 /workover 39,450 Oil well workovers 0.004 0.073 Pipeline Pigging 2.40 scfd of CH 4 /pig station 0 No. of crude pig stations - -

Offshore Platforms, Shallow water

Oil, fugitive, vented and combusted 54,795 scfd CH 4 /platform 1,441 No. of shallow water oil platforms 28.823 554.3

Offshore Platforms, Deepwater oil,

fugitive, vented and combusted 260,274 scfd CH 4 /platform 28 No. of deep water oil platforms 2.649 50.93

Fugitive Emissions 2.539 49

Oil Wellheads (heavy crude) 0.13 scfd/well 14,718 No. of hvy. crude wells 0.001 0.013 Oil Wellheads (light crude) 16.6 scfd/well 194,052 No. of lt. crude wells 1.178 22.65 Separators (heavy crude) 0.15 scfd CH 4 /separator 10,740 No. of hvy. crude seps. 0.001 0.012 Separators (light crude) 14 scfd CH 4 /separator 97,745 No. of lt. crude seps. 0.494 9.504 Heater/Treaters (light crude) 19 scfd CH 4 /heater 73,966 No. of heater treaters 0.518 9.965 Headers (heavy crude) 1 0.08 scfd CH 4 /header 13,721 No. of hvy. crude hdrs. 0.000 0.007 Headers (light crude) 11 scfd CH 4 /header 42,536 No. of lt. crude hdrs. 0.169 3.241 Floating Roof Tanks 338,306 scf CH 4 /floating roof tank/yr. 24 No. of floating roof tanks 0.008 0.159

Compressors 100 scfd CH 4 /compressor 2,473 No. of compressors 0.090 1.736 Large Compressors 16,360 scfd CH 4 /compressor 0 No. of large comprs.

- - Sales Areas 41 scf CH 4 /loading 1,630,377 Loadings/year 0.066 1.271 Pipelines NE scfd of CH 4 /mile of pipeline 11,249 Miles of gathering line NE NE

Well Drilling NE scfd of CH 4 /oil well drilled 13,255 No. of oil wells drilled NE NE

Battery Pumps 0.24 scfd of CH 4 /pump 157,800 No. of battery pumps 0.014 0.266
Combustion Emissions 4.7926 92.22

Gas Engines 0.24 scf CH 4 /HP-hr 15,577 MMHP-hr 3.739 71.89 Heaters 0.52 scf CH 4 /bbl 1956.8 MMbbl/yr 1.020 19.61 Well Drilling 2,453 scf CH 4 /well drilled 13,255 Oil wells drilled 0.033 0.625

### A-167 Activity/Equipment 2009 EPA Inventory Values
Emission Factor Activity Factor Emissions (Bcf/yr) Emissions (Gg/yr) Flares 20 scf CH 4 /Mcf flared 242,355 Mcf flared/yr 0.005 0.932 Process Upset Emissions 0.116 2.233

Pressure Relief Valves 35 scf/yr/PR valve 163,449 No. of PR valves 0.006 0.109 Well Blowouts Onshore 2.5 MMscf/blowout 44.2 No. of blowouts/yr 0.110 2.124
Total 75.08 1,444

- Zero Emissions

1 Emissions are not actually 0, but too smal l to show at this level of precision. NE: Not estimated for lack of data



### Table A-137: 2009 CH 4 

### Emissions from Petroleum Transportation

Activity/Equipment Emission Factor Units Activity Factor Units Emissions (Bcf/yr) Emissions (Gg/yr) Vented Emissions 0.209 4.024

Tanks 0.021 scf CH 4 /yr/bbl of crude delivered to refineries 5,233 MMbbl crude feed/yr 0.108 2.073 Truck Loading 0.520 scf CH 4 /yr/bbl of crude transported by truck 67.8 MMbbl trans. by truck 0.035 0.678 Marine Loading 2.544 scf CH 4 /1000 gal. crude marine loadings 19,609,664 1,000 gal./yr loaded 0.050 0.959 Rail Loading 0.520 scf CH 4 /yr/bbl of crude transported by rail 4.2 MMbbl Crude by rail/yr 0.002 0.042 Pump Station

Maintenance 1 36.80 scf CH 4 /station/yr 496 No. of pump stations 0.000 0.000 Pipeline Pigging 39 scfd of CH 4 /pig station 992 No. of pig stations 0.014 0.271 Fugitive Emissions 0.050 0.958

Pump Stations 25 scf CH 4 /mile/yr. 49,585 No. of miles of crude p/l 0.001 0.024

Pipelines NE scf CH 4 /bbl crude transported by pipeline 6,431 MMbbl crude piped NE NE

Floating Roof Tanks 58,965 scf CH 4 /floating roof tank/yr. 824 No. of floating roof tanks 0.049 0.934

Combustion Emissions NE NE

Pump Engine Drivers 0.24 scf CH 4 /hp-hr NE No. of hp-hrs NE NE Heaters 0.521 scf CH 4 /bbl burned NE No. of bbl Burned NE NE Total 0.259 4.982

1 Emissions are not actually 0, but too smal l to show at this level of precision. NE: Not estimated for lack of data



### Table A-138: 2009 CH 4 

### Emissions from Petroleum Refining

Activity/Equipment 2009 EPA Inventory Values
Emission Factor Activity Factor Emissions (Bcf/yr) Emissions (Gg/yr) Vented Emissions 1.065 20.48 Tanks 20.6 scf CH 4 /Mbbl 1,839 Mbbl/calendar day heavy crude feed 0.014 0.266 System Blowdowns 137 scf CH 4 /Mbbl 14,336 Mbbl/calendar day refinery feed 0.716 13.77 Asphalt Blowing 2,555 scf CH 4 /Mbbl 359 Mbbl/calendar day production 0.335 6.445 Fugitive Emissions 0.088 1.702 Fuel Gas System 439 Mscf CH 4 /refinery/yr 150 Refineries 0.066 1.266 Floating Roof Tanks 1 587 scf CH 4 /floating roof tank/yr. 767 No. of floating roof tanks 0.000 0.009 Wastewater Treating 1.88 scf CH 4 /Mbbl 14,336 Mbbl/calendar day refinery feed 0.010 0.189 Cooling Towers 2.36 scf CH 4 /Mbbl 14,336 Mbbl/calendar day refinery feed 0.012 0.237 Combustion Emissions 0.090 1.723 Atmospheric Distillation 3.61 scf CH 4 /Mbbl 14,659 Mbbl/calendar day refinery feed 0.019 0.371 Vacuum Distillation 3.61 scf CH 4 /Mbbl 6,503 Mbbl/calendar day feed 0.009 0.165

### A-168 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Activity/Equipment 2009 EPA Inventory Values
Emission Factor Activity Factor Emissions (Bcf/yr) Emissions (Gg/yr) Thermal Operations 6.01 scf CH 4 /Mbbl 2,034 Mbbl/calendar day feed 0.004 0.086 Catalytic Cracking 5.17 scf CH 4 /Mbbl 4,694 Mbbl/calendar day feed 0.009 0.170 Catalytic Reforming 7.22 scf CH 4 /Mbbl 2,970 Mbbl/calendar day feed 0.008 0.150 Catalytic Hydrocracking 7.22 scf CH 4 /Mbbl 1,393 Mbbl/calendar day feed 0.004 0.071 Hydrorefining 2.17 scf CH 4 /Mbbl 2,067 Mbbl/calendar day feed 0.002 0.031 Hydrotreating 6.50 scf CH 4 /Mbbl 9,480 Mbbl/calendar day feed 0.022 0.432 Alkylation/Polymerization 12.6 scf CH 4 /Mbbl 1,027 Mbbl/calendar day feed 0.005 0.091 Aromatics/Isomeration 1.80 scf CH 4 /Mbbl 956 Mbbl/calendar day feed 0.001 0.012 Lube Oil Processing 0.00 scf CH 4 /Mbbl 165 Mbbl/calendar day feed 0.000 0.000 Engines 0.006 scf CH 4 /hp-hr 1,128 MMhp-hr/yr 0.006 0.124 Flares 0.189 scf CH 4 /Mbbl 14,336 Mbbl/calendar day refinery feed 0.001 0.019 Total 1.243 23.90

1 Emissions are not actually 0, but too smal l to show at this level of precision.


### Table A-139: Summary of CH 4 

### Emissions from Petroleum Systems (Gg) Activity 1990 1995 2000 2005 2006 2007 2008 2009 Production Field

Operations 1,653 1,557 1,468 1,366 1,365 1,396 1,409 1,444 Pneumatic device venting 489 463 428 397 396 398 416 419 Tank venting 250 226 214 187 188 192 189 212 Combustion & process

upsets 88 82 76 71 71 72 75 94 Misc. venting & fugitives 799 762 727 691 693 714 707 696 Wellhead fugitives 26 25 22 19 17 20 23 23 Crude Oil Transportation 7 6 5 5 5 5 5 5 Refining 25 26 28 28 28 27 25 24 Total 1,685 1,589 1,501 1,398 1,398 1,427 1,439 1,473 Note: Totals may not sum due to independent rounding.



### Table A-140: Ratios of CO 2 

### to CH 4 

### Volume in Emissions from Petrol eum Production Field Operations Whole Crude, Post-Separator Associated Gas Tank Flash Gas Offshore

Ratio %CO 2 

### / %CH 4 

### 0.052 0.020 0.017 0.004



### Table A-141: 2009 CO 2 

### Emissions from Petroleum Production Fi eld Operations and Petroleum Refining

Activity/Equipment 2009 EPA Inventory Values
Emission Factor Activity Factor Emissions (Bcf/yr) Emissions (Gg/yr) Vented Emissions 5.972 316.5

Oil Tanks 3.53 scf of CO 2 /bbl crude 1,490 MMbbl/yr (non stripper wells) 5.256 278.6

Pneumatic Devices, High Bleed 6.704 scfd CO 2 /device 139,797 No. of high-bleed devices 0.342 18.13

Pneumatic Devices, Low Bleed 1.055 scfd CO 2 /device 259,624 No. of low-bleed devices 0.100 5.299 Chemical Injection Pumps 5.033 scfd CO 2 /pump 28,166 No. of pumps 0.052 2.742 Vessel Blowdowns 1 1.583 scfy CO 2 /vessel 182,451 No. of vessels 0.000 0.015 Compressor Blowdowns 1 77 scf/yr of CO 2 /compressor 2,473 No. of compressors 0.000 0.010 Compressor Starts 1 171 scf/yr. of CO 2 /compressor 2,473 No. of compressors 0.000 0.022 Stripper wells 48 scf/yr of CO 2 /stripper well 317,230 No. of stripper wells vented 0.015 0.800

Well Completion Venting 1 14.87 scf/completion 11,804 Oil well completions 0.000 0.009 Well Workovers 1 1.95 scf CO 2 /workover 39,450 Oil well workovers 0.000 0.004 Pipeline Pigging NE scfd of CO 2 /pig station NE No. of crude pig stations NE NE Offshore Platforms, Shallow water

Oil, fugitive, vented and combusted 358 scfd CO 2 /platform 1,441 No. of shallow water oil platforms 0.188 9.984

Offshore Platforms, Deepwater oil, 1,701 scfd CO 2 /platform 28 No. of deep water oil 0.017 0.917

### A-169 Activity/Equipment 2009 EPA Inventory Values
Emission Factor Activity Factor Emissions (Bcf/yr) Emissions (Gg/yr) fugitive, vented and combusted platforms Fugitive Emissions 0.052 2.776

Oil Wellheads (heavy crude) 1 0.003 scfd/well 14,718 No. of hvy. crude wells 0.000 0.001 Oil Wellheads (light crude) 0.337 scfd/well 194,052 No. of lt. crude wells 0.024 1.267 Separators (heavy crude) 1 0.003 scfd CO 2 /separator 10,740 No. of hvy. crude seps. 0.000 0.001 Separators (light crude) 0.281 scfd CO 2 /separator 97,745 No. of lt. crude seps. 0.010 0.531 Heater/Treaters (light crude) 0.319 scfd CO 2 /heater 73,966 No. of heater treaters 0.009 0.456 Headers (heavy crude) 1 0.002 scfd CO 2 /header 13,721 No. of hvy. crude hdrs. 0.000 0.000 Headers (light crude) 0.220 scfd CO 2 /header 42,536 No. of lt. crude hdrs. 0.003 0.181 Floating Roof Tanks 1 17,490 scf CO 2 /floating roof tank/yr. 24 No. of floating roof tanks 0.000 0.023

Compressors 2.029 scfd CO 2 /compressor 2,473 No. of compressors 0.002 0.097 Large Compressors 332 scfd CO 2 /compressor 0 No. of large comprs. 0.000 0.000 Sales Areas 2.096 scf CO 2 /loading 1,630,377 Loadings/year 0.003 0.181 Pipelines NE scfd of CO 2 /mile of pipeline 11,249 Miles of gathering line NE NE

Well Drilling NE scfd of CO 2 /oil well drilled 13,255 No. of oil wells drilled NE NE

Battery Pumps 0.012 scfd of CO 2 /pump 157,800 No. of battery pumps 0.001 0.038 Process Upset Emissions 0.003 0.134

Pressure Relief Valves 1 1.794 scf/yr/PR valve 163,499 No. of PR valves 0.000 0.016 Well Blowouts Onshore 0.051 MMscf/blowout 44 No. of blowouts/yr 0.002 0.119 Refining Emissions 2.720 144.2 Asphalt Blowing 2 20,736 scf CO 2 /Mbbl 359 Mbbl/calendar day production 2.720 144.2

Total 8.747 463.6

1 Emissions are not actually 0, but too smal l to show at this level of precision.

2 Asphalt Blowing emissions are the only significant vented emissions from the refi ning sector; other sources are too small to sh ow at this level of precision. NE: Not estimated for lack of data Energy use CO 2 emissions not estimated to avoid double counting with fossil fuel combustion



### Table A-142: Summary of CO 2 

### Emissions from Petroleum Systems (Gg) Activity 1990 1995 2000 2005 2006 2007 2008 2009 Production Field

Operations 376 341 323 285 285 292 288 319 Pneumatic device venting 27 26 24 22 22 22 23 23 Tank venting 328 296 281 246 246 252 247 278 Misc. venting & fugitives 18 18 17 16 16 16 16 16 Wellhead fugitives 1 1 1 111 1 1 Refining Asphalt Blowing 180 187 211 205 203 182 165 144
Total 555 528 534 490 488 474 453 463

### A-170 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 

# 3.6. Methodology for Estimating CO 2, 

# N 2 

# O and CH 4 

# Emissions from the Incineration of Waste

Emissions of CO 2 

### from the incineration of waste include CO 2 

### generated by the incineration of plastics, synthetic rubber and synthetic fibers in municipal solid waste (MSW), and incineration of tires (which are composed in part of synthetic rubber and C black) in a variety of other combustion facilities (e.g., cement kilns). Incineration of waste also results in emissions of N 2 

### O and CH 4 

### . The methodology for calculating emissions from each of these waste incineration sources is described in this Annex.



## CO 2 

## from Plastics Incineration

In the Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999 through 2003, 2005 through 2011), the flows of pl astics in the U.S. waste stream are reported for seven resin categories. For 2009, the quantity generated, recovered, and discarded for each resin is shown in Table A-143. The data set for 1990 through 2009 is incomplete, and several assu mptions were employed to bridge the data gaps. The EPA reports do not provide estimates for individual materials landfilled and incinerated, although they do provide such an estimate for the waste stream as a whole. To estimate the quantity of plastics landfilled and incinerated, total discards were apportioned based on the proportions of landfilling and inci neration for the entire U.S. waste stream for each year in the time series according to Biocycle’s State of Garbage in America (van Haaren et al. 2010). For those years when distribution by resin category was not reported (1990 through 1994), total values were apportioned according to 1995 (the closest year) distribution ratios. Generation and recovery figures for 2002 and 2004 were linearly interpolated between surrounding years’ data.

Table A-143: 2008 Plastics in the Muni cipal Solid Waste Stream by Resin (Gg)

Waste Pathway PET HDPE PVC LDPE/ LLDPE PP PS Other Total Generation 3,202 4,727 1, 016 5,715 5,017 2,241 5,144 27,261 Recovery 662 535 0 290 45 18 372 1,923 Discard 2,540 4,191 1,016 5,425 4,971 2,223 4,772 25,338 Landfill 2,316 3,822 926 4,947 4,533 2,027 4,351 23,104 Combustion 224 369 90 478 438 196 421 2,234 Recovery* 21% 11% 0% 5% 1% 1% 7% 7% Discard* 79% 89% 100% 95% 99% 99% 93% 93% Landfill* 72% 81% 91% 87% 90% 90% 85% 85% Combustion* 7% 8% 9% 8% 9% 9% 8% 8% *As a percent of waste generation. Note: Totals may not sum due to independent rounding. Abbrevia tions: PET (polyethylene terephthalate), HDPE (high density poly ethylene), PVC (polyvinyl chloride), LDPE/LLDPE (linear low density polyethylene), PP (polypropylene), PS (polystyrene).



### Fossil fuel-based CO 2 

### emissions were calculated as the product of plastic combusted, C content, and fraction oxidized (see Table A-144). The C content of each of the six types of plastics is listed, with the value for “other plastics” assumed equal to the weighted average of the six categorie s. The fraction oxidized was assumed to be 98 percent.

Table A-144: 2008 Plastics Incinerate d (Gg), Carbon Content (%), Fraction Ox idized (%) and Carbon Incinerated (Gg)

Factor PET HDPE PVC LDPE/ LLDPE PP PS Other a Total Quantity Combusted 224 369 90 478 438 196 421 2,234 Carbon Content of Resin 63% 86% 38% 86% 86% 92% 66% - Fraction Oxidized 98% 98% 98% 98% 98% 98% 98% - Carbon in Resin Combusted 137 310 34 402 368 177 271 1,700 Emissions (Tg CO 2 Eq.) 0.5 1.1 0.1 1.5 1.3 0.6 1.0 6.2

a Weighted average of other plastics produced. Note: Totals may not sum due to independent rounding.



## CO 2 

## from Incineration of Synthetic Rubber and Carbon Black in Tires Emissions from tire incineration require two pieces of information: the amount of tires incinerated and the C content of the tires. Scrap Tire Markets in the United States: 9 th 

### Biennial Report (RMA 2009a) reports that 2,484.4

### A-171 thousand of the 4,105.8 thousand tons of scrap tires generate d in 2007 (approximately 61 percent of generation) were used for fuel purposes. Using RMA’s estimates of average tire composition and weight, the mass of synthetic rubber and C black in scrap tires was determined:



### • Synthetic rubber in tires was estimated to be 90 percent C by weight, based on the weighted average C contents of the major elastomers used in new tire consumption. 46 

### Table A- 145 shows consumption and C content of elastomers used for tires and other products in 2002, the most recent year for which data are available.



### • C black is 100 percent C (Aslett Rubber Inc. n.d.).
Multiplying (a) the mass of scrap tires incinerated by (b) the total C content of the synthetic rubber and C black portions of scrap tires and (c) by a 98 percent oxidation factor yielded CO 2 

### emissions, as shown in Table A- 146. The disposal rate of rubber in tires (0.5 Tg C/yr) is smaller than the consumption rate for tires based on summing the elastomers listed in Table A- 145 (1.3 Tg/yr); this is due to the fact that much of the rubber is lost through tire wear during the product’s lifetime and may also reflect the lag time between consumption and disposal of tires. Tire production and fuel use for 1990 through 2007 were taken from RMA 2006 and RMA 2009a; when data were not reported, they were linearly interpolated between bracketing years’ data or, for the ends of time series, set equal to the closest year with reported data.
RMA 2009a changed the reporting of scrap tire data from milli ons of tires to thousands of short tons of scrap tire. As a result, the average weight and percent of the market of light duty and commercial scrap tires was used to convert the previous years from millions of tires to thousands of short tons (STMC 1990 through 1997; RMA 2002 through 2006, 2009b). Table A- 145: Elastomers Consumed in 2002 (Gg)

Elastomer Consumed Carbon Content Carbon Equivalent Styrene butadiene rubber solid 768 91% 700 For Tires 660 91% 602 For Other Products* 108 91% 98 Polybutadiene 583 89% 518 For Tires 408 89% 363 For Other Products 175 89% 155 Ethylene Propylene 301 86% 258 For Tires 6 86% 5 For Other Products 295 86% 253 Polychloroprene 54 59% 32 For Tires 0 59% 0 For Other Products 54 59% 32 Nitrile butadiene rubber solid 84 77% 65 For Tires 1 77% 1 For Other Products 83 77% 64 Polyisoprene 58 88% 51 For Tires 48 88% 42 For Other Products 10 88% 9 Others 367 88% 323 For Tires 184 88% 161 For Other Products 184 88% 161 Total 2,215 - 1,950 For Tires 1,307 - 1,174 *Used to calculate C content of non-tire r ubber products in municipal solid waste. - Not applicable Note: Totals may not sum due to independent rounding.

46 The carbon content of tires (1,158 Gg C) divided by the mass of rubber in tires (1,285 Gg) equals 90 percent.

### A-172 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A- 146: Scrap Tire Constituents and CO 2 

### Emissions from Scrap Tire Incineration in 2007

Material Weight of Material (Tg) Fraction Oxidized Carbon Content Emissions (Tg CO 2 Eq.) Synthetic Rubber 0.5 98% 90% 1.8 Carbon Black 0.6 98% 100% 2.3 Total 1.1 - - 4.1 - Not applicable



## CO 2 

## from Incineration of Syntheti c Rubber in Municipal Solid Waste Similar to the methodology for scrap tires, CO 2 

### emissions from synthetic rubber in MSW were estimated by multiplying the amount of rubber incinerated by an average r ubber C content. The amount of rubber discarded in the MSW stream was estimated from generation and recycling data 47 

### provided in the Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999 through 2003, 2005 through 2011) and unpublished backup data (Schneider 2007). The reports divide rubber found in MSW into three product categories: other durables (not including tires), non-durables (which incl udes clothing and footwear and other non-durables), and containers and packaging. EPA (2011) did not report rubber found in the product category “containers and packaging;” however, containers and packaging from miscellaneous material types were reported for 2009. As a result, EPA assumes that rubber containers and packaging are reported under the “m iscellaneous” category; and therefore, the quantity reported for 2009 was set equal to the quantity reported for 2008. Since there was negligible recovery for these product types, all the waste generated is considered to be discarded. Simi lar to the plastics method, discards were apportioned into landfilling and incineration based on their relative proportions, for each year, for the entire U.S. waste stream. The report aggregates rubber and leather in the MSW stream; an assumed synthetic rubber content of 70% was assigned to each product type, as shown in Table A-147. 48 

### A C content of 85 percent was assigned to synthetic rubber for all product types (based on the weighted average C content of rubber consumed for non-tire uses), and a 98 percent fraction oxidized was assumed.

Table A-147: Rubber and Leather in Municipal Solid Waste in 2009

Product Type Incinerated (Gg) Synthetic Rubber (%) Carbon Content (%) Fraction Oxidized (%) Emissions (Tg CO 2 Eq.) Durables (not Tires) 271 70% 85% 98% 0.8 Non-Durables 85 - - - 0.3 Clothing and Footwear 63 70% 85% 98% 0.2 Other Non-Durables 22 70% 85% 98% 0.1 Containers and Packaging 2 70% 85% 98% + Total 358

-- - 1.1 + Less than 0.05 Tg CO 2 Eq. - Not applicable.



## CO 2 

## from Incineration of Synthetic Fibers

CO 2 

### emissions from synthetic fibers were estimated as the product of the amount of synthetic fiber discarded annually and the average C content of synthetic fiber. Fiber in the MSW stream was estimated from data provided in the Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999 through 2003, 2005 through 2011) for textiles. Production data for the synthetic fibers was based on data from the American Chemical Society (FEB 2009). The amount of synthetic fiber in MSW was estimated by subtracting (a) the amount recovered from (b) the waste generated (see Table A-148). As with the other materials in the MSW stream, discards were apportioned based on the annually variable pr oportions of landfilling and incineration for the entire U.S. waste stream as found in van Haaren et al. (2010). It was a ssumed that approximately 55 percent of the fiber was synthetic in origin, based on information received from the Fiber Economics Bureau (DeZan 2000). An average C content of 70 percent was assigned to synthetic fiber using the production-weighted average of the C contents of the four major fiber

47 Discards = Generation minus recycling. 48 As a sustainably harvested biogenic material, the in cineration of leather is assumed to have no net CO 2 emissions.

### A-173 types (polyester, nylon, olefin, and acrylic) produ ced in 1999 (see Table A-149). The equation relating CO 2 

### emissions to the amount of textiles combusted is shown below.

CO 2 

### Emissions from the Incineration of Synthetic Fibers = Annual Textile Incineration (Gg) × (Percent of Total Fiber that is Synthetic) × (Average C Content of Synthetic Fiber) × (44g CO 2 

### /12 g C) Table A-148: Synthetic Textiles in MSW (Gg) Year Generation Recovery Discards Incineration 1990 2,884 328 2,557 332

1995 3,674 447 3,227 442 1996 3,832 472 3,361 467 1997 4,090 526 3,564 458 1998 4,269 556 3,713 407 1999 4,498 611 3,887 406 2000 4,686 655 4,031 415 2001 4,870 715 4,155 432 2002 5,123 750 4,373 459 2003 5,297 774 4,522 472 2004 5,451 884 4,567 473 2005 5,649 918 4,731 473 2006 5,893 933 4,959 479 2007 5,927 953 4,974 460 2008 6,170 948 5,222 460 2009 6,319 943 5376 474



### Table A-149: Synthetic Fiber Production in 1999 Fiber Production (Tg) Carbon Content Polyester 1.8 63% Nylon 1.2 64% Olefin 1.4 86% Acrylic 0.1 68% Total 4.5 70%



## N 2 

## O and CH 4 

## from Incineration of Waste

Estimates of N 2 

### O emissions from the incineration of waste in the United States are based on the methodology outlined in the EPA’s Compilation of Air Pollutant Emission Factors (EPA 1995) and presented in the Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999 through 2003, 2005 through 2011) and unpublished backup data (Schneider 2007). According to this methodology, emissions of N 2 

### O from waste incineration are the product of the mass of waste incinerated, an emission factor of N 2 

### O emitted per unit mass of waste incinerated, and an N 2 

### O emissions control removal efficiency. The mass of waste incinerated was derived from the information published in BioCycle (van Haaren et al. 2010). For waste incineration in the United States, an emission factor of 50 g N 2 

### O/metric ton MSW based on the 2006 IPCC Guidelines and an estimated emissions control removal efficiency of zero percent were used (IPCC 2006). It was assumed that all MSW incinerators in the United States use continuously-fed stoker technology (Bahor 2009, ERC 2009).

Estimates of CH 4 

### emissions from the incineration of waste in the United States are based on the methodology outlined in IPCC’s 2006 Guidelines for National Greenhouse Gas Inventories (IPCC 2006). According to this methodology, emissions of CH 4 

### from waste incineration are the product of the mass of waste incinerated and an emission factor of CH 4 

### emitted per unit mass of waste incinerated. Similar to the N 2 

### O emissions methodology, the mass of waste incinerated was derived from the information published in Biocycle (van Haaren et al. 2010). For waste incineration in the United States, an emission factor of 0.20 kg CH 4 

### /Gg MSW was used based on the 2006 IPCC Guidelines and assuming that all MSW incinerators in the United States use continuously-fed stoker technology (Bahor 2009, ERC 2009). No information was available on the mass of waste incinerated from Biocycle in 2009, so the value was assumed to remain constant at the 2008 level.
Despite the differences in methodology and data sources, the two series of references (EPA’s and BioCycle’s) provide estimates of total solid waste incinerated that are relatively consistent (see Table A-150).

### A-174 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A-150: U.S. Municipal Solid Waste Incinerated, as Reported by EPA and BioCycle (Metric Tons)

Year EPA BioCycle 1990 28,939,680 30,632,057

1995 32,241,888 29,639,040 1996 32,740,848 29,707,171 1997 33,294,240 27,798,368 1998 31,216,752 25,489,893 1999 30,881,088 24,296,249 2000 30,599,856 25,974,978 2001 30,481,920 25,942,036 a 2002 30,255,120 25,802,917 2003 30,028,320 25,930,542 b 2004 28,585,872 26,037,823 2005 28,685,664 25,973,520 c 2006 28,985,040 25,853,401 2007 29,003,184 24,788,539 d 2008 28,622,160 23,674,017 2009 26,317,872 NA NA (Not Available)

a Interpolated between 2000 and 2002 values.

b Interpolated between 2002 and 2004 values.

c Interpolated between 2004 and 2006 values.

d Interpolated between 2006 and 2008 values



## References ArSova, Ljupka, Rob van Haaren, Nora Goldstein, Scott M. Kaufman, and Nickolas J. Themelis (2008). “16th Annual BioCycle Nationwide Survey: The State of Garbage in America” Biocycle, JG Press, Emmaus, PA. December.
Astlett Rubber Inc. (n.d.) Material Safety Data Sheet, Carbon Black. Available online at: <http://www.astlettrubber.com/pdf/specialty/msd s/carbonblack.pdf>. Accessed 5 November 2009.
Bahor, B (2009) Covanta Energy’s public review comments re: Draft Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007. Submitted via email on April 9, 2009 to Leif Hockstad, U.S. EPA.
DeZan, D.(2000) Personal Communication between Diane DeZan, Fiber Economics Bureau and Joe Casola, ICF Consulting. 4 August 2000.
Energy Recovery Council (2009). “2007 Directory of Waste-to-E nergy Plants in the United States,” accessed September 29, 2009.
EPA (2005 through 20011) Municipal Solid Waste in the United States: Facts and Figures . Office of Solid Waste and Emergency Response, U.S. Environmental Protecti on Agency. Washington, DC. Available online at <http://www.epa.gov/epaoswer/non-hw/muncpl/msw99.htm>.
EPA (1999 through 2003) Characterization of Municipal Solid Waste in the United States: Source Data Update . Office of Solid Waste, U.S. Environmental Protection Agency. Washington, DC.
EPA (1995). Compilation of Air Pollutant Emission Factors, AP-42. Fifth Edition, Vol. I: Stationary Point and Area Sources, Introduction. Office of Air Quality Planning a nd Standards. Research Triangle Park, NC. October.
FEB (2009) Fiber Economics Bureau, as cited in C&EN (2009) Chemical Output Slipped In Most Regions Chemical & Engineering News, American Chemical Society, 6 July . Available online at <http://www.cen-online.org>.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories . The National Greenhouse Gas Inventories Programme, The Intergovernmental Panel on Climate Change, H. S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
RMA (2009a) Scrap Tire Markets in the United States: 9 th 

### Biennial Report. Rubber Manufacturers Association. Washington, DC. May 2009.

### A-175 RMA (2009b) “Scrap Tire Markets: Facts and Figures – Scrap Tire Characteristics.” Available online at: <http://www.rma.org/scrap_tires/scrap_tire_markets/scra p_tire_characteristics/> Ac cessed 17 September 2009.
RMA (2002 through 2006) U.S. Scrap Tire Markets . Rubber Manufacturers Association. Washington, DC. Available online at: <https://www.rma.org/publications/scrap_tires/index.cfm?CategoryID=614>.
Schneider, S. (2007) E-mail communication Shelly Schneider, Fra nklin Associates to Sarah Shapiro, ICF International., A Division of ERG. January 10, 2007.
STMC (1990 through 1997) Scrap Tire Use/Disposal Study. Rubbe r Manufacturers Association: Scrap Tire Management Council. Available online at: <https://www.rma.org/publ ications/scrap_tires/index.cfm?CategoryID=614>.van Haaren, Rob, Thermelis, N., and Goldstein, N. (2010) "T he State of Garbage in America." BioCycle, October 2010. Volume 51, Number 10, pg. 16-23.

### A-176 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 

# 3.7. Methodology for Estimating Emission s from International Bunker Fuels used by the U.S. Military

Bunker fuel emissions estimates for the Department of Defense (DoD) are developed using data generated by the Defense Energy Support Center (DESC) for aviation and naval fuels. The DESC of the Defense Logistics Agency (DLA) prepared a special report based on data in the Fuels Automated System (FAS), a database that recently replaced the Defense Fuels Automated Management System (DFAMS). Data for intermediate fuel oil, however, currently remains in the original DFAMS database. DFAMS/FAS contains data for 1995 through 2009, but the data set was not complete for years prior to 1995. Fuel quantities for 1990 to 1994 were estimated based on a back-calculation of the 1995 DFAMS values using DLA aviation and marine fuel procurement data. The back-calculation was refined in 1999 to better account for the jet fuel conversion from JP4 to JP8 that occurred within DoD between 1992 and 1995.

Step 1: Omit Extra-Territorial Fuel Deliveries
Beginning with the complete FAS data set for each year, the first step in the development of DoD-related emissions from international bunker fuels was to identify data that would be representative of international bunker fuel consumption as that term is defined by decisions of the UNFCCC (i.e., fuel sold to a vessel, aircraft, or installation within the United States or its territories and used in international maritime or aviation transport). Therefore, fuel data were categorized by the location of fuel delivery in order to identify and omit all international fuel transactions/deliveries (i.e.

, sales abroad).

Step 2: Allocate JP-8 between Aviation and Land-based Vehicles

As a result of DoD 49 

### and NATO 50 

### policies on implementing the Single Fuel For the Battlefield concept, DoD activities have been increasingly replacing diesel fuel with JP8 (a type of jet fuel) in compression ignition and turbine engines in land-based equipment. Based on this concept and examination of all data describing jet fuel used in land-based vehicles, it was determined that a portion of JP8 consumption should be attributed to ground vehicle use. Based on available Service data and expert judgment, it was determined that a small fraction of the total JP8 use should be reallocated from the aviation subtotal to a new land-based jet fuel category for 1997 and subsequent years. The amount of JP8 reallocated was determined to be between 1.78 and 2.7 times the amount of diesel fuel used, depending on the Service. As a result of this reallocation, the JP8 use reported for av iation will be reduced and the total fuel use for land-based equipment will increase. DoD’s total fuel use will not change.
Table A-151 displays DoD’s consumption of fuels that remain at the completion of Step 1, summarized by fuel type. Table A-151 reflects the adjustments for jet fuel used in land-based equipment, as described above.

Step 3: Omit Land-Based Fuels
Navy and Air Force land-based fuels (i.e., fuel not used by ships or aircraft) were also omitted for the purpose of calculating international bunker fuels. The remaining fuels, listed below, were considered potential DoD international bunker fuels.



### • Marine: naval distillate fuel (F76), marine ga s oil (MGO), and intermediate fuel oil (IFO).



### • Aviation: jet fuels (JP8, JP5, JP4, JAA, JA1, and JAB).

Step 4: Omit Fuel Transactions Received by Military Services that are not Considered to be International Bunker Fuels
Next, the records were sorted by Military Service. The following assumptions were used regarding bunker fuel use by Service, leaving only the Navy and Air Force as users of military international bunker fuels.

49 DoD Directive 4140.43, Fuel Standardization, 1998; DoD Dir ective 4140.25, DoD Management Policy for Energy Commodities and Related Services, 1999. 50 NATO Standard Agreement NATO STANAG 4362, Fuels for Fu ture Ground Equipments Using Compression Ignition or Turbine Engines, 1987.

### A-177 

### • Only fuel delivered to a ship, aircraft, or installation in the United States was considered a potential international bunker fuel. Fuel consumed in international aviation or marine transport was included in the bunker fuel estimate of the country where the ship or aircraft was fueled. Fuel consumed entirely within a country’s borders was not considered a bunker fuel.



### • Based on discussions with the Army staff, only an extremely small percentage of Army aviation emissions, and none of its watercraft emissions, qualified as bunker fuel emissions. The magnitude of these emissions was judged to be insignificant when compared to Air Force and Navy emissions. Based on this, Army bunker fuel emissions were assumed to be zero.



### • Marine Corps aircraft operating while embarked consumed fuel reported as delivered to the Navy. Bunker fuel emissions from embarked Marine Corps aircraft were reported in the Navy bunker fuel estimates. Bunker fuel emissions from other Marine Corps operations and training were assumed to be zero.



### • Bunker fuel emissions from other DoD and non-DoD activities (i.e., other federal agencies) that purchased fuel from DESC were assumed to be zero.

Step 5: Determine Bunker Fuel Percentages
Next it was necessary to determine what percent of the marine and aviation fuels were used as international bunker fuels. Military aviation bunkers include international operations (i.e., sorties that originate in the United States an d end in a foreign country), operations conducted from naval vessel s at sea, and operations conducted from U.S. installations principally over international water in direct support of militar y operations at sea (e.g., anti-submarine warfare flights). For the Air Force, a bunker fuel weighted average was calculated based on flying hours by major command. International flights were weighted by an adjustment factor to reflect the fact that they typically last longer than domestic flights. In addition, a fuel use correction factor was used to account for the fact that transport aircraft burn more fuel per hour of flight than most tactical aircraft. The Air Force bunker fu el percentage was determined to be 13.2 percent. This percentage was multiplied by total annual Air Force aviation fu el delivered for U.S. activities, producing an estimate for international bunker fuel consumed by the Air Force. Th e Naval Aviation bunker fuel percentage of total fuel was calculated using flying hour data from Chief of Naval Operations Flying Hour Projection System Budget for fiscal year 1998, and estimates of bunker fuel percent of flights provided by the fleet. The Naval Aviation bunker fuel percentage, determined to be 40.4 percent, was multiplied by total annual Navy aviation fuel delivered for U.S. activities, yielding total Navy aviation bunker fuel consumed.
For marine bunkers, fuels consumed while ships were underway were assumed to be bunker fuels. In 2000, the Navy reported that 79 percent of vessel operations were un derway, while the remaining 21 percent of operations occurred in port (i.e., pierside). Therefore, the Navy maritime bunker fuel percentage was determined to be 79 percent. The percentage of time underway may vary from year-to-year. For example, for years prior to 2000, the bunker fuel percentage was 87 percent. Table A-152 and Table A-153 display DoD bunker fuel use totals for the Navy and Air Force.

Step 6: Calculate Emissions from International Bunker Fuels
Bunker fuel totals were multiplied by appropriate emission factors to determine GHG emissions. CO 2 

### emissions from Aviation Bunkers and distillate Marine Bunkers are the total of military aviation and marine bunker fuels, respectively.
The rows labeled “U.S. Military” and “U.S. Military Naval Fuels” in the tables in the International Bunker Fuels section of the Energy Chapter were based on the totals provided in Table A-152 and Table A-153, below. CO 2 

### emissions from aviation bunkers and distillate marine bunkers presented in Table A-156, and are based on emissions from fuels tallied in Table A-152 and Table A-153.A-178 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009Table A-151: Transportation Fuels from Domestic Fuel Deliveriesa(Million Gallons)Vehicle Type/Fuel 19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Aviation 4,598.43,099.9 2,941.9 2,685.6 2,741.4 2,635.2 2,664.4 2,900.6 2,609.8 2,615.0 2,703.1 2,338.1 2,092.0 2,081.0 2,067.8 1,831.1Total Jet Fuels 4,598.43,099.9 2,941.9 2,685.6 2,741.4 2,635.2 2,664.4 2,900.6 2,609.6 2,614.9 2,703.1 2,338.0 2,091.9 2,080.9 2,067.7 1,830.9JP8285.72,182.8 2,253.1 2,072.0 2,122.5 2,066.5 2,122.7 2,326.2 2,091.4 2,094.3 2,126.2 1,838.8 1,709.3 1,618.5 1,616.2 1,374.9JP51,025.4691.2 615.8 552.8 515.6 505.5 472.1 503.2 442.2 409.1 433.7 421.6 325.5 376.1 362.2 361.4Other Jet Fuels3,287.3225.9 72.9 60.9 103.3 63.3 69.6 71.2 76.1 111.4 143.2 77.6 57.0 86.3 89.2 94.6Aviation Gasoline ++ + + + + + + 0.1 0.1 + 0.1 0.1 0.2 0.1 0.2Marine 686.8438.9 493.3 639.8 674.2 598.9 454.4 418.4 455.8 609.1 704.5 604.9 531.6 572.8 563.4 500.5Middle Distillate (MGO) ++ 38.5 47.5 51.1 49.2 48.3 33.0 41.2 88.1 71.2 54.0 45.8 45.7 55.2 56.8Naval Distillate (F76) 686.8438.9 449.0 583.4 608.4 542.9 398.0 369.1 395.1 460.9 583.5 525.9 453.6 516.0 483.4 412.5Intermediate Fuel Oil (IFO)b++ 5.9 9.0 14.7 6.7 8.1 16.3 19.560.2 49.9 25.0 32.2 11.1 24.9 31.2Otherc717.1310.9 276.9 263.3 256.8 256.0 248.2 109.8 211.1 221.2 170.9 205.6 107.3 169.0 173.6 174.6Diesel 93.0119.9 126.1 132.6 139.5 146.8 126.6 26.6 57.7 60.8 46.4 56.8 30.6 47.3 49.1 49.0Gasoline 624.1191.1 150.8 119.0 93.9 74.1 74.8 24.7 27.5 26.5 19.4 24.3 11.7 19.2 19.7 19.7Jet Fueld++ + 11.7 23.4 35.0 46.7 58.4 125.9133.9 105.1 124.4 65.0 102.6 104.8 105.9Total (Including Bunkers) 6,002.43,849.8 3,712.1 3,588.8 3,672.4 3,490.1 3,367.0 3,428.8 3,276.7 3,445.3 3,578.5 3,148.6 2,730.9 2,822.8 2,804.9 2,506.2Note: Totals may not sum due to independent rounding.aIncludes fuel consumption in the United States and U.S. Territories.bIntermediate fuel oil (IFO 180 and IFO 380) is a blend of distillate and residual fuels. IFO is used by the Military SealiftCommand.cPrior to 2001, gasoline and diesel fuel totals were estimated using data provided by the military Services for 1990 and 1996.The 1991 through 1995 data points were interpolated from the Serviceinventory data. The 1997 through1999 gasoline and diesel fuel data were initially extrapolated fromthe 1996 inventory data.Growth factors used for other diesel and gasoline were 5.2 and -21.1percent, respectively. However,prior diesel fuel estimates from 1997 through2000 were reduced according to the estimated consumption of jet fuel that is assumed to have replaced the diesel fuelconsumption in land-based vehicles. Data sets for other dieseland gasoline consumed by the military in 2000 were estimated based on ground fuels consumption trends. This method produced a resultthat was more consistent with expected consumption for 2000. In2001, other gasoline and diesel fuel totals were generated byDESC.dThe fraction of jet fuel consumed inland-based vehicles was estimated using Service data, DESC data,and expert judgment.+ Does not exceed 0.05 million gallons.A-179Table A-152: Total U.S. MilitaryAviation Bunker Fuel (Million Gallons)Fuel Type/Service 19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009JP8 56.7300.4 308.8 292.0 306.4 301.4 307.6 341.2 309.5 305.1 309.8 285.6 262.5 249.1 229.4 212.5Navy 56.738.3 39.8 46.9 53.8 55.5 53.4 73.8 86.6 76.3 79.2 70.9 64.7 62.7 59.2 56.4Air Force +262.2 269.0 245.1 252.6 245.9 254.2 267.4 222.9 228.7 230.6 214.7 197.8 186.5 170.3 156.1JP5 370.5249.8 219.4 194.2 184.4 175.4 160.3 169.7 158.3 146.1 157.9 160.6 125.0 144.5 139.2 136.9Navy 365.3246.3 216.1 191.2 181.4 170.6 155.6 163.7 153.0 141.3 153.8 156.9 122.8 141.8 136.5 133.4Air Force 5.33.5 3.3 3.0 3.0 4.8 4.7 6.15.3 4.9 4.1 3.7 2.3 2.7 2.6 3.5JP4 420.821.5 1.1 0.1 + + + + + + + + + ++ +Navy ++ + + + + + + + + + + + + + 0.0Air Force 420.821.5 1.1 0.1 + + + + + + + + + + + 0.0JAA 13.79.2 10.3 9.4 10.8 10.8 12.5 12.6 13.7 21.7 30.0 15.5 11.7 15.6 16.8 18.1Navy 8.55.7 6.6 5.9 6.6 6.3 7.9 8.0 9.8 15.5 21.5 11.6 9.1 11.7 12.5 12.3Air Force 5.33.5 3.7 3.5 4.2 4.5 4.5 4.63.8 6.2 8.6 3.9 2.6 3.9 4.3 5.8JA1 ++ + + + + + 0.1 0.6 0.2 0.5 0.5 0.4 1.1 1.0 0.5Navy ++ + + + + + + + + + + + 0.1 0.1 +Air Force ++ + + + + + 0.1 0.6 0.2 0.5 0.5 0.4 1.0 0.8 0.6JAB ++ + + + + + + + + + + + + + +Navy ++ + + + + + + + + + + + + + +Air Force ++ + + + + + + + + + + + + + +Navy Subtotal 430.5290.2 262.5 244.0 241.8 232.4 216.9 245.5 249.4 233.1 254.4 239.4 196.6 216.3 208.3 202.1Air Force Subtotal 431.3290.7 277.0 251.7 259.9 255.2 263.5 278.1 232.7 239.9 243.7 222.9 203.1 194.0 178.1 166.1Total 861.8580.9 539.5 495.6 501.7 487.5 480.4 523.6 482.1 473.0 498.1 462.3 399.7 410.3 386.3 368.1+ Does not exceed 0.05 million gallons.Note: Totals may not sum due to independent rounding.Table A-153: Total U.S. DoD Maritime Bunker Fuel (Million Gallons)Marine Distillates 19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Navy—MGO ++ 30.3 35.6 31.9 39.7 23.8 22.5 27.1 63.7 56.2 38.0 33.0 31.6 40.9 39.9Navy—F76 522.4333.8 331.9 441.7 474.2 466.0 298.6 282.6 305.6 347.8 434.4 413.1 355.9 404.1 376.9 320.0Navy—IFO ++ 4.6 7.1 11.6 5.3 6.4 12.9 15.447.5 39.4 19.7 25.4 8.8 19.0 24.1Total 522.4333.8 366.8 484.3 517.7 511.0 328.8 318.0 348.2 459.0 530.0 470.7 414.3 444.4 436.7 384.0+ Does not exceed 0.005 million gallons.Note: Totals may not sum due to independent rounding.

### A-180 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A-154: Aviation and Marine Carbon Cont ents (Tg Carbon/QBtu) and Fraction Oxidized Mode (Fuel) Carbon Content Coefficient Fraction Oxidized Aviation (Jet Fuel) Variable 1.00 Marine (Distillate) 20.17 1.00 Marine (Residual) 20.48 1.00 Source: EPA (2010) and IPCC (2006)



### Table A-155: Annual Variable Carbon Conten t Coefficient for Jet Fuel (Tg Carbon/QBtu)
Fuel 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Jet Fuel 19.40 19.34 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 Source: EPA (2010)



### Table A-156: Total U.S. DoD CO 2 

### Emissions from Bunker Fuels (Tg CO 2 

### Eq.)
Mode 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Aviation 8.1 5.5 5.2 4.8 4.9 4.7 4.7 5.1 4.7 4.6 4.8 4.5 3.9 4.0 3.8 3.6 Marine 5.4 3.4 3.8 5.0 5.3 5.2 3.4 3.3 3.6 4.7 5.4 4.8 4.2 4.6 4.5 3.9 Total 13.4 9.0 9.0 9.8 10.2 10.0 8.0 8.3 8.3 9.3 10.3 9.3 8.1 8.5 8.2 7.5 Note: Totals may not sum due to independent rounding.

### A-181 

# 3.8. Methodology for Estimating HFC and PFC Emissions from Substitution of Ozone Depleting Substances

Emissions of HFCs and PFCs from the substitution of oz one depleting substances (ODS) are developed using a country-specific modeling approach. The Vintaging Model was developed as a tool for estimating the annual chemical emissions from industrial sectors that have historically used ODS in their products. Under the terms of the Montreal Protocol and the United States’ Clean Air Act Amendments of 1990, the domestic U.S. consumption of ODS— chlorofluorocarbons (CFCs), halons, carbon tetrachloride, meth yl chloroform, and hydrochlorofluorocarbons (HCFCs)— has been drastically reduced, forcing these industrial sectors to transition to more ozone friendly chemicals. As these industries have moved toward ODS alternatives such as hy drofluorocarbons (HFCs) and perfluorocarbons (PFCs), the Vintaging Model has evolved into a tool for estimating the rise in consumption and emissions of these alternatives, and the decline of ODS consumption and emissions.
The Vintaging Model estimates emissions from five ODS substitute end-use sectors: air-conditioning and refrigeration, foams, aerosols, solvents, and fire-extinguishing. Within these sectors, there are 59 independently modeled end-uses. The model requires information on the market growth for each of the end-uses, as well as a history of the market transition from ODS to alternatives, as well as the char acteristics of each end-use such as market size or charge sizes and loss rates. As ODS are phased out, a percentage of the market share originally filled by the ODS is allocated to each of its substitutes.
The model, named for its method of tracking the emissions of annual “vintages” of new equipment that enter into service, is a “bottom-up” model. It models the consumption of chemicals based on estimates of the quantity of equipment or products sold, serviced, and retired each year, and the amo unt of the chemical required to manufacture and/or maintain the equipment. The Vintaging Model makes use of this market information to build an inventory of the in-use stocks ofthe equipment and ODS and ODS substitute in each of the end-uses. The simulation is considered to be a “business-asusual” baseline case, and does not incorporate measures to reduce or eliminate the emissions of these gases other than those regulated by U.S. law or otherwise common in the indu stry. Emissions are estimated by applying annual leak rates, service emission rates, and disposal emission rates to each population of equipment. By aggregating the emission and consumption output from the different end-uses, the model produces estimates of total annual use and emissions of each chemical.
The Vintaging Model synthesizes data from a variety of sources, including data from the ODS Tracking System maintained by the Stratospheric Protection Division and information from submissions to EPA under the Significant New Alternatives Policy (SNAP) program. Published sources incl ude documents prepared by the United Nations Environment Programme (UNEP) Technical Options Committees, reports from the Alternative Fluorocarbons Environmental Acceptability Study (AFEAS), and conference proceedings from the International Conferences on Ozone Protection Technologies and Earth Technologies Forums. EPA also coor dinates extensively with numerous trade associations and individual companies. For example, the Alliance for Responsible Atmospheric Policy; the Air-Conditioning, Heating and Refrigeration Institute; the Association of Home Appliance Manufacturers; the American Automobile Manufacturers Association; and many of their member companies have provid ed valuable information over the years. In some instances the unpublished information that the EPA uses in the model is cl assified as Confidential Business Information (CBI). The annual emissions inventories of chemicals are aggregated in such a way that CBI cannot be inferred. Full public disclosure of the inputs to the Vintaging Model would jeopardi ze the security of the CBI that has been entrusted to the EPA.
The following sections discuss the emission equations used in the Vintaging Model for each broad end-use category. These equations are applied separately for each chemical used within each of the different end-uses. In the majority of these end-uses, more than one ODS substitute chemical is used.
In general, the modeled emissions are a function of the amount of chemical consumed in each end-use market. Estimates of the consumption of ODS alternatives can be inferred by determining the transition path of each regulated ODS used in the early 1990s. Using data gleaned from a variety of sources, assessments are made regarding which alternatives have been used, and what fraction of the ODS market in each end-use has been captured by a given alternative. By combining this with estimates of the total end-use market growth, a consumption value can be estimated for each chemical used within each end-use.



## Methodology The Vintaging Model estimates the use and emissions of ODS alternatives by taking the following steps:

### A-182 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 1. Gather historical data . The Vintaging Model is populated with information on each end-use, taken from published sources and industry experts.

2. Simulate the implementation of new, non-ODS technologies . The Vintaging Model uses detailed characterizations of the existing uses of the ODS, as well as data on how the substitutes are replacing the ODS, to simulate the implementation of new technologies that enter the market in compliance with ODS phase-out policies. As part of this simulation, the ODS substitutes are introduced in each of the end- uses over time as seen historically and as needed to comply with the ODS phase-out.

3. Estimate emissions of the ODS substitutes . The chemical use is estimated from the amount of substitutes that are required each year for the manufacture, in stallation, use, or servicing of products. The emissions are estimated from the emission profile for each vintage of equi pment or product in each end-use. By aggregating the emissions from each vintage, a time profile of emissions from each end-use is developed.

Each set of end-uses is discussed in mo re detail in the following sections.

Refrigeration and Air-Conditioning For refrigeration and air conditioning products, emission calculations are split into two categories: emissions during equipment lifetime, which arise from annual leakage and service losses, and disposal emissions, which occur at the time of discard. Two separate steps are required to calcu late the lifetime emissions from leakage and service, and the emissions resulting from disposal of the equipment. For any given year, these lifetime emissions (for existing equipment) and disposal emissions (from discarded eq uipment) are summed to calculate the total emissions from refrigeration and airconditioning. As new technologies replace older ones, it is generally assumed that there are improvements in their leak, service, and disposal emission rates.

Step 1: Calculate lifetime emissions
Emissions from any piece of equipment include both the amount of chemical leaked during equipment operation and the amount emitted during service. Emissions from leakage and servicing can be expressed as follows:

Es j 

### = (l
a 

### + l
s 

### ) × 

# Σ Qc j-i+1 

### for i = 1 → k

Where:

Es = Emissions from Equipment Serviced. Emissions in year j from normal leakage and servicing (including recharging) of equipment.

l
a 

### = Annual Leak Rate. Average annual leak rate during normal equipment operation (expressed as a percentage of total chemical charge).

l
s 

### = Service Leak Rate. Average leakage during equipm ent servicing (expressed as a percentage of total chemical charge).
Qc = Quantity of Chemical in New Equipment. Total amount of a specific chemical used to charge new equipment in a given year by weight.

i = Counter, runs from 1 to lifetime (k).

j = Year of emission.

k = Lifetime. The average lifetime of the equipment.

Step 2: Calculate disposal emissions
The disposal emission equations assume that a certain percentage of the chemical charge will be emitted to the atmosphere when that vintage is discarded. Disposal emissi ons are thus a function of the quantity of chemical contained in the retiring equipment fleet and the proportion of chemical released at disposal:

Ed j 

### = Qc j-k+1 

### × [1 – ( rm × rc )]

Where:

Ed = Emissions from Equipment Disposed. Emissions in year j from the disposal of equipment.

### A-183 Qc = Quantity of Chemical in New Equipment. Total amount of a specific chemical used to charge new equipment in year j-k+1 , by weight.

rm = Chemical Remaining. Amount of chemical remaining in equipment at the time of disposal (expressed as a percentage of total chemical charge).

rc = Chemical Recovery Rate. Amount of chemical that is recovered just prior to disposal (expressed as a percentage of chemical remaining at disposal (rm) ).

j = Year of emission.

k = Lifetime. The average lifetime of the equipment.

Step 3: Calculate total emissions
Finally, lifetime and disposal emissions are summ ed to provide an estimate of total emissions.

E j 

### = Es j 

### + Ed j 

### Where:

E = Total Emissions. Emissions from refrigeration and air conditioning equipment in year j.

Es = Emissions from Equipment Serviced. Emissions in year j from leakage and servicing (including recharging) of equipment.

Ed = Emissions from Equipment Disposed. Emissions in year j from the disposal of equipment.

j = Year of emission.

Assumptions
The assumptions used by the Vintaging Model to trace the transition of each type of equipment away from ODS are presented in Table A- 157, below. As new technologies replace older ones, it is generally assumed that there are improvements in their leak, service, and disposal emission rates. Additionally, the market for each equipment type is assumed to grow independently, according to annual growth rates.A-184 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009Table A- 157: Refrigeration and Air-Conditioning Market Transition AssumptionsInitialMarketSegmentPrimary Substitute Secondary Substitute Tertiary Substitute GrowthRateName ofSubstituteStartDateDate of FullPenetration inNewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetration inNewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetration inNewEquipmentMaximumMarketPenetrationCentrifugal ChillersCFC-11 HCFC-123 1993 1993 45% Unknown 0.5%HCFC-22 1991 1993 16% HFC-134a 2000 2010 100% NoneHFC-134a 1992 1993 39% NoneCFC-12 HFC-134a 1992 1994 53% None 0.5%HCFC-22 1991 1994 16% HFC-134a 2000 2010 100% NoneHCFC-123 1993 1994 31% UnknownR-500 HFC-134a 1992 1994 53% None 0.5%HCFC-22 1991 1994 16% HFC-134a 2000 2010 100% NoneHCFC-123 1993 1994 31% UnknownCFC-114 HFC-236fa 1993 1994 100% HFC-236fa 1996 1996 100% HFC-134a 1998 2009 100% 0.2%Cold StorageCFC-12 HCFC-22 1990 1993 65% R-404A 1996 2010 75% None 2.5%R-507 1996 2010 25% NoneR-404A 1994 1996 26% NoneR-507 1994 1996 9% NoneHCFC-22 HCFC-22 1992 1993 100% R-404A 1996 2009 8% None 2.5%R-507 1996 2009 3% NoneR-404A 2009 2010 68% NoneR-507 2009 2010 23% NoneR-502 HCFC-22 1990 1993 40% R-404A 1996 2010 38% None 2.5%R-507 1996 2010 12% NoneNonODP/GWP 1996 2010 50% NoneR-404A 1993 1996 45% NoneR-507 1994 1996 15% NoneCommercial Unitary AirConditioners (Large)HCFC-22 HCFC-22 1992 1993 100% R-410A 2001 2005 5% None 0.8%R-407C 2006 2009 1% NoneR-410A 2006 2009 9% NoneR-407C 2009 2010 5% NoneR-410A 2009 2010 81% NoneCommercial Unitary Air Conditioners (Small)HCFC-22 HCFC-22 1992 1993 100% R-410A 1996 2000 3% None 0.8%R-410A 2001 2005 18% NoneR-410A 2006 2009 8% NoneR-410A 2009 2010 71% NoneDehumidifiersHCFC-22 HFC-134a 1997 1997 89% None 0.2%A-185InitialMarketSegmentPrimary Substitute Secondary Substitute Tertiary Substitute GrowthRateName ofSubstituteStartDateDate of FullPenetration inNewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetration inNewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetration inNewEquipmentMaximumMarketPenetrationR-410A 2007 2010 11% NoneIce MakersCFC-12 HFC-134a 1993 1995 100% None 2.5%Industrial Process RefrigerationCFC-11 HCFC-123 1992 1994 70% Unknown 2.5%HFC-134a 1992 1994 15% NoneHCFC-22 1991 1994 15% HFC-134a 1995 2010 100% NoneCFC-12 HCFC-22 1991 1994 10% HFC-134a 1995 2010 15% None 2.5%R-404A 1995 2010 50% NoneR-410A 1999 2010 20% NoneR-507 1995 2010 15% NoneHCFC-123 1992 1994 35% UnknownHFC-134a 1992 1994 50% NoneR-401A 1995 1996 5% HFC-134a 1997 2000 100% NoneHCFC-22 HCFC-22 1992 1993 100% HFC-134a 1995 2009 2% None 2.5%R-404A 1995 2009 5% NoneR-410A 1999 2009 2% NoneR-507 1995 2009 2% NoneHFC-134a 2009 2010 14% NoneR-404A 2009 2010 45% NoneR-410A 2009 2010 18% NoneR-507 2009 2010 14% NoneMobile Air Conditioners (Passenger Cars)CFC-12 HFC-134a 1992 1994 100% None 1.9%Mobile Air Conditioners (Light Duty Trucks)CFC-12 HFC-134a 1993 1994 100% None -0.4%Mobile Air Conditioners(School and Tour Buses)CFC-12 HCFC-22HFC-134a19941994199519970.5%99.5%HFC-134aNone20062007100%None2.6%Mobile Air Conditioners (Transit Buses)HCFC-22 HFC-134a 1995 2009 100% None 2.6%Mobile Air Conditioners (Trains)HCFC-22 HFC-134aR-407C200220022009200950%50%NoneNone2.6%Packaged Terminal Air Conditioners and Heat PumpsHCFC-22 R-410A 2006 2009 10% None 0.8%R-410A 2009 2010 90% NonePositive Displacement ChillersHCFC-22 HCFC-22 1996 1996 100% HFC-134a 2000 2009 9% R-407C 2010 2020 60% 0.5%R-410A 2010 2020 40%R-407C 2000 2009 1% NoneA-186 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009InitialMarketSegmentPrimary Substitute Secondary Substitute Tertiary Substitute GrowthRateName ofSubstituteStartDateDate of FullPenetration inNewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetration inNewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetration inNewEquipmentMaximumMarketPenetrationHFC-134a 2009 2010 81% R-407C 2010 2020 60%R-410A 2010 2020 40%R-407C 2009 2010 9% NoneCFC-12 HCFC-22 1993 1993 100% HFC-134a 2000 2009 9% R-407C 2010 2020 60% 0.2%R-410A 2010 2020 40%R-407C 2000 2009 1% NoneHFC-134a 2009 2010 81% R-407C 2010 2020 60%R-410A 2010 2020 40%R-407C 2009 2010 9% NoneRefrigerated AppliancesCFC-12 HFC-134a 1994 1995 100% HFC-134a 2005 2005 100% None 0.5%Residential Unitary Air ConditionersHCFC-22 HCFC-22 2006 2006 70% R-410A 2007 2010 29% None 0.8%R-407C 2010 2010 14% R-410A 2011 2015 100%R-410A 2010 2010 57% NoneR-410A 2000 2005 5% R-410A 2006 2006 100% NoneR-410A 2000 2006 5% NoneR-410A 2006 2006 20% NoneRetail Food (Large)CFC-12 R-502 1988 1990 100% HCFC-22 1991 1993 100% R-404A 1995 2000 17.5% 1.7%R-507 1995 2000 7.5%R-404A 2000 2005 31.5%R-507 2000 2005 13.5%R-404A 2005 2010 18%R-507 2005 2010 12%R-502 HCFC-22 1990 1993 100% R-404A 1995 2000 17.5% None 1.7%R-507 1995 2000 7.5% NoneR-404A 2000 2005 31.5% NoneR-507 2000 2005 13.5% NoneR-404A 2005 2010 18% NoneR-507 2005 2010 12% NoneRetail Food (Medium)HCFC-22 R-404A 1995 2000 17.5% None 1.7%R-507 1995 2000 7.5% NoneR-404A 2000 2005 31.5% NoneR-507 2000 2005 13.5% NoneR-404A 2005 2010 18% NoneR-507 2005 2010 12% NoneRetail Food (Small)CFC-12 HCFC-22 1990 1993 90% HFC-134a 1993 1995 90% CO22010 2010 5% 1.7%A-187InitialMarketSegmentPrimary Substitute Secondary Substitute Tertiary Substitute GrowthRateName ofSubstituteStartDateDate of FullPenetration inNewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetration inNewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetration inNewEquipmentMaximumMarketPenetrationR-404A 2000 2009 7.5% NoneR-507 2000 2009 2.5% NoneR-404A 1993 1996 7.5% NoneR-507 1993 1996 2.5% NoneTransport RefrigerationCFC-12 HFC-134a 1993 1995 98% None 2.5%HCFC-22 1993 1995 2% HFC-134a 1995 1999 100% NoneR-502 HFC-134a 1993 1995 55% None 2.5%R-404A 1993 1995 45% NoneWater-Source and Ground-Source Heat PumpsHCFC-22 R-407C 2000 2006 5% None 0.8%R-410A 2000 2006 5% NoneHFC-134a 2000 2009 2% NoneR-407C 2006 2009 2.5% NoneR-410A 2006 2009 4.5% NoneHFC-134a 2009 2010 18% NoneR-407C 2009 2010 22.5% NoneR-410A 2009 2010 40.5% NoneWindow UnitsHCFC-22 R-407C 2003 2009 3% None 5.0%R-407C 2009 2010 35% NoneR-410A 2003 2009 7% NoneR-410A 2009 2010 55% None

### A-188 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A- 158 presents the average equipment lifetimes and annual HFC emission rates (for servicing and leaks) for each end-use assumed by the Vintaging Model.
Table A- 158. Refrigeration and Air- conditioning Lifetime Assumptions
End-Use Lifetime HFC Emission Rates (Years) (%) Centrifugal Chillers 20 – 27 2.0 – 10.9 Cold Storage 20 – 25 15.0 Commercial Unitary A/C 15 7.9 – 8.6 Dehumidifiers 11 0.5 Ice Makers 20 3.0 Industrial Process Refrigeration 25 3.6 – 12.3 Mobile Air Conditioners 5 –12 2.3 – 18.0 Positive Displacement Chillers 20 0.5 – 1.5 PTAC/PTHP 12 3.9 Retail Food 15 – 20 7.8 – 29.9 Refrigerated Appliances 14 0.6 Residential Unitary A/C 15 7.2 – 8.3 Transport Refrigeration 12 20.6 – 27.9 Water & Ground Source Heat Pumps 20 3.9 Window Units 12 0.6



### Aerosols ODSs, HFCs and many other chemicals are used as propella nt aerosols. Pressurized within a container, a nozzle releases the chemical, which allows the product within the can to also be released. Two types of aerosol products are modeled: metered dose inhalers (MDI) and consumer aerosols. In the United States, the use of CFCs in consumer aerosols was banned in 1978, and many products transitioned to hydrocarbons or “not-in-kind” technologies, such as solid deodorants and finger-pump hair sprays. However, MDIs can continue to use CFCs as propellants because their use has been deemed essential. Essential use exemptions granted to the United States under the Montreal Protocol for CFC use in MDIs are limited to the treatment of asthma and chronic obstructive pulmonary disease.

All HFCs and PFCs used in aerosols are assumed to be emitted in the year of manufacture. Since there is currently no aerosol recycling, it is assumed that all of the annual production of aerosol propellants is released to the atmosphere. The following equation describes the emissions from the aerosols sector.

E j 

### = Qc j 

### Where:

E

= Emissions. Total emissions of a specific chemical in year j from use in aerosol products, by weight.
Qc = Quantity of Chemical. Total quantity of a specific chemical contained in aerosol products sold in year j , by weight.

j = Year of emission.

Transition Assumptions
Transition assumptions and growth rates for those items that use ODSs or HFCs as propellants, including vital medical devices and specialty consumer pr oducts, are presented in Table A- 159.

### A-189 Table A- 159. Aerosol Product Transition Assumptions
Initial Market Segment Primary Substitute Secondary Substitute Growth Rate

Name of Substitute Start Date Date of Full Penetration in New Equipment Maximum Market Penetration Name of Substitute Start Date Date of Full Penetration in New Equipment Maximum Market Penetration

MDIs CFC Mix* HFC-134a 1997 1997 6% None 0.8% Non-ODP/GWP 1998 2007 7% None CFC Mix* 2000 2000 87% HFC-134a 2002 2002 34% HFC-134a 2003 2009 47% HFC-227ea 2006 2009 5% HFC-134a 2010 2011 6% HFC-227ea 2010 2011 1% HFC-134a 2011 2012 3% HFC-227ea 2011 2012 0.3% HFC-134a 2014 2014 3% HFC-227ea 2014 2014 0.3% Consumer Aerosols (Non-MDIs) NA** HFC-152a 1990 1991 50% None 2.0% HFC-134a 1995 1995 50% HFC-152a 1997 1998 44% HFC-152a 2001 2005 36% *CFC Mix consists of CFC-11, CFC-12 and CFC-114 and represents th e weighted average of several CFCs consumed for essential use in MDIs from 1993 to 2008. **Consumer Aerosols transitioned away from ODS prior to 1985, the year in which the Vintaging Model begins. The portion of the market that is now using HFC propellants is modeled.


### Solvents ODSs, HFCs, PFCs and other chemicals are used as solven ts to clean items. For example, electronics may need to be cleaned after production to remove any manufacturing pro cess oils or residues left. Solvents are applied by moving the item to be cleaned within a bath or stream of the solvent. Generally, most solvents are assumed to remain in the liquid phase and are not emitted as gas. Thus, emissions are consid ered “incomplete,” and are a fixed percentage of the amount of solvent consumed in a year. The remainder of the consumed solvent is assumed to be reused or disposed without being released to the atmosphere. The following equation calculates emissions from solvent applications.

E j 

### = l × Qc j 

### Where:

E

= Emissions. Total emissions of a specific chemical in year j from use in solvent applications, by weight.

l = Percent Leakage. The percentage of the total chemical that is leaked to the atmosphere, assumed to be 90 percent.
Qc = Quantity of Chemical. Total quantity of a specific chemical sold for use in solvent applications in the year j, by weight.

j = Year of emission.

Transition Assumptions
The transition assumptions and growth rates used within the Vintaging Model for electronics cleaning, metals cleaning, precision cleaning, and adhesives, coatings and inks, are presented in Table A- 160.

### A-190 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A- 160. Solvent Ma rket Transition Assumptions
Initial Market Segment Primary Substitute Secondary Substitute Growth Rate Name of Substitute Start Date Date of Full Penetration in New Equipment Maximum Market Penetration Name of Substitute Start Date Date of Full Penetration

in New Equipment Maximum Market Penetration

Adhesives CH 3 CCI 3 Non-ODP/GWP 1994 1995 100% None 2.0% Electronics CFC-113 Semi-Aqueous 1994 1995 52% None 2.0% HCFC-225ca/cb 1994 1995 0.2% Unknown HFC-4310mee 1995 1996 0.7% None HFE-7100 1994 1995 0.7% None nPB 1992 1996 5% None Methyl Siloxanes 1992 1996 0.8% None No-Clean 1992 1996 40% None CH 3 CCI 3 Non-ODP/GWP 1996 1997 99.8% None 2.0% PFC/PFPE 1996 1997 0.2% Non-ODP/GWP 2000 2003 90% Non-ODP/GWP 2005 2009 10% Metals CH 3 CCI 3 Non-ODP/GWP 1992 1996 100% None 2.0% CFC-113 Non-ODP/GWP 1992 1996 100% None 2.0% CCI 4 Non-ODP/GWP 1992 1996 100% None 2.0% Precision CH 3 CCI 3 Non-ODP/GWP 1995 1996 99.3% None 2.0% HFC-4310mee 1995 1996 0.6% None PFC/PFPE 1995 1996 0.1% Non-ODP/GWP 2000 2003 90% Non-ODP/GWP 2005 2009 10% CFC-113 Non-ODP/GWP 1995 1996 96% None 2.0% HCFC-225ca/cb 1995 1996 1% Unknown HFE-7100 1995 1996 3% None Non-ODP/GWP includes chemicals with 0 ODP and low GWP, such as hydrocarbons and ammonia, as well as not-in-kind alternatives su ch as “no clean” technologies.



### Fire Extinguishing ODSs, HFCs, PFCs and other chemicals are used as fire-extinguishing agents, in both hand-held “streaming” applications as well as in built-up “flooding” equipment simi lar to water sprinkler systems. Although these systems are generally built to be leak-tight, some leaks do occur and of course emissions occur when the agent is released. Total emissions from fire extinguishing are assumed, in aggregate, to equal a percentage of the total quantity of chemical in operation at a given time. For modeling purposes, it is assumed that fire extinguishing equipment leaks at a constant rate for an average equipment lifetime, as shown in the equation below. In streaming systems, non-halon emissions are assumed to be 3.5 percent of all chemical in use in each year, while in flooding systems 2.5 percent of the installed base of chemical is assumed to leak annually. Halon systems are assumed to leak at higher rates. The equation is applied for a single year, accounting for all fire protection equipment in operation in that year. Each fire protection agent is modeled separately. In the Vintaging Model, streaming applications have a 12-year lifetime and flooding applications have a 20- year lifetime.

E j 

### = r × 

# Σ Qc j-i+1 

### for i=1 → k

Where:

E = Emissions. Total emissions of a specific chemical in year j for streaming fire extinguishing equipment, by weight.

r = Percent Released. The percentage of the total chem ical in operation that is released to the atmosphere.
Qc = Quantity of Chemical

. Total amount of a specific chemical used in new fire extinguishing equipment in a given year, j-i +1, by weight.

i = Counter, runs from 1 to lifetime (k).

j = Year of emission.

### A-191 k

= Lifetime

. The average lifetime of the equipment.

Transition Assumptions
Transition assumptions and growth rates for these two fire extinguishing types are presented in Table A- 161.
Table A- 161. Fire Extinguish ing Market Transition Assumptions
Initial Market Segment Primary Substitute Secondary Substitute Growth Rate

Name of Substitute Start Date Date of Full Penetration

in New Equipment Maximum Market Penetration Name of Substitute Start Date Date of Full Penetration

in New Equipment Maximum Market Penetration

Flooding Agents Halon-1301 Halon-1301* 1994 1994 4% Unknown 2.2% HFC-23 1994 1999 0.2% None HFC-227ea 1994 1999 18% FK-5-1-12 2003 2010 10% HFC-125 2001 2008 10% Non-ODP/GWP 1994 1994 46% FK-5-1-12 2003 2010 7% Non-ODP/GWP 1995 2034 10% None Non-ODP/GWP 1998 2027 10% None C 4 F 10 1994 1999 1% FK-5-1-12 2003 2003 100% HFC-125 1997 2006 11% None Streaming Agents Halon-1211 Halon-1211* 1992 1992 5% Unknown 3.0% HFC-236fa 1997 1999 3% None Halotron 1994 1997 4% Non-ODP/GWP 2015 2015 25% HFC-236fa 2015 2015 75% Non-ODP/GWP 1993 1994 58% None Non-ODP/GWP 1995 2024 20% None Non-ODP/GWP 1999 2018 10% None *Despite the 1994 consumption ban, a small pe rcentage of new halon systems are assumed to continue to be built and filled with stockpiled or recovered supplies. 

### Foam Blowing ODSs, HFCs, and other chemicals are used to produce foams, including such items as the foam insulation panels around refrigerators, insulation sprayed on buildings, etc. The chem ical is used to create pockets of gas within a substrate, increasing the insulating properties of the item. Foams are given emission profiles depending on the foam type (open cell or closed cell). Open cell foams are assumed to be 100 percent emissive in the year of manufacture. Closed cell foams are assumed to emit a portion of their total HFC content upon manufacture, a portion at a constant rate over the lifetime of the foam, a portion at disposal, and a portion af ter disposal; these portions vary by end-use.

Step 1: Calculate manufacturing emissi ons (open-cell and closed-cell foams)
Manufacturing emissions occur in the year of foam manufacture, and are calculated as presented in the following equation.

Em j 

### = lm × Qc j 

### Where: Em j 

### = Emissions from manufacturing. Total em issions of a specific chemical in year j due to manufacturing losses, by weight.

lm = Loss Rate. Percent of original blowing agent emitted during foam manufacture. For open-cell foams, lm is 100%.
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a given year.

j = Year of emission.

### A-192 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Step 2: Calculate lifetime emissions (closed-cell foams)
Lifetime emissions occur annually from closed-cell foams throughout the lifetime of the foam, as calculated as presented in the following equation.

Eu j 

### = lu × 

# Σ Qc j-i+1 

### for i=1 → k

Where:

Eu

j 

### = Emissions from Lifetime Losses. Total emissions of a specific chemical in year j due to lifetime losses during use, by weight.

lu = Leak Rate. Percent of original blowing agent emitted each year during lifetime use.
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a given year.

i = Counter, runs from 1 to lifetime (k).

j = Year of emission.

k

= Lifetime

. The average lifetime of foam product.

Step 3: Calculate disposal emissions (closed-cell foams)
Disposal emissions occur in the year the foam is disposed, and are calculated as presented in the following equation.

Ed j 

### = ld × Qc j-k


### Where: Ed

j 

### = Emissions from disposal. Total emissions of a specific chemical in year j at disposal, by weight.

ld

= Loss Rate. Percent of original blowing agent emitted at disposal.
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a given year.

j = Year of emission.

k

= Lifetime

. The average lifetime of foam product.

Step 4: Calculate post-disposal emissions (closed-cell foams)
Post-Disposal emissions occur in the years after the foam is disposed; for example, emissions might occur while the disposed foam is in a landfill. Currently, the only foam type assumed to have post-disposal emissions is polyurethane foam used as domestic refrigerator and freezer insulation, which is expected to continue to emit for 26 years post-disposal, calculated as presented in the following equation.

Ep

j 

### = lp × 

# Σ Qc j-m 

### for m=k → k + 32

Where: Ep

j 

### = Emissions from post disposal. Total post-disposa l emissions of a specific chemical in year j, by weight.
lp = Leak Rate. Percent of original blowing agent emitted post disposal.
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a given year.

k

= Lifetime

. The average lifetime of foam product.

m = Counter

. Runs from lifetime (k) to (k+26).

j = Year of emission.

### A-193 Step 5: Calculate total emissions (open-cell and closed-cell foams)
To calculate total emissions from foams in any given year, emissions from all foam stages must be summed, as presented in the following equation.

E j 

### = Em j 

### + Eu j 

### + Ed j 

### + Ep j 

### Where: E

j 

### = Total Emissions. Total emissions of a specific chemical in year j , by weight.

Em = Emissions from manufacturing. Total emissions of a specific chemical in year j due to manufacturing losses, by weight.

Eu

j 

### = Emissions from Lifetime Losses. Total emissions of a specific chemical in year j due to lifetime losses during use, by weight.

Ed

j 

### = Emissions from disposal. Total emissions of a specific chemical in year j at disposal, by weight.
Ep

j 

### = Emissions from post disposal. Total post-disposal emissions of a specific chemical in year j, by weight.

Assumptions
The Vintaging Model contains 13 foam types, whose transition assumptions away from ODS and growth rates are presented in Table A- 162. The emission profiles of these 13 foam types are shown in Table A- 163.A-194 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009Table A- 162. Foam BlowingMarket Transition AssumptionsInitialMarketSegmentPrimary Substitute Secondary Substitute Tertiary Substitute GrowthRateName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetration inNewEquipmentMaximumMarketPenetrationCommercial Refrigeration FoamCFC-11 HCFC-141b 1989 1996 40% HFC-245fa 2002 2003 80% None 6.0%Non-ODP/GWP 2002 2003 20% NoneHCFC-142b 1989 1996 8% Non-ODP/GWP 2009 2010 80% NoneHFC-245fa 2009 2010 20% NoneHCFC-22 1989 1996 52% Non-ODP/GWP 2009 2010 80% NoneHFC-245fa 2009 2010 20% NoneFlexible PU Foam: Integral Skin FoamCFC-11 HCFC-141b 1989 1990 100% HFC-134a 1993 1996 25% None 2.0%HFC-134a 1994 1996 25% NoneCO21993 1996 25% NoneCO21994 1996 25% NoneFlexible PU Foam: Slabstock Foam, Moulded FoamCFC-11 Non-ODP/GWP 1992 1992 100% None 2.0%Phenolic FoamCFC-11 HCFC-141b 1989 1990 100% Non-ODP/GWP 1992 1992 100% None 2.0%Polyolefin FoamCFC-114 HFC-152a 1989 1993 10% Non-ODP/GWP 2005 2010 100% None 2.0%HCFC-142b 1989 1993 90% Non-ODP/GWP 1994 1996 100% NonePU and PIR Rigid: BoardstockCFC-11 HCFC-141b 1993 1996 100% Non-ODP/GWP 2000 2003 95% None 6.0%HC/HFC-245faBlend 2000 2003 5% NonePU Rigid: Domestic Refrigerator and Freezer InsulationCFC-11 HCFC-141b 1993 1995 100% HFC-134a 1996 2001 7% Non-ODP/GWP 2002 2003 100% 0.8%HFC-245fa 2001 2003 50% Non-ODP/GWP 2015 2029 100%HFC-245fa 2006 2009 10% Non-ODP/GWP 2015 2029 100%Non-ODP/GWP 2002 2005 10% NoneNon-ODP/GWP 2006 2009 3% NoneNon-ODP/GWP 2009 2014 20% NonePU Rigid: One Component FoamCFC-12 HCFC-142b/22Blend 1989 1996 70% Non-ODP/GWP 2009 2010 80% None 4.0%HFC-134a 2009 2010 10% NoneHFC-152a 2009 2010 10% NoneA-195InitialMarketSegmentPrimary Substitute Secondary Substitute Tertiary Substitute GrowthRateName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetration inNewEquipmentMaximumMarketPenetrationHCFC-22 1989 1996 30% Non-ODP/GWP 2009 2010 80% NoneHFC-134a 2009 2010 10% NoneHFC-152a 2009 2010 10% NonePU Rigid: Other: Slabstock FoamCFC-11 HCFC-141b 1989 1996 100% CO21999 2003 45% None 2.0%Non-ODP/GWP 2001 2003 45% NoneHCFC-22 2003 2003 10% Non-ODP/GWP 2009 2010 100%PU Rigid: Sandwich Panels:Continuous and DiscontinuousCFC-11 HCFC-141b 1989 1996 82%HCFC-22/WaterBlend 2001 2003 20% HFC Blend 2009 2010 50% 6.0%Non-ODP/GWP 2009 2010 50%HFC-245fa/CO2Blend 2002 2004 20% NoneNon-ODP/GWP 2001 2004 40% NoneHFC-134a 2002 2004 20% NoneHCFC-22 1989 1996 18%HFC-245fa/CO2Blend 2009 2010 40% NoneNon-ODP/GWP 2009 2010 20% NoneCO22009 2010 20% NoneHFC-134a 2009 2010 20% NonePU Rigid: Spray FoamCFC-11 HCFC-141b 1989 1996 100% HFC-245fa 2002 2003 30% None 6.0%HFC-245fa/CO2Blend 2002 2003 60% NoneNon-ODP/GWP 2001 2003 10% NoneXPS: Boardstock FoamCFC-12HCFC-142b/22Blend 1989 1994 10% HFC-134a 2009 2010 70% None 2.5%HFC-152a 2009 2010 10% NoneCO22009 2010 10% NoneNon-ODP/GWP 2009 2010 10% NoneHCFC-142b 1989 1994 90% HFC-134a 2009 2010 70% NoneHFC-152a 2009 2010 10% NoneCO22009 2010 10% NoneNon-ODP/GWP 2009 2010 10% NoneXPS: Sheet FoamCFC-12 CO21989 1994 1% None 2.0%Non-ODP/GWP 1989 1994 99% CO21995 1999 9% NoneHFC-152a 1995 1999 10% None

### A-196 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A- 163. Emission profile for the foam end-uses

Foam End-Use Loss at Manufacturing (%) Annual Leakage Rate (%) Leakage Lifetime (years) Loss at Disposal (%) Total* (%) Flexible PU Foam: Slabstock Foam, Moulded Foam 100 0 1 0 100 Commercial Refrigeration 6 0.25 15 90.25 100 Rigid PU: Spray Foam 15 1.5 56 1 100 Rigid PU: Slabstock and Other 37.5 0.75 15 51.25 100 Phenolic Foam 23 0.875 32 49 100 Polyolefin Foam 95 2.5 2 0 100 Rigid PU: One Component Foam 100 0 1 0 100 XPS: Sheet Foam* 40 2 25 0 90 XPS: Boardstock Foam 25 0.75 50 37.5 100 Flexible PU Foam: Integral Skin Foam 95 2.5 2 0 100 Rigid PU: Domestic Refrigerator and Freezer

Insulation* 4 0.25 14 40.0 47.5 PU and PIR Rigid: Boardstock 6 1 50 44 100 PU Sandwich Panels: Continuous and Discontinuous 5.5 0.5 50 69.5 100 PIR (Polyisicyanurate) PU (Polyurethane) XPS (Extruded Polystyrene) *In general, total emissions from foam end-uses are assumed to be 100 percent, although work is underway to investigate that as sumption. In the XPS Sheet/Insulation Board end-use, the source of emission rates and lifetimes did not yield 100 percent emission; it is unclea r at this time whether that was intentional. In the Rigi d PU Appliance Foam end-use, the source of emission rates and lifetimes did not yield 100 percent emission; the remainder is anticipated to be emitted at a rate of 2.0%/year post-disposal for the next 26 years.



### Sterilization Sterilants kill microorganisms on medical equipment and devices. The principal ODS used in this sector was a blend of 12% ethylene oxide (EtO) and 88% CFC-12, known as “12/88.” In that blend, ethylene oxide sterilizes the equipment and CFC-12 is a dilutent solvent to form a non-flamma ble blend. The sterilization sector is modeled as a single end-use. For sterilization applications, all chemicals that are used in the equipment in any given year are assumed to be emitted in that year, as shown in the following equation.

E j 

### = Qc j 

### Where:

E = Emissions. Total emissions of a specific chemical in year j from use in sterilization equipment, by weight.
Qc = Quantity of Chemical. Total quantity of a specific chemical used in sterilization equipment in year j , by weight.

j = Year of emission.

Assumptions
The Vintaging Model contains 1 sterilization end-use, whose transition assumptions away from ODS and growth rates are presented in Table A- 164.

Table A- 164. Sterilization Market Transition Assumptions

Initial Market Segment Primary Substitute Growth Rate Name of Substitute Start Date Date of Full Penetration in New Equipment Maximum Market Penetration 12/88 EtO 1994 1995 95% 2.0% Non-ODP/GWP 1994 1995 1% HCFC/EtO Blends 1993 1994 4%

### A-197 

## Model Output By repeating these calculations for each year, the Vintag ing Model creates annual profiles of use and emissions for ODS and ODS substitutes. The results can be shown for each year in two ways: 1) on a chemical-by-chemical basis, summed across the end-uses, or 2) on an end-use or sector ba sis. Values for use and emissions are calculated both in metric tons and in teragrams of CO 2 

### equivalents (Tg CO 2 

### Eq.). The conversion of metric tons of chemical to Tg CO 2 

### Eq. is accomplished through a linear scaling of tonnage by the global warming potential (GWP) of each chemical.
Throughout its development, the Vintaging Model has undergone annual modifications. As new or more accurate information becomes available, the model is adjusted in such a way that both past and future emission estimates are often altered.

Bank of ODS and ODS Substitutes
The bank of an ODS or an ODS substitute is “the cumulative difference between the chemical that has been consumed in an application or sub-application and that which has already been released” (IPCC 2006). For any given year, the bank is equal to the previous year’s bank, less th e chemical in equipment disposed of during the year, plus chemical in new equipment entering the market during that year, less the amount emitted but not replaced, plus the amount added to replace chemical emitted prior to the given year, as shown in the following equation:

Bc j 

### = Bc j-1 

### -Qd j 

### +Qp j 

### +E e 

### -Q
r 

### Where:

Bc j 

### = Bank of Chemical. Total bank of a specific chemical in year j , by weight.
Qd j 

### = Quantity of Chemical in Equipment Disposed. Total quantity of a specific chemical in equipment disposed of in year j , by weight.
Qp j 

### = Quantity of Chemical Penetrating the Market. Total quantity of a specific chemical that is entering the market in year j , by weight.

E e 

### = Emissions of Chemical Not Replaced. Total quantity of a specific chemical that is emitted during year j but is not replaced in that year. The Vintaging Model assumes all chemical emitted from refrigeration, air conditioning and fire extinguishing equipment is replaced in the year it is emitted, hence this term is zero for all sectors except foam blowing.
Q
r



### = Chemical Replacing Previous Year’s Emissions. Total quantity of a specific chemical that is used to replace emissions that occurred prior to year j. The Vintaging Model assumes all chemical emitted from refrigeration, air conditioning and fire extinguishing equipment is replaced in the year it is emitted, hence this term is zero for all sectors.

j = Year of emission.

Table A- 165 provides the bank for ODS and ODS substitutes by chemical grouping in metric tons (MT) for 1990-2009.

### A-198 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A- 165. Banks of ODS and ODS Substitutes, 1990-2009 (MT)
CFC HCFC HFC 1990 653,338 235,625 868

1995 757,485 454,719 50,761

2000 628,584 878,343 181,693 2001 602,580 947,180 210,207 2002 578,017 999,467 241,960 2003 554,078 1,033,925 280,053 2004 531,714 1,067,106 318,505 2005 513,896 1,103,085 354,544 2006 500,430 1,135,099 390,237 2007 489,582 1,160,421 425,719 2008 482,541 1,174,094 457,860 2009 479,890 1,165,278 498,695



## References IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories Programme, The Intergovernmental Panel on Climate Change, H. S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.

### A-199 

# 3.9. Methodology for Estimating CH 4 

# Emissions from Enteric Fermentation

Methane emissions from enteric fermentation were estimated for five livestock categories: cattle, horses, sheep, swine, and goats. Emissions from cattle represent the majority of U.S. emissions from enteric fermentation; consequently, the more detailed IPCC Tier 2 methodology was used to estimate emissions from all cattle (except for bulls). The IPCC Tier 1 methodology was used to estimate emissions from bulls and the other types of livestock.



## Estimate Methane Emissions from Cattle This section describes the process used to estimate me thane emissions from cattle enteric fermentation using the Cattle Enteric Fermentation Model (CEFM). 51 

### The CEFM, based on recommendations provided in IPCC/UNEP/OECD/IEA (1997), IPCC (2000) and IPCC (2006), uses information on population, energy requirements, digestible energy, and methane conversion rates to estimate methane emissions. 52 

### The emission methodology consists of the following three steps: (1) characterize the cattle population to account for animal population categories with different emission profiles; (2) characterize cattle diets to generate information needed to estimate emission factors; and (3) estimate emissions using these data and the IPCC Tier 2 equations.

Step 1: Characterize U.S. Cattle Population
The national cattle population estimates in the inventory submission are based on data obtained from the U.S. Department of Agriculture’s (USDA) National Agricultural Statistics Service Quick Stats database (USDA 2010). Asummary of the annual average populations upon which all livestock-related emissions are based is provided in Table A166. Cattle populations used in the Enteric Fermentation sector were estimated using the cattle transition matrix in the CEFM, which uses January 1 USDA population estimates and weight data to simulate the population of U.S. cattle from birth to slaughter, and results in an estimate of the number of animals in a particular cattle grouping while taking into account the monthly rate of weight gain, the average weight of the animals, and the death and calving rates. The use of supplemental USDA data and the cattle transition matrix in the CEFM results in the cattle population estimates for this sector differing slightly from the January 1 or July 1 USDA point estimates and the cattle population data obtained from the Food and Agriculture Organization of the United Nations (FAO).
Table A-166: Cattle Population Estimates from the CEFM Transition Matrix for 1990-2009 Livestock Type 1990 1995 2000 2005 2006 2007 2008 2009 Calves 0-6 months 22,561 23,499 22,569 21,678 21,621 21,483 21,155 20,940 Dairy

Dairy Cows 10,015 9,482 9,183 9,004 9,104 9,145 9,257 9,333 Dairy Replacements 7-11 months 1,214 1,216 1,196 1,257 1,277 1,299 1,304 1,325 Dairy Replacements 12-23 months 2,915 2,892 2,812 2,905 3,017 3,043 3,097 3,101 Beef

Bulls 2,160 2,385 2,293 2,214 2,258 2,214 2,207 2,184 Beef Cows 32,455 35,190 33,575 32,674 32,703 32,644 32,435 31,712 Beef Replacements 7-11 months 1,269 1,493 1,313 1,363 1,380 1,349 1,312 1,287 Beef Replacements 12-23 months 2,967 3,637 3,097 3,171 3,294 3,276 3,169 3,098 Steer Stockers 10,321 11,716 8,724 8,185 8,248 8,302 8,233 8,501 Heifer Stockers 5,946 6,699 5,371 5,015 5,041 4,966 4,868 5,051 Feedlot Cattle 9,549 11,064 13,006 12,652 13,526 13,404 13,070 12,964 Note: Mature animal populations are not assumed to have signi ficant monthly fluctuations, and therefore the populations utilize d are the January estimates downloaded from USDA (2010).



### The population transition matrix in the CEFM simulate s the U.S. cattle population over time and provides an estimate of the population age and weight structure by cattle ty pe on a monthly basis. Since cattle often do not remain in a single population type for an entire year (e.g., calves become stockers, stockers become feedlot animals), and emission

51 Emissions from bulls are estimated in the CEFM using a Tier 1 approach based on published population statistics and national a verage emission factors because the variation in diets and within year population is assumed to be minimal. The IPCC recommends the us e of a methane conversion factor of zero for calves, because they consum e mainly milk, therefore this results in no methane emissions from calves through 6 months. 52 Additional information on the Cattle Enteric Fermentation Model can be found in ICF (2006).

### A-200 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 profiles vary both between and within each cattle type, these monthly age groups are tracked in the enteric fermentation model to obtain more accurate emission estimates than would be available from annual point estimates of population (such as available from USDA statistics) and weight for each cattle type.
The transition matrix tracks both dairy and beef populations, and divides the populations into males and females, and subdivides the population further into specific cattle groupings for replacement, stocker, feedlot, and mature animals. The matrix is based primarily on two types of data: population st atistics and weight statistics (including target weights, slaughter weights, and weight gain). Using the weight data , the transition matrix simulates the growth of animals over time by month. The matrix also relies on supplementary data , such as feedlot placement statistics, slaughter statistics, death rates, and calving rates.
The basic method for tracking population of animals per category is based on the number of births (or graduates) into the monthly age group minus those animals that die or are slaughtered and those that graduate to the next category (such as stockers to feedlot placements).
Each stage in the cattle lifecycle was modeled to simulate the cattle population from birth to slaughter. This level of detail accounts for the variability in CH 4 

### emissions associated with each life stag e. Given that a stage can last less than one year (e.g., beef calves are weaned at 7 months), each is modeled on a per-month basis. The type of cattle also impacts CH 4 

### emissions (e.g., beef versus dairy). Consequently, ther e is an independent transition matrix for each of three separate lifecycle phases, 1) calves, 2) replacements and stoc kers, and 3) feedlot animals. In addition, the number of mature cows is tracked for both dairy and beef stock. Each lifecycle is discussed separately below, and the categories tracked are listed in Table A-167.
Table A-167: Cattle Population Ca tegories Used for Estimating CH 4 

### Emissions Dairy Cattle Beef Cattle Calves Calves Heifer Replacements He ifer Replacements Cows Heifer and Steer Stockers Animals in Feedlots (Heifers & Steers) Cows Bulls* * Bulls (beef and dairy) are accounted for in a single category.



### The key variables tracked for each of these cattle population categories (except bulls) are as follows:

Calves . The number of animals born on a monthly basis was used to initiate monthly cohorts and to determine population age structure. The number of calves born each month was obtained by multiplying annual births by the percentage of births by month. Annual birth information for each year was taken from USDA (2010). For dairy cows, the number of births is assumed to be distributed equally th roughout the year (approximately 8.3 percent per month), beef births are distributed according to Table A-168, based on estimates from the National Animal Health Monitoring System (NAHMS) (USDA/APHIS/VS 1998, 1994, 1993). To determine whethe r calves were born to dairy or beef cows, the dairy cow calving rate (USDA/APHIS/VS 2002, USDA/APHIS/VS 1996) was multiplied by the total dairy cow population to determine the number of births attributable to dairy cows, with the remainder assumed to be attributable to beef cows. Total annual calf births are obtained from USDA, and distributed into monthly cohorts by cattle type (beef or dairy). Calf growth is modeled by month, based on estimated monthly weight gain for each cohort (approximatey 61 pounds per month). Total calf population is modified through time to account for veal calf slaughter at 4 months and a calf death loss of 0.35 percent annually (distributed across age cohorts up to six months of age). An example of a transition matrix for calves is shown in Table A-169. Note that calves age one through six months available in January have been tracked through the model based on births and death loss from the previous year.
Table A-168: Estimated Beef Cow Births by Month

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
7% 15% 28% 22% 9% 3% 2% 2% 3% 4% 3% 3%

### A-201 Table A-169: Example of Monthly Average Populations from Calf Transition Matrix

Replacements and Stockers. At seven months of age, calves ‘graduate’ and are separated into the applicable cattle types. First the number of replacements required for beef and dairy cattle are calculated based on estimated death losses and population changes between beginning and end of year population estimates. All steer, and remaining heifers (after subtracting required replacements), are considered ‘sto ckers,’ that is backgrounding animals that are eligible for placement into feedlots as they reach the appropriate weight class. During the stocker phase animals are subtracted out of the transition matrix for placement into feedlots based on feedlot placement statistics from USDA (2010).
The data and calculations that occur for the stocker category include matrices that estimate the population of backgrounding heifers and steer, as well as a matrix for total combined stockers. The matrices start with the beginning of year populations in January and model the progression of each cohort. The age structure of the January population is based on estimated births by month from the previous two years, although in order to balance the population properly, an adjustment is added that slightly reduces population percentages in the older populations. The populations are modified through addition of graduating calves (month 7, bottom row of Table A-170) and subtraction through death loss and animals placed in feedlots. Eventually, an entire cohort population of stockers may be zero, indicating that the complete cohort has been transitioned into feedlots. An example of th e transition matrix for stockers is shown in Table A-170.
Table A-170: Example of Monthly Average Po pulations from Stocker Transition Matrix Age (month) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 23 202 214 126 47 20 12 10 11 9 7 5 51 22 353 174 59 23 15 12 13 13 9 7 103 311 21 286 81 29 17 15 15 15 12 9 153 421 559 20 133 40 22 17 18 18 14 12 197 526 765 457 19 66 30 22 21 21 17 14 273 620 965 629 212 18 49 30 27 25 20 17 321 779 1,141 795 291 305 17 49 37 31 24 20 379 882 1,442 942 368 546 49 16 60 43 30 24 453 1,004 1,636 1,193 435 779 49 49 15 71 41 30 531 1,160 1,867 1,355 551 985 49 49 61 14 68 41 669 1,324 2,162 1,547 625 1,337 49 49 61 71 13 67 918 1,607 2,471 1,796 716 1,669 49 49 61 71 68 12 1,076 1,774 2,813 2,017 857 2,242 282 49 61 71 68 67 11 2,027 3,105 2,297 962 2,765 365 179 82 91 76 72 1,094 10 3,532 2,542 1,082 3,229 507 253 209 261 213 128 1,151 2,683 9 2,860 1,204 3,588 607 451 425 504 464 429 1,353 2,916 6,195 8 1,328 4,083 770 617 664 915 782 831 1,682 3,596 6,832 5,605 7 4,450 878 871 1,060 1,295 1,232 1, 230 2,114 4,188 7,484 6,012 2,665



### In order to ensure a balanced population of both stockers and placements, additional data tables are utilized in the stocker matrix calculations. The tables summarize the placement data by weight class and month, and is based on the total number of animals within the population that are available to be placed in feedlots and the actual feedlot placement statistics provided by USDA (2010). In cases where there are discrepancies between the USDA estimated placements by weight class and the calculated animals available by weight, the model pulls available stockers from one higher weight category if available. If there are still not enough animals to fulfill requirements the model pulls animals from the next lower category. Note that in the current time series, this method was able to ensure that total placement data matched USDA estimates, and no shortfalls have occurred.

In addition, average weights were tracked for each monthly age group using starting weight and monthly weight gain estimates. Weight gain (i.e., pounds per month) was estimated based on weight gain needed to reach a set target weight, divided by the number of months remaining before target weight was achieved. Birth weight was assumed to be 88 pounds for both beef and dairy animals. Weaning weights were estimated at 515 lbs. Other reported target weights

Age (month) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 6 1,207 1,199 1,494 1,751 1,684 1,666 2,665 5,005 8,720 7,062 3,279 1,631 5 1,199 1,494 1,751 1,684 1,666 2,665 5,005 8,720 7,062 3,279 1,631 1,212 4 1,494 1,751 1,684 1,666 2,665 5,005 8,720 7,062 3,279 1,631 1,212 1,202 3 1,751 1,684 1,666 2,665 5,005 8,720 7,062 3,279 1,631 1,212 1,202 1,495 2 1,684 1,666 2,665 5,005 8,720 7,062 3,279 1,631 1,212 1,202 1,495 1,748 1 1,666 2,665 5,005 8,720 7,062 3,279 1,631 1,212 1,202 1,495 1,748 1,680 0 2,665 5,005 8,720 7,062 3,279 1,631 1,212 1,202 1,495 1,748 1,680 1,661

### A-202 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 were available for 12, 15, 24, and 36 month-old animals, depending on the animal type. Beef cow mature weight was taken from measurements provided by a major British Bos taurus breed (Enns 2008). Beef replacement weight was calculated as 70 percent of mature weight at 15 months and 85 percent of mature weight at 24 months. As dairy weights are not a trait that is typically tracked, mature weight for dairy cows was estimated at 1,500 for all years, based on a personal communication with Kris Johnson (2010) and an estimate from Holstein Association USA (2010). Dairy replacement at 15 months was assumed to be 875 lbs and replace ment at 24 months is 1,300 lbs. Live slaughter weights were derived from dressed slaughter weight data for each year (USDA 2010). The annual typical animal mass for each livestock type are presented in Table A- 171

Weight gain for stocker animals was based on monthly gain estimates from Johnson (1999) for 1989, and from average daily estimates from Lippke et al. 2000, Pinchack et al., 2004, Platter et al., 2003, and Skogerboe et al., 2000 for 2000 through 2007. Interim years were calculated linearly, as shown in Table A-172, and weight gain was held constant starting in 2008. Live slaughter weight was estimated as dressed weight divided by 0.63. This ratio represents the dressed weight (i.e. weight of the carcass after removal of the internal organs), to the live weight (i.e. weight taken immediately before slaughter). Table A-172 provides weights and weight gains that vary by year in the CEFM.
Table A- 171: Typical Animal Ma ss that Varies by Year (lbs)
Year/Cattle Type Dairy Cows Dairy Replacements Beef Cows Beef Replacements Steer Stockers Heifer Stockers Steer Feedlot Heifer Feedlot 1990 1,500 900 1,221 820 692 652 923 846 1991 1,500 898 1,225 822 695 656 975 867 1992 1,500 897 1,263 841 714 673 984 878 1993 1,500 899 1,280 852 721 683 930 864 1994 1,500 898 1,280 854 721 689 944 876 1995 1,500 898 1,282 858 735 701 947 880 1996 1,500 898 1,285 859 739 707 940 878 1997 1,500 900 1,286 861 737 708 939 877 1998 1,500 897 1,296 866 736 710 957 892 1999 1,500 899 1,292 862 731 709 960 895 2000 1,500 897 1,272 849 720 702 961 899 2001 1,500 898 1,272 850 726 707 963 901 2002 1,500 897 1,276 852 726 708 982 915 2003 1,500 900 1,308 872 719 702 973 905 2004 1,500 897 1,323 878 719 702 967 905 2005 1,500 895 1,327 880 718 706 975 917 2006 1,500 898 1,341 890 725 713 984 925 2007 1,500 897 1,348 895 721 707 992 928 2008 1,500 898 1,348 895 721 705 1,000 939 2009 1,500 897 1,348 895 731 715 1,007 948

### A-203 Table A-172: Weight Gains that Vary by Year (lbs)
Year/Cattle Type Steer Stockers to 12 months(lbs/day) Steer Stockers to 24 months (lbs/day) Heifer Stockers to 12 months(lbs/day) Heifer Stockers to 24 months(lbs/day) 1990 1.53 1.23 1.23 1.08 1991 1.56 1.29 1.29 1.15 1992 1.59 1.35 1.35 1.23 1993 1.62 1.41 1.41 1.30 1994 1.65 1.47 1.47 1.38 1995 1.68 1.53 1.53 1.45 1996 1.71 1.59 1.59 1.53 1997 1.74 1.65 1.65 1.60 1998 1.77 1.71 1.71 1.68 1999 1.80 1.77 1.77 1.75 2000 1.83 1.83 1.83 1.83 2001 1.83 1.83 1.83 1.83 2002 1.83 1.83 1.83 1.83 2003 1.83 1.83 1.83 1.83 2004 1.83 1.83 1.83 1.83 2005 1.83 1.83 1.83 1.83 2006 1.83 1.83 1.83 1.83 2007 1.83 1.83 1.83 1.83 2008 1.83 1.83 1.83 1.83 2009 1.83 1.83 1.83 1.83 Sources: Enns (2008), Johnson (1999), Lippke et al. (2000), NRC (1999), Pinchack et al. (2004), Platter et al. (2003), Skogerbo e et al. (2000)



### Feedlot Animals. Feedlot placement statistics from USDA provide data on the placement of animals from the stocker population into feedlots on a monthly basis by weight class. The model uses these data to shift a sufficient number of animals from the stocker cohorts into the feedlot populations to match the reported placement data. After animals are placed in feedlots they progress through two steps. First, anim als spend 25 days on a step-up diet to become acclimated to the new feed type, during this time weight gain is estima ted to be 2.8 to 3 pounds per day (Johnson 1999). Animals are then switched to a finishing diet for a period of time before they are slaughtered. Weight gain during finishing diets is estimated to be 3 to 3.3 pounds per day (Johnson 1999). The length of time an animal spends in a feedlot depends on the start weight (i.e., placement weight), the rate of weight gain during the start-up and finishing phase of diet, and the target weight (as determined by weights at slaughter). Additionally, animals remaining in feedlots at the end of the year are tracked for inclusion in the following year’s emission and population counts. For 1990 to 1995, only the total placement data were available, therefore placements for each weight cate gory (displayed in Table A-173) for those years are based on the average of monthly placements from the 1996 to 1998 reported figures. Placement data is available by weight class for all years from 1996 onward. Table A-173 provides a summary of the reported feedlot placement statistics for 2009.

Table A-173: Feedlot Placements in the United States for 2009 (Number of animals placed in Thousand Head) Weight Placed When: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec < 600 lbs 380 320 305 355 395 315 455 435 490 615 510 430 600 – 700 lbs 505 385 340 315 305 290 365 395 450 645 565 420 700 – 800 lbs 553 538 593 405 433 371 458 514 593 579 394 401 > 800 lbs 420 435 570 525 505 415 585 775 855 635 375 295 Total 1,858 1,678 1,808 1,600 1,638 1, 391 1,863 2,119 2,388 2,474 1,844 1,546 Source: USDA (2010). Note: Totals may not sum due to independent rounding.



### Mature Animals. Energy requirements and hence, composition of diets, level of intake, and emissions for particular animals, are greatly influenced by whether the animal is pregnant or lactating. Information is therefore needed on the percentage of all mature animals that are pregnant each month, as well as milk production, to estimate CH 4 

### emissions. A weighted average percent of pregnant cows each month was estimated using information on births by month and average pregnancy term. For beef cattle, a weighted average total milk production per animal per month was estimated using information on typical lactation cycles and amounts (NRC 1999), and data on births by month. This process results in a range of weighted monthly lactation esti mates expressed as lbs/animal/month. The monthly estimates from January to December are 3.3, 5.1, 8.7, 12.0, 13.6, 13.3, 11.7, 9.3, 6.9, 4.4, 3.0, and 2.8 lbs milk/animal/day. Annual estimates for dairy cattle were taken from USDA milk production statistics. Dairy lactation estimates for 1990 through 2009 are shown in

### A-204 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A-174. Beef and dairy cow and bull populations are assumed to remain relatively static throughout the year, as large fluctuations in population size are assumed to not occur. These estimates are taken from the USDA beginning and end of year population datasets.

Table A-174: Dairy Lactation by State (lbs/ year/cow)*

State/Year 1990 1995 2000 2005 2006 2007 2008 2009 Alabama 12,214 14,176 13,920 14,000 14,500 15,154 15,333 14,909 Alaska 13,300 17,000 14,500 12,273 12,250 14,667 12,000 10,000 Arizona 17,500 19,735 21,820 22,679 22,855 23,260 23,382 23,028 Arkansas 11,841 12,150 12,436 13,545 12,900 12,941 12,400 12,615 California 18,456 19,573 21,130 21,404 21,815 22,440 22,344 22,000 Colorado 17,182 18,687 21,618 22,577 23,155 22,932 22,930 23,089 Connecticut 15,606 16,438 17,778 19,200 19,316 19,211 19,158 18,684 Delaware 13,667 14,500 14,747 16,622 16,286 16,618 16,923 17,000 Florida 14,033 14,698 15,688 16,591 16,447 16,832 17,167 18,061 Georgia 12,973 15,550 16,284 17,259 18,234 18,169 17,829 18,600 Hawaii 13,604 13,654 14,358 12,889 13,256 12,241 10,882 14,200 Idaho 16,475 18,147 20,816 22,332 22,346 22,513 22,432 22,091 Illinois 14,707 15,887 17,450 18,827 19,252 18,612 18,569 18,873 Indiana 14,590 15,375 16,568 20,295 19,861 20,307 19,683 20,137 Iowa 15,118 16,124 18,298 20,641 20,127 20,085 19,995 20,367 Kansas 12,576 14,390 16,923 20,505 20,938 19,882 20,641 21,085 Kentucky 10,947 12,469 12,841 12,896 13,296 13,889 13,444 14,190 Louisiana 11,605 11,908 12,034 12,400 12,375 12,034 12,269 11,870 Maine 14,619 16,025 17,128 18,030 17,938 17,788 18,273 18,061 Maryland 13,461 14,725 16,083 16,099 17,281 18,121 18,375 18,255 Massachusetts 14,871 16,000 17,091 17,059 17,375 17,000 16,933 17,571 Michigan 15,394 17,071 19,017 21,635 22,234 22,761 22,180 22,445 Minnesota 14,127 15,894 17,777 18,091 18,598 18,817 18,927 19,230 Mississippi 12,081 12,909 15,028 15,280 14,957 15,429 14,550 13,889 Missouri 13,632 14,158 14,662 16,026 16,000 14,982 14,682 14,654 Montana 13,542 15,000 17,789 19,579 18,632 18,500 18,412 19,933 Nebraska 13,866 14,797 16,513 17,950 18,328 18,220 18,672 19,721 Nevada 16,400 18,128 19,000 21,680 20,148 20,481 20,704 21,821 New Hampshire 15,100 16,300 17,333 18,875 19,533 19,333 19,933 19,533 New Jersey 13,538 13,913 15,250 16,000 16,182 16,800 16,900 17,889 New Mexico 18,815 18,969 20,944 21,192 21,853 21,958 23,269 24,320 New York 14,658 16,501 17,378 18,639 18,879 19,303 19,859 20,071 North Carolina 15,220 16,314 16,746 18,741 18,510 19,188 18,979 19,644 North Dakota 12,624 13,094 14,292 14,182 14,688 15,310 16,077 16,739 Ohio 13,767 15,917 17,027 17,567 17,737 18,109 18,321 18,744 Oklahoma 12,327 13,611 14,440 16,480 16,630 16,580 16,578 16,983 Oregon 16,273 17,289 18,222 18,876 19,000 19,417 19,772 19,719 Pennsylvania 14,726 16,492 18,081 18,722 19,390 19,422 19,262 19,360 Rhode Island 14,250 14,773 15,667 17,000 17,273 16,455 18,091 17,818 South Carolina 12,771 14,481 16,087 16,000 16,294 17,889 17,889 18,765 South Dakota 12,257 13,398 15,516 17,741 18,580 19,306 19,956 20,128 Tennessee 11,825 13,740 14,789 15,743 15,657 15,857 16,068 16,232 Texas 14,350 15,244 16,503 19,646 19,501 18,982 20,134 20,898 Utah 15,838 16,739 17,573 18,875 20,314 20,376 20,894 20,988 Vermont 14,528 16,210 17,199 18,469 18,383 18,079 18,400 18,289 Virginia 14,213 15,116 15,833 16,990 17,363 17,530 17,612 18,083 Washington 18,532 20,091 22,644 23,270 23,055 23,239 23,344 23,171 West Virginia 11,250 12,667 15,588 14,923 15,385 15,000 15,083 14,727 Wisconsin 13,973 15,397 17,306 18,500 18,824 19,310 19,546 20,079 Wyoming 12,337 13,197 13,571 14,878 17,612 18,831 19,386 18,811 Source: USDA (2010). * Beef lactation data were developed using the methodol ogy described in Step 1.

### A-205 Step 2: Characterize U.S. Cattle Population Diets
To support development of digestible energy (DE, the per cent of gross energy intake digested by the animal) and CH 4 

### conversion rate (Y
m 

### , the fraction of gross energy converted to CH 4 

### ) values for each of the cattle population categories, data were collected on diets considered representative of different regions. For both grazing animals and animals being fed mixed rations, representative regional diets were estimated using information collected from state livestock specialists and from USDA (1996). The data for each of the diets (e.g., proportions of different feed constituents, such as hay or grains) were used to determine feed chemical composition for use in estimating digestible energy and Y
m 

### for each animal type.

DE and Y
m 

### vary by diet and animal type. The IPCC recommends Y
m 

### values of 3.0+ 1.0 percent for feedlot cattle and 6.5+ 1.0 percent for all other cattle (IPCC 2006). Given the availability of detailed diet information for different regions and animal types in the United States, digestible energy and Y
m 

### values unique to the United States 53 

### were developed. Digestible energy and Y
m 

### values were estimated for each cattle population category, for each year in the time series based on physiological modeling, published values, and/or expert opinion.
For dairy cows, ruminant digestion models were used to estimate Y
m 

### . The three major categories of input required by the models are animal description (e.g., cattle type, mature weight), animal performance (e.g., initial and final weight, age at start of period), and feed characteristics (e.g., ch emical composition, habitat, grain or forage). Data used to simulate ruminant digestion is provided for a particular animal that is then used to represent a group of animals with similar characteristics. The Y
m 

### values were estimated for 1990 using the Donovan and Baldwin model (1999) that represents physiological processes in the ruminant animals and diet characteristics from USDA (1996). The Donovan and Baldwin model accounts for differing diets (i.e., grain-based or forage-based), so that Y
m 

### values for the variable feeding characteristics within the U.S. cattle population can be estimate d. Subsequently, a literature review of dairy diets was conducted and nearly 250 diets were analyzed from 1990 through 2008 across 23 states. Kebreab et al (2008). conducted an evaluation of models and found that the COWPOLL model was the best model for estimating Y
m 

### for dairy. Therefore, the COWPOLL model was used to estimate Y
m 

### values for each of the diets. Due to the variability associated with cattle diets, a function based on the national trend observed from the analysis of the diary diets was used to calculate 1991 and beyond values based on the regional 1990 Y
m 

### values from Donovan and Baldwin.. The resulting scaling factor is shown below:



# ( ) ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

− = 1980 22

. 1 ) 1990 ( Year EXP Ym Y

m 

# ( ) ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

− 1980 1990 22

. 1 / EXP

DE values for dairy cows were estimated from the litera ture search based on the on annual trends observed in the recent data collection effort. The regional variability observed in the literature search was not statistically significant, a nd therefore DE was not varied by region.
Considerably less data was available for dairy heifers, so assumptions were based on the relationship of the collected data literature on dairy heifers to the data on dairy cow diets. From this relationship, DE was estimated as the mature cow DE minus three percent, and Y
m 

### was estimated as that of the mature dairy cow plus 0.1 percent.
To calculate the DE values for grazing beef cattle, diet composition assumptions were used to estimate weighted DE values for a combination of forage and supplemental diets. Where DE values were not available for specific feed types, total digestible nutrients (TDN) as a percent of dry matt er (DM) intake was used as a proxy for DE, as listed in NRC (2000). Forage diets make up the majority of beef cattle diets, and two separate regional DE values were used to account for the generally lower forage quality in the western United States. For all non-western beef cattle, the forage DE was an average of the seasonal values for Grass Pasture diets listed in Appendix Table 1 of the NRC (2000). This resulted in a DE of 64.7 percent for the forage portion of the diet for all beef cattle in regions other than the west. For beef cattle in t he west, the forage DE was calculated as the seasonal average for meadow and range diets listed in Appendix Table 1 of the NRC (2000). This resulted in a DE for the forage portion of the western region diet of 58.5 percent. In addition, it was assumed that each region fed a supplemental diet, as shown in Table A- 175. By weighting the calculated DE values from the forage and supplemental diets, the DE values for the composite diet were calculated. 54 

### The percent of each diet that is assumed to be supplemental and the forage DE values used for each region, and the resulting weighted DE values are

53 In some cases, the Y m values used for this analysis extend beyond the range pr ovided by the IPCC. However, EPA believes that these values are representative for the U.S. due to research conduc ted to characterize the diets of U.S. cattle and assess the Y m values associated with different animal performance an d feed characteristics in the United States. 54 For example, in California the forage DE of 64.7 was used for 95 percent of the grazing cattle diet and a supplemented diet DE of 69.3 percent was used for five percent of the diet, for a total weighted DE of 64.9 percent, as shown in 

### Table A-176

.

### A-206 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 shown in Table A-176. These values are used for steer and he ifer stockers and beef replacements. Finally, for beef cows, the DE value was adjusted downward by two percent to reflect the lower digestibility diets of the mature beef cow based on Johnson (2002). Y
m 

### values for all grazing beef cattle were set at 6.5 percent based on Johnson (2002).
For feedlot animals, DE and Y
m 

### values for 1990 were taken from Johns on (1999). . In the CEFM, values for 1991 through 1999 were linearly extrapolated based on values for 1990 and 2000. DE and Y
m 

### values for 2000 onwards were estimated using the MOLLY model as described in Kebreab et al (2008). In addition, feedlot animals are assumed to spend the first 25 days in the feedlot on a “step-up” diet to become accustomed to the higher quality feedlot diets. The step-up DE and Y
m 

### are calculated as the average of all state forage and feedlot diet DE and Y
m 

### values.
Table A-177 shows the regional DE and Y
m 

### for U.S. cattle in each region for 2009.
Table A- 175: DE Values and Representati ve Regional Diets (Percent of Diet for each Region) for the Supplemental Diet of Grazing Beef Cattle

Source of representative regional diets: Donovan (1999).

a Note that emissions are currently calculate d on a state-by-state basis, but diets are applied by the regions shown in the table above. The Western region includes AK, WA, OR, ID, NV, UT, AZ, HI, and NM; the Northeastern region includes PA, NY, MD, DE, NJ, CT, RI, MA, VT, N H, ME, and WV; the Southcentral region includes AR, LA, OK, and TX; the Midwestern region includes MO, IL, IN, OH, MN, WI, MI, and IA; the Northern Great Plains include MT, WY, ND, SD, NE, KS, and CO; and the Southeastern region includes VA, NC, KY, TN, MS, AL, GA, SC, and FL.


### Table A-176: Percent of Each Diet that is Supplem ental and the Resulting DE Values for Each Region
Region a Percent Supplement Percent Forage Forage DE Used Calculated Weighted Average DE West 10 90 59 59 N ortheas t 15 85 65 65 Southcentral 10 90 65 64 Midwest 15 85 65 65 N orthern Great Plains 15 85 65 66 Southeast 5 95 65 64 California 5 95 65 65 Sources: Percent of total diet that is supplem ental diet, Donovan (1999); Forage DE, NRC (2000).

a Note that emissions are currently calculate d on a state-by-state basis, but diets are applied by the regions shown in the table above. The Western region includes AK, WA, OR, ID, NV, UT, AZ, HI, and NM; the Northeastern region includes PA, NY, MD, DE, NJ, CT, RI, MA, VT, N H, ME, and WV; the Southcentral region includes AR, LA, OK, and TX; the Midwestern region includes MO, IL, IN, OH, MN, WI, MI, and IA; the Northern Great Plains include MT, WY, ND, SD, NE, KS, and CO; and the Southeastern region includes VA, NC, KY, TN, MS, AL, GA, SC, and FL. Feed Source of TDN (NRC 2000) Unweighted TDN or DE California a West Northern Great Plains Southcentral Northeast Midwest Southeast Alfalfa Hay Table 11-1, feed #4 59.6% 65% 30% 30% 29% 12% 30% Barley Table 11-1, feed #12 86.3% 10% 15% Bermuda Table 11-1, feed #17 48.5% 35% Bermuda Hay Table 11-1, feed #17 48.5% 40% Corn Table 11-1, feed #38 88.1% 10% 10% 25% 11% 13% 13% Corn Silage Table 11-1, feed #39 71.2% 25% 20% 20% Cotton Seed Meal Table 11-1, feed #42 74.4% 7% Grass Hay Table 1a, feed #129, 147, 148 53.7% 40% 30%
Orchard Table 11-1, feed #61 53.5% 40% Soybean Meal Supplement Table 11-1, feed #70 83.1% 5% 5% 5% Sorghum Table 11-1, feed #67 81.3% 20% Soybean Hulls Table 11-1, feed #69 76.4% 7% Timothy Hay Table 11-1, feed #77 55.5% 50% Whole Cotton Seed Table 11-1, feed #41 89.2% 5% 5% Wheat Middlings Table 1a, feed #433 83.0% 15% 13% Wheat Table 11-1, feed #83 87.2% 10% Weighted Total 69% 65% 74% 62% 65% 65% 59%

### A-207 Table A-177: Regional Digestible Energy (DE) and CH 4 

### Conversion Rates (Y m 

### ) for Cattle in 2009

Animal Type Data California c West Northern Great Plains Southcentral Northeast Midwest Southeast
Beef Repl. Heif. DE a 65 59 66 64 65 65 64 Y m b 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% Dairy Repl. Heif. DE 64 64 64 64 64 64 64 Y m 6.0% 6.0% 5.7% 6.5% 6.4% 5.7% 7.0% Steer Stockers DE 65 59 66 64 65 65 64 Y m 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% Heifer Stockers DE 65 59 66 64 65 65 64 Y m 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% Steer Feedlot DE 83 83 83 83 83 83 83 Y m 3.9% 3.9% 3.9% 3.9% 3.9% 3.9% 3.9% Heifer Feedlot DE 83 83 83 83 83 83 83 Y m 3.9% 3.9% 3.9% 3.9% 3.9% 3.9% 3.9% Beef Cows DE 63 57 64 62 63 63 62 Y m 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% Dairy Cows DE 67 67 67 67 67 67 67 Y m 5.9% 5.9% 5.6% 6.4% 6.3% 5.6% 6.9% Steer Step-Up DE 72 72 72 72 72 72 72 Y m 5.2% 5.2% 5.2% 5.2% 5.2% 5.2% 5.2% Heifer Step-Up DE 72 72 72 72 72 72 72 Y m 5.2% 5.2% 5.2% 5.2% 5.2% 5.2% 5.2%

a Digestible Energy in units of percent of GE (MJ/Day).

b Methane Conversion Rate is the fraction of GE in feed converted to methane.

c Note that emissions are currently calculate d on a state-by-state basis, but diets are applied by the regions shown in the table above. The Western region includes AK, WA, OR, ID, NV, UT, AZ, HI, and NM; the Northeastern region includes PA, NY, MD, DE, NJ, CT, RI, MA, VT, N H, ME, and WV; the Southcentral region includes AR, LA, OK, and TX; the Midwestern region includes MO, IL, IN, OH, MN, WI, MI, and IA; the Northern Great Plains include MT, WY, ND, SD, NE, KS, and CO; and the Southeastern region includes VA, NC, KY, TN, MS, AL, GA, SC, and FL.



### Step 3: Estimate CH 4 

### Emissions from Cattle
Emissions by state were estimated in three steps: a) de termine gross energy (GE) intake using the IPCC (2006) equations, b) determine an emission factor using the GE values and other factors, and c) sum the daily emissions for each animal type. Finally, the state emissions were aggregated to obtain the national emissions estimate. The necessary data values for each state and animal type include:



### • Body Weight (kg)



### • Weight Gain (kg/day)



### • Net Energy for Activity (C
a 

### , MJ/day) 55



### • Standard Reference Weight (kg) 56



### • Milk Production (kg/day)



### • Milk Fat (percent of fat in milk = 4)



### • Pregnancy (percent of population that is pregnant)



### • DE (percent of gross energy intake digestible)



### • Y m 

### (the fraction of gross energy converted to CH 4 

### )



### • Population

Step 3a: Determine Gross Energy, GE
As shown in the following equation, gross energy (GE) is derived based on the net energy estimates and the feed characteristics. Only variables relevant to each animal cate gory are used (e.g., estimates for feedlot animals do not require the NE l 

### factor). All net energy equations are provided in IPCC (2006).

55 Zero for feedlot conditions, 0.17 for high quality confined pa sture conditions, and 0.36 for extensive open range or hilly ter rain grazing conditions. C a factor for dairy cows is weighted to account for the fr action of the population in the region that grazes during the
year. 56 Standard Reference Weight is the mature weight of a female an imal of the animal type being estimated, used in the model to ac count for breed potential.

### A-208 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 

## ⎥ ⎥ ⎥ ⎥ ⎥

⎦ ⎤

⎢ ⎢ ⎢ ⎢ ⎢

⎣ ⎡ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

+ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ + + + +

=

100 % DE REG NE

REM NE NE NE NE NE

GE g p work l a m



### Where, 

### GE = Gross energy (MJ/day) NE m 

### = Net energy required by the animal for maintenance (MJ/day) NE a 

### = Net energy for animal activity (MJ/day) NE l 

### = Net energy for lactation (MJ/day) NE work 

### = Net energy for work (MJ/day) NE p 

### = Net energy required for pregnancy (MJ/day) REM = Ratio of net energy available in a diet for maintenance to digestible energy consumed NE g 

### = Net energy needed for growth (MJ/day) REG = Ratio of net energy available for growth in a diet to digestible energy consumed DE = Digestible energy expressed as a percent of gross energy (percent)

Step 3b: Determine Emission Factor
The emission factor (DayEmit) was determined using the gross energy value and the methane conversion factor (Y
m 

### ) for each category. This relationship is shown in the following equation:



## 65

. 55 Y ×
m 

## GE DayEmit =

Where, 

### DayEmit = Emission factor (kg CH 4 

### /head/day) GE = Gross energy intake (MJ/head/day) Y m 

### = CH 4 

### conversion rate, which is the fraction of gross energy in feed converted to CH 4 

### (%) 55.65 = A factor for the energy content of methane (MJ/kg CH 4 

### )

The daily emission factors were estimated for each animal type and state, calculated national emission factors are shown by animal type in Table A-178.

Table A-178: Calculated National Emission Factors for Cattle by Animal Type (kg CH 4 

### /head/year) Cattle Type 1990 1995 2000 2005 2006 2007 2008 2009 Dairy

Cows 124 125 132 133 134 139 139 140 Replacements 7-11

months 48 46 46 45 45 46 46 46 Replacements 12-23

months 73 69 70 67 67 70 69 70 Beef

Bulls 53 53 53 53 53 53 53 53 Cows 89 92 91 94 94 94 94 94 Replacements 7-11

months 54 57 57 59 60 60 60 60 Replacements 12-23

months 63 66 66 68 69 69 69 69 Steer Stockers 55 56 58 58 58 58 57 57 Heifer Stockers 51 56 60 59 59 59 59 59 Feedlot Cattle 39 38 39 39 39 42 42 43 Note: To convert to a daily emission factor, the yearly emission factor can be divided by 365 (the number of days in a year).

### A-209 Step 3c: Estimate Total Emission s Emissions were summed for each month and for each state population category using the daily emission factor for a representative animal and the number of animals in the category. The following equation was used:

Emissions state 

### = DayEmit state 

### × Days/Month × SubPop state 

### Where, 

### DayEmit State 

### = The emission factor for the subcategory and state (kg CH 4 

### /head/day) Days/Month = The number of days in the month SubPop State 

### = The number of animals in the subcategory and state during the month

This process was repeated for each month, and the totals for each state subcategory were summed to achieve an emission estimate for a state for the entire year and state estimates were summed to obtain the national total. The estimates for each of the 10 subcategories of cattle are list ed in Table A-179. The emissions for each subcategory were then summed to estimate total emissions from beef cattle and dairy cattle for the entire year.

Table A-179: CH 4 

### Emissions from Cattle (Gg) Cattle Type 1990 1995 2000 2005 2006 2007 2008 2009 Dairy 1,513 1,440 1,460 1,449 1,479 1,544 1,564 1,581

Cows 1,242 1,183 1,209 1,197 1,219 1,271 1,289 1,304 Replacements 7-11 months 58 56 55 56 57 60 60 61 Replacements 12-23 months 212 201 196 196 203 213 214 216 Beef 4,502 5,128 4,790 4,731 4,803 4,837 4,796 4,742

Bulls 114 126 122 117 120 117 117 116 Cows 2,887 3,223 3,059 3,056 3,079 3,083 3,065 2,995 Replacements 7-11 months 69 85 74 80 82 81 79 77 Replacements 12-23 months 188 241 204 217 228 228 220 215 Steer Stockers 563 661 508 472 475 478 473 489 Heifer Stockers 305 374 322 298 298 294 288 298 Feedlot Cattle 375 416 502 489 521 556 554 553 Total 6,015 6,567 6,251 6,180 6,282 6,381 6,374 6,323 Notes: Totals may not sum due to independent rounding. Because calves younger than 7 months consume mainly milk the IPCC recom mends the use of methane conversion factor of zero, resulting in no methane emissions from this subcategory of cattle.


## Emission Estimates from Other Livestock All livestock population data, except for horses, were ta ken from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) agricultural statistics database (USDA 2009). Table A- 183of the Manure Management Annex shows the population data for all livestock species that were used for estimating all livestock-related emissions. For each animal category, the USDA publishes monthly, annual, and multi-year livestock population and production estimates. All data were downloaded from the USDA-NASS agricultural database (USDA 2010). The Food and Agriculture Organization (FAO) publishes horse population data. These data were accessed from the FAOSTAT database (FAO 2010). Methane emissions from sheep, goats, swine, and horses were estimated by multiplying published national population estimates by the IPCC emission factor for each year. Table A-180 shows the emission factors used for these other livestock.
Enteric fermentation emissions from all livestock types are shown in Table A-181 and Table A-182. Table A-180: Emission Factors for Other Livestock (kg CH 4 

### /head/year) Livestock Type Emission Factor Sheep 8 Goats 5 Horses 18 Swine 1.5 Source: IPCC (2006)

### A-210 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A-181: CH 4 

### Emissions from Enteric Fermentation (Tg CO 2 

### Eq.) Livestock Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Beef Cattle 94.5 107.7 100.6 99.9 100.0 100.0 98.3 99.3 100.9 101.6 100.7 99.6 Dairy Cattle 31.8 30.2 30.7 30.5 30.6 28.4 29.9 30.4 31.1 32.4 32.9 33.2 Horses 1.9 1.9 2.0 2.1 2.3 2.6 3.0 3.5 3.6 3.6 3.6 3.6 Sheep 1.9 1.5 1.2 1.2 1.1 1.1 1.0 1.0 1.0 1.0 1.0 1.0 Swine 1.7 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 2.1 2.1 2.1 Goats 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 Total 132.1 143.5 136.5 135.7 136.1 134.4 134.4 136.5 138.8 141.0 140.6 139.8 Note: Totals may not sum due to independent rounding.



### Table A-182: CH 4 

### Emissions from Enteric Fermentation (Gg)
Livestock Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Beef Cattle 4,502 5,128 4,790 4,757 4,761 4,762 4,680 4, 731 4,803 4,837 4,796 4,742 Dairy Cattle 1,513 1,440 1,460 1,453 1,457 1,354 1,422 1, 449 1,479 1,544 1,564 1,581 Horses 91 92 94 99 108 126 144 166 171 171 171 171 Sheep 91 72 56 55 53 51 49 49 50 49 48 46 Swine 81 88 88 88 90 90 91 92 93 98 101 99 Goats 13 12 12 12 13 13 14 15 15 16 16 16 Total 6,290 6,832 6,502 6,464 6,481 6,396 6,400 6, 500 6,611 6,715 6,696 6,655 Note: Totals may not sum due to independent rounding.

### A-211 

# 3.10. Methodology for Estimating CH 4 

# and N 2 

# O Emissions from Manure Management

The following steps were used to estimate methane (CH 4 

### ) and nitrous oxide (N 2 

### O) emissions from the management of livestock manure. Nitrous oxide emissions associated with pasture, range, or paddock systems and daily spread systems are included in the emission estimates for Agricultural Soil Management (see the Agricultural Soils Management Annex).

Step 1: Livestock Population Characterization Data
Annual animal population data for 1990 through 2009 for all livestock types, except horses and goats were obtained from the USDA National Agricultural Statistics Service (NASS). The population data used in the emissions calculations for cattle, swine, and sheep were downloaded from the USDA NASS Population Estimates Database (USDA 2009a). Poultry population data were obtained from USDA NASS reports (USDA 1995a, 1995b, 1998a, 1999, 2004a, 2004b, 2010a, and 2010b). Horse population data were obtained from the Food and Agriculture Organization (FAO) FAOSTAT database (FAO 2010). Goat population data for 1992, 1997, 2002, and 2007 were obtained from the Census of Agriculture (USDA 2009a). Additional data sources used and adjustments to these data sets are described below.

Cattle: For all cattle groups (cows, heifers, steers, bulls, and calves), the US DA data provide cattle inventories from January (for each state) and July (as a U.S. total only) of each year. Cattle inventories change over the course of the year, sometimes significantly, as new calves are born and as cattle are moved into feedlots and subsequently slaughtered; therefore, to develop the best estimate for the annual animal population, the populations and the individual characteristics, such as weight and weight gain, pregnancy, and lactation of each animal type were tracked in the Cattle Enteric Fermentation Model (CEFM). For animals that have relatively static populations throughout the year, such as mature cows and bulls, the January 1 values were used. For animals that have flucatuating populations throughout the year, such as calves and growing heifers and steer, the populations ar e modeled based on a transition matrix that uses annual population data from USDA along with USDA data on animal birt hs, placement into feedlots, and slaughter statistics.

Swine: The USDA provides quarterly data for each swine subcategory: breeding, market under 50 pounds (under 23 kg), market 50 to 119 pounds (23 to 54 kg), market 120 to 179 pounds (54 to 81 kg), and market 180 pounds and over (greater than 82 kg). The average of the quarterly data was used in the emissions calculations. For states where only December inventory is reported, the December data were used directly.
Sheep: Population data for lamb and sheep on feed are not available after 1993 (USDA 1994). The number of lamb and sheep on feed for 1994 through 2009 were calculated using the average of the percent of lamb and sheep on feed from 1990 through 1993. In addition, all of the sheep and lamb “on feed” are not necessarily on “feedlots;” they may be on pasture/crop residue supplemented by feed. Data for those animals on feed that are in feedlots versus pasture/crop residue were provided only for lamb in 1993. To calculate the populations of sheep and lamb in feedlots for all years, it was assumed that the percentage of sheep and lamb on feed that are in feedlots versus pasture/crop residue is the same as that for lambs in 1993 (Anderson 2000).

Goats: Annual goat population data by state were available for 1992, 1997, 2002, and 2007 (USDA 2009a). The data for 1992 were used for 1990 through 1992 and the data for 2007 were used for 2007 through 2009. Data for 1993 through 1996, 1998 through 2001, and 2003 through 2006 were ex trapolated based on the 1992, 1997, and 2002 Census data.
Poultry: The USDA provides population data for hens (one year old or older), pullets (hens younger than one year old), broilers, other chickens, and turkeys (U SDA 1995a, 1995b, 1998a, 1999, 2004a, 2004b, 2010a, and 2010b). The annual population data for boilers and turkeys were adjusted for the turnover (i.e., slaughter) rate (Lange 2000). All poultry population data were adjusted to account for states that report non-disclosed populations to USDA NASS. The combined populations of the states reporting non-disclosed populations are reported as “other” states. State populations for the non-disclosed states were estimated by equally distribu ting the population attributed to “other” states to each of the non-disclosed states.

Horses: The FAO publishes annual total U.S. horse population, which were accessed from the FAOSTAT database (FAO 2010). State horse population data were estimated using state population distributions from the 1992, 1997, and 2002 Census of Agriculture and the FAO national population data. A summary of the livestock population characterization data used to calculate CH4 and N2O emissions is presented in Table A- 183.

### A-212 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Step 2: Waste Characteristics Data

Methane and N 2 

### O emissions calculations are based on the following animal characteristics for each relevant livestock population:



### • Volatile solids (VS) excretion rate;



### • Maximum methane producing capacity (B o 

### ) for U.S. animal waste;



### • Nitrogen excretion rate (Nex); and



### • Typical animal mass (TAM).

Table A- 184 presents a summary of the waste characteristics used in the emissions estimates. Published sources were reviewed for U.S.-specific livestock waste characterization data that would be consistent with the animal population data discussed in Step 1. The USDA’s Agricultural Waste Management Field Handbook (AWMFH; USDA 1996a, 2008) is one of the primary sources of waste characteristics. Data from the 1996 and 2008 USDA AWMFH were used to estimate VS and Nex for most animal groups across the time se ries of the inventory, as shown in Table A-3. In some cases, data from the American Society of Agricultural Engineers, Standard D384.1 (ASAE 1998) were used to supplement the USDA data. The VS and Nex data for breeding swine are from a combination of the types of animals that make up this animal group, namely gestating and farrowing swine and boars. It is assumed that a group of breeding swine is typically broken out as 80 percent gestating sows, 15 percent farrowing swine, and 5 percent boars (Safley 2000). Due to the change in USDA reporting of hens and pullets, new nitrogen and VS excretion rates were calculated for the combined population of hens and pullets; a weighted average rate was calcula ted based on hen and pullet population data from 1990 to 2004.
The method for calculating VS excretion and Nex from beef and dairy cows, heifers, and steers is based on the relationship between animal performance characteristics such as diet, lactation, and weight gain and energy utilization. The method used is outlined by the IPCC Tier II methodology, and is modeled in the enteric fermentation portion of the inventory in order to take advantage of the detailed diet and animal performance data assembled as part of the Tier II analysis for cattle.
Volatile solids content of manure is the fraction of the diet consumed by cattle that is not digested and thus excreted as fecal material; fecal material combined with urinary excretions constitutes manure. The enteric fermentation model requires the estimation of gross energy intake and its fractional digestibility to estimate enteric CH4 emissions (see the Enteric Fermentation Annex for details on the enteric ener gy model). These two inputs are used to calculate the indigestible energy per animal unit as gross energy minus di gestible energy plus the amount of gross energy for urinary energy excretion per animal unit (2 or 4 percent). This value is then converted to VS production per animal unit using the typical conversion of dietary gross energy to dry organic ma tter of 18.45 MJ/kg, after subtracting out the ash content of manure. The current equation recommended by the 2006 IPCC Guidelines is: 

# ( )( ) 

# [ ] 18.45 1 GE UE DE

- GE (kg) production VS ASH

− × × + =

Where, 

### GE = Gross energy intake (MJ) DE = Digestible energy (MJ) (UE × GE) = Urinary energy expressed as fr action of GE, assumed to be 0.04 except for feedlots which are reduced 0.02 as a result of the high grain content of their diet. ASH = Ash content of manure calculated as a fraction of the dry matter feed intake (assumed to be 0.08). 18.45 = Conversion factor for dietary GE per kg of dry matter (MJ per kg). This value is relatively constant across a wide range of forage and grain-based feeds commonly consumed by livestock.

Nitrogen uptake in cattle is carried out through dietary protein intake. However, when feed intake of protein exceeds the nutrient requirements of the animal, the excess nitr ogen is excreted, primarily through the urine. To calculate the nitrogen excreted by each animal type, the cattle enteric fermentation model (CEFM) utilizes the energy balance calculations recommended by the IPCC (2006) for gross energy and the energy required for growth along with inputs of weight gain, milk production, and the percent of crude protein in the diets. The total nitrogen excreted is measured in the CEFM as nitrogen consumed minus nitrogen retained by the animal for growth and in milk. The basic equation for calculating Nex is shown below, followed by the equations for each of the constituent parts.

### A-213 

# ( )

milk growth consumed excreted 

## N N N N +

− =

Where,

N excreted 

### = Daily N excreted per animal, kg per animal per day. N consumed 

### = Daily N intake per animal, kg per animal per day N growth 

### = Nitrogen retained by the animal for growth, kg per animal per day N milk 

### = Nitrogen retained in milk, kg per animal per day

The equation for nitrogen consumed is based on the 2006 IPCC Guidelines, and is estimated as:



## ⎥ ⎥ ⎥ ⎥

⎦ ⎤

⎢ ⎢ ⎢ ⎢

⎣ ⎡

⎟ ⎟ ⎟ ⎟

⎠ ⎞

⎜ ⎜ ⎜ ⎜

⎝ ⎛

= 25

. 6 100 %

* 45

. 18 CP

GE N consumed



### Where:

N consumed 

### = Daily N intake per animal, kg per animal per day GE = Gross energy intake, as calculated in the CEFM, MJ per animal per day 18.45 = Conversion factor for dietary GE per kg of dry matter, MJ per kg. CP% = Percent crude protein in diet, input into the CEFM 6.25 = Conversion from kg of dietary protein to kg of dietary N, kg feed per kg N

The portion of consumed nitrogen that is retained as product equals the nitrogen required for weight gain plus that in milk. The nitrogen retained in body weight gain by stockers, replacements, or feedlot animals is calculated using the net energy for growth (NEg), weight gain (WG), and other conversion factors and constants. The equation matches current 2006 IPCC Guidelines recommendations, and is as follows: 

# ( )

25

. 6 1000 * 03

. 7 268 * ⎭ ⎬ ⎫ ⎩ ⎨ ⎧ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

−

= WG NEg WG

N growth



### Where,

N growth 

### = Nitrogen retained by the animal for growth, kg per animal per day WG = Daily weight gain of the animal, kg per day 268 = Constant from 2006 IPCC Guidelines 7.03 = Constant from 2006 IPCC Guidelines NEg = Net energy required for growth, MJ per animal per day 1,000 = Conversion from grams to kilograms 6.25 = Conversion from kg of dietary protein to kg of dietary N, kg feed per kg N

The N content of milk produced also currently matches the 2006 IPCC Guidelines. Milk nitrogen retained as product is calculated using the following equation:



## 38

. 6 100 % * ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

= pr milk
N milk

### A-214 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Where,

N milk 

### = Nitrogen retained in milk, kg per animal per day milk = Milk production, kg per day pr% = Percent protein in milk, estimated from the fat content as 1.9 + 0.4 * %Fat 100 = Conversion from percent to value (e.g., 4% to 0.04) 6.38 = Conversion from kg Protein to kg N

The VS and N equations above were used to calculate VS and Nex rates for each state, cattle type, and year. Table A- 186 presents the state-specific VS and Nex production rates used for cattle in 2009.

Step 3: Waste Management System Usage Data
Table A- 187 summarizes 2009 manure distribution data among waste management systems (WMS) at beef feedlots, dairies, dairy heifer facilities , and swine, layer, broiler, and turk ey operations. Manure from the remaining animal types (beef cattle not on feed, sheep, horses, and goats ) is managed on pasture, range, or paddocks, on drylot, or with solids storage systems. Additional information on the development of the manure distribution estimates for each animal type is presented below. Definitions of each WMS type are presented in Table A- 188.
Beef Cattle and Dairy Heifers : The beef feedlot and dairy heifer WMS data were developed using information from EPA's Office of Water's engineering cost analyses conduc ted to support the development of effluent limitations guidelines for Concentrated Animal Feeding Operations (EPA 2002b). Based on EPA site visits and state contacts supporting this work and additional contacts with the national USDA office to estimate the percent of beef steers and heifers in feedlots (Milton 2000), feedlot manure is almost ex clusively managed in drylots. Therefore, for these animal groups, the percent of manure deposited in drylots is assumed to be 100 percent. In addition, there is a small amount of manure contained in runoff, which may or may not be collected in runoff ponds. The runoff from feedlots was calculated by region in Calculations: Percent Distribution of Manure for Waste Management Systems (ERG 2000b) and was used to estimate the percentage of manure managed in runoff ponds in a ddition to drylots; this percentage ranges from 0.4 to 1.3 percent. The percentage of manure generating emissions from beef feedlots is therefore greater than 100 percent. The remaining population categories of beef cattle outside of feedlots are managed through pasture, range, or paddock systems, which are utilized for the majority of the population of beef cattle in the country.
Dairy Cows: The WMS data for dairy cows were developed using data from the Census of Agriculture, EPA’s Office of Water, USDA, and expert sources. Farm-size di stribution data are reported in the 1992, 1997, and 2002 Census of Agriculture (USDA 2009a). It was assumed that the data provided for 1992 were the same as that for 1990 and 1991, and data provided for 2002 were the same as that for 2003 through 2009. Data for 1993 through 1996 and 1998 through 2001 were extrapolated using the 1992, 1997, and 2002 data. The percent of waste by system was estimated using the USDA data broken out by geographic region and farm size.
Based on EPA site visits and state contacts, manure from dairy cows at medium (200 through 700 head) and large (greater than 700 head) operations are managed using either flush systems or scrape/slurry systems. In addition, they may have a solids separator in place prior to their storage component. Estimates of the percent of farms that use each type of system (by geographic region) were developed by EPA's O ffice of Water, and were used to estimate the percent of waste managed in lagoons (flush systems), liquid/slurry syst ems (scrape systems), and solid storage (separated solids) (EPA 2002b). Manure management system data for small (fewer than 200 head) dairies were obtained from USDA (2000a). These operations are more likely to use liquid/slurry and solid storage management systems than anaerobiclagoon systems. The reported manure management systems were deep pit, liquid/slurry (includes slurry tank, slurry earthbasin, and aerated lagoon), anaerobic lagoon, and solid storage (i ncludes manure pack, outside storage, and inside storage).
Data regarding the use of daily spread and pasture, range, or paddock systems for dairy cattle were obtained from personal communications with personnel from several organizations. These organizations include state NRCS offices, state extension services, state universities, USDA NASS, and other experts (Deal 2000, Johnson 2000, Miller 2000, Stettler 2000, Sweeten 2000, and Wright 2000). Contacts at Cornell University provided survey data on dairy manure management practices in New York (Poe et al. 1999). Census of Agriculture population data for 1992, 1997, and 2002 (USDA 2009a) were used in conjunction with the state data obtained from personal communications to determine regional percentages of total dairy cattle and dairy waste that are mana ged using these systems. These percentages were applied to the total annual dairy cow and heifer state population data for 1990 through 2009, which were obtained from the USDA NASS (USDA 2009b).

### A-215 Of the dairies using systems other than daily spread and pasture, range, or paddock systems, some dairies reported using more than one type of manure management system. Due to limitations in how USDA reports the manure management data, the total percent of systems for a region and farm size is greater than 100 percent. However, manure is typically partitioned to use only one manure management system, rather than transferred between several different systems. Emissions estimates are only calculated for the final manure management system used for each portion of manure. To avoid double counting emissions, the reported percentages of systems in use were adjusted to equal a total of 100 percent using the same distribution of systems. For exam ple, if USDA reported that 65 percent of dairies use deep pits to manage manure and 55 percent of dairies use anaerobi c lagoons to manage manure, it was assumed that 54 percent (i.e., 65 percent divided by 120 percent) of the manure is ma naged with deep pits and 46 percent (i.e., 55 percent divided by 120 percent) of the manure is managed with anaerobic lagoons (ERG 2000a).

Swine: The distribution of manure managed in each WMS was estimated using data from a USDA report and EPA’s Office of Water site visits (USDA 1998, ERG 2000a ). For operations with less than 200 head, manure management system data were obtained from USDA (USDA 1998). It was assumed that those operations use pasture, range, or paddock systems. For swine operations with greater than 200 head, the percent of waste managed in each system was estimated using the EPA and USDA data broken out by geographic region and farm size. Farm-size distribution data reported in the 1992, 1997, and 2002 Census of Agriculture (US DA 2009a) were used to determine the percentage of all swine utilizing the various manure management systems. It was assumed that the swine farm size data provided for 1992 were the same as that for 1990 and 1991, and data provided for 2002 were the same as that for 2003 through 2009. Data for 1993 through 1996 and 1998 through 2001 were extrapolated using the 1992, 1997, and 2002 data. The reported manure management systems were deep pit, liquid/slurry (i ncludes above- and below-ground slurry), anaerobic lagoon, and solid storage (includes solids separated from liquids).
Some swine operations reported using more than one mana gement system; therefore, the total percent of systems reported by USDA for a region and farm size was greater than 100 percent. Typically, this means that a portion of the manure at a swine operation is handled in one system (e.g., liquid system), and a separate portion of the manure is handled in another system (e.g., dry system). However, it is unlikely that the same manure is moved from one system to another, which could result in increased emissions, so reported systems data were normalized to 100 percent for incorporation into the WMS distribution, using the same method as described above for dairy operations.
Sheep: Waste management system data for sheep were obtained from USDA NASS sheep report for years 1990 through 1993 (USDA 1994). Data for 2001 are obtained from USDA APHIS sheep report (USDA 2003). The data for years 1994-2000 are calculated assuming a lin ear progression from 1993 to 2001. Due to lack of additional data, data for years 2002 and beyond are assumed to be the same as 2001. It was assumed that all sheep manure not deposited in feedlots was deposited on pasture, range, or paddock lands (Anderson 2000).

Goats and Horses: Waste management system data for 1990 to 2009 were obtained from Appendix H of Global Methane Emissions from Livestock and Poultry Manure (EPA 1992). It was assumed that all manure not deposited in pasture, range, or paddock lands was managed in dry systems.
Poultry—Hens (one year old or older), Pullet s (hens less than one year old), and Other Chickens: Waste management system data for 1992 were obtained from Global Methane Emissions from Livestock and Poultry Manure (EPA 1992). These data were also used to represent 1990 and 1991. The percentage of layer operations using a shallow pit flush house with anaerobic lagoon or high-rise house without bedding was obtained for 1999 from a United Egg Producers voluntary survey (UEP 1999). These data were a ugmented for key poultry states (AL, AR, CA, FL, GA, IA, IN, MN, MO, NC, NE, OH, PA, TX, and WA) with USDA data (USDA 2000b). It was assumed that the change in system usage between 1990 and 1999 is proportionally distribut ed among those years of the inventory. It was assumed that system usage in 2000 through 2009 was equal to that estimated for 1999. Data collected for EPA's Office of Water, including information collected during site visits (EPA 2002b), were used to estimate the distribution of waste by management system and animal type.
Poultry—Broilers and Turkeys: The percentage of turkeys and broilers on pasture was obtained from Global Methane Emissions from Livestock and Poultry Manure (EPA 1992). It was assumed that one percent of poultry waste is deposited in pastures, ranges, and paddocks (EPA 1992). The re mainder of waste is assumed to be deposited in operations with bedding management.

Step 4: Emission Factor Calculations
Methane conversion factors (MCFs) and N 2 

### O emission factors (EFs) used in the emission calculations were determined using the methodologies presented below.

### A-216 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Methane Conversion Factors (MCFs)
Climate-based IPCC default MCFs (IPCC 2006) were used for all dry systems; these factors are presented in Table A- 189. A U.S.-specific methodology was used to develop MCFs for all lagoon and liquid systems.
For animal waste managed in dry systems, the appropriate IPCC default MCF was applied based on annual average temperature data. Each state and year in the inventor y was assigned a climate classifi cation of cool, temperate or

warm. For anaerobic lagoons and other liquid systems a climate-based approach based on the van’t Hoff-Arrheniusequation was developed to estimate MCFs that reflects the seasonal changes in temperatures, and also accounts for longterm retention time. This approach is consistent with th e recently revised guidelines from IPCC (IPCC 2006). The van’t Hoff-Arrhenius equation, with a base temp erature of 30°C, is shown in the following equation (Safley and Westerman 1990):

Where,



### T 1 

### = 303.15K T 2 

### = Ambient temperature (K) for climate zone (i n this case, a weighted value for each state) E = Activation energy constant (15,175 cal/mol) R = Ideal gas constant (1.987 cal/K mol)

The factor f represents the proportion of VS that are biologically available for conversion to CH 4 

### based on the temperature of the system. For those animal populations using liquid manure management systems or manure runoff ponds (i.e., dairy cow, dairy heifer, layers, beef in feedlots, and swine) monthly average state temperatures were based on the counties where the specific animal population resides (i.e., the temperatures were weighted based on the percent of animals located in each county). The average county and state temperature data were obtained from the National Climate Data Center (NOAA 2009). County population data were calculated from state-level population data from NASS and county-state distribution data from the 1992, 1997, and 2002 Census data (USDA 2009a). County population distribution data for 1990 and 1991 were assumed to be the same as 1992; county population distribution data for 1993 through 1996 were extrapolated based on 1992 and 1997 data; county population data for 1998 through 2001 were extrapolated based on 1997 and 2002 data; and county population data for 2003 to 2009 were assumed to be the same as 2002.
Annual MCFs for liquid systems are calculated as follows for each animal type, state, and year of the inventory:



### • The weighted-average temperature for a state is calculated using the population estimates and average monthly temperature in each county.



### • Monthly temperatures are used to calculate a monthl y van't Hoff-Arrhenius “f” factor, using the equation presented above. A minimum temperature of 5°C is used for uncovered anaerobic lagoons and 7.5°C is used for liquid/slurry and deep pit systems.



### • Monthly production of VS added to the system is estimated based on the number of animals present.



### • For lagoon systems, the calculation of methane includes a management and design practices (MDP) factor. This factor, equal to 0.8, was developed based on model comparisons to empirical CH 4 

### measurement data from anaerobic lagoon systems in the United States (ERG 2001). The MDP factor represents a variety of factors that may affect methane production in lagoon systems.



### • The amount of VS available for conversion to CH 4 

### is assumed to be equal to the amount of VS produced during the month (from Step 3). For anaerobic lagoons, the amount of VS available also includes VS that may remain in the system from previous months.



### • The amount of VS consumed during the month is equal to the amount available for conversion multiplied by the “f” factor. ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

−

=
2 1 1 2 ) ( exp T RT T T E f

### A-217 

### • For anaerobic lagoons, the amount of VS carried over from one month to the next is equal to the amount available for conversion minus the amount consumed. Lagoons are also modeled to have a solids clean-out once per year, occurring after the month of September.



### • The estimated amount of CH 4 

### generated during the month is equal to the monthly VS consumed multiplied by the maximum CH 4 

### potential of the waste (B o 

### ).

The annual MCF is then calculated as:

o annual annual 4

annual 

## B produced VS generated

CH MCF

× =

Where,



### MCF annual 

### = Methane conversion factor VS produced annual 

### = Volatile solids excreted annually B o 

### = Maximum CH 4 

### producing potential of the waste

In order to account for the carry-over of VS from one year to the next, it is assumed that a portion of the VS from the previous year are available in the lagoon system in the next year. For example, the VS from October, November, and December of 2005 are available in the lagoon system starting January of 2006 in the MCF calculation for lagoons in 2006. Following this procedure, the resulting MCF for lagoons acco unts for temperature variation throughout the year, residual VS in a system (carry-over), and management and design practices that may reduce the VS available for conversion to CH 4 

### . It is assumed that liquid-slurry systems have a retention time less than 30 days, so the liquid-slurry MCF calculation doesn’t reflect the VS carry-over.
The liquid system MCFs are presented in Table A-8 by state, WMS, and animal group for 2009.

Nitrous Oxide Emission Factors

Direct N 2 

### O emission factors for manure management systems (kg N 2 

### O-N/kg excreted N) were set equal to the most recent default IPCC factors (IPCC 2006), presented in Table A- 191.

Indirect N 2 

### O emission factors account for two fractions of nitrogen losses: volatilization of ammonia (NH 3 

### ) and NO X 

### (Frac gas 

### ) and runoff/leaching (Frac runoff/leach 

### ). IPCC default indirect N 2 

### O emission factors were used to estimate indirect N 2 

### O emissions. These factors are 0.010 kg N 2 

### O-N/kg N for volatilization and 0.0075 kg N 2 

### O/kg N for runoff/leaching.
EPA developed country-specific estimates of nitrogen losses for Frac gas 

### and Frac runoff/leach 

### for the U.S. The vast majority of volatilization losses are NH 3 

### . Although there are also some small losses of NO X 

### , no quantified estimates were available for use and those losses are believed to be small (about 1 percent) in comparison to the NH 3 

### losses. Therefore, Frac gas 

### values were based on WMS-specific volatilization values estimated from U.S. EPA’s National Emission Inventory - Ammonia Emissions from Animal Agriculture Operations (EPA 2005). To estimate Frac runoff/leach 

### , EPA used data from EPA’s Office of Water that estimate the amount of runoff from beef, dairy, and heifer operations in five geographic regions of the country (EPA 2002b). These estimates were used to develop U.S. runoff factors by animal type, WMS, and region. Nitrogen losses from leaching are believed to be small in comparison to the runoff losses; therefore, Frac runoff/leach 

### was set equal to the runoff loss factor. Nitrogen losses fro m volatilization and runoff/leaching are presented in Table A192.

Step 5: CH 4 

### and N 2 

### O Emission Calculations
To calculate methane emissions for animals other than cattle, EPA first estimated the amount of volatile solids excreted in manure that is managed in each WMS:



### 365.25 WMS VS 1000 TAM Population excreted VS Animal State, WMS Animal, State, 

### × × × × =



### Where, VS excreted State, Animal, WMS 

### = Amount of VS excreted in ma nure managed in each WMS for each animal type (kg/yr)

### A-218 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 

### Population State, Animal 

### = Annual average state animal population by animal type (head) TAM = Typical animal mass (kg) VS = Volatile solids production rate (kg VS/1000 kg animal mass/day) WMS = Distribution of manure by WMS for each animal type in a state (percent) 

### 365.25 = Days per year

Using the CEFM VS data for cattle, the amount of VS excreted was calculated using the following equation:



### WMS VS Population excreted VS Animal State, WMS Animal, State, 

### × × =


### Where, VS excreted State, Animal, WMS 

### = Amount of VS excreted in ma nure managed in each WMS for each animal type (kg/yr) Population State, Animal 

### = Annual average state animal population by animal type (head) VS = Volatile solids produc tion rate (kg VS/animal/year) WMS = Distribution of manure by WMS for each animal type in a state (percent)

For all animals, the estimated amount of VS was used to calculate methane emissions using the following equation: 

# ( ) ∑ × × × =

WMS Animal, State, o WMS Animal, State, 4 

## 0.662 MCF B excreted VS CH

Where, 

### CH 4 

### = CH 4 

### emissions (kg CH 4 

### /yr) VS excreted WMS, State 

### = Amount of VS excreted in ma nure managed in each WMS (kg/yr) B o 

### = Maximum CH 4 

### producing capacity (m 3 

### CH 4 

### /kg VS) MCF animal, state, WMS 

### = MCF for the animal group, state and WMS (percent) 0.662 = Density of methane at 25 o 

### C (kg CH 4 

### /m 3 

### CH 4 

### )

EPA developed a calculation to estimate the amount of CH 4 

### emitted from anaerobic digestion (AD) systems utilizing CH 4 

### capture and combustion technology. First, EPA as sumed that AD systems produce 90 percent of the maximum CH 4 

### producing capacity. This value is applied for all climate regions and AD system types. However, EPA realizes that the actual amount of CH 4 

### produced by each AD system is very variable and will change based on operational and climate conditions and an assumption of 90 percent is likely overestimating CH 4 

### production from some systems and underestimating CH 4 

### production in other systems. The CH 4 

### production of AD systems is calculated using the equation below:



## 0.90 365.25 0.662 B VS 1000 TAM AD Population AD Production CH

o System AD System AD 4 

## × × × × × × =

Where, CH 4 

### Production AD AD system 

### = CH 4 

### production from a particular AD system, (kg/yr) Population AD state 

### = Number of animals on a particular AD system VS = Volatile solids production rate (kg VS/1000 kg animal mass-day) TAM = Typical Animal Mass (kg/head) B o 

### = Maximum CH 4 

### producing capacity (CH4 m 3 

### /kg VS) 0.662 = Density of methane at 25 o 

### C (kg CH 4 

### /m 3 

### CH 4 

### ) 365.25 = Days/year 0.90 = CH 4 

### production factor for AD systems

Next, EPA considered the collection efficiency (CE) and destruction efficiency (DE) of the AD system. The CE of covered lagoon systems was assumed to be 75 percent, and the CE of complete mix and plug flow AD systems was assumed to be 99 percent (EPA 2008). The CH 4 

### DE from flaring or burning in an engine was assumed to be 98 percent;

### A-219 therefore, the amount of CH 4 

### that would not be flared or combusted was assumed to be 2 percent (EPA 2008). The amount of CH 4 

### produced by systems with anaerobic digestion was calculated with the following equation: 

# ( ) 

# [ ] ( ) 

# [ ] ∑ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

×

+ − × ×

= Systems AD Animal, State, system AD system AD 4 system AD system AD 4
4 

### CE

- 1 AD Production CH DE 1 CE AD Production CH AD Emissions CH

Where: CH 4 

### Emissions AD = CH 4 

### emissions from AD systems, (kg/yr) CH 4 

### Production AD AD system 

### = CH 4 

### production from a particular AD system, (kg/yr) CE AD system 

### = Collection efficiency of the AD system, varies by AD system type DE = Destruction efficiency of the AD system, 0.98 for all systems

In addition to methane emissions, EPA also estimated total N 2 

### O emissions from manure management systems. Total N 2 

### O emissions were calculated by summing direct and indirect N 2 

### O emissions. The first step in estimating direct and indirect N 2 

### O emissions was calculating the amount of nitrogen excreted in manure and managed in each WMS for animals other than cattle using the following equation:



### 365.25 Nex 1000 TAM WMS Population excreted N Animal State, WMS Animal, State, 

### × × × × =

Where,



### N excreted State, Animal, WMS 

### = Amount of N excreted in ma nure managed in each WMS for each animal type (kg/yr) Population state 

### = Annual average state animal population by animal type (head) WMS = Distribution of manure by waste management system for each animal type in a state (percent) TAM = Typical animal mass (kg) Nex = Total Kjeldahl nitrogen excretion rate (kg N/1000 kg animal mass/day) 365.25 = Days per year

Using the CEFM Nex data for cattle, the amount of N excreted was calculated using the following equation:



### Nex WMS Population excreted N Animal State, WMS Animal, State, 

### × × =

Where,



### N excreted State, Animal, WMS 

### = Amount of N excreted in ma nure managed in each WMS for each animal type (kg/yr) Population state 

### = Annual average state animal population by animal type (head) WMS = Distribution of manure by waste management system for each animal type in a state (percent) Nex = Total Kjeldahl nitrogen excretion rate (kg N/animal/year)

For all animals, direct N 2 

### O emissions were calculated as follows:



### ∑ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

× × = WMS Animal, State, WMS WMS Animal, State, 2 

### 28 44 EF excreted N O N Direct

Where,



### Direct N 2 

### O = Direct N 2 

### O emissions (kg N 2 

### O/yr) N excreted State, Animal, WMS 

### = Amount of N excreted in ma nure managed in each WMS for each animal type (kg/yr) EF WMS 

### = Direct N 2 

### O emission factor from IPCC guidelines (kg N 2 

### O-N /kg N) 44/28 = Conversion factor of N 2 

### O-N to N 2 

### O

### A-220 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Indirect N 2 

### O emissions were calculated for all animals with the following equation:



### ∑
⎟ ⎟ ⎟ ⎟ ⎟ ⎟

⎠ ⎞

⎜ ⎜ ⎜ ⎜ ⎜ ⎜

⎝ ⎛

⎥ ⎦ ⎤ ⎢ ⎣ ⎡

× × × + ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎣ ⎡

× × ×

= WMS Animal, State,

ach runnoff/le WMS ch, runoff/lea WMS Animal, State, tion volatiliza WMS gas, WMS Animal, State,

2



### 28 44 EF 100 Frac excreted N 28 44 EF 100 Frac excreted N

O N Indirect

Where,



### Indirect N 2 

### O = Indirect N 2 

### O emissions (kg N 2 

### O/yr) N excreted State, Animal, WMS 

### = Amount of N excreted in ma nure managed in each WMS for each animal type (kg/yr) Frac gas,WMS 

### = Nitrogen lost through volatilization in each WMS Frac runoff/leach,WMS 

### = Nitrogen lost through runoff and leaching in each WMS; data were not available for leaching so the value reflects only runoff EF volatilization 

### = Emission factor for volatilization (0.010 kg N 2 

### O-N/kg N) EF runoff/leach 

### = Emission factor for runoff/leaching (0.0075 kg N 2 

### O-N/kg N) 

### 44/28 = Conversion factor of N 2 

### O-N to N 2 

### O

Emission estimates of CH 4 

### and N 2 

### O by animal type are presented for all years of the inventory in Table A- 193 and Table A- 194, respectively. Emission estimates for 2009 are presented by animal type and state in Table A- 195 and Table A- 196, respectively.A-221Table A- 183: Livestock Population (1,000 Head)Animal Type 199019952000 2001 2002 2003 2004 2005 2006 2007 2008 2009Dairy Cattle14,14413,59013,19113,21713,16513,21513,02113,16513,39813,48713,658 13,759Dairy Cows 10,0159,4829,183 9,172 9,106 9,142 8,9889,004 9,104 9,145 9,257 9,333Dairy Heifer 4,1294,1084,008 4,045 4,060 4,073 4,0334,162 4,294 4,343 4,401 4,426Swine153,94158,89958,86458,91360,02859,82760,73561,07361,88765,41767,408 66,142Market <50 lb. 18,35919,65619,574 19,659 19,863 19,929 20,22220,228 20,514 21,812 19,964 19,559Market 50-119 lb. 11,73412,83612,926 12,900 13,284 13,138 13,40013,519 13,727 14,557 17,219 17,077Market 120-179 lb. 9,44010,54510,748 10,708 11,013 11,050 11,22711,336 11,443 12,185 12,931 12,517Market >180 lb. 7,5108,9379,385 9,465 9,738 9,701 9,9229,997 10,113 10,673 11,193 11,084Breeding 6,8996,9266,231 6,181 6,129 6,011 5,9635,993 6,090 6,190 6,102 5,905Beef Cattle287,22895,68389,94889,11889,10288,23286,44186,95488,07087,63986,450 85,736Feedlot Steers 6,3577,2338,304 7,932 8,116 8,416 8,0188,116 8,724 8,674 8,481 8,450Feedlot Heifers 3,1923,8314,702 4,569 4,557 4,676 4,5214,536 4,801 4,730 4,589 4,514NOF Bulls 2,1602,3852,293 2,274 2,244 2,248 2,2012,214 2,258 2,214 2,207 2,184NOF Calves 22,56123,49922,569 22,389 22,325 21,997 21,78121,678 21,621 21,483 21,155 20,940NOF Heifers 10,18211,8299,781 9,832 9,843 9,564 9,3219,550 9,716 9,592 9,350 9,436NOF Steers 10,32111,7168,724 8,724 8,883 8,347 8,0678,185 8,248 8,302 8,233 8,501NOF Cows 32,45535,19033,575 33,398 33,134 32,983 32,53132,674 32,703 32,644 32,435 31,712Sheep11,3588,9897,0366,9086,6236,3216,1056,1356,2306,1205,950 5,747Sheep On Feed 1,1801,7712,963 3,256 3,143 3,049 2,9432,982 3,043 3,000 2,911 2,805Sheep NOF 10,1787,2184,073 3,652 3,480 3,272 3,1623,153 3,187 3,120 3,039 2,942Goats2,5162,3572,4192,4752,5302,6522,7742,8973,0193,1413,141 3,141Poultry31,537,0741,826,9772,033,1232,060,3982,097,6912,085,2682,130,8772,150,4102,154,2362,166,9362,175,990 2,086,699Hens >1 yr. 273,467299,071333,593 340,317 340,209 340,979 343,922348,203 349,888 346,613 339,859 339,526Pullets 73,16781,36995,159 95,656 95,289 100,346 101,42996,809 96,596 103,816 99,458 101,588Chickens 6,5457,6378,088 8,126 8,353 8,439 8,2488,289 7,938 8,164 7,589 8,496Broilers 1,066,2091,331,9401,506,127 1,525,413 1,562,015 1,544,155 1,589,209 1,613,091 1,612,3271,619,400 1,638,055 1,554,636Turkeys 117,685106,96090,155 90,887 91,826 91,349 88,06984,018 87,487 88,943 91,029 82,453Horses5,0695,1305,2405,5006,0007,0008,0009,2009,5009,5009,500 9,500Note: Totals may not sum due to independent rounding.1Prior to 2008, the Market <50 lbs category was <60 lbs and the Market 50-119 lbs category was Market 60-119 lbs; USDA updated the categories to be more consistent with international animalcategories.2NOF = Not on Feed3Pullets includes laying pullets, pullets younger than3 months, and pullets older than 3 months.A-222 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009Table A- 184: Waste Characteristics DataAnimal Group TAM (kg)TAMSourceTotal KjeldahlNitrogen, Nex(kg/day per1,000 kg mass)NexSourceMaximumMethaneGenerationPotential, Bo(m3CH4/kg VSadded)BoSourceVolatile Solids, VS(kg/day per 1,000 kgmass)VSSourceDairy Cows 680 EntericFermentationTable A- 171Table A-185Moffroid and Pape, 2010 0.24 Morris 1976Table A-185Moffroid and Pape, 2010Dairy Heifers 406-408 EntericFermentationTable A- 171Table A-185Moffroid and Pape, 2010 0.17 Bryant et. al. 1976Table A-185Moffroid and Pape, 2010Feedlot Steers 419-457 EntericFermentationTable A- 171Table A-185Moffroid and Pape, 2010 0.33 Hashimoto 1981Table A-185Moffroid and Pape, 2010Feedlot Heifers 384-430 EntericFermentationTable A- 171Table A-185Moffroid and Pape, 2010 0.33 Hashimoto 1981Table A-185Moffroid and Pape, 2010NOF Bulls 750 Shuyler 2000Table A- 186USDA 1996a 0.17 Hashimoto 1981Table A- 186USDA 1996aNOF Calves 118 USDA 1996aTable A- 186USDA 1996a, 2008 0.17 Hashimoto 1981Table A- 186USDA 1996a, 2008NOF Heifers 296-406 EntericFermentationTable A- 171Table A-185Moffroid and Pape, 2010 0.17 Hashimoto 1981Table A-185Moffroid and Pape, 2010NOF Steers 314-335 EntericFermentationTable A- 171Table A-185Moffroid and Pape, 2010 0.17 Hashimoto 1981Table A-185Moffroid and Pape, 2010NOF Cows 554-611 EntericFermentationTable A- 171Table A-185Moffroid and Pape, 2010 0.17 Hashimoto 1981Table A-185Moffroid and Pape, 2010Market Swine <50 lbs. 13 ERG 2010aTable A- 186USDA 1996a, 2008 0.48 Hashimoto 1984Table A- 186USDA 1996a, 2008Market Swine <60 lbs. 16 Safley 2000Table A- 186USDA 1996a, 2008 0.48 Hashimoto 1984Table A- 186USDA 1996a, 2008Market Swine 50-119 lbs. 39 ERG 2010aTable A- 186USDA 1996a, 2008 0.48 Hashimoto 1984Table A- 186USDA 1996a, 2008Market Swine 60-119 lbs. 41 Safley 2000Table A- 186USDA 1996a, 2008 0.48 Hashimoto 1984Table A- 186USDA 1996a, 2008Market Swine 120-179 lbs. 68 Safley 2000Table A- 186USDA 1996a, 2008 0.48 Hashimoto 1984Table A- 186USDA 1996a, 2008Market Swine >180 lbs. 91 Safley 2000Table A- 186USDA 1996a, 2008 0.48 Hashimoto 1984Table A- 186USDA 1996a, 2008Breeding Swine 198 Safley 2000Table A- 186USDA 1996a, 2008 0.48 Hashimoto 1984Table A- 186USDA 1996a, 2008Feedlot Sheep 25 EPA 1992Table A- 186ASAE 1998, USDA 2008 0.36 EPA 1992Table A- 186ASAE 1998, USDA 2008NOF Sheep 80 EPA 1992Table A- 186ASAE 1998, USDA 2008 0.19 EPA 1992Table A- 186ASAE 1998, USDA 2008Goats 64 ASAE 1999Table A- 186ASAE 1998 0.17 EPA 1992Table A- 186ASAE 1998Horses 450 ASAE 1999Table A- 186ASAE 1998, USDA 2008 0.33 EPA 1992Table A- 186ASAE 1998, USDA 2008Hens >/= 1 yr 1.8 ASAE 1999Table A- 186USDA 1996a, 2008 0.39 Hill 1982Table A- 186USDA 1996a, 2008Pullets 1.8 ASAE 1999Table A- 186USDA 1996a, 2008 0.39 Hill 1982Table A- 186USDA 1996a, 2008Other Chickens 1.8 ASAE 1999Table A- 186USDA 1996a, 2008 0.39 Hill 1982Table A- 186USDA 1996a, 2008A-223Broilers 0.9 ASAE 1999Table A- 186USDA 1996a, 2008 0.36 Hill 1984Table A- 186USDA 1996a, 2008Turkeys 6.8 ASAE 1999Table A- 186USDA 1996a, 2008 0.36 Hill 1984Table A- 186USDA 1996a, 2008A-224 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009Table A- 185: Estimated Volatile Solids andNitrogen Excreted Production Rate byyear for Animals Other Than Cattle (kg/day/1000 kg animal mass)Animal Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009VSMarket Swine<50 lb. 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8Market Swine 50-119 lb. 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4Market Swine120-179 lb. 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4Market Swine>180 lb. 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4Breeding Swine 2.6 2.6 2.6 2.62.6 2.6 2.6 2.611 2.623 2.634 2.645 2.656 2.668 2.679 2.69 2.701 2.713 2.724 2.735 2.735NOF Bulls 6.04 6.04 6.04 6.046.04 6.04 6.04 6.04 6.04 6.04 6.04 6.04 6.04 6.04 6.046.04 6.04 6.04 6.04 6.04NOF Calves 6.41 6.41 6.41 6.41 6.41 6.41 6.41 6.5175 6.625 6.733 6.84 6.948 7.055 7.163 7.27 7.3787.485 7.593 7.7 7.7Sheep 9.2 9.2 9.2 9.2 9.2 9.2 9.2 9.2 9.2 9.11 9.02 8.93 8.84 8.75 8.668.57 8.48 8.39 8.3 8.3Goats 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5Hens >1 yr. 10.091 10.091 10.091 10.091 10.091 10.091 10.091 10.097 10.103 10.109 10.115 10.121 10.1265 10.132 10.138 10.144 10.150 10.156 10.162 10.162Pullets 10.091 10.091 10.091 10.091 10.091 10.091 10.091 10.097 10.103 10.109 10.115 10.121 10.126510.132 10.138 10.144 10.150 10.156 10.162 10.162Chickens 10.8 10.8 10.8 10.8 10.8 10.8 10.8 10.817 10.833 10.85 10.867 10.883 10.9 10.917 10.933 10.95 10.967 10.983 11 11Broilers 15 15 15 15 15 15 15 15.167 15.333 15.5 15.667 15.833 16 16.167 16.333 16.5 16.667 16.833 17 17Turkeys 9.7 9.7 9.7 9.7 9.7 9.7 9.7 9.596 9.492 9.388 9.283 9.179 9.0758.971 8.867 8.7625 8.658 8.554 8.45 8.45Horses1010101010101010109.619.228.838.448.057.667.276.886.496.1 6.1NexMarket Swine<50 lb. 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.627 0.653 0.68 0.707 0.733 0.76 0.787 0.813 0.84 0.867 0.893 0.92 0.92Market Swine50-119 lb. 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5 0.51 0.52 0.53 0.54 0.54Market Swine120-179 lb. 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5 0.51 0.52 0.53 0.54 0.54Market Swine>180 lb. 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5 0.51 0.52 0.53 0.54 0.54Breeding Swine 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.232 0.230 0.227 0.224 0.221 0.219 0.216 0.2130.211 0.208 0.205 0.203 0.203NOF Bulls 0.31 0.31 0.31 0.310.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.310.31 0.31 0.31 0.31 0.31NOF Calves 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.313 0.325 0.338 0.35 0.363 0.3750.388 0.4 0.413 0.425 0.438 0.45 0.45Sheep 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.423 0.426 0.429 0.432 0.435 0.438 0.441 0.444 0.447 0.45 0.45Goats 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45Hens >1 yr. 0.695 0.695 0.695 0.695 0.695 0.695 0.695 0.703 0.711 0.719 0.727 0.735 0.743 0.750 0.7580.766 0.774 0.782 0.79 0.79Pullets 0.695 0.695 0.695 0.6950.695 0.695 0.695 0.703 0.711 0.719 0.727 0.735 0.743 0.750 0.7580.766 0.774 0.782 0.79 0.79Chickens 0.83 0.83 0.83 0.830.83 0.83 0.83 0.853 0.875 0.898 0.92 0.943 0.965 0.988 1.01 1.0331.055 1.078 1.1 1.1Broilers 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.088 1.077 1.065 1.053 1.042 1.031.018 1.007 0.995 0.983 0.972 0.96 0.96Turkeys 0.74 0.74 0.74 0.740.74 0.74 0.74 0.730 0.721 0.7110.702 0.692 0.683 0.673 0.663 0.654 0.644 0.635 0.625 0.625Horses0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.295 0.2890.284 0.278 0.273 0.267 0.2620.256 0.251 0.245 0.245

### A-225 Table A- 186: Estimated Volatile Solids and Nitrogen Excreted Production Rate by State for Cattle for 2009 (kg/animal/year) Volatile Solids Nitrogen Excreted

State Dairy Cow Dairy Heifers Beef NOF Cow Beef NOF Heifers Beef NOF Steer Beef OF Heifers Beef OF Steer Dairy Cow Dairy Heifers Beef NOF Cow Beef NOF Heifers Beef NOF Steer Beef OF Heifers Beef OF Steer Alabama 2,267.41 1,251.47 1,680.43 1,103.73 979.63 673.81 656.14 135.78 68.85 70.16 48.18 40.04 53.69 55.01 Alaska 1,846.07 1,251.47 2,158.74 1,475.35 1,287.98 758.17 723.58 115.86 68.85 84.14 60.31 49.59 61.06 61.25 Arizona 2,819.52 1,251.47 2,158.74 1,440.02 1,287.98 674.59 656.77 157.68 68.85 84.14 58.35 49.59 53.76 55.07 Arkansas 2,046.05 1,251.47 1,675.36 1,093.22 976.43 731.19 702.01 124.61 68.85 72.93 49.63 41.72 58.70 59.26 California 2,723.70 1,251.47 1,638.85 1,042.04 953.48 688.37 667.78 152.93 68.85 71.23 46.75 40.63 54.96 56.09 Colorado 2,824.09 1,251.47 1,546.94 951.51 896.01 685.35 665.37 157.87 68.85 71.65 44.97 40.74 54.70 55.87 Connecticut 2,494.94 1,251.47 1,652.50 1,093.00 962.06 674.37 656.59 143.73 68.85 68.93 47.85 39.24 53.74 55.06 Delaware 2,369.10 1,251.47 1,652.50 1,062.76 962.06 740.93 709.80 138.33 68.85 68.93 45.95 39.24 59.55 59.98 Florida 2,502.90 1,251.47 1,680.43 1,117.18 979.63 723.70 696.02 145.89 68.85 70.16 49.02 40.04 58.05 58.70 Georgia 2,543.18 1,251.47 1,680.43 1,103.56 979.63 706.46 682.24 147.62 68.85 70.16 48.17 40.04 56.54 57.43 Hawaii 2,159.89 1,251.47 2,158.74 1,450.79 1,287.98 706.46 682.24 129.34 68.85 84.14 58.95 49.59 56.54 57.43 Idaho 2,749.48 1,251.47 2,158.74 1,392.62 1,287.98 679.06 660.34 154.67 68.85 84.14 55.73 49.59 54.15 55.40 Illinois 2,509.01 1,251.47 1,653.05 1,037.84 962.40 664.58 648.76 144.34 68.85 70.73 45.55 40.34 52.88 54.33 Indiana 2,603.48 1,251.47 1,653.05 1,050.80 962.40 661.25 646.10 148.40 68.85 70.73 46.38 40.34 52.59 54.09 Iowa 2,620.71 1,251.47 1,653.05 1,022.85 962.40 675.86 657.78 149.14 68.85 70.73 44.59 40.34 53.87 55.17 Kansas 2,674.30 1,251.47 1,546.94 945.66 896.01 684.40 664.60 151.44 68.85 71.65 44.57 40.74 54.61 55.80 Kentucky 2,213.71 1,251.47 1,680.43 1,092.37 979.63 731.19 702.01 133.47 68.85 70.16 47.47 40.04 58.70 59.26 Louisiana 1,990.33 1,251.47 1,675.36 1,112.51 976.43 714.03 688.29 122.21 68.85 72.93 50.88 41.72 57.20 57.99 Maine 2,448.34 1,251.47 1,652.50 1,080.42 962.06 692.36 670.97 141.73 68.85 68.93 47.06 39.24 55.31 56.39 Maryland 2,462.83 1,251.47 1,652.50 1,066.48 962.06 641.27 630.13 142.36 68.85 68.93 46.18 39.24 50.85 52.61 Massachusetts 2,411.79 1,251.47 1,652.50 1,080.42 962.06 706.46 682.24 140.16 68.85 68.93 47.06 39.24 56.54 57.43 Michigan 2,775.94 1,251.47 1,653.05 1,045.14 962.40 674.88 656.99 155.80 68.85 70.73 46.02 40.34 53.78 55.09 Minnesota 2,535.74 1,251.47 1,653.05 1,047.02 962.40 690.51 669.49 145.49 68.85 70.73 46.14 40.34 55.15 56.25 Mississippi 2,191.18 1,251.47 1,680.43 1,103.12 979.63 680.89 661.80 132.50 68.85 70.16 48.14 40.04 54.31 55.54 Missouri 2,193.82 1,251.47 1,653.05 1,067.85 962.40 681.24 662.08 130.80 68.85 70.73 47.47 40.34 54.34 55.56 Montana 2,588.27 1,251.47 1,546.94 1,003.22 896.01 657.92 643.44 147.74 68.85 71.65 48.53 40.74 52.30 53.84 Nebraska 2,572.43 1,251.47 1,546.94 950.97 896.01 680.84 661.76 147.06 68.85 71.65 44.93 40.74 54.30 55.53 Nevada 2,729.35 1,251.47 2,158.74 1,428.01 1,287.98 691.23 670.06 153.80 68.85 84.14 57.69 49.59 55.21 56.30 New

Hampshire 2,558.38 1,251.47 1,652.50 1,073.63 962.06 706.46 682.24 146.46 68.85 68.93 46.63 39.24 56.54 57.43 New Jersey 2,435.51 1,251.47 1,652.50 1,068.91 962.06 745.24 713.24 141.18 68.85 68.93 46.34 39.24 59.93 60.30 New Mexico 2,916.03 1,251.47 2,158.74 1,417.32 1,287.98 667.61 651.18 161.82 68.85 84.14 57.10 49.59 53.15 54.56 New York 2,598.56 1,251.47 1,652.50 1,050.97 962.06 675.38 657.39 148.18 68.85 68.93 45.21 39.24 53.83 55.13 North

Carolina 2,621.22 1,251.47 1,680.43 1,103.07 979.63 731.19 702.01 150.97 68.85 70.16 48.14 40.04 58.70 59.26 North Dakota 2,349.60 1,251.47 1,546.94 985.19 896.01 671.24 654.09 137.49 68.85 71.65 47.29 40.74 53.46 54.83 Ohio 2,499.38 1,251.47 1,653.05 1,061.17 962.40 668.17 651.63 143.92 68.85 70.73 47.04 40.34 53.20 54.60 Oklahoma 2,372.40 1,251.47 1,675.36 1,066.91 976.43 672.96 655.46 138.62 68.85 72.93 47.94 41.72 53.61 54.95 Oregon 2,572.28 1,251.47 2,158.74 1,414.00 1,287.98 671.24 654.09 147.06 68.85 84.14 56.91 49.59 53.46 54.83 Pennsylvania 2,545.40 1,251.47 1,652.50 1,050.97 962.06 671.24 654.09 145.90 68.85 68.93 45.21 39.24 53.46 54.83 Rhode Island 2,430.23 1,251.47 1,652.50 1,095.96 962.06 675.44 657.44 140.95 68.85 68.93 48.03 39.24 53.83 55.14 South

Carolina 2,555.49 1,251.47 1,680.43 1,107.64 979.63 688.56 667.94 148.15 68.85 70.16 48.42 40.04 54.98 56.11 South Dakota 2,602.79 1,251.47 1,546.94 970.02 896.01 674.32 656.55 148.37 68.85 71.65 46.25 40.74 53.73 55.05 Tennessee 2,366.26 1,251.47 1,680.43 1,100.05 979.63 671.24 654.09 140.02 68.85 70.16 47.95 40.04 53.46 54.83 Texas 2,664.94 1,251.47 1,675.36 1,058.67 976.43 678.95 660.25 151.19 68.85 72.93 47.41 41.72 54.14 55.40 Utah 2,667.08 1,251.47 2,158.74 1,424.78 1,287.98 719.20 692.43 151.13 68.85 84.14 57.51 49.59 57.66 58.37 Vermont 2,465.40 1,251.47 1,652.50 1,055.51 962.06 727.14 698.78 142.47 68.85 68.93 45.49 39.24 58.35 58.96 Virginia 2,504.58 1,251.47 1,680.43 1,097.87 979.63 675.38 657.39 145.96 68.85 70.16 47.81 40.04 53.83 55.13 Washington 2,830.17 1,251.47 2,158.74 1,401.02 1,287.98 674.99 657.08 158.13 68.85 84.14 56.19 49.59 53.79 55.10 West Virginia 2,199.28 1,251.47 1,652.50 1,071.08 962.06 617.96 611.49 131.04 68.85 68.93 46.47 39.24 48.81 50.89 Wisconsin 2,599.14 1,251.47 1,653.05 1,067.78 962.40 676.24 658.08 148.21 68.85 70.73 47.46 40.34 53.90 55.19 Wyoming 2,504.44 1,251.47 1,546.94 989.11 896.01 671.24 654.09 144.14 68.85 71.65 47.56 40.74 53.46 54.83 Source: Moffroid and Pape, 2010.A-226 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009Table A- 187: 2009 Manure Distribution AmongWaste Management Systems by Operation (Percent)Beef Feedlots Dairies1Dairy Heifer Facilities Swine Operations1Layer OperationsBroiler andTurkeyOperationsStateDryLot2Liquid/Slurry2PastureDailySpreadSolidStorageLiquid/SlurryAnaerobicLagoonDeepPitDailySpread2DryLot2Liquid/Slurry2Pasture2PastureSolidStorageLiquid/SlurryAnaerobicLagoonDeepPitAnaerobicLagoonPoultrywithoutLitterPasturePoultrywithLitterAlabama 100 1.3 45 17 11 10 17 0 17 38 0 45 5 4 8 51 32 41.9 58.1 1 99Alaska 100 1.3 4 6 28 24 30 8 6 90 1 4 53 2 13 10 22 25 75 1 99Arizona 100 0.4 0 10 9 20 61 0 10 90 0 0 14 3 5 50 27 60 40 1 99Arkansas 100 1.3 57 15 11 7 10 1 15 28 0 57 4 4 12 46 35 0 100 1 99California 100 1.3 1 11 9 21 58 0 11 88 1 1 13 3 8 47 29 12 88 1 99Colorado 100 0.4 1 1 12 24 62 1 1 98 0 1 2 5 26 17 50 60 40 1 99Connecticut 100 1 6 43 17 20 12 2 43 51 0 6 57 2 12 9 20 5 95 1 99Delaware 100 1 6 44 19 19 10 2 44 50 0 6 12 4 24 18 42 5 95 1 99Florida 100 1.3 17 22 8 15 39 0 22 61 1 17 71 1 8 6 14 41.9 58.1 1 99Georgia 100 1.3 40 18 10 11 21 0 18 42 0 40 8 4 10 47 32 41.9 58.1 1 99Hawaii 100 1.3 1 0 11 21 67 0 0 99 1 1 23 3 18 21 34 25 75 1 99Idaho 100 0.4 0 1 12 23 63 1 1 99 0 0 46 3 15 10 26 60 40 1 99Illinois 100 0.6 5 8 43 26 13 5 8 87 0 5 2 5 28 15 50 2 98 1 99Indiana 100 0.6 8 13 35 24 16 3 13 79 0 8 3 5 28 15 50 0 100 1 99Iowa 100 0.6 6 10 41 25 14 4 10 83 0 6 1 4 12 48 35 0 100 1 99Kansas 100 0.6 3 5 28 33 29 3 5 92 0 3 2 5 28 13 51 2 98 1 99Kentucky 100 1 61 14 14 6 2 2 14 24 0 61 5 4 12 45 34 5 95 1 99Louisiana 100 1.3 60 14 10 6 9 1 14 26 0 60 54 2 13 10 22 60 40 1 99Maine 100 1 7 45 20 17 9 2 45 48 0 7 73 1 7 6 13 5 95 1 99Maryland 100 1 7 44 23 15 8 3 44 49 0 7 21 4 21 16 38 5 95 1 99Massachusetts 100 1 7 45 24 15 6 3 45 47 0 7 31 3 19 14 32 5 95 1 99Michigan 100 0.6 3 6 32 33 22 4 6 91 0 3 5 5 25 17 48 2 98 1 99Minnesota 100 0.6 6 10 44 24 12 5 10 84 0 6 2 5 26 18 49 0 100 1 99Mississippi 100 1.3 57 15 10 7 10 0 15 28 0 57 2 4 6 57 31 60 40 1 99Missouri 100 0.6 8 14 48 18 6 5 14 77 0 8 3 5 28 14 51 0 100 1 99Montana 100 0.4 3 4 25 26 36 6 4 93 0 3 6 5 25 17 47 60 40 1 99Nebraska 100 0.6 4 6 35 30 21 4 6 90 0 4 3 5 28 15 50 2 98 1 99Nevada 100 0.4 0 0 11 24 64 0 0 99 0 0 35 2 4 39 20 0 100 1 99New Hampshire 100 1 7 44 21 16 9 3 44 49 0 7 57 2 12 9 20 5 95 1 99New Jersey 100 1 8 45 24 14 6 3 45 47 0 8 31 3 19 14 33 5 95 1 99New Mexico 100 0.4 0 10 9 19 61 0 10 90 0 0 92 0 2 2 4 60 40 1 99New York 100 1 7 45 20 16 10 2 45 48 0 7 20 4 21 15 40 5 95 1 99North Carolina 100 1 54 15 12 11 6 1 15 31 0 54 0 4 6 58 31 41.9 58.1 1 99North Dakota 100 0.6 6 11 45 22 12 4 11 83 0 6 9 5 24 17 45 2 98 1 99Ohio 100 0.6 8 14 41 23 11 4 14 78 0 8 7 4 27 15 47 0 100 1 99Oklahoma 100 0.4 0 6 25 23 40 6 6 94 0 0 1 4 6 58 31 60 40 1 99Oregon 100 1.3 20 0 13 21 44 2 0 80 1 20 58 2 12 9 20 25 75 1 99Pennsylvania 100 1 9 47 25 12 5 2 47 44 0 9 5 5 25 18 47 0 100 1 99A-227Beef Feedlots Dairies1Dairy Heifer Facilities Swine Operations1Layer OperationsBroiler andTurkeyOperationsStateDryLot2Liquid/Slurry2PastureDailySpreadSolidStorageLiquid/SlurryAnaerobicLagoonDeepPitDailySpread2DryLot2Liquid/Slurry2Pasture2PastureSolidStorageLiquid/SlurryAnaerobicLagoonDeepPitAnaerobicLagoonPoultrywithoutLitterPasturePoultrywithLitterRhode Island 100 1 9 47 26 12 4 3 47 44 0 9 56 2 12 9 21 5 95 1 99South Carolina 100 1.3 54 15 8 9 13 0 15 31 0 54 6 4 9 50 32 60 40 1 99South Dakota 100 0.6 5 8 38 28 18 4 8 87 0 5 3 5 26 18 48 2 98 1 99Tennessee 100 1 59 15 12 9 4 2 15 26 0 59 10 4 12 41 33 5 95 1 99Texas 100 0.4 0 8 13 24 53 2 8 92 0 0 7 3 6 54 29 12 88 1 99Utah 100 0.4 1 1 17 26 51 3 1 98 0 1 1 6 26 17 51 60 40 1 99Vermont 100 1 7 44 19 17 10 2 44 49 0 7 86 1 4 3 7 5 95 1 99Virginia 100 1 57 15 12 9 4 2 15 28 0 57 3 4 7 55 31 5 95 1 99Washington 100 1.3 17 0 11 22 49 1 0 83 1 17 37 3 17 12 31 12 88 1 99West Virginia 100 1 7 45 23 16 7 3 45 48 0 7 58 2 11 8 21 5 95 1 99Wisconsin 100 0.6 7 12 42 24 12 4 12 82 0 7 13 4 24 17 42 2 98 1 99Wyoming 100 0.4 7 12 22 23 30 6 12 81 0 7 3 5 26 17 49 60 40 1 991In the methane inventory for manure management, the percent ofdairy cows and swine with anaerobic digestion systems is estimated using data from EPA’s AgSTAR Program.2Because manure from beef feedlots and dairy heifers may be managed for long periods oftime in multiple systems (i.e., both drylot and runoff collection pond), the percent of manure that generatesemissions is greater than 100 percent.

### A-228 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A- 188: Manure Manag ement System Descriptions Manure Management System Description 1

Pasture The manure from pasture and range grazing animals is allowed to lie as is, and is not managed. N 2 O emissions from deposited manure are covered under the N 2 O from Agricultural Soils category.
Daily Spread Manure is routinely removed from a confinement facility and is applied to cropland or pasture within 24 hours of excretion. N 2 O emissions during storage and treatment are assumed to be zero. N 2 O emissions from land application are covered under the Agricultural Soils category.
Solid Storage The storage of manure, typically for a period of several months, in unconfined piles or stacks. Manure is able to be stacked due to the presence of a sufficient amount of bedding material or loss of moisture by evaporation.
Dry Lot A paved or unpaved open confinement area without any significant vegetative cover where accumulating manure may be removed periodically. Dry lots are most typically found in dry climates but also are used in humid climates.
Liquid/ Slurry Manure is stored as excreted or with some minima l addition of water to facilitate handling and is stored in either tanks or earthen ponds, usually for periods less than one year.
Anaerobic Lagoon Uncovered anaerobic lagoons are designed and operated to combine waste stabilization and storage. Lagoon supernatant is usually used to remove ma nure from the associated confinement facilities to the lagoon. Anaerobic lagoons are designed with varying lengths of storage (up to a year or greater), depending on the climate region, the volatile solid s loading rate, and other operational factors. Anaerobic lagoons accumulate sludge over time, diminishing treatment capacity. Lagoons must be cleaned out once every 5 to 15 years, and the sludge is typically applied to agricultural lands. The water from the lagoon may be recycled as flush water or used to irrigate and fertilize fields. Lagoons are sometimes used in combinati on with a solids separator, typically for dairy waste. Solids separators help control the buildup of nondegradab le material such as straw or other bedding materials.
Anaerobic Digester Animal excreta with or without stra w are collected and anaerobically digested in a large containment vessel or covered lagoon. Digesters are designed and operated for waste stabilization by the microbial reduction of complex organic compounds to CO2 and CH4, which is captured and flared or used as a fuel.
Deep Pit Collection and storage of manure usually with little or no added water typically below a slatted floor in an enclosed animal confinement facility. Typical storage periods range from 5 to 12 months, after which manure is removed from the pit and transfe rred to a treatment system or applied to land.
Poultry with Litter Enclosed poultry houses use bedding derived from w ood shavings, rice hulls, chopped straw, peanut hulls, or other products, dependi ng on availability. The bedding ab sorbs moisture and dilutes the manure produced by the birds. Litter is typically cl eaned out completely once a year. These manure systems are typically used for all poultry breeder fl ocks and for the production of meat type chickens (broilers) and other fowl.
Poultry without Litter In high-rise cages or scrape-out/belt systems, manure is excreted onto the floor below with no bedding to absorb moisture. The ve ntilation system dries the manure as it is stored. When designed and operated properly, this high-rise system is a form of passive windrow composting.

1 Manure management system descriptions are from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (Volume 4: Agriculture, Forestry and Other Land Use, Chapter 10: Emissions from Livestock and Manure Ma nagement, Tables 10.18 and 10.21) and the Development Document for the Final Revisions to the National Pollutant Discharge Elimination Sy stem Regulation and the Effluent Guidelines for Concentrated Animal Feeding Operations (EPA-821-R-03-001, December 2002).


### Table A- 189: Methane Conversion Factors (percent) for Dry Systems Waste Management System Cool Climate MC F Temperate Climate MCF Warm Climate MCF Aerobic Treatment 0 0 0 Cattle Deep Litter (<1 month) 0.03 0.03 0.3 Cattle Deep Litter (>1 month) 0.21 0.44 0.76 Composting - In Vessel 0.005 0.005 0.005 Composting - Static Pile 0.005 0.005 0.005 Composting-Extensive/ Passive 0.005 0.01 0.015 Composting-Intensive 0.005 0.01 0.015 Daily Spread 0.001 0.005 0.01 Dry Lot 0.01 0.015 0.05 Fuel 0.1 0.1 0.1

### A-229 Waste Management System Cool Climate MC F Temperate Climate MCF Warm Climate MCF Pasture 0.01 0.015 0.02 Poultry with bedding 0.015 0.015 0.015 Poultry without bedding 0.015 0.015 0.015 Solid Storage 0.02 0.04 0.05



### Table A- 190: Methane Conversion Factors by State for Liquid Systems for 2009 (percent) State Dairy Swine Beef Poultry Anaerobic Lagoon Liquid/Slurry and Deep Pit Anaerobic Lagoon Liquid/Slurry and Deep Pit Liquid/Slurry Anaerobic Lagoon Alabama 0.76 0.39 0.76 0.38 0.40 0.76 Alaska 0.46 0.14 0.46 0.14 0.14 0.46 Arizona 0.80 0.62 0.75 0.37 0.48 0.76 Arkansas 0.74 0.34 0.75 0.36 0.34 0.74 California 0.75 0.35 0.74 0.32 0.42 0.75 Colorado 0.63 0.21 0.67 0.24 0.22 0.63 Connecticut 0.68 0.24 0.68 0.24 0.25 0.69 Delaware 0.74 0.31 0.74 0.31 0.31 0.74 Florida 0.78 0.52 0.78 0.50 0.51 0.78 Georgia 0.76 0.39 0.76 0.38 0.38 0.75 Hawaii 0.77 0.58 0.77 0.58 0.58 0.77 Idaho 0.67 0.23 0.65 0.22 0.22 0.65 Illinois 0.71 0.28 0.71 0.28 0.27 0.71 Indiana 0.70 0.27 0.71 0.27 0.27 0.71 Iowa 0.68 0.24 0.69 0.25 0.25 0.69 Kansas 0.73 0.30 0.73 0.30 0.30 0.73 Kentucky 0.74 0.31 0.74 0.31 0.31 0.74 Louisiana 0.77 0.46 0.77 0.46 0.47 0.77 Maine 0.63 0.20 0.63 0.20 0.20 0.63 Maryland 0.72 0.29 0.73 0.30 0.30 0.73 Massachusetts 0.67 0.23 0.68 0.24 0.24 0.67 Michigan 0.67 0.23 0.68 0.24 0.23 0.68 Minnesota 0.66 0.23 0.67 0.23 0.23 0.66 Mississippi 0.76 0.41 0.76 0.39 0.41 0.76 Missouri 0.73 0.30 0.72 0.30 0.30 0.73 Montana 0.59 0.19 0.61 0.20 0.20 0.61 Nebraska 0.70 0.26 0.70 0.26 0.25 0.70 Nevada 0.71 0.26 0.71 0.27 0.26 0.71 New Hampshire 0.63 0.20 0.65 0.22 0.20 0.64 New Jersey 0.72 0.28 0.72 0.29 0.28 0.72 New Mexico 0.72 0.30 0.71 0.28 0.28 0.68 New York 0.65 0.22 0.66 0.22 0.22 0.67 North Carolina 0.73 0.31 0.75 0.37 0.32 0.73 North Dakota 0.65 0.21 0.65 0.21 0.21 0.64 Ohio 0.70 0.26 0.71 0.27 0.26 0.71 Oklahoma 0.75 0.37 0.74 0.35 0.34 0.75 Oregon 0.62 0.20 0.63 0.21 0.22 0.64 Pennsylvania 0.69 0.25 0.70 0.26 0.26 0.71 Rhode Island 0.69 0.26 0.69 0.26 0.26 0.69 South Carolina 0.76 0.38 0.76 0.39 0.39 0.76 South Dakota 0.68 0.24 0.68 0.24 0.24 0.68 Tennessee 0.74 0.31 0.75 0.34 0.32 0.74 Texas 0.76 0.44 0.76 0.43 0.37 0.77 Utah 0.67 0.23 0.66 0.22 0.23 0.66 Vermont 0.63 0.21 0.63 0.21 0.21 0.63 Virginia 0.71 0.28 0.73 0.31 0.28 0.72 Washington 0.62 0.20 0.64 0.21 0.22 0.63 West Virginia 0.69 0.26 0.70 0.26 0.26 0.69 Wisconsin 0.66 0.22 0.67 0.23 0.23 0.67 Wyoming 0.59 0.18 0.64 0.21 0.21 0.62

### A-230 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A- 191: Direct Nitrous Oxid e Emission Factors for 2009 (kg N 2 

### O-N/kg Kjdl N) Waste Management System Direct N 2 O Emission

Factor Aerobic Treatment (forced aeration) 0.005 Aerobic Treatment (natural aeration) 0.01 Anaerobic Digester 0 Anaerobic Lagoon 0 Cattle Deep Bed (active mix) 0.07 Cattle Deep Bed (no mix) 0.01 Composting_in vessel 0.006 Composting_intensive 0.1 Composting_passive 0.01 Composting_static 0.006 Daily Spread 0 Deep Pit 0.002 Dry Lot 0.02 Fuel 0 Liquid/Slurry 0.005 Pasture 0 Poultry with bedding 0.001 Poultry without bedding 0.001 Solid Storage 0.005



### Table A- 192: Indirect Nitrou s Oxide Loss Factors (percent)

Animal Type Waste Management System Volatilization Nitrogen Loss Runoff/Leaching Nitrogen Loss 1

Central Pacific MidAtlantic Midwest South Beef Cattle Dry Lot 23 1.1 3.9 3.6 1.9 4.3 Beef Cattle Liquid/Slurry 26 0 0 0 0 0 Beef Cattle Pasture 0 0 0 0 0 0 Dairy Cattle Anaerobic Lagoon 43 0.2 0.8 0.7 0.4 0.9 Dairy Cattle Daily Spread 10 0 0 0 0 0 Dairy Cattle Deep Pit 24 0 0 0 0 0 Dairy Cattle Dry Lot 15 0.6 2 1.8 0.9 2.2 Dairy Cattle Liquid/Slurry 26 0.2 0.8 0.7 0.4 0.9 Dairy Cattle Pasture 0 0 0 0 0 0 Dairy Cattle Solid Storage 27 0.2 0 0 0 0
Goats Dry Lot 23 1.1 3.9 3.6 1.9 4.3
Goats Pasture 0 0 0 0 0 0 Horses Dry Lot 23 0 0 0 0 0
Horses Pasture 0 0 0 0 0 0 Poultry Anaerobic Lagoon 54 0.2 0.8 0.7 0.4 0.9 Poultry Liquid/Slurry 26 0.2 0.8 0.7 0.4 0.9 Poultry Pasture 0 0 0 0 0 0 Poultry Poultry with bedding 26 0 0 0 0 0

Poultry Poultry without bedding 34 0 0 0 0 0 Poultry Solid Storage 8 0 0 0 0 0 Sheep Dry Lot 23 1.1 3.9 3.6 1.9 4.3 Sheep Pasture 0 0 0 0 0 0 Swine Anaerobic Lagoon 58 0.2 0.8 0.7 0.4 0.9 Swine Deep Pit 34 0 0 0 0 0 Swine Liquid/Slurry 26 0.2 0.8 0.7 0.4 0.9 Swine Pasture 0 0 0 0 0 0 Swine Solid Storage 45 0 0 0 0 0

1 Data for nitrogen losses due to leaching were not available, so the values repr esent only nitrogen losses due to runoff.A-231Table A- 193: Methane Emissions from Livestock Manure Management (Gg)aAnimal Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Dairy Cattle599 615 598 623 663 693 702 734 781 854 900 960 997 856 969 1018 1034 1151 1147 1168Dairy Cows 592 608 591 616 656 686 695 727 774 846 893 952 990 850 962 1011 1027 1143 1139 1159Dairy Heifer 7 7 7 7 7 7 7 7 7 7 7 7 7 6 7 7 7 8 8 8Swine624 676 639 680 741 764 730 783 892 849 834 854 879 857 851 905 889 965 918 903Market Swine 484 524 500 534 585 608 582 626 720 692 680 696 720 703 701 745 730 798 763 753Market <50 lbs. 102 110 104 109 119 121 116 125 141 133 131 134 137 135 134 141 139 152 107 105Market 50-119 lbs. 101 111 105 110 120 124 117 127 144 138 136 138 144 140 140 148 146 159 170 169Market 120-179 lbs. 136 147 140 151 164 170 163 175 201 193 189 192 199 196 194 208 203 223 223 218Market >180 lbs. 145 156 152 165 182 194 185 198 235 229 225 232 240 233 233 248 243 264 262 260Breeding Swine 140 152 139 146 156 155 148 157 172 157 155 158 158 154 150 160 159 166 155 150Beef Cattle128 128 131 131 137 140 139 136 139 139 133 135 133 133 130 132 139 136 131 130Feedlot Steers 14 14 14 13 14 14 14 13 13 14 15 15 15 16 15 15 16 16 16 16Feedlot Heifers 7 7 7 7 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9NOF Bulls 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5NOF Calves 8 8 8 8 9 9 8 8 9 9 9 9 9 9 9 9 10 10 9 9NOF Heifers 12 12 13 14 14 15 15 14 14 14 13 13 13 13 12 13 13 13 12 12NOF Steers 12 12 13 14 13 14 14 13 13 12 11 11 11 10 10 10 10 10 10 10NOF Cows 69 69 70 71 74 76 76 74 76 76 71 73 71 71 71 71 74 73 70 69Sheep7 7 7 6 6 5 5 5 5 4 4 4 4 4 3 3 3 3 3 3Goats1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1Poultry131 132 127 131 131 128 126 128 130 126 127 131 130 130 129 129 131 134 129 127Hens >1 yr. 73 72 70 73 72 69 68 67 70 66 67 70 67 68 66 66 66 67 64 64Total Pullets 25 26 23 23 23 22 21 23 23 21 22 22 22 22 23 22 23 25 23 23Chickens 4 4 4 4 4 4 3 3 4 4 3 3 4 4 3 3 3 3 3 4Broilers 19 20 20 21 22 23 24 25 26 27 28 28 29 29 30 31 32 32 33 31Turkeys 10 10 10 10 9 9 9 9 8 7 7 7 7 7 7 7 7 7 7 6Horses22 22 21 21 21 21 21 21 22 21 20 20 21 24 26 28 28 27 24 24aAccounts for CH4reductions due to capture and destruction of CH4at facilities using anaerobic digesters.Table A- 194: Total (Direct and Indirect) Nitrous Oxide Emissions fromLivestock Manure Management (Gg)Animal Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Dairy Cattle17.1 17.01 17.03 17.27 17.4 17.7 17.7 17.9 18.0 17.6 17.9 18.2 18.5 16.5 17.8 18.2 18.7 18.8 18.5 18.7Dairy Cows 10.0 10.07 9.97 10.05 10.1 10.3 10.3 10.4 10.5 10.2 10.5 10.5 10.6 9.610.2 10.4 10.7 10.7 10.5 10.7Dairy Heifer 7.0 6.93 7.06 7.22 7.3 7.4 7.4 7.4 7.5 7.4 7.5 7.7 7.8 7.0 7.5 7.8 8.1 8.1 8.0 8.0Swine 4.0 4.18 4.33 4.37 4.6 4.5 4.4 4.7 5.1 5.0 5.0 5.1 5.3 5.4 5.6 5.7 5.9 6.3 6.4 6.3Market Swine 3.0 3.11 3.26 3.29 3.5 3.5 3.3 3.6 4.0 4.1 4.1 4.2 4.4 4.5 4.7 4.8 5.0 5.4 5.6 5.5Market <50 lbs. 0.6 0.59 0.61 0.60 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 1.0 1.1 0.8 0.8Market 50-119 lbs. 0.6 0.67 0.70 0.70 0.7 0.7 0.7 0.8 0.8 0.8 0.8 0.8 0.9 0.9 0.9 1.0 1.0 1.1 1.3 1.2Market 120-179 lbs. 0.9 0.90 0.94 0.95 1.0 1.0 1.0 1.0 1.1 1.1 1.1 1.2 1.2 1.3 1.3 1.4 1.4 1.5 1.6 1.6Market >180 lbs. 0.9 0.95 1.02 1.04 1.1 1.1 1.1 1.2 1.3 1.3 1.3 1.4 1.5 1.5 1.5 1.6 1.6 1.8 1.9 1.9Breeding Swine 1.0 1.07 1.08 1.07 1.1 1.1 1.0 1.1 1.1 1.0 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.8 0.8Beef Cattle19.8 20.33 20.15 19.10 21.0 21.8 21.5 21.5 21.7 24.1 25.0 24.1 24.8 25.1 23.7 24.1 25.8 25.6 25.2 25.2Feedlot Steers 13.4 13.67 13.56 12.83 13.9 14.4 14.0 13.9 14.1 15.5 16.1 15.416.1 16.3 15.3 15.5 16.8 16.7 16.5 16.5A-232 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009Feedlot Heifers 6.4 6.65 6.60 6.27 7.0 7.4 7.4 7.6 7.6 8.5 8.9 8.7 8.8 8.8 8.4 8.5 9.0 8.9 8.7 8.6Sheep0.4 0.41 0.44 0.44 0.6 0.7 0.8 0.9 0.9 1.0 1.1 1.2 1.2 1.2 1.1 1.2 1.2 1.2 1.2 1.1Goats0.1 0.07 0.07 0.07 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1Poultry4.7 4.79 4.88 4.96 5.1 5.1 5.3 5.3 5.3 5.3 5.3 5.3 5.4 5.3 5.4 5.4 5.4 5.4 5.4 5.1Hens >1 yr. 1.0 1.02 1.02 1.03 1.0 1.0 1.0 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.2 1.3 1.3 1.3 1.3 1.3Total Pullets 0.3 0.29 0.29 0.29 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4Chickens0.0 0.03 0.03 0.03 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Broilers2.2 2.26 2.36 2.46 2.6 2.7 2.8 2.8 2.9 2.9 2.9 2.9 3.0 2.9 2.9 3.0 2.9 2.9 2.9 2.7Turkeys1.2 1.19 1.18 1.14 1.1 1.1 1.1 1.1 1.0 0.9 0.9 0.9 0.9 0.9 0.8 0.8 0.8 0.8 0.8 0.7Horses0.7 0.70 0.71 0.71 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.8 0.9 1.0 1.1 1.1 1.1 1.1 1.1

### A-233 Table A- 195: Methane Emissions by State from Livestock Manure Management for 2009 (Gg) a

State Beef on Feedlots Beef Not on Feed Dairy Cow Dairy Heifer Swine— Market Swine— Breedin g Layer Broiler Turkey Sheep Goats Horses Alabama 0.0091 2.5130 0.7878 0.0133 1. 8870 0.5402 8.8601 3.6406 0. 0528 0.0090 0.0302 0.6750 Alaska 0.0001 0.0214 0.0336 0.0003 0. 0036 0.0019 0.1801 0.0000 0. 0526 0.0060 0.0001 0.0096 Arizona 0.8202 1.3914 52.2827 0.1192 2. 2931 0.6103 0.6572 0.0000 0. 0528 0.1057 0.0158 0.5327 Arkansas 0.0042 2.3427 0.4741 0.0095 1. 5543 2.1073 0.5189 3.8171 0. 7252 0.0090 0.0190 0.6119 California 1.3793 3.5354 401.7280 2.0424 1.5433 0.2113 4.2273 0.2654 0. 3751 0.4652 0.0490 1.4005 Colorado 1.5272 2.2547 25.9551 0.0901 4.7356 2.7422 3.1055 + 0. 0526 0.1927 0.0122 0.6150 Connecticut 0.0002 0.0214 0.9206 0.0186 0.0086 0.0054 0.2664 0.2645 0. 0526 0.0038 0.0011 0.0595 Delaware 0.0002 0.0127 0.3593 0.0041 0. 0533 0.0427 0.0727 0.8381 0. 0526 0.0060 0.0009 0.0205 Florida 0.0049 3.3731 17.9106 0.0998 0. 0643 0.0530 6.8326 0.1526 0. 0528 0.0090 0.0216 0.9338 Georgia 0.0078 2.0242 6.0643 0.0614 2.6177 1.0343 15.2216 4.8018 0. 0528 0.0090 0.0315 0.5944 Hawaii 0.0014 0.3871 0.3521 0.0029 0. 1295 0.0854 0.1325 + 0.0528 0.0090 0.0034 0.0507 Idaho 0.3405 1.7547 113.6925 0.4155 0. 1240 0.0714 0.5614 + 0.0526 0.0987 0.0044 0.3824 Illinois 0.2797 1.1587 7.1004 0.0951 36. 9889 9.7843 0.2512 + 0.0526 0.0273 0.0084 0.4106 Indiana 0.1859 0.6572 9.9651 0.1057 31.395 3 5.4684 0.8165 0.2645 0. 3739 0.0235 0.0118 0.4193 Iowa 2.0285 3.3553 15.5070 0.2062 264.5844 28.7171 1.7071 0.2645 0. 0526 0.0940 0.0140 0.3719 Kansas 3.8479 5.2668 17.1442 0.1058 18. 8129 3.5728 0.0416 + 0.0526 0.0376 0.0124 0.4644 Kentucky 0.0190 2.8498 1.5234 0.0881 5. 0055 1.0488 0.5920 1.1114 0. 0526 0.0188 0.0245 0.9063 Louisiana 0.0043 1.8598 0.8950 0.0194 0. 0565 0.0306 2.1219 0.2654 0. 0528 0.0090 0.0081 0.4687 Maine 0.0003 0.0387 1.3237 0.0296 0. 0078 0.0058 0.3816 + 0.0526 0.0038 0.0015 0.0628 Maryland 0.0202 0.1159 2.5013 0.0453 0. 2579 0.0736 0.2180 1.0567 0. 0526 0.0060 0.0042 0.1588 Massachusetts 0.0002 0.0237 0.4876 0. 0117 0.0643 0.0141 0.0106 + 0. 0526 0.0038 0.0021 0.1062 Michigan 0.2544 0.4613 34.7198 0.2332 8. 3149 1.9980 0.6120 0.2645 0. 0526 0.0367 0.0070 0.5225 Minnesota 0.4411 1.5054 28.1062 0.4485 58.758 1 10.6826 0.3644 0.1622 0. 0526 0.0658 0.0092 0.4657 Mississippi 0.0088 1.8861 0.8255 0.0190 5.8277 1.2846 8.2188 2.8818 0. 0528 0.0090 0.0115 0.5058 Missouri 0.0965 5.1365 4.7540 0.0650 25.002 9 7.4122 0.2482 0.2645 0. 4611 0.0390 0.0241 0.7706 Montana 0.0645 3.4419 1.6729 0.0149 1. 1172 0.3543 0.3486 + 0.0526 0.1198 0.0031 0.5437 Nebraska 3.9357 5.7938 5.8908 0.0319 25. 5224 7.3081 0.5819 0.2645 0. 0526 0.0334 0.0086 0.3390 Nevada 0.0092 0.7279 6.3173 0.0153 0. 0285 0.0121 0.0214 + 0.0526 0.0315 0.0030 0.0950 New Hampshire 0.0001 0.0181 0.6294 0. 0123 0.0107 0.0034 0.0659 + 0. 0526 0.0038 0.0010 0.0511 New Jersey 0.0003 0.0283 0.3514 0.0087 0.0531 0.0160 0.0715 + 0. 0526 0.0060 0.0027 0.1549 New Mexico 0.2468 1.6482 77.4893 0.2155 0.0015 0.0010 0.5926 + 0. 0526 0.0564 0.0089 0.2770 New York 0.0481 0.3778 27.1686 0.5322 0. 4586 0.1625 0.4220 0.2645 0. 0526 0.0291 0.0100 0.4391 North Carolina 0.0038 1.0137 1.7797 0.0354 151. 1504 35.4253 11.0133 2.7590 0.8878 0.0197 0.0369 0.6074 North Dakota 0.1065 2.1409 1.3253 0.0313 0.8294 0.6165 0.0390 + 0. 0526 0.0413 0.0011 0.2312 Ohio 0.3035 0.9541 16.8066 0.1837 16.406 1 3.1993 0.9148 0.2042 0. 1296 0.0611 0.0174 0.6158 Oklahoma 0.5381 6.0089 9.8078 0.0684 32.208 1 14.2156 3.5948 0.8181 0. 0526 0.0376 0.0313 0.8553 Oregon 0.1395 1.7579 13.5532 0.0933 0. 0571 0.0239 0.8226 0.2645 0. 0526 0.1034 0.0095 0.4620 Pennsylvania 0.1272 0.6633 15.4864 0.4585 10. 2107 1.8110 0.7260 0.5557 0. 2243 0.0470 0.0148 0.6010 Rhode Island + 0.0042 0.0310 0.0009 0.0049 0.0039 0.0696 + 0. 0526 0.0038 0.0002 0.0180 South Carolina 0.0025 0.7246 1.0262 0.0186 4.2497 0.4742 5.0711 0.8637 0. 2976 0.0090 0.0163 0.3354 South Dakota 0.6037 4.0757 7.4168 0.0632 10.1931 2.8293 0.1373 + 0. 1122 0.1433 0.0027 0.3628 Tennessee 0.0071 2.4632 1.5864 0.0796 2. 6957 0.4871 0.2492 0.6867 0. 0526 0.0160 0.0327 0.7329 Texas 6.2856 22.3466 93.4873 0.5075 14.721 5 2.4491 4.3726 2.4289 0. 0528 0.6132 0.4273 3.4006 Utah 0.0392 1.1290 14.7238 0.0679 4. 0454 1.0264 2.7412 + 0.0798 0.1363 0.0043 0.3088 Vermont 0.0005 0.0547 5.2580 0.1005 0. 0032 0.0018 0.0188 + 0.0526 0.0038 0.0016 0.0686 Virginia 0.0505 1.6854 2.7183 0.0776 5. 7130 0.8152 0.4380 0.8717 0. 4237 0.0352 0.0158 0.4668 Washington 0.2798 1.0249 37.9185 0.1870 0. 0989 0.0570 1.1358 0.2645 0. 0526 0.0249 0.0082 0.4636 West Virginia 0.0142 0.5231 0.3899 0.0051 0.0223 0.0137 0.0971 0.2994 0. 0823 0.0155 0.0070 0.1949 Wisconsin 0.3705 1.1936 70.4229 1.0204 2. 4065 0.8365 0.2969 0.1658 0. 0526 0.0399 0.0140 0.6197 Wyoming 0.1025 1.6831 0.6483 0.0075 0. 2398 0.2827 0.0085 + 0.0526 0.1973 0.0021 0.4158 + Emission estimate is less than 0.00005 Gg.

a Accounts for CH 4 reductions due to capture and destruction of CH 4 at facilities using anaerobic digesters.

### A-234 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Table A- 196: Total (Direct and Indirect) Nitrous Oxide Em issions by State from Livestock Manure Management for 2009 (Gg)

State BeefFeedlotHeifer BeefFeedlotSteers Dairy Cow Dairy Heifer Swine— Market Swine— Breeding Layer Broiler Turkey Sheep Goats Horses Alabama 0.0021 0.0040 0.0060 0.0045 0. 0099 0.0021 0.0639 0.3221 0. 0061 0.0049 0.0024 0.0232 Alaska + + 0.0006 0.0004 0.0001 + 0.0031 + 0.0061 0.0016 + 0.0005 Arizona 0.2208 0.4235 0.2285 0.1056 0. 0116 0.0024 0.0034 + 0.0061 0.0165 0.0012 0.0183 Arkansas 0.0014 0.0026 0.0050 0.0033 0. 0088 0.0087 0.0736 0.3377 0. 0840 0.0042 0.0015 0.0210 California 0.3129 0.5977 2.1407 1.6247 0. 0085 0.0008 0.0873 0.0235 0. 0435 0.0822 0.0039 0.0481 Colorado 0.6402 1.2240 0.1751 0.1381 0.0493 0.0212 0.0192 + 0. 0061 0.0452 0.0014 0.0317 Connecticut 0.0001 0.0001 0.0149 0.0133 0.0001 + 0.0116 0.0235 0. 0061 0.0030 0.0001 0.0031 Delaware 0.0001 0.0002 0.0049 0.0027 0. 0004 0.0003 0.0030 0.0744 0. 0061 0.0049 0.0001 0.0011 Florida 0.0011 0.0020 0.0924 0.0506 0. 0004 0.0002 0.0469 0.0135 0. 0061 0.0049 0.0017 0.0321 Georgia 0.0018 0.0035 0.0434 0.0228 0. 0141 0.0041 0.1106 0.4248 0. 0061 0.0049 0.0025 0.0204 Hawaii 0.0003 0.0006 0.0018 0.0023 0. 0007 0.0003 0.0014 + 0.0061 0.0016 0.0003 0.0017 Idaho 0.1429 0.2737 0.7334 0.6375 0. 0013 0.0005 0.0034 + 0.0061 0.0232 0.0005 0.0197 Illinois 0.1096 0.2108 0.1408 0.1205 0. 3498 0.0678 0.0181 + 0.0061 0.0190 0.0010 0.0212 Indiana 0.0727 0.1399 0.2096 0.1218 0. 3033 0.0388 0.1134 0.0235 0. 0435 0.0164 0.0014 0.0216 Iowa 0.8062 1.5456 0.2924 0.2542 1.7628 0.1407 0.2370 0.0235 0.0061 0.0656 0.0017 0.0192 Kansas 1.4902 2.8499 0.1782 0.1401 0. 1705 0.0239 0.0030 + 0.0061 0.0262 0.0015 0.0239 Kentucky 0.0068 0.0129 0.0316 0.0288 0. 0299 0.0046 0.0246 0.0987 0. 0061 0.0152 0.0029 0.0467 Louisiana 0.0010 0.0018 0.0081 0.0043 0. 0003 0.0001 0.0111 0.0235 0. 0061 0.0042 0.0006 0.0161 Maine 0.0001 0.0002 0.0244 0.0204 0. 0001 + 0.0176 + 0.0061 0.0030 0.0002 0.0032 Maryland 0.0071 0.0137 0.0427 0.0300 0. 0022 0.0005 0.0091 0.0938 0. 0061 0.0049 0.0005 0.0082 Massachusetts 0.0001 0.0001 0.0107 0. 0078 0.0006 0.0001 0.0005 + 0. 0061 0.0030 0.0002 0.0055 Michigan 0.1022 0.1959 0.5270 0.3146 0. 0858 0.0152 0.0453 0.0235 0. 0061 0.0256 0.0008 0.0269 Minnesota 0.1778 0.3394 0.6319 0.5617 0. 6070 0.0812 0.0506 0.0144 0. 0061 0.0459 0.0011 0.0240 Mississippi 0.0020 0.0039 0.0071 0.0047 0.0291 0.0047 0.0430 0.2550 0. 0061 0.0049 0.0009 0.0174 Missouri 0.0375 0.0718 0.1234 0.0724 0. 2284 0.0499 0.0346 0.0235 0. 0536 0.0272 0.0029 0.0397 Montana 0.0270 0.0520 0.0213 0.0218 0. 0134 0.0031 0.0022 + 0.0061 0.0281 0.0004 0.0280 Nebraska 1.5630 2.9921 0.0829 0.0423 0. 2541 0.0536 0.0425 0.0235 0. 0061 0.0233 0.0010 0.0175 Nevada 0.0038 0.0073 0.0368 0.0233 0. 0002 0.0001 0.0030 + 0.0061 0.0074 0.0004 0.0049 New Hampshire + 0.0001 0.0117 0.0087 0.0001 + 0.0030 + 0.0061 0.0030 0.0001 0.0026 New Jersey 0.0001 0.0002 0.0070 0.0055 0.0005 0.0001 0.0030 + 0. 0061 0.0049 0.0003 0.0080 New Mexico 0.1006 0.1934 0.4144 0.2955 + + 0.0034 + 0.0061 0.0132 0.0011 0.0143 New York 0.0181 0.0347 0.4805 0.3629 0. 0049 0.0013 0.0186 0.0235 0. 0061 0.0236 0.0012 0.0226 North Carolina 0.0014 0.0026 0.0228 0.0144 0.7694 0.1326 0.0811 0.2441 0. 1029 0.0107 0.0029 0.0209 North Dakota 0.0431 0.0827 0.0314 0.0387 0.0092 0.0050 0.0030 + 0. 0061 0.0289 0.0001 0.0119 Ohio 0.1194 0.2294 0.3446 0.2115 0.1571 0.0225 0.1270 0.0181 0.0151 0.0493 0.0021 0.0317 Oklahoma 0.2153 0.4131 0.0760 0.0657 0. 1696 0.0546 0.0190 0.0726 0. 0061 0.0262 0.0037 0.0441 Oregon 0.0497 0.0954 0.1223 0.1036 0. 0006 0.0002 0.0106 0.0235 0. 0061 0.0274 0.0011 0.0238 Pennsylvania 0.0465 0.0892 0.3984 0.2817 0.0966 0.0127 0.1009 0.0493 0. 0261 0.0381 0.0018 0.0310 Rhode Island + + 0.0008 0. 0005 + + 0.0030 + 0.0061 0.0030 + 0.0009 South Carolina 0.0006 0.0011 0.0076 0.0051 0.0220 0.0018 0.0265 0.0764 0. 0345 0.0049 0.0013 0.0115 South Dakota 0.2413 0.4627 0.1309 0.0819 0.1030 0.0210 0.0101 + 0. 0130 0.1001 0.0003 0.0187 Tennessee 0.0025 0.0048 0.0238 0.0279 0. 0157 0.0021 0.0105 0.0610 0. 0061 0.0130 0.0039 0.0378 Texas 1.7394 3.3315 0.5319 0.4742 0.0833 0.0102 0.0892 0.2149 0.0061 0.0959 0.0337 0.1168 Utah 0.0165 0.0313 0.1145 0.1031 0. 0599 0.0109 0.0163 + 0.0093 0.0320 0.0005 0.0159 Vermont 0.0002 0.0003 0.1044 0. 0703 + + 0.0009 + 0.0061 0.0030 0.0002 0.0035 Virginia 0.0181 0.0347 0.0430 0.0295 0. 0317 0.0033 0.0186 0.0774 0. 0493 0.0286 0.0019 0.0241 Washington 0.1000 0.1917 0.2872 0.2164 0. 0011 0.0005 0.0274 0.0235 0. 0061 0.0066 0.0010 0.0239 West Virginia 0.0051 0.0099 0.0077 0.0034 0.0002 0.0001 0.0043 0.0266 0. 0096 0.0126 0.0008 0.0100 Wisconsin 0.1489 0.2855 1.6751 1.2450 0. 0246 0.0063 0.0221 0.0147 0. 0061 0.0279 0.0017 0.0319 Wyoming 0.0429 0.0824 0.0079 0.0094 0. 0045 0.0039 0.0001 + 0.0061 0.0463 0.0002 0.0214 + Emission estimate is less than 0.00005 Gg.

### A-235 

# 3.11. Methodology for Estimating N 2 

# O Emissions from Agricultural Soil Management

Nitrous oxide emissions from agricultural soils result from the interaction of the natural processes of denitrification and nitrification with management practices that add or release mineral nitrogen (N) in the soil profile. Emissions can occur directly in the soil where the N is made available or can be transported to another location following volatilization, leaching, or runoff, and then converted into N 2 

### O.
A combination of Tier 1 and Tier 3 approaches was used to estimate direct and indirect N 2 

### O emissions from agricultural soils. The process-based biogeochemical model DAYCENT (a Tier 3 approach) was used to estimate N 2 

### O emissions resulting from croplands on mineral soils that were used to produce major crops, while the IPCC (2006) Tier 1 methodology was applied to estimate N 2 

### O emissions for non-major crop types on mineral soils. The Tier 1 method was also used to estimate direct N 2 

### O emissions due to drainage and cultivation of organic cropland soils. Direct N 2 

### O emissions from grasslands were estimated by using a comb ination of DAYCENT and IPCC (2006) Tier 1 methods. A combination of DAYCENT and Tier 1 methods was also used to estimate indirect emissions from all managed lands (i.e., croplands, grasslands, forest lands, and settlements). Specifically, the amount of N volatilized from soils, as well as leaching or transport of nitrate (NO 3 - 

### ) off-site in surface runoff waters was computed by DAYCENT for the direct emission analyses, while IPCC default factors were used to estimate N transport for the analyses using the Tier 1 methodology. The indirect N 2 

### O emissions resulting from off-site transport of N were then computed using the IPCC (2006) Tier 1 default emission factor. Overall, the Tier 3 appr oach is used to estimate approximately 85 percent of direct soil emissions and 70 percent of total soil N 2 

### O emissions associated with agricultural soil management in the United States.
DAYCENT (Del Grosso et al. 2001, Parton et al. 1998) simulates biogeochemical N fluxes between the atmosphere, vegetation, and soil, allowing for a more complete estimation of N 2 

### O emissions than IPCC Tier 1 methods by accounting for the influence of environmental conditions including soil characteristics and weather patterns, specific crop and forage qualities that influence the N cycle, and management practices at a daily time step. For example, plant growth is controlled by nutrient availability, water, and temperature stress; moreover, growth removes mineral N from the soil before it can potentially be converted into N 2 

### O. Nutrient supply is a function of external nutrient additions as well as litter and soil organic matter (SOM) decomposition rates, and increasing decomposition can lead to greater N 2 

### O emissions by enhancing mineral N availability in soils. In this model-based assessment framework, daily maximum/minimum temperature and precipitation, timing and description of management events (e.g., fertilization, tillage, harvest), and soil texture data are model inputs to DAYCENT, which form the basis to simulate key processes and generate robust estimates of N 2 

### O emissions from soils. Key processes simulated within sub-models of DAYCENT include plant production, organic matter formation and decomposition, soil water and soil temper ature regimes by layer, and nitrification and denitrification processes (Figure A- 7). Comparison of model results and pl ot level data show that DAYCENT reliably simulates crop yields, soil organic matter levels, and trace gas fluxes for a nu mber of native and managed systems (Del Grosso et al. 2001, 2005). Comparisons with measured data showed that DAYCENT estimated emissions more accurately and precisely than the IPCC Tier 1 methodology (Figure A- 8). Th e linear regression of simulated vs. measured emissions for DAYCENT had higher r 2 

### and a fitted line closer to a perfect 1:1 relationship between measured and modeled N 2 

### O emissions (Del Grosso et al. 2005, 2008). This is not surpri sing, since DAYCENT includes site-specific factors (climate, soil properties, and previous management) that influence N 2 

### O emissions. Furthermore, DAYCENT also simulated NO 3 - 

### leaching (root mean square error = 20 percent) more accurately than IPCC Tier 1 methodology (root mean square error = 69 percent) (Del Grosso et al. 2005). Thus, the Tier 3 a pproach has reduced uncertainties in the agricultural soil management section relative to earlier Inventory years where the IPCC Tier 1 method was used. The latest operational version of DAYCENT has several improvements, including (1 ) elimination of the influence of labile (i.e., easily decomposable by microbes) C availability on surface litter denitrification rates, (2) incorporation of precipitation events on surface litter denitrification, and (3) having the wettest soil la yer within the rooting zone control plant transpiration.

[Begin Text Box]

Box 1. DAYCENT Model Simulation of Nitrification and Denitrification

The DAYCENT model simulates the two biogeochemical proces ses, nitrification and denitrification, that result in N 2 

### O emissions from soils (Del Grosso et al. 2000, Parton et al. 2001). Nitrification is calculated for the top 15 cm of soil, while denitrification is calculated for the entire so il profile. The equations and key parameters controlling N 2 

### O emissions from nitrification and denitrification are described below.

### A-236 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Nitrification is controlled by soil ammonium (NH 4 + 

### ) concentration, water filled pore space (WFPS), temperature (t), and pH according to the following equation:

Nit = NH 4 

### × K max 

### × F(t) × F(WFPS) × F(pH)

where,
Nit = the soil nitrification rate (g N/m 2 

### /day) NH 4 

### = the model-derived soil ammonium concentration (g N/m 2 

### ) K

max 

### = the maximum fraction of NH 4 + 

### nitrified (K
max 

### = 0.10/day) F(t) = the effect of soil temperature on nitrification ( Figure A- 5 

### a) F(WFPS) = the effect of soil water content and soil texture on nitrification ( Figure A- 5 

### b) F(pH) = the effect of soil pH on nitrification ( Figure A- 5 

### c)

The current parameterization used in the model assumes that 1.2 percent of nitrified N is converted to N 2 

### O.

N 2 

### O emissions from denitrification are a function of soil NO 3 - 

### concentration, WFPS, heterotrophic (i.e., microbial) respiration, and texture. Denitrification is calculated for each soil layer in the profile, and N 2 

### O emissions from each layer are summed to obtain total soil emissions. The model assumes that denitrification rates are controlled by the availability of soil NO 3 - 

### (electron acceptor), labile C compounds (electron donor) and oxygen (competing electron acceptor). Heterotrophic soil respiration is used as a proxy for labile C availability, while oxygen availability is a functio n of soil physical properties that influence gas diffusivity, so il WFPS, and oxygen demand. The model selects the minimum of the NO 3 - 

### and CO 2 

### functions to establish a maximum potential denitrification rate for particular levels of electron acceptor and C substrate and accounts for limitations of oxygen availability to estimate daily denitrification rates according to the following equation:

Den = min[F(CO 2 

### ), F(NO 3 

### )] × F(WFPS)
where,
Den = the soil denitrification rate ( μ g N/g soil/day) F(CO 2 

### ) = a function relating N gas flux to soil respiration ( Figure A- 6 

### a) F(NO 3 

### ) = a function relating N gas flux to nitrate levels ( Figure A- 5 

### b) F(WFPS) = a dimensionless multiplier ( Figure A- 6 

### c).

The x inflection point of F(WFPS) is a function of respiration and soil gas diffusivity at field capacity (D FC 

### ):

x inflection = 0.90 - M(CO 2 

### )
where,
M = a multiplier that is a function of D FC 

### .

Respiration has a much stronger effect on the water curve in clay soils with low D FC 

### than in loam or sandy soils with high D FC 

### (Figure A- 6c). The model assumes th at microsites in fine-textured soils can become anaerobic at relatively low water contents when oxygen demand is high.
After calculating total N gas flux, the ratio of N 2 

### /N 2 

### O is estimated so that total N gas emissions can be partitioned between N 2 

### O and N 2 

### :

R N2/N2O 

### = F r 

### (NO 3 

### /CO 2 

### ) × F
r 

### (WFPS).
where,

R N2/N2O 

### = the ratio of N 2 

### /N 2 

### O F r 

### (NO 3 

### /CO 2 

### ) = a function estimating the impact of the availability of electron donor relative to substrate F r 

### (WFPS) = a multiplier to account for the effect of soil water on N 2 

### :N 2 

### O.

For F r 

### (NO 3 

### /CO 2 

### ), as the ratio of electron donor to substrate increases, a higher portion of N gas is assumed to be in the form of N 2 

### O. For F r 

### (WFPS), as WFPS increases, a higher portion of N gas is assumed to be in the form of N 2 

### . [End Box]

### A-237 Figure A- 5: Effect of Soil Temperature, Water-F illed Pore Space, and pH on Nitrification Rates

Figure A- 6: Effect of Soil Nitrite Concentration, Hetero trophic Respiration Rates, and Water-Filled Pore Space on Denitrification Rates

There are five steps in estimating direct N 2 

### O emissions from cropland and grassland soils, and indirect N 2 

### O emissions from volatilization, leaching, and runoff from all mana ged lands (i.e., croplands, grasslands, forest lands, and settlements). First, the activity data are derived from a combination of land-use, livestock, crop, and grassland management surveys, as well as expert knowledge. In th e second, third, and fourth st eps, direct and indirect N 2 

### O emissions are estimated using DAYCENT and/or the Tier 1 method. In the fifth step, total emissions are computed by summing all components. The remainder of this annex describes the methods underlying each step.

Step 1: Derive Activity Data
The activity data requirements vary for major crops, non-major crops, grasslands, organic cropland soils, settlements and forest lands. Activity data were derived for direct and indirect N 2 

### O emission calculations as described below.

Step 1a: Activity Data for Direct Emissions from Crop Production on Mineral Soils
Nitrous oxide emissions from mineral cropland soils include emissions from both major and non-major cropping systems and were estimated using a Tier 3 and Tier 1 approach, respectively.

Major Crop Types: Tier 3 DAYCENT Simulations
The activity data requirements for estimating N 2 

### O emissions from major crop types (corn, soybeans, wheat, alfalfa hay, other hay, sorghum, and cotton) include the following: (1) crop-specific mineral N fertilizer rates and timing, (2) crop-specific manure amendment N rates and timing, (3) other N inputs, (4) crop-specific land management information, (5) native vegetation, (6) daily weather data fo r every county, (7) sub-county-level soil texture data, and (8) county-level crop areas. The United States was divided into 63 agricultural regions based on common cropping practices as defined by McCarl et al. (1993), and data were assembled and provided as inputs to the DAYCENT biogeochemical ecosystem model.
Synthetic N Fertilizer Application : Data on N fertilizer rates were obtained primarily from the U.S. Department of Agriculture–Economic Research Service 1995 Cropping Practices Survey (ERS 1997). In this survey, data on inorganic N fertilization rates were collected for major crops (corn, cotton, soybeans, and wheat) in the high production states during 1995. It is assumed that the fertilization ra tes have not changed much during the Inventory reporting period, which is confirmed by the sales data showing relatively minor change in the amount of fertilizer sold for on-farm use across the time series of this Inventory (Ruddy et al. 2006). The trend and therefore the rates and uncertainties reflected in the 1995 survey data are considered representative for 1990 through 2008 (trends will be re-evaluated when new fertilization data are released by U.S. Department of Agricu lture). Note that all wheat data were combined into one category and assumed to represent small grains in aggregate. Estimates for sorghum fertilizer rates were derived from corn fertilizer rates using a ratio of national average corn fert ilizer rates to national average sorghum fertilizer rates, whi ch were derived from additional publications (NASS 1992, 1999, 2004; ERS 1988; Grant and Krenz 1985; USDA 1954, 1957, 1966). Alfalfa hay is assumed to not be fertilized, but grass hay is fertilized according to rates from published farm enterprise budgets (NRIAI 2003).

The ERS survey parameter “TOT N” (total amount of N applied per acre), with a small number of records deleted as outliers, was used in determining the fraction of crop acres receiving fertilizer and the average fertilizer rates f or each region. Mean fertilizer rates and standard deviations fo r irrigated and rainfed crops were produced for each state with a minimum of 15 data points for irrigated and rainfed, respectively. If a state was not surveyed for a particular crop or if fewer than 15 data points existed for one of the categories, then data were aggregated to U.S. Department of Agriculture Farm Production Regions in order to estimate a mean and standard deviation for fertilization rates (Farm Production Regions are groups of states in the United States with similar agricultural commodities). If Farm Production Region data were not available, crop data were aggregated to the entire Un ited States (all major states surveyed) to estimate a mean and standard deviation for a particular crop in a state lacking sufficient data. Standard deviations for fertilizer rates were use d

### A-238 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 to construct probability distribution functions (PDFs) with log-normal densities in order to address uncertainties in application rates (see Step 2a for discussion of uncertainty methods). Total fertilizer application data are found in Table A- 197.

Simulations were conducted for the period prior to 1990 in order to initialize the DAYCENT model (see Step 2a), and estimates for crop-specific regional fertilizer rates prior to 1990 were based largely on extrapolation/interpolation of fertilizer rates from the years with available data. For cr ops in some agricultural regions, little or no data were available, and, therefore, a geographic regional mean was us ed to simulate N fertilization rates (e.g., no data were available from Alabama during the 1970s and 1980s for corn fertilization rates; therefore, mean values from the southeastern United States were used to simulate fertilization to corn fields in this state).
Managed Livestock Manure 57 

### N Amendment Rates and Timing: County-level manure addition estimates have been derived from manure N addition rates developed by the Natural Resources Conservation Service (NRCS) (Edmonds et al. 2003). Working with the farm-level crop and animal data from the 1997 Census of Agriculture, NRCS has coupled estimates of manure N produced with estimates of manure N recoverability by animal waste management system to produce county-level estimates of manure N applied to cropland and pasture. Edmonds et al. (2003) defined a hierarchy that included 24 crops, cropland used as pasture, and perman ent pasture. They estimated the area amended with manure and application rates in 1997 for both manure-producing farms and manure-receiving farms within a county and for two scenarios—before implementation of Comprehensive Nutrient Management Plans (baseline) and after implementation (Edmonds et al. 2003). The goal of nutrient management plans is to apply manure nutrients at a rate meeting plant demand, thus limiting leaching losses of nutrients to gr oundwater and waterways. For DAYCENT simulations, the baseline scenario estimates have been used as the basis for manure amendment applications under the assumption that Comprehensive Nutrient Management Plans have not been fully implemented. This is a conservative assumption because it allows for higher leaching rates due to some over-application of manure to soils. The rates for manure-producing farms and manure-receiving farms have been area-weighted and combined to produce a single county-level estimate for the amount of land amended with manure and the manure N application rate for each crop in each county. Several of the crops in Edmonds et al. (2003) have been area-weighted and combined into broader crop categories. For example, all small grain crops have been combined into one category. In order to address uncertainty in these data, uniform probability distributions were constructed based on the proportion of land receiving manure versus the amount not receiving manure for each crop type and pasture. For example, if 20 percent of land producing corn in a county was amended with manure, randomly drawing a value equal to or greater than 0 and less than 20 would lead to a simulation with a manure amendment, while drawing a value greater than or equal to 20 and less than 100 would lead to no amendment in the simulation (see Step 2a for further discussion of uncertainty methods).
Edmonds et al. (2003) only provides manure application rate data for 1997, but the amount of managed manure available for soil application changes annually, so the area amended with manure was adjusted relative to 1997 to account for all the manure available for application in other years. Specifically, the manure N available for application in other years was divided by the manure N available in 1997. If the ratio was greater than 1, there was more manure N available in that county relative to the amount in 1997, and so it was assumed a larger area was amended with manure. In contrast, ratios less than one implied less area was amended with manure because there was a lower amount available in the year compared to 1997. The amendment area in each county for 1997 was multiplied by the ratio to reflect the impact of manure N availability on the area amended. The amount of managed manure N available for application to soils was calculated by determining the populations of animals that were on feedlots or otherwise housed in order to collect and manage the manure, as described in the Manure Management section (Section 6.2) and annex (Annex 3.10).
To estimate C inputs associated with manure N application rates derived from Edmonds et al. (2003), carbonnitrogen (C:N) ratios for livestock-specific manure types we re adapted from the Agricultural Waste Management Field Handbook (USDA 1996), On-Farm Composting Handbook (NRAES 1992), and recoverability factors provided by Edmonds et al (2003). The C:N ratios were applied to county-level estimates of manure N excreted by animal type and management system to produce a weighted county average C:N ratio for manure amendments. The average C:N ratio was used to determine the associated C input for crop amendments derived from Edmonds et al. (2003).
To account for the common practice of reducing inorganic N fertilizer inputs when manure is added to a cropland soil, crop-specific reduction factors were derived from mineral fertilization data for land amended with manure versus land

57 For purposes of the Inventory, total livestock manure is divide d into two general categories: (1) managed manure, and (2) unma naged manure. Managed manure includes manure that is stored in manure management systems such as pits and lagoons, as well as manure applied to soils through daily spread manure operations. Unma naged manure encompasses all manure deposited on soils by animals on PRP.

### A-239 not amended with manure in the ERS 1995 Cropping Practices Survey (ERS 1997). Mineral N fertilization rates were reduced for crops receiving manure N based on a fraction of the amount of manure N applied, depending on the crop and whether it was irrigated or rainfed. The reduction factors were randomly selected from PDFs with normal densities in order to address uncertainties in the dependence between manure amendments and mineral fertilizer application.
Manure N that was not applied to major crops and grassla nd was assumed to be applied to non-major crop types. The fate of manure N is summarized in Table A- 198.
Residue N Inputs: Residue N input is estimated as part of the DAYCENT simulation and is not an input to the model. Unlike the Tier 1 approach, N inputs from crop residues are not considered activity data in the DAYCENT simulations because N availability from this source is simulated by the model based on N uptake during crop growth according to environmental and management conditions, such as temperature, precipitation, and edaphic (i.e., soil) characteristics, in combination with the harvest practices. That is, while the model accounts for the contribution of N from crop residues to the soil profile and subsequent N 2 

### O emissions, this source of mineral soil N is not activity data in the sense that it is not a model input. Similarly, N from mineralization of soil organic matter and asymbiotic N fixation are also simulated by the model. The simulated total N inputs of above- and below-ground residue N and fixed N that was not harvested and not burned (the DAYCENT simulations assumed that 3 percent of non-harvested above ground residues for grain crops were burned) are provided in Table A- 199.
Other N Inputs: Other N inputs are estimated within the DAYCE NT simulation, and thus input data are not required, including mineralization from decomposition of soil organic matter and asymbiotic fixation of N from the atmosphere. The influence of additional inputs of N are estimated in the simulations so that there is full accounting of all emissions from managed lands, as recommended by IPCC (2006). The simulated total N inputs from other sources are provided in Table A- 199.
Crop Rotation and Land Management Information : Data were obtained on specific timing and type of cultivation, timing of planting/harvest, and crop rotation sc hedules for the 63 agricultural regions (Hurd 1930, 1929, Latta 1938, Iowa State College Staff Members 1946, Bogue 1963, Hurt 1994, USDA 2000a, 2000b, CTIC 1998, Piper et al. 1924, Hardies and Hume 1927, Holmes 1902, 1929, Spillm an 1902, 1905, 1907, 1908, Chilcott 1910, Smith 1911, Kezer ca 1917, Hargreaves 1993, ERS 2002, Warren 1911, Langston et al. 1922, Russell et al. 1922, Elliott and Tapp 1928, Elliott 1933, Ellsworth 1929, Garey 1929, Holmes 1929, Hodges et al. 1930, Bonnen and Elliott 1931, Brenner et al. 2002, 2001, Smith et al. 2002). As with N fertilizer and manure add itions, data were not complete, so regional averages were used to fill spatial gaps in the datasets and interpolation/ extrapolation was used to fill temporal gaps. The amount of agricultural residue burning was based on state inventory da ta (ILENR 1993, Oregon Department of Energy 1995, Noller 1996, Wisconsin Department of Natural Resources 1993, and Cibrowski 1996)

. 

### Native Vegetation by County : Pre-agricultural land cover for each county was designated according to the potential native vegetation used in the Vegetation-Ecosystem Modeling and Analysis Project (VEMAP 1995), which was based on the Kuchler (1964) Potential Vegetation Map for the conterminous United States.
Daily Weather Data by County : Daily maximum/minimum temperature and precipitation data were obtained from the DAYMET model, which generates daily surface precipi tation, temperature, and other meteorological data at 1 km 2 

### resolution driven by weather station observations and an elevation model (Thornton et al. 2000 and 1997, Thornton and Running 1999, DAYMET no date). It is necessary to us e computer-generated weather data because weather station data do not exist in each county, and moreover weather station data are for a point in space, while the DAYMET uses this information with interpolation algorithms to derive weathe r patterns for areas between these stations. DAYMET weather data are available for the United States at 1 km 2 

### resolution for 1980 through 2003. For each county, DAYMET weather data were selected from the 1 km 2 

### cell that occurred in agricultural lands according the National Land Cover Dataset (Vogelman et al. 2001). The grid cells formed the basis for county-scale PDFs based on the frequency of cells with same weather patterns. Separate PDFs were developed for croplan d, pasture/hay land, and rangeland. A weather record was then randomly selected from the PDFs in each iteration of the Monte Carlo analysis to represent variation in precipitation and temperature at the county scale. Weather data were randomly selected from the previous 23 years to represent 2004 through 2009, accounting for uncertainty in the weather during the years that have no data. The time series will be updated when new weather data are available.
Soil Properties by County : Soil texture data required by DAYCENT were obtained from STATSGO (Soil Survey Staff 2005). Observed data for soil hydraulic properties needed for model inputs were not available, so they were calculated from STATSGO (Soil Survey Staff 2005) texture class and Saxton et al.’s (1986) hydraulic properties calculator. Similar to the weather data, soil types within the STATSGO map that occurred in agricultural lands according to the National Land Cover Dataset (Vogelman et al. 2001) were used to form a county-scale PDF. Specifically, the PDFs were an area-weighted proportion for the extent of overlap between STATSGO map units and agricultural land. Separate PDFs were developed for cropland, pasture/hay land, and rangeland. Individual soil types were randomly selected from

### A-240 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 the PDFs during each iteration of the Monte Carlo analysis to represent variation in soil texture and depth at the county scale.
Crop Areas by County : County-level total crop area data were downloaded from the NASS web site for the years 1990 through 2009 (USDA 2010a, 2010b), and these data formed the basis to scale emissions from individual crop types to an entire county.

Non-Major Crop Types: Tier 1 Method
The activity data required for calculating emissions from non-major crop types include: (1) the amount of mineral N in synthetic fertilizers that are applied annually, (2) managed manure N, (3) the amount of N in other commercial organic fertilizers and (4) the amount of N in the above- and below-ground residue retained on and in soils of all non-major crops.
Application of Synthetic Commercial Fertilizers: A process-of-elimination approach was used to estimate synthetic N fertilizer additions to non-major crop types. The total amount of fertilizer used on farms has been estimatedby the USGS from 1990-2001 on a county scale from fertilizer sales data (Ruddy et al. 2006). For 2002-2009, countylevel fertilizer used on farms was adjusted based on annual fluctuations in total U.S. fertilizer sales (AAPFCO 1995 through 2009). In addition, fertilizer application data are avai lable for major crops and grasslands (discussed in Step 1 sections for Major Crops and Grasslands). Thus, the amount of N applied to non-major crops was assumed to be the remainder of the fertilizer used on farms after subtracting the amount applied to major crops and grasslands. The differences were aggregated to the state level and PDFs were derived based on uncertainties in the amount of N applied to major crops and grasslands. Total fertilizer application is found in Table A- 200.
Manure and Other Commercial Organic Fertilizers: 58 

### Manure N applied to non-major crops was estimated using the activity data described for major crops (Table A- 198 ). Estimates of total national annual N additions from other commercial organic fertilizers were derived from orga nic fertilizer statistics (TVA 1991 through 1994; AAPFCO 1995 through 2009). AAPFCO fertilizer data were not yet available for 2009, so 2008 values were used as a placeholder until data become available. Commercial organic fertilizers includ e dried blood, tankage, compost, and other; dried manure and sewage sludge that are used as commercial fertilizer were subtracted from totals to avoid double counting. The dried manure N is counted with the non-commercial manure applicati ons, and sewage sludge is assumed to be applied only to grasslands. The organic fertilizer data, which are recorded in mass units of fertilizer, had to be converted to mass units of N by multiplying the consumption values by the average organic fertilizer N contents provided in the annual fertilizer publications. These N contents are weighted average values, and vary from year to year (ranging from 2.3 percent to 3.9 percent over the period 1990 through 2008). The fertilizer consum ption data are recorded in “fertilizer year” totals, (i.e., July to June), but were converted to calendar year totals. This was done by assuming that approximately 35 percent of fertilizer usage occurred from July to December and 65 percent from January to June (TVA 1992b). July to December values were not available for calendar year 2008 so a “least squares line” statistical extrapolation using the previous 14 years of data was used to arrive at an approximate value. PDFs were derived for the organic fertilizer applications assuming a default ±50 percent uncertainty. Annual consumption of other organic fertilizers is presented in Table A- 201.
Retention of Crop Residue: Annual crop yield (metric tons per hectare) and area harvested (hectare) statistics for non-major N-fixing crops, including bean and pulse crops, were taken from U.S. Department of Agriculture crop production reports (USDA 1994, 1998, 2003, 2005, 2006, 2008, 2009, 2010a,b). Crop yield per hectare and area planted were multiplied to determine total crop yield for each crop, which was then converted to tons of dry matter product using the residue dry matter fractions shown in
Table A- 202. Dry matter yield was then converted to tons of above- and below-ground biomass N. Aboveground biomass was calculated by using linear equations to estimate above-ground biomass given dry matter crop yields, and below-ground biomass was calculated by multiplying above-ground biomass by the below-to-above-ground biomass ratio. N inputs were estimated by multiplying above- and below-ground biomass by respective N concentrations. All ratios and equations used to calculate residue N inputs (s hown in Table A- 203) are from IPCC (2006) and Williams (2006). PDFs were derived assuming a ±50 percent uncertainty in the yield estimates (NASS does not provide uncertainty), along with uncertainties provided by the IPCC (2 006) for dry matter fractions, above-ground residue, ratio of

58 Other commercial organic fertilizers include, dried blood, dried manure, tankage, compost, sewage, and other minor organic fer tilizer types, but manure and sewage sludge have been excluded in or der to avoid double-counting and ensure consistency across the Inve ntory as these inputs are calculated using alternative data sources and methods.

### A-241 below-ground to above-ground biomass, and residue N fractions . The resulting annual biomass N inputs are presented in Table A- 203.

Step 1b: Activity Data for Direct Emissions from Drainage and Cultivation of Organic Cropland Soils

Tier 1 Method
Estimates and associated uncertainty for the area of dr ained and cultivated organic cropland soils in 1992 and 1997 were obtained from the U.S. Department of Agriculture 1997 National Resources Inventory (USDA 2000a, as extracted by Eve 2001, and revised by Ogle 2002). 59 

### These areas were grouped by broad climatic region 60 

### using temperature and precipitation estimates from Daly et al. (1994, 1998) and then further aggregated to derive total land in temperate and sub-tropical regions. Areas for 1992 were assumed to represent 1990 through 1992 and areas for 1997 were assumed to represent 1993 through 2009 (Table A- 204).

Step 1c: Activity Data for Direct Emissions from Grassland Management

N 2 

### O emissions from non-federal grasslands were estimated using DAYCENT. DAYCENT simulations addressed the influence of legume seeding, managed manure N amendments, unmanaged manure N excreted by livestock and deposited directly onto pasture, range, and paddock (P RP) soils, and synthetic fertilizer applications. N 2 

### O emissions from PRP manure N deposition on federal grasslands and sewage sludge amendments to agricultural soils were addressed using the Tier 1 method.

Tier 3 DAYCENT Simulations
Activity data for DAYCENT simulations of grasslands (i.e ., climate, soils, and N inputs) were based on the same sources as those used for major crop types described in Step 1a. In addition to the data sources used for major crops, county-level area data on non-federal pasture and rangeland (i .e., mostly privately-owned) were needed for U.S. grasslands. This information was based on U.S. Land Repr esentation Analysis for Land Use, Land Use Change and Forestry sector (See Section 7.1), and included data compiled from the U.S. Department of Agriculture National Resources Inventory (USDA 2000a, Nusser and Goebel 1997, http://www.ncgc.nrcs.usda.gov/products/nri/index.htm ), the USDA Forest Service (USFS) Forest Inventory and Analysis Data base (FIA, http://fia.fs.fed .us/tools-data/data) and the U.S. Geological Survey (USGS) National Land Cover Dataset (NLCD, Vogelman et al. 2001, http://www.mrlc.gov). Grassland on non-federal lands is classified using the NRI a nd grassland on federal lands is classified using the NLCD. Grassland area data from the NRI and NLCD were adjusted to achieve consistency with FIA estimates of Forest Land. Another key source of N for grasslands is PRP manure N depo sition. Activity data for PRP manure N excretion from dairy cattle, beef cattle, swine, sheep, goats, poultry, and horses were derived from multiple sources as described in the Manure Management section (Section 6.2) and Annex 3.10. The am ount of PRP manure N deposited on non-federal grasslands(non-federal grasslands are mostly under private ownership) was based on the relative proportion of federal and nonfederal grasslands in each county based on the U.S. Land Repres entation Analysis (See Section 7.1) . For example, if 75 percent of the grasslands in a county were non-federal then 75 percent of PRP manure N was assumed to be deposited on non-federal grasslands.
Nitrogen fixation by legumes, and N residue inputs from senesced grass litter were included as sources of N to the soil, and were estimated in the DAYCENT simulations as a function of vegetation type, weather, and soil properties. Similar to the methodology for major crops, “other N inputs” were simulated within the DAYCENT model framework in order to estimate all greenhouse gas emissions from managed lands (IPCC 2006), including mineralization from decomposition of soil organic matter and litter, as well as asymbiotic N fixation from the atmosphere. Decomposition rates are a function of litter quality and quantity, soil textur e, water content and temperatur e, and other factors. Total annual amounts of PRP manure N, mineral N fertilizer application, manure N amendments, forage legume N and “other N inputs” can be found in Table A- 205.

59 These areas do not include Alaska, but Al aska’s cropland area accounts for less than 0. 1 percent of total U.S. cropland area, so this omission is not significant. 60 The climatic regions were: (1) cold temperate, dry, (2) cold temperate, moist, (3) sub-tropical, dry, (4) sub-tropical, moist, (5) warm temperate, dry, and (6) warm temperate, moist.

### A-242 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Tier 1 Method: Additional Direct Soil N 2 

### O Emissions
The Tier 1 method was used to estimate emissions from PRP manure that were not simulated with DAYCENT, in addition to emissions due to sewage sludge amendments to agricultural soils.

PRP Manure: PRP manure N data were derived using methods described in the Manure Management section (Section 6.2) and Annex 3.10. The amount of PRP manure N deposited on federal grasslands was based on the relative proportion of federal to non-federal grassland area in each c ounty. As discussed in the Tier 3 DAYCENT Simulations section, the area data were based on the U.S. Department of Agriculture’s National Resources Inventory (USDA 2000a) and the National Land Cover Dataset (Vogelman et al. 2001), respectively, and were reconciled with the Forest Inventory and Analysis dataset in order to produce the U.S. Land Representation (See Section 7.1). Soil N 2 

### O emissions from the proportion of PRP manure N deposited on federal grassl ands were estimated with the Tier 1 method.
Sewage Sludge Amendments: Sewage sludge is generated from the treatment of raw sewage in public or private wastewater treatment works and is typically used as a soil amendment or is sent to waste disposal facilities such as landfills. In this Inventory, all sewage sludge that is amended to agricultural soils is assumed to be applied to grasslands. Estimates of the amounts of sewage sludge N applied to agricultural lands were derived from national data on sewage sludge generation, disposition, and N content. Total sewage sludge generation data for 1988, 1996, and 1998, in dry mass units, were obtained from EPA (1999) and estimates for 2 004 were obtained from an independent national biosolids survey (NEBRA 2007). These values were linearly interpolated to estimate values for the intervening years. Sewage sludge generation data are not available after 2004 (Bastian 2007), so the 1990 through 2004 data were linearly extrapolated for the most recent years. The total sludge generation estimates were then converted to units of N by applying an average N content of 3.9 percent (McFarland 2001) , and disaggregated into use and disposal practices using historical data in EPA (1993) and NEBRA (2007). The use and disposal practices were agricultural land application, other land application, surface disposal, incineration, landfilling, ocean dumping (ended in 1992), and other disposal. The resulting estimates of sewage sludge N applied to agricultural land were used here; the estimates of sewage sludge N applied to other land and surface-disposed were used in estimating N 2 

### O fluxes from soils in Settlements Remaining Settlements (see section 7.5 of the Land Use, Land-Use Change, and Forestry chapter). Sewage sludge disposal data are provided in Table A- 206.

Step 1d: Activity Data for Indirect N 2 

### O Emissions from Managed Soils of all Land-Use Types and Managed Manure Systems
Volatilization of N that was applied or deposited as synt hetic fertilizer, livestock manure, sewage sludge, and other organic amendments leads to emissions of NH 3 

### and NO
x 

### to the atmosphere. In turn, this N is returned to soils through atmospheric deposition, thereby increasing mineral N availability and enhancing N 2 

### O production. Additional N is lost from soils through leaching as water percolates through a soil profile and through runoff with overland water flow. N losses from leaching and runoff enter groundwater and waterways, from which a portion is emitted as N 2 

### O. However, N leaching was assumed to be an insignificant source of indirect N 2 

### O in cropland and grassland systems where the amount of precipitation plus irrigation did not exceed 80 percent of the potential evapotranspiration. These areas are typically semiarid to arid, and nitrate leaching to groundwater is a re latively uncommon event; moreover IPCC (2006) recommends limiting the amount of nitrate leaching assumed to be a source of indirect N 2 

### O emissions based on precipitation, irrigation and potential evapotranspiration.
The activity data for synthetic fertilizer, livestock manure, other organic amendments, residue N inputs, sewage sludge N, and other N inputs are the same as those used in the calculation of direct emissions from agricultural mineral soils, and may be found in Table A- 197 through Table A- 201, Table A- 203, and Table A- 206. The activity data for computing direct and indirect N 2 

### O emissions from settlements and forest lands are described in the Land Use, Land-Use Change, and Forestry chapter.
Using the DAYCENT model, volatilization and leaching/su rface run-off of N from soils was computed internally for major crop types and non-federal grasslands. DAYCENT simula tes the processes leading to these losses of N based on environmental conditions (i.e., weather patterns and soil char acteristics), management impacts (e.g., plowing, irrigation, harvest), and soil N availability. Note that the DAYCEN T method accounts for losses of N from all anthropogenic activity, not just the inputs of N from mineral fertilization and organic amendments, which are addressed in the Tier 1 methodology. Similarly, the N available for producing indirect emissions resulting from grassland management as well as deposited PRP manure was also estimated by DAYCENT. Esti mated leaching losses of N from DAYCENT were not used in the indirect N 2 

### O calculation if the amount of precipitation plus irrigation did not exceed 80 percent of the potential evapotranspiration. Volatilized losses of N were summed for each day in the annual cycle to provide an estimate of the

### A-243 amount of N subject to indirect N 2 

### O emissions. In addition, the daily losses of N through leaching and runoff in overland flow were summed for the annual cycle. Uncertainty in the estimates was derived from uncertainties in the activity data for the N inputs (i.e., fertilizer and organic amendments; see Step 1a for further information)
The Tier 1 method was used to estimate N losses from mineral soils due to volatilization and leaching/runoff for non-major crop types, forestland, settlements, sewage sl udge applications, and PRP manure on federal grasslands not accounted for by DAYCENT simulations. To estimate volatilized losses, synthetic fertilizers, manure, sewage sludge, and other organic N inputs were multiplied by the fraction subject to gaseous losses using the respective default values of 0.1 kg N/kg N added as mineral fertilizers and 0.2 kg N/kg N added as manure (IPCC 2006). Uncertainty in the volatilized N ranged from 0.03-0.3 kg NH 3 

### -N+NOx-N/kg N for synthetic fertilizer and 0.05-0.5 kg NH 3 

### -N+NOx-N/kg N for organic amendments (IPCC 2006). Leaching/runoff losses of N were estimated by summing the N additions from synthetic and other organic fertilizers, manure, sewage sludge, and above- and below-ground crop residues, and then multiplying by the default fraction subject to leaching/runoff losses of 0.3 kg N/kg N applied, with an uncertainty from 0.1–0.8 kg NO 3 

### -N/kg N (IPCC 2006). However, N leaching was assumed to be an insignificant source of indirect N 2 

### O emissions if the amount of precipitation plus irrigation did not exceed 80 percent of the potential evapotranspiration. PDFs were derived for each of the N inputs in the same manner as direct N 2 

### O emissions, discussed in Steps 1a and 1c.
Volatilized N was summed for losses from major crop type s, minor crop types, gra sslands, settlements, and forest lands. Similarly, the annual amounts of N lost from soil profiles through leaching and surface runoff were summed to obtain the total losses for this pathway.

Step 2: Estimate Direct N 2 

### O Emissions from Cropland Soils
In this step, N 2 

### O emissions were calculated for major crop types and non-major crop types on mineral soils, in addition to emissions associated with drainage and cultivation of organic soils.

Step 2a: Direct N 2 

### O Emissions from Cropland Mineral Soils

Two methods were used to estimate direct N 2 

### O emissions from N additions and crop production on mineral soils. The DAYCENT ecosystem model was used to estimate emissions from major crop types, while the Tier 1 methodology was used to estimate emissions from crops considered non -major types, which are grown on a considerably smaller portion of land than the major types.

Major Crops: Tier 3 DAYCENT Simulations
Simulations were performed over three major time periods for each county in the United States using the DAYCENT model. The first time period was used for simulation of native vegetation up to date of cultivation in the county (1 A.D. to plow out). Plow out was assumed to occur between 1600 and 1850, depending on the state in which the county lies. Simulation of at least 1600 years of native vege tation was needed to initialize soil organic matter (SOM) pools in the model. The second time period of the simulation started at plowout and represents historical agricultural practices up to the modern period (plow out to 1970). Simulation of the historical cropping period was needed to establish modern day SOM levels, which is important because N 2 

### O emissions are sensitive to the amount of SOM. Lastly, simulations were performed for the modern agricultural period (1971 through 2009).
Corn, soybeans, wheat, alfalfa hay, other hay, sorghum, and cotton are defined as major crops and were simulated in every county where they were grown. These crops represent approximately 90 percent of total principal cropland in the United States as defined by the U.S. Depa rtment of Agriculture (USDA 2003). Overall, the DAYCENT simulations included approximately 86 percent of total cropland ar ea. For rotations that include a cycle that repeats every two or more years (e.g., corn/soybeans, wheat/corn/fallow), di fferent simulations were performed where each phase of the rotation was simulated every year. For example, 3 rotations were simulated in regions where wheat/corn/fallow cropping is a dominant rotation—one with wheat grown the first year, a second with corn the first year and a third with fallow the first year. This ensured that each crop was represented durin g each year in one of the three simulations. In cases where the same crop was grown in the same year in two or more distinct rotations for a region, N 2 

### O emissions were averaged across the different rotations to obtain a value for that crop. Emissions from cultivated fallow land were also included. Fallow area was assumed to be equal to winter wheat area in regions where winter wheat/fallow rotations are the dominant land management for winter wheat.
The simulations reported here assumed conventional tilla ge cultivation, gradual improvement of cultivars, and gradual increases in fertilizer application until 1989. Note that there is a planned improvement to incorporate use of conservation tillage in the United States into this Inventor y. The productivity of cultivars (cultivated varieties) has

### A-244 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 steadily improved over the last century and therefore it is unr ealistic to assume that modern varieties of crops, such as corn, are identical to the popular varieties grown in 1900 in te rms of yield potential, N demand, etc. Realistic simulations of historical land management and vegetation type are impo rtant because they influence present day soil C and N levels, which influence present-day N cycling and associated N 2 

### O emissions.
Uncertainty estimation was an integral part of this analysis; uncertainty in the input data for the county-scale simulations and structural uncertainty associated with the DAYCENT model predictions were both addressed (Del Grosso et al. 2010). In the first step, a Monte Carlo Analysis was used to propagate input data uncertainty through the modeling process. Thus, input data were randomly selected from PDFs for weather records, soil type, mineral N fertilization rate, and organic amendments. See Step 1a for additional discussion about the PDFs. After selecting a set of inputs for a county, DAYCENT was used to simulate each crop and then the process was repeated until 100 iterations were completed. Due to the computationally intensive requirements for DAYCENT, it was not possible to simulate every county with the Monte Carlo Analysis. Two counties were selected from each of the 63 agricultural regions, and additional counties were added based on the variance in N 2 

### O emissions across regions from the past year’s Inventory, using a Neyman allocation (Cochran 1977). A Neyman allocation is based on the variance in N 2 

### O emissions across the 63 regions; regions with larger variances were allocated a larger number of counties for the Monte Carlo Analysis. A total of 300 counties were included in the Monte Carlo Analysis, which is approximately 10 percent of all counties with agricultural land. In addition, all counties were simulated once based on the dominant conditions from the PDFs (i.e., most common soil type, weather condition, manure amendment, and mineral fertilizer rate).
In the second step of the uncertainty analysis, a structural uncertainty estimator was developed to account for uncertainty inherent in model formulation and parameterization using an empirically-based procedure described by Ogle et al. (2007). The procedure is based on developing a statistical relationship between modeled results and field measurements. Specifically, DAYCENT was used to simulate 11 agricultural experiments with 108 treatments, and the results were analyzed using a linear-mixed effect model in wh ich measurements were statistically modeled as a function ofsimulated emissions. Simulated DAYCENT emissions were a highly significant predictor of the measurements, with a pvalue of <0.01. Several other variables were tested in the statistical model to evaluate if DAYCENT exhibited bias under certain conditions related to climate, so il types, and management practices. The ty pe of crop or grassland was significant at an alpha level of 0.05, demonstrating that DAYCENT tended to over-estimate emissions for small grains systems and grassland, but was accurate in predicting the N 2 

### O emissions for other crops. Random effects were included in the model to capture the dependence in time series and data collected from the same site, which were needed to estimate appropriate standard deviations for parameter coefficients.
The structural uncertainty estimator accounted for bias and prediction error in the DAYCENT model results, as well as random error associated with fine-scale emission predictions in counties over a time series from 1990 to 2009. To apply the uncertainty estimator, DAYCENT N 2 

### O emission estimates were used as an input to the linear mixed effect model after randomly selecting statistical parameter coefficients from their joint probability distribution, in addition to random draws from PDFs representing the uncertainty due to site, site by year random effects and the residual error from the linear-mixed effect model (Del Grosso et al. 2010).
In DAYCENT, once N enters the plant/soil system, the model cannot distinguish among the original sources of the N to determine which management activity led to specific N 2 

### O emissions. This means, for example, that N 2 

### O emissions from applied synthetic fertilizer cannot be separated from emissions due to other N inputs, such as crop residues. It is desirable, however, to report emissions associat ed with specific N inputs. Thus, for each crop in a county, the N inputs in a simulation were determined for anthropogenic practices discussed in IPCC (2006), including synthetic mineral N fertilization, organic amendments, and crop residue N added to soils (including N-fixing crops). The percentage of N input for anthropogenic practices was divided by the total N input, and this proportion was used to determine the amount of N 2 

### O emissions assigned to each of the practices. 61 

### For example, if 70 percent of the mineral N made available in the soil was due to mineral fertilization, then 70 percent of the N 2 

### O emissions were assigned to this practice. The remainder of soil N 2 

### O emissions is reported under “other N inputs,” which includes mineralization due to decomposition of soil organic matter and litter, as well as asymbiotic fixa tion of mineral N in soils from the atmosphere. Asymbiotic N fixation by soil bacteria is a minor source of N, typically not exceeding 10 percent of total N inputs to agroecosystems. Mineralization of soil organic matter is a more significant source of N, but is still typically less than half of the amount of N made available in the soil compared to fertilization, manure amendments, and symbiotic fixation. Accounting for the

61 This method is a simplification of reality to allow partitioning of N 2 O emissions, as it assumes that all N inputs have an identical chance of being converted to N 2 O. This is unlikely to be the case, but DAYCENT does not track N 2 O emissions by source of mineral N so this approximation is the only appro ach that can be used for partitioning N 2 O emissions by source of N input. Moreover, this approach is similar to the IPCC Tier 1 method (IPCC 2006), which uses the same direct emissions factor for most N sources (e.g. , PRP).

### A-245 influence of “other N inputs” was necessary in order to meet the recommendation of reporting all emissions from managed lands (IPCC 2006). While this method allows for attribution of N 2 

### O emissions to the individual N inputs to the soils, it is important to realize that sources such as synthetic fertilization may have a larger impact on N 2 

### O emissions than would be suggested by the associated level of N input for this source (Delgado et al. 2009). Further research will be needed to improve upon this attribution method, however.

The final N 2 

### O emission estimate was determined by summing the estimates from the single simulation conducted in each county for the dominant condition to the 63 regions. Estimates were then adjusted to account for the difference between the emissions estimated in the Monte Carlo analysis and the dominant condition simulation on a region-by-region basis (i.e., if the Monte Carlo mean was slightly higher than the dominant condition among the counties included in the Monte Carlo analysis, the total emission estimate for the regi on would be raised by the difference) (Del Grosso et al. 2010). In turn, regional values were summed to produce the national total. The uncertainty was based on the variance in simulated N 2 

### O emissions for the iterations in the Monte Carlo Analysis and the variance associated with difference between the means from the Monte Carlo Analysis and the simulated N 2 

### O emissions for the dominant condition, expressed as a 95 percent confidence interval (Del Grosso et al. 2010).

Non-Major Crops: Tier 1 Method

To estimate direct N 2 

### O emissions from N additions to non-major crops, the amount of N in applied synthetic fertilizer, manure and other commercial organic fertilizers (i.e., dried blood, tankage, compost, and other) was added to N inputs from crop residues, and the resulting annual totals were multiplied by the IPCC default emission factor of 0.01 kg N 2 

### O-N/kg N (IPCC 2006). The uncertainty was determined based on simple error propagation methods (IPCC 2006). The uncertainty in the default emission factor ranged from 0.3–3.0 kg N 2 

### O-N/kg N (IPCC 2006). Uncertainty in activity data is ± 20 percent for fertilizer additions (Mosier 2004). 62 

### Uncertainties in the emission factor and fertilizer additions were combined with uncertainty in the equations used to calculate residue N additions from above- and below-ground biomass dry matter and N concentration to derive overall uncertainty.

Step 2b: Direct N 2 

### O Emissions Due to Drainage and Cultivation of Organic Cropland Soils

To estimate annual N 2 

### O emissions from drainage and cultivation of organic soils, the area of cultivated organic soils in temperate regions was multiplied by the IPCC (2006) default emission factor for temperate soils and the corresponding area in sub-tropical regions was multiplied by the average (12 kg N 2 

### O-N/ha cultivated) of IPCC (2006) default emission factors for temperate (8 kg N 2 

### O-N/ha cultivated) and tropical (16 kg N 2 

### O-N/ha cultivated) organic soils. The uncertainty was determined based on simple error propagation methods (IPCC 2006), including uncertainty in the default emission factor ranging from 2–24 kg N 2 

### O-N/ha (IPCC 2006).

Step 2c: Estimate Total Direct N 2 

### O Emissions from Cropland Soils
In this step, total direct N 2 

### O emissions from cropland soils are calculated by summing direct emissions on mineral soils with emissions resulting from the drainage and cultivation of organic soils (i.e., histosols) (Table A- 207). Uncertainties were combined using the simple error propagation method (IPCC 2006).

Step 3: Estimate Direct N 2 

### O Emissions from Grasslands

DAYCENT was used to estimate direct N 2 

### O emissions from soils in non-federal grasslands (pastures and rangeland), and the Tier 1 method was used for federal grassl ands. Managed pastures were simulated with DAYCENT by assuming that the vegetation mix includes forage legumes and gr asses, and that grazing intensity was moderate to heavy. Rangelands were simulated without forage legumes and grazing intensity was assumed to be light to moderate. The methodology used to conduct the DAYCENT simulations of grasslands was similar to that for major crop types described above in Step 2a, including the analysis addressing uncertainty in the model inputs and model structure. Carbon and nitrogen additions to grasslands from grazing animals were obtai ned from county level animal excretion data and area data for federal and non-federal grasslands, as described in Step1c.

62 Note that due to lack of data, uncertainties in managed ma nure N production, PRP manure N production, other commercial organic fertilizer amendments, indirect losses of N in the DAYCENT simulations, and sewage sludge amendments to soils are currently tre ated as certain; these sources of uncertainty w ill be included in future Inventories.

### A-246 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 The Tier 1 method was used to estimate emissions from N excreted by livestock on federal lands (i.e., PRP manure N). The Tier 1 method was also used to estimate emis sions from sewage sludge application to grasslands. These two sources of N inputs to soils were multiplied by the IPCC (2006) default emission factors (0.01 kg N 2 

### O-N/kg N from sludge and horse, sheep, and goat manure, and 0.02 kg N 2 

### O-N/kg N from cattle, swine, and poultry manure) to estimate N 2 

### O emissions. This emission estimate was summed with the DAYCENT simulated emissions to provide the national total for direct N 2 

### O losses from grasslands (Table A- 208). The uncertainty was determined based on the Tier 1 error propagation methods provided by the IPCC (2006) with uncertain ty in the default emission factor ranging from 0.007 to 0.06 kg N 2 

### O-N/kg N (IPCC 2006).

Step 4: Estimate Indirect N 2 

### O Emissions for All Land-Use Types
In this step, N 2 

### O emissions were calculated for the two indirect emission pathways (N 2 

### O emissions due to volatilization, and N 2 

### O emissions due to leaching and runoff of N), which were then summed to yield total indirect N 2 

### O emissions from croplands, grasslands, forest lands, and settlements.

Step 4a: Indirect Emissions Due to Volatilization
Indirect emissions from volatilization of N inputs from synthetic and commercial organic fertilizers, and PRP manure, were calculated according to the amount of mineral N that was transported in gaseous forms from the soil profile and later emitted as soil N 2 

### O following atmospheric deposition. See Step 1d for additional information about the methods used to compute N losses due to volatilization. The estima ted N volatilized for all land-use and livestock activities was multiplied by the IPCC default emission factor of 0.01 kg N 2 

### O-N/kg N (IPCC 2006) to compute total N 2 

### O emissions from volatilization. The resulting estimates are provided in Table A- 209. The uncertainty was determined using simple error propagation methods (IPCC 2006), by combining uncertainties in the amount of N volatilized, with uncertainty in the default emission factor ranging from 0.002–0.05 kg N 2 

### O-N/kg N (IPCC 2006).

Step 4b: Indirect Emissions Due to Leaching and Runoff
The amount of mineral N (i.e., synthetic fertilizers, comm ercial organic fertilizers, PRP manure, crop residue, N mineralization, asymbiotic fixation) that was transported from the soil profile in aqueous form was used to calculate indirect emissions from (1) leaching of mineral N from soils and (2) losses in runoff of water associated with overland flow. See Step 1d for additional information about the methods used to compute N losses from soils due to leaching and runoff in overland water flows.
The total amount of N transported from soil profiles through leaching and surface runoff was multiplied by the IPCC default emission factor of 0.0075 kg N 2 

### O-N/kg N (IPCC 2006) to estimate emissions for this source. The resulting emission estimates are provided in Table A- 210. The uncertainty was determined based on simple error propagation methods (IPCC 2006), including uncertainty in the default emission factor ranging from 0.0005 to 0.025 kg N 2 

### O-N/kg N (IPCC 2006).

Step 5: Estimate Total N 2 

### O Emissions for U.S. Soils
Total emissions were estimated by adding total direct emissions (from major crop types and non-major crop types on mineral cropland soils, drainage and cultivation of organic soils, and grassland management) to indirect emissions for all land use and management activities. U.S. national estimates for this source category are provided in Table A- 210. Uncertainties in the final estimate were combined using simple error propagation methods (IPCC 2006), and expressed as a 95 percent confidence interval.

Direct and indirect emissions of soil N 2 

### O vary regionally in both croplands and grasslands as a function of N inputs, weather, and soil type. A little more than half of the total N 2 

### O emissions from major crops occur in Iowa, Illinois, Nebraska, Minnesota, Texas, Kansas and Indiana where N input s associated with corn rotations are high or where large land areas are cropped (Table A- 211). On a per area unit basis, direct N 2 

### O emissions are also high in many of the Mississippi River Basin states where there are also high N in put to corn and soybean crops (Figure A- 9). Emissions are also high in some western and New England states. Only a small portion of the land in these regions is used for crop production, but management and conditions lead to higher emissions on a per unit area basis than other regions. For example, emissions are high in California, Arizona, and other western states due to intensive irrigation management systems. For some New England states, emissions are high on a per unit area because subsurface soil layers remain frozen

### A-247 when surface soil layers thaw in spring. This creates saturate d conditions near the surface that facilitate denitrification and N 2 

### O emissions. Indirect emissions tend to be high on an area basis in the central and eastern United States because relatively high rainfall facilitates N losses from leaching and runoff and in some western states where irrigation can contribute to leaching and runoff (Figure A- 10).
Direct and indirect emissions from grasslands are typi cally lower than those from croplands (Table A- 211, Figure A- 11, and Figure A- 12) because N inputs tend to be lowe r, particularly from synthetic fertilizer. Texas was by far the highest emitter for this category, followed by Nebr aska, Montana, Oklahoma, New Mexico Colorado and South Dakota. On a per area unit basis, emissions are lower in the western United States because grasslands in the East and Central regoins are more intensively managed (legume seeding, fertilization) while western rangelands receive few, if any, N inputs. Also, rainfall is limited in most of the western United States, and grasslands are not typically irrigated so minimal leaching and runoff of N occurs in these grasslands.

Figure A- 7: DAYCENT Model Flow Diagram

Figure A- 8: Comparisons of Results from DAYCENT Mode l and IPCC Tier 1 Method wi th Measurements of Soil N 2 

### O Emissions

Figure A- 9: Major Crops, Average Annual Direct N 2 

### O Emissions, Estimated Using the DAYCENT Model, 1990-2009 (Metric Tons CO 2 

### Eq./ha/year)

Figure A- 10: Major Crops, Average Annual N Losses Leading to Indirect N 2 

### O Emissions, Estimated Us ing the DAYCENT Model, 1990-2009 (kg N/ha/year)

Figure A- 11: Grasslands, Average Annual Direct N 2 

### O Emissions, Estimated Using the DAYCENT Model, 1990-2009 (Metric Tons CO 2 

### Eq./ha/year)

Figure A- 12: Grasslands, Average Annu al N Losses Leading to Indirect N 2 

### O Emissions, Estimated Us ing the DAYCENT Model, 1990-2009 (kg N/ha/year)A-248 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009Table A- 197: Synthetic Fertilizer N Added to Major Crops (Gg N)19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Fertilizer N 7,4687,307 7,915 7,705 7,641 7,412 7,575 7,450 7,547 7,370 7,355 7,000 6,855 7,428 6,663 6194Table A- 198: Fate of Livestock Manure Nitrogen (Gg N)Activity 19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Managed Manure N Appliedto Major Crops andGrasslandsa,b993968 1,038 1,063 1,116 1,103 1,099 1,086 1,164 1,079 1,077 1,036 847 1,195 1,110 948Managed Manure N Appliedto Non-Major Cropsb1,4731,618 1,568 1,563 1,519 1,561 1,587 1,583 1,585 1,549 1,598 1,6871,878 1,655 1,649 1,722Managed Manure N Appliedto Grasslands 446515 514 503 542 478 463 466 470 470 472 475 479 475 474 462Pasture, Range, & PaddockManure N 4,0834,458 4,412 4,296 4,237 4,158 4,056 4,043 4,049 4,072 4,077 4,1574,209 4,201 4,170 4,123Total 6,9947,559 7,532 7,426 7,414 7,300 7,204 7,177 7,268 7,171 7,223 7,3557,413 7,527 7,403 7,255aAccounts for N volatilized and leached/runoff during treatment, storage andtransport before soil application.bIncludes managed manure and daily spread manure amendmentscTotals may not sum exactly due to rounding.Table A- 199: Crop Residue N and Other N Inputsto Major Crops as Simulated by DAYCENT (Gg N)Activity 19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Residue Na2,9823,3053,3223,3293,0993,6593,4503,1513,2533,4073,2313,0413,1863,1643,2102,996Mineralization &Asymbiotic Fixation12,40612,01012,59012,81114,16612,92613,54813,52013,23813,21613,36112,74012,80013,08412,94312,345aResidue N inputs include unharvested fixed Nfrom legumes as well as crop residue N.Table A- 200: Synthetic Fertilizer N Added to Non-Major Crops (Gg N)19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Fertilizer N 1,0761,884 1,326 1,638 1,669 1,841 1,546 1,571 1,540 1,838 1,905 2,1552,039 1,692 2,229 2,653Table A- 201: Other Organic Commercial Fertilizer Consumption on Agricultural Lands (Gg N)19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Other Commercial Organic Fertilizer Na410 13 14 12 11 9 7 8 8 9 10 12 15 13 13aIncludes dried blood, tankage, compost, other. Excludes dried manure and sewage sludge used as commercial fertilizer to avoid double counting.A-249Table A- 202: Key Assumptions forProduction of Non-Major Crops and Retention of Crop ResiduesCropDry MatterFraction ofHarvestedProductAbove-ground Residue Ratio ofBelow-groundResidue toAbove-groundBiomassResidue N FractionSlope Intercept Above-ground Below-groundPeanuts for Nuts 0.94 1.071.54 0.20 0.016 0.014Dry Edible Beans 0.900.36 0.68 0.19 0.010 0.010Dry Edible Peas 0.91 1.13 0.85 0.19 0.008 0.008Austrian WinterPeas0.91 1.13 0.85 0.19 0.008 0.008Lentils 0.91 1.13 0.85 0.19 0.008 0.008Wrinkled Seed Peas 0.91 1.13 0.85 0.19 0.008 0.008Barley 0.89 0.98 0.59 0.22 0.007 0.014Oats 0.89 0.91 0.89 0.25 0.007 0.008Rye 0.88 1.09 0.88 0.22 0.005 0.011Millet 0.90 1.43 0.140.22 0.007 0.009Rice 0.89 0.95 2.46 0.16 0.007 0.009Table A- 203: Nitrogen in Crop Residues Retained on Soils Producing Non-Major Crops (Gg N)Crop 19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Peanuts for Nuts 6463 65 64 68 66 61 71 61 69 71 77 63 65 79 65Dry Edible Beans 1616 15 16 16 16 15 13 16 14 13 15 14 15 15 14Dry Edible Peas 911 9 11 11 10 10 10 10 11 14 15 8 8 14 17Austrian Winter Peas 88 8 8 8 8 8 8 8 8 8 8 8 8 8 8Lentils 99 9 9 9 9 10 10 9 9 10 11 10 10 9 11Wrinkled Seed Peas 99 8 8 8 8 8 8 8 8 9 8 8 8 8 9Barley 11296 105 96 94 74 86 68 63 76 76 59 51 59 66 63Oats 5529 28 30 30 27 28 24 23 27 23 23 20 20 20 20Rye 99 9 8 9 9 9 8 8 9 8 8 8 8 8 8Millet 77 7 7 7 7 3 7 2 5 6 5 4 6 6 4Rice 8087 86 91 91 99 94 103 101 97 109 106 95 96 99 105Total 378343 349 348 352 336 331 330 311 333 347 336 290 304 332 324A-250 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009Table A- 204: Drained and CultivatedOrganic Soil Area (Thousand Hectares)Year Temperate Area Sub-Tropical Area1990 444 1941995 450 1961996 450 1961997 450 1961998 450 1961999 450 1962000 450 1962001 450 1962002 450 1962003 450 1962004 450 1962005 450 1962006 450 1962007 450 1962008 450 1962009 450 196Table A- 205: Synthetic Fertilizer N, PRP Manure N, Organic Manure N Amendment, Forage Legume N, and Other N Inputs Simulated with the DAYCENT Model (Gg N)N Source19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Fertilizer N 9,6789,662 10,245 9,973 9,891 9,628 9,747 9,620 9,723 9,553 9,536 9,213 9,088 9,656 8,871 8,385PRP Manure N 3,7184,065 4,021 3,906 3,856 3,787 3,697 3,688 3,701 3,728 3,731 3,8063,856 3,850 3,819 3,781Managed Manure 1,4381,483 1,552 1,566 1,658 1,581 1,561 1,552 1,634 1,549 1,549 1,5111,326 1,670 1,584 1,410Residue Na11,16411,560 11,054 11,069 10,667 12,264 11,056 11,078 10,631 11,234 10,855 10,731 10,840 10,875 10,890 10,577Mineralization &AsymbioticFixation 23,86423,237 23,649 24,172 25,721 23,703 24,190 24,412 23,731 23,942 24,175 23,635 23,549 23,830 23,731 23,285aResidue N inputs include unharvested fixed Nfrom legumes as well as crop residue N.Table A- 206: Sewage Sludge Nitrogen by Disposal Practice (Gg N)Disposal Practice 19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Applied to Agricultural Soils 5269 72 75 78 81 30 86 89 91 94 98 101 104 106 109Other Land Application 2528 29 29 29 30 30 30 30 30 30 31 31 32 32 32Surface Disposal 2016 15 14 13 12 10 9 8 6 5 5 4 4 3 3Total 98113 116 118 121 122 124 125 127 128 130 134 136 139 141 144Note: Totals may not sum due to independent rounding.A-251Table A- 207: Direct N2O Emissions from Cropland Soils (Tg CO2Eq.)Activity 19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Mineral Soils 100.1107.1 115.5 112.9 116.5 111.1 112.7 120.1 112.5 109.7 116.2 115.2 112.7 114.9 115.0 109.1Major Crops 85.888.3 99.6 95.6 99.2 92.9 95.7 103.1 95.7 91.5 97.4 94.8 92.1 97.1 94.4 86.1Synthetic Fertilizer 27.127.4 31.7 29.6 29.4 27.4 28.4 30.4 28.8 27.0 28.7 28.1 26.7 29.1 26.5 23.9Managed Manure 3.63.6 4.2 4.1 4.1 4.0 4.0 4.4 4.3 3.9 4.1 4.1 3.3 4.6 4.4 3.6Residue Na10.612.4 13.5 12.8 11.6 13.7 12.7 12.7 12.4 12.4 12.6 12.1 12.4 12.4 12.6 11.5Mineralization and AsymbioticFixation 44.644.8 50.2 49.0 54.0 47.7 50.6 55.5 50.2 48.3 52.1 50.5 49.7 50.9 50.9 47.1Non-Major Crops 14.318.8 15.9 17.4 17.3 18.3 16.9 17.0 16.8 18.2 18.8 20.4 20.6 17.9 20.6 23.0Synthetic Fertilizer 5.29.2 6.5 8.0 8.1 9.0 7.5 7.7 7.5 9.0 9.3 10.5 9.9 8.2 10.9 12.9Managed Manure and OtherOrganic Commercial Fertilizer 7.27.9 7.7 7.7 7.5 7.7 7.8 7.7 7.8 7.6 7.8 8.3 9.2 8.1 8.1 8.5Residue N 1.81.7 1.7 1.7 1.7 1.6 1.6 1.6 1.5 1.6 1.7 1.6 1.4 1.5 1.6 1.6Organic Soils 2.92.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9Total* 102.9110.0 118.4 115.8 119.4 114.0 115.6 123.0 115.4 112.6 119.1 118.1 115.6 117.8 117.9 112.0+ Less than 0.05 Tg CO2Eq.aResidue N inputs include unharvested fixed Nfrom legumes as well as crop residue N.Table A- 208: Direct N2O Emissions from Grasslands (Tg CO2Eq.)19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009DAYCENT47.145.2 49.4 46.2 47.3 42.9 43.3 47.3 47.5 42.6 44.5 45.6 44.3 43.4 44.9 44.4Synthetic Fertilizer 3.94.1 4.5 4.1 4.2 3.7 3.9 4.1 4.3 3.8 4.0 4.1 4.0 3.9 4.0 3.9PRP Manure 4.64.7 5.2 4.7 4.6 4.4 4.5 4.7 4.7 4.4 4.6 4.9 4.8 4.7 4.9 4.7Managed Manure 1.51.8 2.0 1.8 1.8 1.5 1.6 1.7 1.8 1.5 1.6 1.6 1.6 1.6 1.6 1.6Residue Na15.614.6 15.5 14.6 14.5 14.9 13.8 15.5 15.3 13.9 14.2 14.6 14.2 13.9 14.4 14.1Mineralization andAsymbiotic Fixation 21.520.0 22.3 21.2 22.2 18.4 19.5 21.2 21.3 19.0 20.1 20.4 19.7 19.3 20.0 20.1Tier 1 3.74.1 4.1 4.0 4.0 3.9 3.8 3.7 3.7 3.7 3.7 3.8 3.8 3.8 3.8 3.8PRP Manure 3.53.7 3.7 3.7 3.6 3.5 3.4 3.3 3.3 3.2 3.3 3.3 3.3 3.3 3.3 3.2Sewage Sludge 0.30.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.5Total 50.949.2 53.5 50.3 51.3 46.8 47.1 51.0 51.2 46.3 48.2 49.4 48.1 47.3 48.7 48.2+ Less than 0.05 Tg CO2Eq.aResidue N inputs include unharvested fixed Nfrom legumes as well as crop residue N.A-252 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009Table A- 209: Indirect N2O Emissions (Tg CO2Eq.)Activity 19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Volatilization and Atm. Deposition 16.918.2 17.6 17.9 17.8 17.8 17.6 17.6 17.5 17.6 17.8 18.2 19.3 17.7 17.8 18.3Croplands 11.612.8 12.4 12.6 12.4 12.6 12.7 12.5 12.5 12.5 12.8 13.1 14.2 12.8 12.9 13.4Settlements 0.10.2 0.2 0.2 0.2 0.2 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2Forest Land ++ + + + + + + + + + + + + + +Grasslands 5.15.2 5.0 5.1 5.2 5.0 4.7 4.9 4.7 4.8 4.8 4.8 4.8 4.7 4.7 4.7Surface Leaching & Run-off 27.130.3 29.8 25.8 29.1 24.5 26.5 28.8 24.8 25.9 26.7 25.7 25.9 26.6 26.3 26.1Croplands 25.829.1 28.1 24.4 27.5 23.0 25.0 27.2 23.2 24.0 24.9 23.7 24.4 24.9 24.5 24.1Settlements 0.20.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4Forest Land ++ 0.1 0.1 0.1 0.1 0.1 0.1 0.10.1 0.1 0.1 0.1 0.1 0.1 0.1Grasslands 1.00.8 1.4 0.9 1.3 1.0 1.2 1.1 1.2 1.4 1.3 1.5 1.1 1.2 1.2 1.5Total 44.048.5 47.4 43.7 46.9 42.3 44.1 46.3 42.3 43.5 44.5 43.9 45.2 44.3 44.1 44.4+ Less than 0.05 Tg CO2Eq.Table A- 210: Total N2O Emissions from Agricultural Soil Management (Tg CO2Eq.)Activity 19901995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Total Direct 153.8159.2 171.9 166.1 170.7 160.9 162.6 174.0 166.6 158.9 167.3 167.5 163.7 165.1 166.6 160.2Direct Emissions from MineralCropland Soils 100.1107.1 115.5 112.9 116.5 111.1 112.7 120.1 112.5 109.7 116.2 115.2 112.7 114.9 115.0 109.1Synthetic Fertilizer 32.336.6 38.2 37.6 37.5 36.4 36.0 38.1 36.3 35.9 37.9 38.6 36.7 37.4 37.3 36.9Organic Amendmenta10.811.6 11.9 11.8 11.6 11.7 11.8 12.2 12.1 11.5 12.0 12.3 12.5 12.8 12.5 12.1Residue Nb12.414.0 15.2 14.5 13.4 15.3 14.3 14.3 13.9 14.0 14.3 13.7 13.8 13.9 14.3 13.1Mineralization and Asymbiotic Fixation 44.644.8 50.2 49.0 54.0 47.7 50.6 55.5 50.2 48.3 52.1 50.5 49.7 50.9 50.9 47.1Direct Emissions from DrainedOrganic Cropland Soils 2.92.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9Direct Emissions from Grasslands* 50.949.2 53.5 50.3 51.3 46.8 47.1 51.0 51.2 46.3 48.2 49.4 48.1 47.3 48.7 48.2Synthetic Mineral Fertilizer 3.94.1 4.5 4.1 4.2 3.7 3.9 4.1 4.3 3.8 4.0 4.1 4.0 3.9 4.0 3.9PRP Manure* 8.18.4 8.9 8.4 8.2 7.9 7.9 8.1 8.0 7.6 7.8 8.2 8.1 8.0 8.2 7.9Managed Manure 1.51.8 2.0 1.8 1.8 1.5 1.6 1.7 1.8 1.5 1.6 1.6 1.6 1.6 1.6 1.6Sewage Sludge 0.30.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.5Residueb15.614.6 15.5 14.6 14.5 14.9 13.8 15.5 15.3 13.9 14.2 14.6 14.2 13.9 14.4 14.1Mineralization and Asymbiotic Fixation 21.520.0 22.3 21.2 22.2 18.4 19.5 21.2 21.3 19.0 20.1 20.4 19.7 19.3 20.0 20.1Total Indirect 44.048.5 47.4 43.7 46.9 42.3 44.1 46.3 42.3 43.5 44.5 43.9 45.2 44.3 44.1 44.4Volatilization 16.918.2 17.6 17.9 17.8 17.8 17.6 17.6 17.5 17.6 17.8 18.2 19.3 17.7 17.8 18.3Leaching/Runoff 27.130.3 29.8 25.8 29.1 24.5 26.5 28.8 24.8 25.9 26.7 25.7 25.9 26.6 26.3 26.1Total Emissions 197.8207.6 219.3 209.7 217.6 203.2 206.8 220.4 208.8 202.4 211.8 211.3 208.9 209.4 210.7 204.6+ Less than 0.05 Tg CO2Eq.aOrganic amendment inputs include managed manure amendments, daily spread manure and other commercial organic fertilizer (i.e.,dried blood, tankage, compost, and other).bResidue N inputs include unharvested fixed Nfrom legumes as well as crop residue N.

### A-253 Table A- 211: Total 2009 N 2 

### O Emissions (Direct and Indirect) from Ag ricultural Soil Management by State (Tg CO 2 

### Eq.)

State Croplands 1 Grasslands 2 Settlements 3 Forest Lands 4 Total Lower Bound Upper Bound AL 0.67 0.71 0.03 n.e. 1.42 0.87 2.51 AR 3.07 0.76 0.02 n.e. 3.85 2.47 7.37 AZ 0.91 0.93 0.02 n.e. 1.86 0.95 4.58 CA 6.96 1.80 0.19 n.e. 8.95 5.73 17.46 CO 1.23 2.46 0.02 n.e. 3.71 2.36 6.14 CT 0.57 0.03 0.02 n.e. 0.62 0.36 1.58 DE 0.77 0.01 0.01 n.e. 0.78 0.45 2.38 FL 2.02 1.03 0.22 n.e. 3.27 2.28 5.58 GA 1.22 0.36 0.01 n.e. 1.59 1.02 3.23 HI5 0.00 n.e. n.e. n.e. 1.59 1.02 3.23

IA 15.79 0.85 0.07 n.e. 16.71 11.99 33.98 ID 2.19 1.57 0.02 n.e. 3.78 2.34 7.60 IL 11.86 0.37 0.12 n.e. 12.34 8.81 19.28 IN 7.55 0.30 0.07 n.e. 7.92 5.05 12.74 KS 5.66 2.21 0.07 n.e. 7.94 5.58 12.16 KY 2.00 1.14 0.03 n.e. 3.17 1.91 5.69 LA 1.58 0.54 0.04 n.e. 2.16 1.65 3.44 MA 0.13 0.02 0.04 n.e. 0.19 0.13 0.77 MD 1.03 0.09 0.05 n.e. 1.18 0.30 3.27 ME 0.22 0.02 0.01 n.e. 0.25 0.17 1.36 MI 4.48 0.37 0.08 n.e. 4.92 3.50 8.35 MN 9.71 0.80 0.03 n.e. 10.54 7.38 16.41 MO 6.07 1.82 0.07 n.e. 7.96 5.34 12.65 MS 2.44 0.42 0.03 n.e. 2.90 1.81 6.33 MT 2.01 3.09 0.01 n.e. 5.11 3.21 8.22 NC 2.38 0.65 0.05 n.e. 3.08 2.14 6.28 ND 5.46 1.17 0.03 n.e. 6.66 4.46 10.67 NE 6.38 2.98 0.07 n.e. 9.43 6.72 14.63 NH 0.06 0.01 0.01 n.e. 0.08 0.06 0.41 NJ 1.61 0.02 0.07 n.e. 1.71 0.34 5.10 NM 0.54 2.91 0.01 n.e. 3.46 1.66 6.09 NV 0.31 0.61 0.01 n.e. 0.93 0.04 9.01 NY 3.19 0.33 0.07 n.e. 3.60 2.35 7.02 OH 7.37 0.49 0.11 n.e. 7.97 5.67 12.54 OK 1.21 3.02 0.03 n.e. 4.27 1.97 7.83 OR 1.12 1.43 0.01 n.e. 2.56 1.60 5.42 PA 3.05 0.12 0.06 n.e. 3.24 1.77 7.60 RI 0.02 0.00 0.01 n.e. 0.03 0.02 0.58 SC 0.66 0.15 0.03 n.e. 0.85 0.16 2.10 SD 3.25 2.53 0.02 n.e. 5.79 3.97 9.35 TN 1.60 0.77 0.05 n.e. 2.42 1.46 6.22 TX 5.32 8.93 0.09 n.e. 14.34 8.26 22.86 UT 0.64 0.92 0.01 n.e. 1.57 1.06 3.24 VA 4.26 0.84 0.06 n.e. 5.16 3.34 12.17 VT 0.46 0.06 0.00 n.e. 0.52 0.37 1.11 WA 2.28 1.00 0.03 n.e. 3.31 1.24 7.93 WI 6.64 0.77 0.04 n.e. 7.45 5.23 12.13 WV 0.44 0.21 0.01 n.e. 0.66 0.36 1.62 WY 0.51 1.95 0.01 n.e. 2.47 1.69 4.02

1 Emissions from non-manure organic N inputs for minor cr ops were not estimated (n.e.) at the state level.

2 Emissions from sewage sludge app lied to grasslands and were not es timated (n.e.) at the state level

3 Emissions from sewage sludge applied to settlement s were not estimated (n.e.) at the state level.

4 Forestland emissions were not estimated (n.e.) at the state level.

5 N 2 O emissions are not reported for Hawa ii except from cropland organic soils.

### A-254 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 

# 3.12. Methodology for Estimating Net Ca rbon Stock Changes in Forest Lands Remaining Forest Lands

This sub-annex expands on the methodology used to calculate net changes in carbon (C) stocks in forest ecosystems and in harvested wood products. Some of the deta ils of C conversion factors and procedures for calculating net CO 2 

### flux for forests are provided below; full details of selected topics may be found in the cited references.

Carbon Stocks and Net Changes in Forest Ecosystem Carbon Stocks

At least two forest inventories exist for most forest land in the United States. C stocks are estimated based on data from each inventory, at the level of permanent inventory pl ots. C per hectare (for a sample location) is multiplied by the total number of hectares that the plot represents, and then totals are summed for an area of interest, such as the state of Maine. Net annual C stock changes are calculated by taking the difference between the inventories and dividing by the number of years between the inventories for a selected state or sub-state area.

Forest inventory data
The estimates of forest C stocks are based on data derived from forest inventory surveys. Forest inventory data were obtained from the USDA Forest Service, Forest Inventory and Analysis (FIA) program (Frayer and Furnival 1999). FIA data include remote sensing information and collection of measurements in the field at sample locations called plots. Tree measurements include diameter and species. On a subset of plots, additional measurements or samples are taken of down dead wood, litter, and soil C; however, these are not yet available nationwide for C estimation. The field protocols are thoroughly documented and available for download from the USDA Forest Service (2010c). The inventory was designed for timber volume estimation rather than C stock estimation, so most C pools are not measured directly or are sampled only on a subset of plots. Bechtold and Patterson (2005) provide the estimation procedures for standard forest inventory results. The data are freely available for download at USDA Forest Service (2010b) as the Forest Inventory and Analysis Database (FIADB) Version 4.0; these data are the prim ary sources of forest inventory used to estimate forest C stocks.
Forest surveys have begun in the U.S. territories and in Hawaii. Meanwhile this inventory assumes that these areas account for a net C change of zero. Survey data ar e available for the temperate oceanic ecoregion of Alaska (southeast and south central). Inventory data are publicly av ailable for 6.1 million hectares of forest land, and these inventoried lands, comprising 12% of the total forest land in Alaska, contribute to the forest carbon stocks presented here.
Agroforestry systems are also not currently accounted for in the U.S. Inventory, since they are not explicitly inventoried by either of the two primary national natura l resource inventory programs: the FIA program of the USDA Forest Service and the National Resources Inventory (NRI) of the USDA Natural Resources Conservation Service (Perry et al. 2005). The majority of these tree-based practices do not meet the size and definitions for forests within each of these resource inventories. The size characteristics that exclude th em from inventories also allow these systems to provide their many services without taking the land out of agricultural production, making them an appealing C sequestration option. Agroforestry in the United States has been defined as “intensive land-use management that optimizes the benefits (physical, biological, ecological, economic, social) from bio-phy sical interactions created when trees and/or shrubs are deliberately combined with crops and/or livestock” (Gold et al. 2000). In the United States, there are six categories of agroforestry practices: riparian forest buffers, windbreaks, alley cropping, silvopasture, forest farming and special applications. 63 

### These practices are used to address many issues faci ng agricultural lands, such as economic diversification, habitat fragmentation, and water quality. While providing th ese services and regardless of intent, these tree-based plantings will also reduce atmospheric CO 2 

### . This occurs directly through CO 2 

### sequestration into woody biomass, and indirectly through enhancement of agricultural production, trapping wind-blown and surface runoff sediments, and/or reducing CO 2 

### emissions through fuel-use savings (Quam et al. 1992). The effects of these individual practices canpotentially be quite large when taken into account within a whole-farm or within an aggregating larger entity (i.e., statelevel) (Quam et al. 1992, Schoeneberger 2006). One estimate of the sequestration potential through agroforestry practices in the United States is 90.3 Mt C/year by 2025 (Nair and Nair 2003).

63 

### More information on agroforest ry practices can be found onlin e at <http://www.unl.edu/nac>.

### A-255 Summing state-level C stocks to calculate United States net C flux in forest ecosystems
The overall approach for determining forest C stocks and stock change is based on methodology and algorithms coded into the computer tool described in Smith et al. (2010). It focuses on estimating forest C stocks based on data from two or more forest surveys conducted several years apart for each state or sub-state. There are generally two or more surveys available for download for each state. C stocks are calculated separately for each state based on available inventories conducted since 1990 and for the inventory closest to, but prior to, 1990 if such data are available and consistent with these methods. This approach ensures that the period 1990 (the base year) to present can be adequately represented. Surveys conducted prior to and in the early to mid 1990s focused on land capable of supporting timber production (timberland). 64 

### As a result, information on less productive forest land or lands reserved from harvest was limited. Inventory field crews periodically measured all the plots in a state at a frequency of every 5 to 14 years. Generally, forests in states with fast-growing (and therefore rapidly changing) forests tended to be surveyed more often than states with slower-growing (and therefore slowly changing) forests. Older surveys for some states, particularly in the West, also have National Forest System (NFS) lands or reserved lands that were surveyed at different times than productive, privately-owned forest land in the state. Periodic data for each state thus became available at irregular intervals and determining the year of data collection associated with the survey can sometimes be difficult.
Table A-212: Source of Unique Forest Inventory and Average Year of Field Surv ey Used to Estimate Statewide Carbon Stocks

State/Substate a Source of Inventory Data, Report/Inventory Year b Average Year Assigned to Inventory c Alabama FIADB 4.0, 1982 1982 FIADB 4.0, 1990 1990 FIADB 4.0, 2000 1999 FIADB 4.0, 2005 2003 Alaska, non-reserved Southcentral FIADB 4.0, 2009 2007 Alaska, non-reserved Southeast FIADB 4.0, 2009 2007 Alaska, reserved Southcentral FIADB 4.0, 2009 2006 Alaska, reserved Southeast FIADB 4.0, 2009 2006 Arizona, NFS non-woodlands 1987 RPA 1985 FIADB 4.0, 1999 1996 FIADB 4.0, 2009 2005 Arizona, NFS woodlands 1987 RPA 1984 FIADB 4.0, 1999 1996 FIADB 4.0, 2009 2005 Arizona, non-NFS non-woodlands FIADB 4.0, 1985 1986 FIADB 4.0, 1999 1996 FIADB 4.0, 2009 2006 Arizona, non-NFS woodlands FIADB 4.0, 1999 1990 FIADB 4.0, 2009 2006 Arkansas FIADB 4.0, 1988 1988 FIADB 4.0, 1995 1996

64 

### Forest land is defined as land at least 120 feet wide and 1 acr e in size with at least 10 percent cover (or equivalent stockin g by live trees of any size, including land that formerly had such tree cover and that will be naturally or artificially regenerated

. . Forest land includes transition zones, such as areas between forest and nonforest lands that have at least 10 percent cover (or equivalent stocking) with live trees and forest areas adjacent to urban and built-up lands. Roadside, streamside, and shelterbe lt strips of trees must have a crown width of at least 120 feet and continuous length of at least 363 feet to qualify as forest la nd. Unimproved roads and trails, streams, and clearings in forest areas are classi fi ed as forest if they are less than 120 feet wide or an acre in size. Tree-covered areas in agricultural production settings , such as fruit orchards, or tree-covered areas in urban se ttings, such as city parks, are not considered fo rest land. (Smith et al. 2009) Timberland is the most productive type of forest land, which is on unreserved land and is producing or capable of producing crops of industrial wood. Productivity is at a minimum rate of 20 cubic feet of industrial wood per acre per year (Woudenberg and Farrenkopf 1995). There are a bout 203 million hectares of timberland in the conterminous Unite d States, which represents 81 pe rcent of all forest lands over the same area (Smith et al. 2009).

### A-256 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 FIADB 4.0, 2005 2003 FIADB 4.0, 2009 2007 California, NFS IDB, 1990s 1997 FIADB 4.0, 2009 2006 California, non-NFS IDB, 1990s 1993 FIADB 4.0, 2009 2006 Colorado, NFS non-woodlands 1997 RPA 1981 FIADB 4.0, 2009 2006 Colorado, NFS woodlands 1997 RPA 1975 FIADB 4.0, 1984 1997 FIADB 4.0, 2009 2006 Colorado, non-NFS non-woodlands Westwide, 1983 1980 FIADB 4.0, 2009 2006 Colorado, non-NFS woodlands Westwide, 1983 1983 FIADB 4.0, 2009 2006 Connecticut FIADB 4.0, 1985 1985 FIADB 4.0, 1998 1998 FIADB 4.0, 2007 2006 Delaware FIADB 4.0, 1986 1986 FIADB 4.0, 1999 1999 FIADB 4.0, 2008 2007 Florida FIADB 4.0, 1987 1987 FIADB 4.0, 1995 1995 FIADB 4.0, 2007 2005 Georgia FIADB 4.0, 1989 1989 FIADB 4.0, 1997 1997 FIADB 4.0, 2004 2001 FIADB 4.0, 2008 2006 Idaho, Caribou-Targhee NF Westwide, 1991 1992 FIADB 4.0, 2009 2007 Idaho, Kootenai NF 1987 RPA 1988 FIADB 4.0, 1991 1995 FIADB 4.0, 2009 2007 Idaho, non-NFS non-woodlands FIADB 4.0, 1991 1990 FIADB 4.0, 2009 2007 Idaho, non-NFS woodlands FIADB 4.0, 1991 1982 FIADB 4.0, 2009 2007 Idaho, other NFS Westwide, 1991 1988 FIADB 4.0, 1991 2000 FIADB 4.0, 2009 2007 Idaho, Payette NF 1987 RPA 1982 FIADB 4.0, 2009 2007 Idaho, Salmon-Challis NF 1987 RPA 1978 FIADB 4.0, 2009 2007 Idaho, Sawtooth NF Westwide, 1991 1983 FIADB 4.0, 1991 1996 FIADB 4.0, 2009 2007 Illinois FIADB 4.0, 1985 1985 FIADB 4.0, 1998 1998 FIADB 4.0, 2005 2004

### A-257 FIADB 4.0, 2008 2006 Indiana FIADB 4.0, 1986 1986 FIADB 4.0, 1998 1998 FIADB 4.0, 2003 2001 FIADB 4.0, 2008 2007 Iowa FIADB 4.0, 1990 1990 FIADB 4.0, 2003 2002 FIADB 4.0, 2008 2006 Kansas FIADB 4.0, 1981 1981 FIADB 4.0, 1994 1994 FIADB 4.0, 2005 2003 FIADB 4.0, 2008 2007 Kentucky FIADB 4.0, 1988 1987 FIADB 4.0, 2004 2002 FIADB 4.0, 2007 2005 Louisiana FIADB 4.0, 1984 1984 FIADB 4.0, 1991 1991 FIADB 4.0, 2005 2004 Maine Eastwide, 1982 1983 FIADB 4.0, 1995 1995 FIADB 4.0, 2003 2002 FIADB 4.0, 2008 2007 Maryland FIADB 4.0, 1986 1986 FIADB 4.0, 1999 2000 FIADB 4.0, 2008 2007 Massachusetts FIADB 4.0, 1985 1985 FIADB 4.0, 1998 1998 FIADB 4.0, 2007 2006 Michigan FIADB 4.0, 1980 1980 FIADB 4.0, 1993 1993 FIADB 4.0, 2004 2003 FIADB 4.0, 2008 2006 Minnesota FIADB 4.0, 1990 1989 FIADB 4.0, 2003 2001 FIADB 4.0, 2008 2006 Mississippi FIADB 4.0, 1987 1987 FIADB 4.0, 1994 1994 FIADB 4.0, 2006 2007 Missouri FIADB 4.0, 1989 1988 FIADB 4.0, 2003 2002 FIADB 4.0, 2008 2006 Montana, NFS 1987 RPA 1988 FIADB 4.0, 1989 1996 FIADB 4.0, 2009 2007 Montana, non-NFS non-reserved FIADB 4.0, 1989 1989 FIADB 4.0, 2009 2006 Montana, non-NFS reserved 1997 RPA 1990 FIADB 4.0, 2009 2007 Nebraska FIADB 4.0, 1983 1983 FIADB 4.0, 1994 1995

### A-258 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 FIADB 4.0, 2005 2004 FIADB 4.0, 2008 2006 Nevada, NFS non-woodlands 1987 RPA 1974 FIADB 4.0, 1989 1997 FIADB 4.0, 2005 2005 Nevada, NFS woodlands 1987 RPA 1978 FIADB 4.0, 1989 1997 FIADB 4.0, 2005 2005 Nevada, non-NFS non-woodlands 1997 RPA 1985 FIADB 4.0, 2005 2005 Nevada, non-NFS woodlands FIADB 4.0, 1989 1980 FIADB 4.0, 2005 2005 New Hampshire FIADB 4.0, 1983 1983 FIADB 4.0, 1997 1997 FIADB 4.0, 2007 2005 New Jersey FIADB 4.0, 1987 1987 FIADB 4.0, 1999 1999 FIADB 4.0, 2008 2007 New Mexico, NFS non-woodlands 1987 RPA 1986 FIADB 4.0, 1999 1997 New Mexico, NFS woodlands 1987 RPA 1986 FIADB 4.0, 1999 1997 New Mexico, non-NFS non-woodlands FIADB 4.0, 1987 1987 FIADB 4.0, 1999 1999 New Mexico, non-NFS woodlands FIADB 4.0, 1999 1989 New York, non-reserved Eastwide, 1980 1981 FIADB 4.0, 1993 1993 FIADB 4.0, 2007 2005 New York, reserved 1987 RPA 1988 FIADB 4.0, 2007 2005 North Carolina FIADB 4.0, 1984 1984 FIADB 4.0, 1990 1990 FIADB 4.0, 2002 2001 FIADB 4.0, 2007 2006 North Dakota FIADB 4.0, 1980 1979 FIADB 4.0, 1995 1995 FIADB 4.0, 2005 2003 FIADB 4.0, 2008 2007 Ohio FIADB 4.0, 1991 1991 FIADB 4.0, 2006 2005 Oklahoma, Central & West FIADB 4.0, 1989 1989 Oklahoma, East FIADB 4.0, 1986 1986 FIADB 4.0, 1993 1993 FIADB 4.0, 2008 2008 Oregon, NFS East IDB, 1990s 1995 FIADB 4.0, 2009 2006 Oregon, NFS West IDB, 1990s 1996 FIADB 4.0, 2009 2006 Oregon, non-NFS East Westwide, 1992 1991 IDB, 1990s 1999

### A-259 FIADB 4.0, 2009 2005 Oregon, non-NFS West Westwide, 1992 1989 IDB, 1990s 1997 FIADB 4.0, 2009 2006 Pennsylvania FIADB 4.0, 1989 1990 FIADB 4.0, 2004 2003 FIADB 4.0, 2008 2007 Rhode Island FIADB 4.0, 1985 1985 FIADB 4.0, 1998 1999 FIADB 4.0, 2007 2006 South Carolina FIADB 4.0, 1986 1986 FIADB 4.0, 1993 1993 FIADB 4.0, 2001 2001 FIADB 4.0, 2006 2005 South Dakota, NFS 1997 RPA 1986 FIADB 4.0, 1995 1999 FIADB 4.0, 2005 2004 FIADB 4.0, 2008 2006 South Dakota, non-NFS 1987 RPA 1986 FIADB 4.0, 1995 1995 FIADB 4.0, 2005 2004 FIADB 4.0, 2008 2007 Tennessee FIADB 4.0, 1989 1989 FIADB 4.0, 1999 1998 FIADB 4.0, 2004 2003 FIADB 4.0, 2007 2006 Texas, Central & West FIADB 4.0, 2007 2006 Texas, East FIADB 4.0, 1986 1986 FIADB 4.0, 1992 1992 FIADB 4.0, 2003 2003 FIADB 4.0, 2008 2006 Utah, non-woodlands FIADB 4.0, 1993 1993 FIADB 4.0, 2009 2005 Utah, woodlands FIADB 4.0, 1993 1994 FIADB 4.0, 2009 2005 Vermont FIADB 4.0, 1983 1983 FIADB 4.0, 1997 1997 FIADB 4.0, 2007 2006 Virginia FIADB 4.0, 1985 1985 FIADB 4.0, 1992 1991 FIADB 4.0, 2001 2000 FIADB 4.0, 2007 2005 Washington, NFS East IDB, 1990s 1996 FIADB 4.0, 2009 2006 Washington, NFS West IDB, 1990s 1996 FIADB 4.0, 2009 2006 Washington, non-NFS East IDB, 1990s 1992 FIADB 4.0, 2009 2006 Washington, non-NFS West IDB, 1990s 1990 FIADB 4.0, 2009 2006

### A-260 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 West Virginia FIADB 4.0, 1989 1988 FIADB 4.0, 2000 2001 FIADB 4.0, 2008 2007 Wisconsin FIADB 4.0, 1983 1982 FIADB 4.0, 1996 1995 FIADB 4.0, 2004 2002 FIADB 4.0, 2008 2006 Wyoming, NFS 1997 RPA 1982 FIADB 4.0, 2000 2000Wyoming, non-NFS non-reserved nonwoodlands FIADB 4.0, 1984 1984 FIADB 4.0, 2000 2002 Wyoming, non-NFS non-reserved woodlands FIADB 4.0, 1984 1984 FIADB 4.0, 2000 2002 Wyoming, non-NFS reserved 1997 RPA 1985 FIADB 4.0, 2000 2000

a Substate areas (Smith et al. 2010) include National Forests (NFS ), all forest ownerships except National Forest (non-NFS), woo dlands (forest land dominated by woodland species, such as pinyon and juniper, where st ocking cannot be determined (USDA Forest Service 2010c) ), nonwoodlands (used for clarity to emphasize that woodlands are classified separately), re served (forest land withdrawn from timber utilization through statute, administrative regulation, or designation, Smith et al. (2009)), and non-reserved (forest land that is not res erved, used for clarity). Some National Forests are listed individually by name, e.g., Paye tte NF. Oregon and Washington we re divided into eastern and w estern forests (east or west of the crest of the Cascade Mountains). Oklahoma and Texas are divided into East versus Central & West according to forest inventroy survey units (USDA Forest Service 2010d). Alaska is re presented by a portion of forest land, in the southcentral and southeast part of the state.

b FIADB 4.0 is the current, publicly available, format of FIA i nventory data, and these files were downloaded from the Internet September 12, 2010 (USDA Forest Service 2010b). IDB (Integrat ed Database) data are a compilation of periodic inventory data from the 1990s f or California, Oregon, and Washington (Waddell and Hisero te 2005). Eastwide (Hansen et al. 1992) and Westwide (Woudenbe rg and Farrenkopf 1995 ) inventory data are formats that predate th e FIADB data. RPA data are periodic national summaries. The year is the nominal, or reporting, year associated with each dataset.

c Average year is based on average measurement year of forest land survey plots and rounded to the nearest integer year. 

### A new national plot design and annualized sampling (USDA Forest Service 2010a) was introduced by FIA with most new surveys beginning after 1998. These surveys include sampling of all forest land including reserved and lower productivity lands. Most states have annualized inventory data available as of September 2010. Annualized sampling means that a portion of plots throughout the state is sampled each year, with the goal of measuring all plots once every 5 to 10 years, depending on the region of the United States. The full unique set of data with all measured plots, such that each plot has been measured one time, is called a cycle. Sampling is designed such that partial inventory cycles provide usable, unbiased samples of forest inventory, but with higher sampling errors than the full cycle. After all plots have been measured once, the sequence continues with remeasurement of the first year’s plots, starting the next new cycle. Most Eastern states have completed the first full cycle of the annualized inventories and are providing annual updates to the state’s forest inventory with each year’s remeasurements, such that one plot’s measurements are included in subsequent year’s annual updates. Thus, annually updated estimates of forest C stocks are accurate, but estimates of stock change cannot utilize the annually updated inventory measurements direc tly, as there is redundancy in the data used to generate the annual updates of C stock. For example, a typical annual inventory update for an eastern state will include new data from remeasurement on 20 percent of plots; data from the remaining 80 percent of plots is identical to that included in the previous year’s annual update. The interpretation and use of the sequence of annual inventory updates can affect trends in annualized stock and stock change. In general, the C stock and stock change calculations use annual inventory summaries (updates) with unique sets of plot-level data (that is, without redundant sets); the most-recent annual update is the exception because it is included in stock change calculations if at least half of the plots in a state include new measurements. Table A-212 lists the specific surveys used in this report, and this list can be compared with the full set of summaries available for download (USDA Forest Service 2010b).
For each pool in each state in each year, C stocks are esti mated by linear interpolation between survey years. Similarly, fluxes, or net stock changes, are estimated for each pool in each state by dividing the difference between two successive stocks by the number of intervening years betw een surveys. Thus, the number of separate stock change estimates for each state or sub-state is one less than the number of available inventories. Annual estimates of stock and net change since the most recent survey are based on linear extrapolation. C stock and flux estimates for each pool are summed over all forest land in all states as identified in Table A-212 to form estimates for the United States. Summed net annual stock change and stock are presented in Table A-213 a nd Table A-214, respectively. Table A-214 also provides an

### A-261 estimate of forest area based on the interpolation and extrapolation procedure described above. Estimated net stock change of non-soil forest ecosystem carbon for 2009 for each of the states is shown in Table A-215, which also includes estimated forest area and total non-soil forest carbon stock. The state-level forest areas and carbon stocks are from the most recent inventory available (USDA Forest Service 2010a), and the estimate for net stock change is the mean of the 2000 through 2009 estimates from the carbon calculator (Smith et al. 2010).A-262 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009Table A-213: Net Annual Changes in Carbon Stocks (Tg C yr-1) in Forest and Harvested Wood Pools, 1990–2009Carbon Pool 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Forest(149.8) (151.4) (149.8) (139.5) (147.1) (148.4) (130.3) (128.6) (102.8) (76.9) (72.4) (113.0) (162.3) (210.3) (219.6) (219.9) (220.6) (220.6) (220.6) (220.6)Live,aboveground (98.2) (99.9) (100.5) (97.4)(101.5) (102.8) (92.7) (95.8) (84.7) (78.3) (78.3) (89.3) (103.0) (118.9) (122.1) (122.1) (122.1)(122.1) (122.1) (122.1)Live,belowground (19.3) (19.7) (19.8) (19.3)(20.1) (20.5) (18.4) (19.0) (16.9)(15.7) (15.7) (17.9) (20.5) (23.5) (24.1) (24.1) (24.1) (24.1) (24.1) (24.1)Dead Wood (8.6) (8.6) (8.3) (7.6) (7.7) (7.7) (7.1) (6.2)(4.8) (4.0) (3.5) (4.0) (5.2) (7.6) (8.1) (8.4) (9.1) (9.1) (9.1) (9.1)Litter (8.8) (8.8) (9.0) (8.1) (7.6) (6.0) (5.1) (5.1) (0.5) 4.9 7.5 1.8 (5.3) (9.9) (11.4) (11.4) (11.4) (11.4) (11.4) (11.4)Soil OrganicCarbon (14.9) (14.4) (12.1) (7.2) (10.2) (11.4) (7.1) (2.5) 4.1 16.2 17.6 (3.7) (28.3)(50.3) (53.8) (53.8) (53.8) (53.8) (53.8) (53.8)Harvested Wood(35.9) (33.8) (33.8) (32.9) (33.4) (32.3)(30.6) (32.0) (31.1) (32.5) (30.8)(25.5) (26.8) (25.9) (28.7) (28.7) (29.6) (28.1) (22.4) (14.8)Products in Use (17.7) (14.9) (16.3)(15.0) (15.9) (15.1) (14.1) (14.7) (13.4) (14.1) (12.8) (8.7) (9.6) (9.7) (12.4) (12.4) (12.3) (10.7) (5.2) 1.9SWDS (18.3) (18.8) (17.4) (17.9) (17.5) (17.2) (16.5) (17.3) (17.7) (18.4) (18.0) (16.8) (17.2) (16.2)(16.3) (16.3) (17.3) (17.4) (17.2)(16.7)Total Net Flux(185.7) (185.1) (183.6) (172.4) (180.5) (180.7) (160.9) (160.7) (133.9) (109.4) (103.2) (138.5) (189.1) (236.1) (248.3) (248.6) (250.2) (248.7) (243.0) (235.4)Table A-214: Carbon Stocks (Tg C) in Forest and Harvested Wood Pools, 1990–20101990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010Forest Area(1000 ha)269,137 269,764 270,395 270,992 271,535 272,071272,602 273,081 273,553 273,950 274,183 274,386274,717 275,234 275,976 276,769 277,561 278,354 279,147 279,939 280,732Carbon PoolForest42,783 42,933 43,085 43,234 43,374 43,521 43,669 43,800 43,928 44,031 44,10844,180 44,293 44,456 44,666 44,88645,105 45,326 45,547 45,767 45,988Live,aboveground 15,072 15,171 15,271 15,371 15,46815,570 15,673 15,765 15,861 15,94616,024 16,102 16,192 16,295 16,41416,536 16,658 16,780 16,902 17,024 17,147Live,belowground 2,995 3,014 3,034 3,053 3,0733,093 3,113 3,132 3,151 3,168 3,183 3,199 3,217 3,237 3,261 3,2853,309 3,333 3,357 3,381 3,405Dead Wood 2,960 2,969 2,978 2,986 2,994 3,001 3,009 3,016 3,022 3,027 3,0313,035 3,039 3,044 3,052 3,0603,068 3,077 3,086 3,096 3,105Litter 4,791 4,799 4,808 4,817 4,825 4,833 4,839 4,844 4,849 4,850 4,845 4,837 4,835 4,841 4,851 4,862 4,873 4,885 4,896 4,908 4,919SoilOrganicCarbon 16,965 16,980 16,995 17,007 17,014 17,024 17,035 17,043 17,045 17,041 17,025 17,007 17,011 17,039 17,089 17,143 17,197 17,251 17,304 17,35817,412HarvestedWood1,859 1,895 1,929 1,963 1,996 2,029 2,0612,092 2,124 2,155 2,187 2,218 2,244 2,2712,296 2,325 2,354 2,383 2,412 2,434 2,449Products inUse 1,231 1,249 1,264 1,280 1,295 1,311 1,326 1,340 1,355 1,368 1,382 1,395 1,404 1,413 1,423 1,436 1,448 1,460 1,471 1,476 1,474SWDS 628 646 665 683 701 718 735 752 769 787 805 823 840 857 873 890 906 923 941 958 974Total CarbonStock44,643 44,828 45,014 45,197 45,370 45,550 45,731 45,892 46,052 46,186 46,29646,399 46,537 46,726 46,962 47,21147,459 47,710 47,958 48,201 48,437

### A-263 Table A-215: State-level forest area, ca rbon stock, and net annual stock change. Estimates are forest ecosystem carbon and do not include harvested wood

State Mean year of field data collection Forest area (1000 ha) Nonsoil C stock (Mt C) Mean net annual nonsoil stock change 2000–2009 (Mt C/yr) Alabama 2006 9,219 682 (1.2) Alaska 2007 6,132 1,217 0.0 Arizona 2006 7,572 436 2.0 Arkansas 2007 7,560 644 (3.6) California 2006 13,333 2,165 (5.2) Colorado 2006 9,294 915 (5.2) Connecticut 2007 698 89 0.7 Delaware 2007 143 18 (0.1) Florida 2005 6,838 471 (5.0) Georgia 2006 10,046 802 (7.7) Idaho 2007 8,656 1,053 2.1 Illinois 2006 1,942 215 (5.3) Indiana 2007 1,920 225 (3.7) Iowa 2006 1,227 113 (2.8) Kansas 2007 902 74 (1.5) Kentucky 2005 5,006 502 (5.3) Louisiana 2004 5,722 447 0.1 Maine 2007 7,146 741 (1.9) Maryland 2007 1,009 131 (1.2) Massachusetts 2007 1,222 166 (0.4) Michigan 2006 8,021 826 (6.5) Minnesota 2006 6,876 536 (4.1) Mississippi 2007 7,941 610 (6.2) Missouri 2006 6,231 605 (10.5) Montana 2007 10,356 1,132 (4.2) Nebraska 2006 538 44 (0.6) Nevada 2005 4,520 247 (0.8) New Hampshire 2006 1,944 254 (0.5) New Jersey 2007 810 86 (0.0) New Mexico 1994 6,753 484 (3.8) New York 2006 7,669 943 (6.3) North Carolina 2006 7,521 727 (6.8) North Dakota 2007 289 20 0.2 Ohio 2007 3,255 353 (3.0) Oklahoma 2003 3,235 237 (0.8) Oregon 2006 12,163 2,007 (8.2) Pennsylvania 2007 6,739 804 (7.0) Rhode Island 2007 141 17 (0.0) South Carolina 2006 5,239 440 (5.7) South Dakota 2007 753 51 (0.9) Tennessee 2006 5,659 576 (3.8) Texas 2006 24,363 977 (2.4) Utah 2005 7,396 510 (3.4) Vermont 2007 1,857 250 (0.3) Virginia 2006 6,400 683 (3.4) Washington 2006 9,060 1,711 (10.0) West Virginia 2007 4,846 592 (4.6) Wisconsin 2006 6,757 620 (6.0) Wyoming 2001 4,633 468 (1.1)



### Table A-216 shows average C density values for forest ecosystem C pools according to region and forest types based on forest lands in this Inventory. These values were calculated by applying plot-level C estimation procedures as described below to the most recent inventory per state as available 12 September 2010 (USDA 2010b). C density values

### A-264 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 reflect the most recent survey for each state as available in the FIADB, not potential maximum C storage. C densities are affected by the distribution of stand sizes within a forest type, which can range from regenerating to mature stands. A large proportion of young stands in a particular forest type are likely to reduce the regional average for C density. Table A-216: Average carbon density (Mg C/ha) by carbon p ool and forest area (1000 ha) according to region and forest type, based on the most recent inventory survey available fo r each state from FIA, corresponding to an average year of 2006 Region (States)

Forest Types Aboveground Biomass Belowground Biomass Dead Wood Litter Soil Organic Carbon Forest Area Carbon Density (Mg C/ha) (1,000 ha) Northeast (CT,DE,MA,MD,ME,NH,NJ,NY,OH,PA,RI,VT,WV)

White/Red/Jack Pine 91.6 19.0 11.9 13.7 78.1 1,584 Spruce/Fir 52.7 11.2 13.3 30.7 98.0 2,970 Oak/Pine 77.0 15.2 9.8 27.4 66.9 1,234 Oak/Hickory 82.5 15.6 11.1 8.1 53.1 13,007 Elm/Ash/Cottonwood 59.3 11.3 9.9 6.9 111.7 1,450 Maple/Beech/Birch 77.9 15.0 13.3 27.1 69.6 13,673 Aspen/Birch 48.1 9.5 9.3 8.6 87.4 1,704 Minor Types and Nonstocked 48.2 9.5 10.3 10.9 73.7 1,855 All 74.5 14.4 11.9 17.8 69.0 37,478 Northern Lake States (MI,MN,WI)

White/Red/Jack Pine 54.1 11.3 9.0 12.3 120.8 1,821 Spruce/Fir 40.5 8.6 8.6 33.1 261.8 3,213 Oak/Hickory 70.4 13.3 10.8 7.9 97.1 3,815 Elm/Ash/Cottonwood 52.4 10.0 9.2 7.5 179.9 2,118 Maple/Beech/Birch 74.1 14.2 11.5 27.3 134.3 4,301 Aspen/Birch 40.9 7.9 9.5 8.3 146.1 5,272 Minor Types and Nonstocked 34.5 6.8 8.8 18.0 122.6 1,113 All 54.5 10.7 9.9 16.4 152.3 21,654 Northern Prairie States (IA,IL,IN,KS,MO,ND,NE,SD)

Ponderosa Pine 40.3 8.5 7.1 14.3 48.5 576 Oak/Pine 52.0 10.1 7.5 25.5 40.1 551 Oak/Hickory 69.8 13.2 9.7 7.7 49.4 9,570 Elm/Ash/Cottonwood 74.7 14.0 11.8 6.8 83.2 1,874 Minor Types and Nonstocked 41.6 8.1 8.3 17.9 60.3 1,231 All 66.0 12.5 9.7 9.5 54.5 13,803 Ponderosa Pine 40.3 8.5 7.1 14.3 48.5 576 South Central (AL,AR,KY,LA,MS,OK,TN,TX) 40.6 8.3 5.5 10.9 55.5 1,198

Loblolly/Shortleaf Pine 45. 8 9.4 7.0 9.6 41.9 13,256 Pinyon/Juniper 16.4 3.2 1.9 12.2 37.7 3,894 Oak/Pine 48.1 9.4 6.6 9.3 41.7 5,115 Oak/Hickory 58.5 11.0 6.8 6.4 38.6 24,619 Oak/Gum/Cypress 76.2 14.5 9.2 6.5 52.8 5,131 Elm/Ash/Cottonwood 51.9 9.8 7.1 5.9 49.9 3,441 Woodland Hardwoods 10.3 1.7 0.9 5.0 65.0 8,977 Minor Types and Nonstocked 32.3 6.2 5.8 7.1 54.3 4,271 All 45.9 8.8 5.9 7.4 45.5 68,704 Loblolly/Shortleaf Pine 45. 8 9.4 7.0 9.6 41.9 13,256 Southeast (FL,GA,NC,SC,VA)

Longleaf/Slash Pine 34.7 7.1 6.2 9.7 110.0 4,139 Loblolly/Shortleaf Pine 48. 6 10.0 8.5 9.6 72.9 9,137 Oak/Pine 52.6 10.3 6.7 9.3 61.4 4,054 Oak/Hickory 73.1 13.8 8.9 6.4 45.3 12,014 Oak/Gum/Cypress 74.2 14.3 9.7 6.5 158.0 4,551 Elm/Ash/Cottonwood 57.9 10.9 9.7 5.6 95.7 760 Minor Types and Nonstocked 45.7 8.7 9.0 5.8 107.0 1,389 All 59.0 11.5 8.4 7.9 79.2 36,044 Coastal Alaska

(approximately 12 percent of

### A-265 forest land in Alaska) Spruce/Fir 24.2 5.0 5.6 33.8 62.1 367 Fir/Spruce/Mountain Hemlock 96.0 20.3 26.8 43.2 62.1 2,233 Hemlock/Sitka Spruce 142.2 30.0 37.5 50.5 116.3 2,754 Aspen/Birch 42.0 8.0 9.1 10.6 42.5 310 Minor Types and Nonstocked 40.0 7.8 10.7 19.5 75.7 469 All 105.5 22.2 28.2 42.5 86.5 6,132 Spruce/Fir 24.2 5.0 5.6 33.8 62.1 367 Pacific Northwest, Westside (Western OR and WA)

Douglas-fir 146.2 30.7 39.0 32.0 94.8 5,956 Fir/Spruce/Mountain Hemlock 150.1 31.7 41.6 38.3 62.1 1,187 Hemlock/Sitka Spruce 174.7 36.8 50.0 37.8 116.3 1,566 Alder/Maple 84.8 16.6 24.2 7.6 115.2 1,189 Minor Types and Nonstocked 68.9 13.7 18.0 13.5 85.9 1,216 All 135.6 28.3 37.0 28.8 95.5 11,114 Pacific Northwest, Eastside (Eastern OR and WA)

Douglas-fir 77.5 16.2 19.5 36.3 94.8 2,089 Ponderosa Pine 48.8 10.2 11.2 22.5 50.7 2,742 Fir/Spruce/Mountain Hemlock 95.3 20.1 27.8 37.9 62.1 1,781 Lodgepole Pine 41.2 8.7 12.0 21.1 52.0 1,041 Western Larch 63.5 13.3 19.2 35.7 45.1 204 Other Western Softwoods 15. 7 3.0 4.5 36.2 78.8 1,252 Minor Types and Nonstocked 36.0 7.1 16.1 25.1 81.5 999 All 57.1 11.9 15.8 30.1 68.4 10,109 Pacific Southwest (CA)

Pinyon/Juniper 24.1 4.7 2.3 21.1 26.3 742 Douglas-fir 162.8 33.8 37.3 35.7 40.1 442 Ponderosa Pine 63.7 13.2 15.0 22.4 41.3 899 Fir/Spruce/Mountain Hemlock 158.0 33.4 45.0 38.3 51.9 824 Redwood 219.6 45.9 48.1 60.5 53.8 299 Other Western Softwoods 33.7 6.6 7.6 37.5 49.8 806 California Mixed Conifer 127. 8 26.8 32.8 37.9 49.8 3,159 Western Oak 67.0 12.8 7.9 29.7 27.6 3,791 Tanoak/Laurel 132.0 26.0 20.1 28.0 27.6 830 Minor Types and Nonstocked 57.8 11.7 17.7 25.2 37.0 1,540 All 92.0 18.7 20.0 31.9 38.6 13,333 Rocky Mountain, North (ID,MT)

Douglas-fir 72.8 15.4 14.5 37.0 38.8 5,587 Ponderosa Pine 40.0 8.3 8.2 22.9 34.3 1,865 Fir/Spruce/Mountain Hemlock 67.6 14.3 22.1 37.4 44.1 4,471 Lodgepole Pine 50.3 10.7 10.9 23.1 37.2 2,761 Western Larch 60.1 12.7 15.4 36.3 34.2 492 Other Western Softwoods 44.8 9.4 6.9 39.3 31.4 649 Aspen/Birch 31.8 6.0 12.5 26.8 56.6 533 Minor Types and Nonstocked 28.4 5.7 17.0 22.5 42.9 2,655 Rocky Mountain, South (AZ,CO,NM,NV,UT,WY)

Pinyon/Juniper 22.9 4.7 1.0 21.1 19.7 18,738 Douglas-fir 74.7 15.9 16.9 38.1 30.9 1,797 Ponderosa Pine 47.3 10.0 8.2 23.6 24.1 3,570 Fir/Spruce/Mountain Hemlock 79.6 16.9 22.9 38.8 31.5 4,262 Lodgepole Pine 52.0 11.1 13.0 24.0 27.0 2,024 Aspen/Birch 56.9 11.0 13.2 28.5 58.8 2,555 Woodland Hardwoods 19.0 3.6 4.6 28.2 25.9 4,135 Minor Types and Nonstocked 18.7 3.6 7.9 22.6 25.4 3,088 All 36.3 7.5 6.9 25.4 25.8 40,168 United States (forest land included in Inventory) 60.0 11.9 11.1 17.6 62.0 277,552

Note: The forest area values in this tabl e do not equal the forest area values repor ted in Table A-214, because the forest area values in this table are estimated using the most recent dataset per state, with an aver age year of 2006. The time series of forest area values rep orted in Table A-214,

### A-266 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 in contrast, is constructed following the CCT methods used to construct the carbon stock series. The forest area values report ed in Table A-214 and Table A-216 would only be identical if all states were measur ed simultaneously or they all had identical rates of change.



### The Inventory is derived primarily from the current FIADB 4.0 data (USDA Forest Service 2010b), but it also draws on older FIA survey data where necessary. The Resources Planning Act Assessment (RPA) database, which includes periodic summaries of state inventories, is one example. Information about the RPA data is available on the Internet (USDA Forest Service 2010a, see Program Features), and compilations of analytical estimates based on these databases are found in Waddell et al. (1989) and Smith et al. (2001). The basic difference between the RPA database and the FIADB is that the FIADB includes some informative additi onal details such as individual-tree data. Having only plotlevel information (such as volume per hectare) limits the conve rsion to biomass. This does not constitute a substantial difference for the overall state-wide estimates, but it does affect plot-level precision (Smith et al. 2004). In the past, FIA made their data available in tree-level Eastwide (Hansen et al. 1992) or Westwide (Woudenberg and Farrenkopf 1995) formats, which included inventories for Eastern and Western states, respectively. The current Inventory estimates rely in part on older tree-level data that are not available on the current FIADB site. The Integrated Database (IDB) is a compilation of periodic forest inventory data from the 1990s for California, Oregon, and Washington (Waddell and Hiserote 2005). These data were identified by Heath et al. (submitted) as the most appropriate non-FIADB sources for these states.
An historical focus of the FIA program was to provide in formation on timber resources of the United States. For this reason, prior to 1998, some forest land, which were less productive or reserved (i.e., land where harvesting was prohibited by law), were less intensively surveyed. This generally meant that on these less productive lands, forest type and area were identified but data were not collected on individual tree measurements. The practical effect that this evolution in inventories has had on estimating forest C stocks from 1990 through the present is that some older surveys of lands do not have the stand-level values for merchantable volume of wood or stand age. Any data gaps identified in the surveys taken before 1998 were filled by assigning average C densities calculated from the more complete, later inventories from the respective states. The overall effect of th is necessary approach to generate estimates for C stock is that no net change in C density occurs on those lands with gaps in past surveys.

Estimating C stocks from forest inventory data
For each inventory summary in each state, data are converted to C units or augmented by other ecological data. This collection of conversion factors and models used for inventory-based forest carbon estimates (Smith et al. 2010, Heath et al. in press) was intitially developed as an offshoot of the forest carbon simulation model FORCARB (Heath et al. 2010) and is incorporated into a number of applications (Birdsey and Heath 1995, Birdsey and Heath 2001, Heath et al. 2003, Smith et al. 2004, Smith et al. 2007, 

### Hoover and Rebain 2008 ). The conversion factors and model coefficients are usually categorized by region, and forest type. Classifications for both region and forest type are subject to change depending on the particular coefficient set. Thus, region and type are specifically defined for each set of estimates. Factors are applied to the survey data at the scale of FIA i nventory plots. The results are estimates of C density (Mg per hectare) for the various forest pools. C density for live tr ees, standing dead trees, understory vegetation, down dead wood, litter, and soil organic matter are estimated. All non-soil pools except litter can be separated into aboveground and belowground components. The live tree and understory C pools are pooled as biomass in this inventory. Similarly, standing dead trees and down dead wood are pooled as dead wood in this inventory. C stocks and fluxes for Forest Land Remaining Forest Land are reported in pools following IPCC (2003).

Live tree C pools
The tree C pools include aboveground and belowground (coars e root) C mass of live trees. Separate estimates are made for full-tree and aboveground-only biomass to estima te the belowground component. Most tree C estimates are based on Jenkins et al. (2003) and are functions of species gro ups and diameter. For example, the equation for estimating aboveground biomass for a live tree of a species in the aspen/alder/cottonwood/willow group is:
Biomass (kg dry weight) = e (-2.2094 + 2.3867 × ln(diameter))



### Diameter is cm at diameter breast height (d.b.h.), which is measured at 1.37 m above the forest floor. C is calculated by multiplying biomass by 0.5 because biomass is 50 percent of dry weight (IPCC/UNEP/OECD/IEA 1997). A full set of coefficients can be found in Jenkins et al. (2003; Table 4). Belowground root biomass is estimated as a ratio of roots to total aboveground biomass. The equation for ratio of root biomass of a live tree in the aspen/alder/cottonwood/willow group is:

Ratio = e (-1.6911 + 0.8160/diameter)

### A-267 Belowground biomass is calculated by multiplying the ratio by total aboveground biomass. A full set of coefficients can be found in Jenkins et al. (2003; Table 6). The C per tree is summed for each plot, and multiplied by the appropriate expansion factors to obtain a C stock estimate for the plot.
Some inventory data do not provide measurements of individual trees; tree C in these plots is estimated from plot-level growing stock volume of live trees and equations given in Table A-217 and Table A-218. These equations are updates of those in Smith et al. (2003), modified to reduce error and correspond to common forest types defined by inventories. Separate estimates are made for whole-tree and aboveground-only biomass based on forest type group and region. The belowground portion is determined as the differen ce between the two estimates. C density is estimated based on the growing stock volume of the plot, where growing stock in cludes live trees of commercial species meeting specified standards. Only trees 12.7 cm (5 inches) d.b.h. and larger are included in growing stock volume. The full sets of coefficients are in Table A-217 and Table A-218. For example, the total C in tree biomass per hectare of aspen-birch in the North averages 8.1 Mg C/ha if growing-stock volume is zero. If growing-stock volume is greater than zero, the estimate is in two parts. Average C density of non-growi ng-stock trees (sapling and cull trees) is 14.3 Mg C/ha, and the equation for C in growing-stock trees is:
Growing-stock trees (Mg C/ha) = e (-0.337 + ln(volume) × 0.933)



### Units for volume are m 3 

### /ha.
Table A-217: Coefficients for estimating carbon density of live trees (above- and below-ground, MgC/ha) by region and type for plot-level data such as RPA data a

Region b Forest type group c Carbon density, if Growing Stock Volume (GSV) = 0 C density for nonGrowing Stock (GS), if GSV > 0 Coefficient A Coefficient B

North Aspen/Birch 8.138 14.335 -0.337 0.933 Elm/Ash/Cottonwood 16.187 18.707 -0.206 0.920 Maple/Beech/Birch 6.938 17.054 -0.170 0.925 Oak/Hickory 13.083 15.914 -0.079 0.932 Hardwood minor types 10.376 14.127 0.002 0.890 Oak/Pine 4.079 15.473 -0.146 0.908 Ponderosa Pine & Exotic Softwood 2.595 6.895 -0.074 0.886 Loblolly/Shortleaf Pine 6.277 9.766 -0.415 0.943 Spruce/Fir 6.424 16.903 -0.487 0.947 White/Red/Jack Pine 3.908 12.117 -0.349 0.924 Softwood minor types 6.277 17.234 -0.380 0.970 Non-stocked 1.054 1.238 -0.174 0.866

Pacific Coast Alder/Maple 8.425 4.444 0.056 0.828 Other Western Hardwoods 8.425 10.483 0.041 0.864 Tanoak/Laurel 8.425 10.203 -0.167 0.917 Western Oak 8.425 7.400 0.344 0.850 Hardwood minor types 8.425 4.802 0.333 0.770 California Mixed Conifer 10.102 4.727 0.137 0.843 Douglas-fir 2.752 4.961 0.180 0.834 Fir/Spruce/Mt. Hemlock 10.102 6.462 0.171 0.834 Hemlock/Sitka Spruce 10.102 8.034 0.085 0.830 Lodgepole Pine 10.102 5.733 -0.129 0.857 Pinyon/Juniper 22.552 5.065 -0.070 0.842 Ponderosa Pine 10.102 2.262 0.145 0.813 Western Larch 10.102 5.254 -0.264 0.853 Softwood minor types 10.102 6.771 0.466 0.783 RPA Western Hardwoods 8.425 7.460 0.302 0.831 Non-stocked 0.880 0.300 0.049 0.806

Rocky Mountain Aspen/Birch 4.594 9.516 0.324 0.792 Harwood minor types 4.866 11.844 0.266 0.814 Douglas-fir 1.987 5.363 0.331 0.825 Fir/Spruce/Mt. Hemlock 1.987 6.693 0.065 0.825 Lodgepole Pine 1.080 8.051 0.003 0.804 Other Western Softwoods 1.987 12.217 0.361 0.796 Ponderosa Pine 1.987 5.574 0.382 0.771 Softwood minor types 1.987 5.496 -0.152 0.836 RPA Western Hardwood 13.714 11.678 0.246 0.807 Pinyon/Juniper 22.927 23.301 0.254 0.794

### A-268 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 West. Oak/Other West. Hardwoods 14.441 18.544 0.215 0.796

Non-stocked 1.111 0.568 0.257 0.732

South Elm/Ash/Cottonwood 12.841 21.633 -0.144 0.896 Oak/Gum/Cypress 7.176 23.919 -0.216 0.907 Oak/Hickory 14.594 20.007 -0.031 0.886 Hardwood minor types 47.316 40.194 -0.442 0.960 Oak/Pine 4.106 17.933 -0.086 0.858 Loblolly/Shortleaf Pine 3.892 12.466 0.206 0.773 Longleaf/Slash Pine 4.441 8.694 0.110 0.772 Softwood minor types 7.161 20.189 -0.085 0.868 Non-stocked 0.467 0.943 0.019 0.734

a Prediction of C in growing-stock trees is based on exp(A + B*ln(growing stock volume)).

b Regions are North (CT, DE, IA, IL, IN, KS, MA, MD, ME, MI, MN, MO, ND, NE, NH, NJ, NY, OH, PA, RI, SD, VT, WI, WV); Pacific Co ast (CA, OR, WA); Rocky Mountain (AZ, CO, ID, MT, NM, NV, UT, WY); and South (AL, AR, FL, GA, KY , LA, MS, NC, OK, SC, TN, TX, VA).

c Forest type groups are identified in FIADB and RPA data (Smith et al. (2001, USDA Forest Service 2010d).


### Table A-218: Coefficients for estimating carbon density of live trees (abovegrou nd only, MgC/ha) by region and type for plot-level data such as RPA data a

Region b Forest type group c Carbon density, if Growing Stock Volume (GSV)=0 C density for nonGS, if GSV > 0 Coefficient A Coefficient B

North Aspen/Birch 6.697 11.880 -0.521 0.934 Elm/Ash/Cottonwood 13.585 15.653 -0.387 0.922 Maple/Beech/Birch 5.762 14.219 -0.352 0.926 Oak/Hickory 10.960 13.306 -0.260 0.933 Hardwood minor types 8.647 11.796 -0.166 0.888 Oak/Pine 3.368 12.881 -0.335 0.909 Ponderosa Pine & Exotic Softw ood 2.116 5.671 -0.269 0.886 Loblolly/Shortleaf Pine 5.098 8.070 -0.620 0.946 Spruce/Fir 5.206 13.833 -0.684 0.948 White/Red/Jack Pine 3.174 10.010 -0.548 0.926 Softwood minor types 5.098 14.246 -0.570 0.971 Non-stocked 0.880 1.032 -0.357 0.866

Pacific Coast Alder/Maple 7.006 3.676 -0.138 0.830 Other Western Hardwoods 7.006 8.709 -0.154 0.867 Tanoak/Laurel 7.006 8.469 -0.355 0.918 Western Oak 7.006 6.163 0.167 0.850 Hardwood minor types 7.006 3.974 0.136 0.773 California Mixed Conifer 8.309 3.883 -0.061 0.844 Douglas-fir 2.235 4.072 -0.017 0.835 Fir/Spruce/Mt. Hemlock 8.309 5.285 -0.027 0.835 Hemlock/Sitka Spruce 8.309 6.586 -0.113 0.831 Lodgepole Pine 8.309 4.674 -0.327 0.858 Pinyon/Juniper 18.583 4.170 -0.263 0.842 Ponderosa Pine 8.309 1.849 -0.053 0.814 Western Larch 8.309 4.282 -0.461 0.853 Softwood minor types 8.309 5.563 0.267 0.784 RPA Western Hardwoods 7.006 6.202 0.119 0.831 Non-stocked 0.724 0.247 -0.146 0.808

Rocky Mountain Aspen/Birch 3.798 7.914 0.139 0.793 Harwood minor types 4.027 9.936 0.084 0.815 Douglas-fir 1.616 4.388 0.134 0.826 Fir/Spruce/Mt. Hemlock 1.616 5.466 -0.133 0.826 Lodgepole Pine 0.871 6.571 -0.195 0.805 Other Western Softwoods 1.616 10.031 0.165 0.797 Ponderosa Pine 1.616 4.569 0.185 0.772 Softwood minor types 1.616 4.473 -0.350 0.837 RPA Western Hardwood 11.341 9.704 0.054 0.809 Pinyon/Juniper 18.867 19.173 0.059 0.794 West. Oak/Other West. Hardw oods 11.942 15.353 0.021 0.796 Non-stocked 0.916 0.466 0.061 0.733

South Elm/Ash/Cottonwood 10.749 18.129 -0.323 0.897 Oak/Gum/Cypress 5.987 20.004 -0.400 0.909 Oak/Hickory 12.223 16.731 -0.215 0.888

### A-269 Hardwood minor types 39.737 33.739 -0.631 0.964 Oak/Pine 3.394 14.923 -0.277 0.859 Loblolly/Shortleaf Pine 3.172 10.288 0.012 0.773 Longleaf/Slash Pine 3.634 7.176 -0.088 0.773 Softwood minor types 5.893 16.751 -0.280 0.869 Non-stocked 0.388 0.788 -0.171 0.735

a Prediction of aboveground C in growing-stock trees is based on exp(A + B*ln(growing stock volume).

b Regions are North (CT, DE, IA, IL, IN, KS, MA, MD, ME, MI, MN, MO, ND, NE, NH, NJ, NY, OH, PA, RI, SD, VT, WI, WV); Pacific Co ast (CA, OR, WA); Rocky Mountain (AZ, CO, ID, MT, NM, NV, UT, WY); and South (AL, AR, FL, GA, KY , LA, MS, NC, OK, SC, TN, TX, VA).

c Forest type groups are identified in FIADB and RP A data (Smith et al. 2001, USDA Forest Service 2010d).



### Understory vegetation
Understory vegetation is a minor component of biomass. Understory vegetation is defined as all biomass of undergrowth plants in a forest, including woody shrubs and trees le ss than one-inch d.b.h. In this inventory, it is assumed that 10 percent of understory C mass is belowground. This ge neral root-to-shoot ratio (0.11) is near the lower range of temperate forest values provided in IPCC (2006) and was selected based on two general assumptions: ratios are likely to be lower for light-limited understory vegetation as compared with larger trees, and a greater proportion of all root mass will be less than 2 mm diameter.
Estimates of C density are based on information in Birdsey (1996), which was applied to FIA permanent plots. These were fit to the equation:

Ratio = e (A - B × ln(live tree C density)



### In this equation, “ratio” is the ratio of understory C density (Mg C/ha) to live tree C density (above- and belowground) in Mg C/ha. An additional coefficient is provided as a maximum ratio; that is, any estimate predicted from theequation that is greater than the maximum ratio is set equal to the maximum ratio. A full set of coefficients is in Table A219. Regions and forest types are the same classifications de scribed in Smith et al. (2003). As an example, the basic calculation for understory C in aspen-birch forests in the Northeast is:
Understory (Mg C/ha) = (live tree C density) × e (0.855 – 1.03 × ln(tree C density)



### This calculation is followed by three possible modifications . First, the maximum value for the ratio is set to 2.02 (see value in column “maximum ratio”); this also applies to stands with zero tree C, which is undefined in the above equation. Second, the minimum ratio is set to 0.005 (Birdsey 1996). Third, nonstocked and pinyon/juniper stands are set to coefficient A, which is a carbon density (Mg C/ha) for these types only.

Table A-219: Coefficients for estimating the ratio of carbon density of un derstory vegetation (above- and belowground, MgC/ha) a 

### by region and forest type. The ratio is multiplied by tree carbon density on each plot to produce understory vegetation

Region b Forest Type b A B Maximum ratio c

NE Aspen-Birch 0.855 1.032 2.023 MBB/Other Hardwood 0.892 1.079 2.076 Oak-Hickory 0.842 1.053 2.057 Oak-Pine 1.960 1.235 4.203 Other Pine 2.149 1.268 4.191 Spruce-Fir 0.825 1.121 2.140 White-Red-Jack Pine 1.000 1.116 2.098 Nonstocked 2.020 2.020 2.060

NLS Aspen-Birch 0.777 1.018 2.023 Lowland Hardwood 0.650 0.997 2.037 Maple-Beech-Birch 0.863 1.120 2.129 Oak-Hickory 0.965 1.091 2.072 Pine 0.740 1.014 2.046 Spruce-Fir 1.656 1.318 2.136 Nonstocked 1.928 1.928 2.117

NPS Conifer 1.189 1.190 2.114 Lowland Hardwood 1.370 1.177 2.055 Maple-Beech-Birch 1.126 1.201 2.130 Oak-Hickory 1.139 1.138 2.072 Oak-Pine 2.014 1.215 4.185

### A-270 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Nonstocked 2.052 2.052 2.072

PSW Douglas-fir 2.084 1.201 4.626 Fir-Spruce 1.983 1.268 4.806 Hardwoods 1.571 1.038 4.745 Other Conifer 4.032 1.785 4.768 Pinyon-Juniper 4.430 4.430 4.820 Redwood 2.513 1.312 4.698 Nonstocked 4.431 4.431 4.626

PWE Douglas-fir 1.544 1.064 4.626 Fir-Spruce 1.583 1.156 4.806 Hardwoods 1.900 1.133 4.745 Lodgepole Pine 1.790 1.257 4.823 Pinyon-Juniper 2.708 2.708 4.820 Ponderosa Pine 1.768 1.213 4.768 Nonstocked 4.315 4.315 4.626

PWW Douglas-fir 1.727 1.108 4.609 Fir-Spruce 1.770 1.164 4.807 Other Conifer 2.874 1.534 4.768 Other Hardwoods 2.157 1.220 4.745 Red Alder 2.094 1.230 4.745 Western Hemlock 2.081 1.218 4.693 Nonstocked 4.401 4.401 4.589

RMN Douglas-fir 2.342 1.360 4.731 Fir-Spruce 2.129 1.315 4.749 Hardwoods 1.860 1.110 4.745 Lodgepole Pine 2.571 1.500 4.773 Other Conifer 2.614 1.518 4.821 Pinyon-Juniper 2.708 2.708 4.820 Ponderosa Pine 2.099 1.344 4.776 Nonstocked 4.430 4.430 4.773

RMS Douglas-fir 5.145 2.232 4.829 Fir-Spruce 2.861 1.568 4.822 Hardwoods 1.858 1.110 4.745 Lodgepole Pine 3.305 1.737 4.797 Other Conifer 2.134 1.382 4.821 Pinyon-Juniper 2.757 2.757 4.820 Ponderosa Pine 3.214 1.732 4.820 Nonstocked 4.243 4.243 4.797

SC Bottomland Hardwood 0.917 1.109 1.842 Misc. Conifer 1.601 1.129 4.191 Natural Pine 2.166 1.260 4.161 Oak-Pine 1.903 1.190 4.173 Planted Pine 1.489 1.037 4.124 Upland Hardwood 2.089 1.235 4.170 Nonstocked 4.044 4.044 4.170

SE Bottomland Hardwood 0.834 1.089 1.842 Misc. Conifer 1.601 1.129 4.191 Natural Pine 1.752 1.155 4.178 Oak-Pine 1.642 1.117 4.195 Planted Pine 1.470 1.036 4.141 Upland Hardwood 1.903 1.191 4.182 Nonstocked 4.033 4.033 4.182

a Prediction of ratio of understory C to liv e tree C is based on the equation: Ratio=e xp(A-B*ln(tree_carbon_tph)), where “ratio” is the ratio of understory C density to live tree (above-and below- ground) C density, and “tree_carbon_density” is live tree (above-and below- ground) C density in Mg C/ha.

b Regions and types as defi ned in Smith et al. (2003)

c Maximum ratio: any estimate predicted from the equation that is greater than the maximum ratio is set equal to the maximum rati o.



### Dead Wood
The standing dead tree C pools include aboveground and belowground (coarse root) mass. Estimates for standing dead tree C are not based on newly available FIA standing dead tree data because updated methodology is not yet available for integrating dead wood carbon estimates based on both older (which do not have consistent measurements of dead wood) and newer surveys. Instead, the estimates are based on a ratio of growing stock volume of live trees, stratified by region and forest type groups, applied at the FIA plot-level. The standing dead tree equations estimate mass; they are

### A-271 converted to C mass by multiplying by 0.5. An example calculation for standing dead tree C in aspen-birch forests in the Northeast is:
Dry weight of dead trees (Mg/ha) = 1.0 × (growing stock volume) 0.499



### It is multiplied by 0.5 to obtain Mg C/ha. All coefficients are provided in Table A-220. Note that nonstocked stands are assigned a constant C density (the value of Coefficient A).
Down dead wood is defined as pieces of dead wood greater th an 7.5 cm diameter, at transect intersection, that are not attached to live or standing dead trees. Down dead wood includes stumps and roots of harvested trees. Ratio estimates of down dead wood to live tree biomass were developed using FORCARB2 simulations and applied at the plot level (Smith et al. 2004). Estimates for down dead wood correspond to the region and forest type classifications described in Smith et al. (2003). A full set of ratios is provided in Table A-221. An additional component of down dead wood is a regional average estimate of logging residue based on Smith et al. (2006) applied at the plot level. These are based on a regional average C density at age zero and first order decay; in itial densities and decay coefficients are provided in Table A-222. These amounts are added to explicitly account for down dead wood following harvest. In practice, this modification resulted in minor changes to the estimates. Example calculations of the two components for down dead wood in 25-year-old aspen-birch forests in the Northeast are:
C density (Mg C/ha) = (live tree C density, above- and below-ground) × (0.078) = 7.8% of live tree C
Conversion to C units is not necessary because the live tree value is already in terms of C.
C density additional for logging residue (Mg C/ha) = 13.9 × e (-25/12.1) 

### = 1.8 (Mg C/ha)

Table A-220: Coefficients for estima ting standing dead tree carbon (MgC/ha) by region and forest type group a Region b Forest type group c Coefficient A Coefficient B

MTN Douglas-fir 3.935 0.312 Fir/Spruce/Mt. Hemlock 4.550 0.358 Hemlock/Sitka Spruce 1.000 0.569 Lodgepole Pine 1.177 0.501 Ponderosa Pine 1.000 0.455 Nonstocked 12.855 --

MTS Douglas-fir 2.200 0.460 Fir/Spruce/Mt. Hemlock 6.923 0.293 Lodgepole Pine 1.177 0.501 Ponderosa Pine 1.944 0.292 Nonstocked 4.232 --

NC Aspen/Birch 1.962 0.400 Elm/Ash/Cottonwood 3.755 0.253 Maple/Beech/Birch 3.442 0.219 Planted Pine 1.000 0.298 Oak/Hickory 2.949 0.236 Oak/Pine 1.364 0.394 Spruce/Fir 1.320 0.472 White/Red/Jack Pine 2.844 0.266 Nonstocked 2.634 --

NE Aspen/Birch 1.000 0.499 Elm/Ash/Cottonwood 4.992 0.134 Maple/Beech/Birch 3.041 0.306 Oak/Hickory 3.332 0.191 Oak/Pine 1.725 0.311 Spruce/Fir 5.893 0.190 White/Red/Jack Pine 2.841 0.254 Nonstocked 2.876 --

PC California Mixed Conifer 1.000 0.608 Douglas-fir 1.237 0.559 Douglas-fir Planted 10.145 0.112 Fir/Spruce/Mt. Hemlock 4.235 0.415 Hemlock/Sitka Spruce 1.546 0.562 Redwood 5.385 0.287 Nonstocked 7.377 --

SC Elm/Ash/Cottonwood 2.393 0.284 Loblolly/Shortleaf Pine 1.203 0.271

### A-272 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Loblolly/Shortleaf Planted 1.000 0.138 Oak/Gum/Cypress 4.234 0.121 Oak/Hickory 2.396 0.186 Oak/Pine 1.133 0.337 Nonstocked 0.286 --

SE Elm/Ash/Cottonwood 1.358 0.476 Loblolly/Shortleaf Pine 1.000 0.324 Loblolly/Shortleaf Planted 1.000 0.265 Oak/Gum/Cypress 1.770 0.329 Oak/Hickory 2.256 0.257 Oak/Pine 1.000 0.351 Nonstocked 0.349 --

South Longleaf/Slash Pine 1.000 0.184 Longleaf/Slash Planted 1.000 0.106

West Alder/Maple 2.190 0.466 Aspen/Birch 3.062 0.376 Pinyon/Juniper 3.163 0.100 Tanoak/Laurel 1.000 0.593 Western Hardwood/Woodlands 5.595 0.181 Western Larch 2.049 0.449 Western Oak 1.996 0.348

a Standing dead tree C is based on the equati on: mass (Mg/ha) = A* (live-tree growing st ock volume)^B. Note that nonstocked stand s are assigned a constant C density (the value listed under coefficient A). Note that the standing dead tree equa tions are for biomass. To con vert to C mass, multiply by 0.5.

b Regions are PC (CA,OR-West,WA-West), MTN (OR-East,WA-East,ID,MT), MTS (AZ,CO,NM,NV,UT,WY), West (regions PC, MTN, and MTS), NC (IA, IL, IN, KS, MI, MN, MO, ND, NE, SD, WI), NE (CT, DE, MA, MD, ME, NH, NJ, NY, OH, PA, RI, VT, WV), SC (AL, AR, KY, LA, MS, OK, TN, TX), SE (FL, GA, NC, SC, VA), and South (regions SC and SE).

c Forest types are described in FIADB documentation (USDA Forest Service, 2010d). Minor forest types within a region that are no t explicitly defined/listed in the table of coefficients are assi gned to a similar hardwood or softwood forest type.



### Table A-221: Ratio for estimating down dead wood by region and forest type. The ratio is multiplied by the live tree carbon density on a plot to produce down dead wood carbon density (MgC/ha)

Region a Forest type a Ratio Region (cont’d) Forest type (cont’d) Ratio (cont’d)

NE Aspen-Birch 0.078

PWW Douglas-fir 0.100 MBB/Other Hardwood 0.071 Fir-Spruce 0.090 Oak-Hickory 0.068 Other Conifer 0.073 Oak-Pine 0.061 Other Hardwoods 0.062 Other Pine 0.065 Red Alder 0.095 Spruce-Fir 0.092 Western Hemlock 0.099 White-Red-Jack Pine 0.055 Nonstocked 0.020 Nonstocked 0.019

RMN Douglas-fir 0.062

NLS Aspen-Birch 0.081 Fir-Spruce 0.100 Lowland Hardwood 0.061 Hardwoods 0.112 Maple-Beech-Birch 0.076 Lodgepole Pine 0.058 Oak-Hickory 0.077 Other Conifer 0.060 Pine 0.072 Pinyon-Juniper 0.030 Spruce-Fir 0.087 Ponderosa Pine 0.087 Nonstocked 0.027 Nonstocked 0.018

NPS Conifer 0.073

RMS Douglas-fir 0.077 Lowland Hardwood 0.069 Fir-Spruce 0.079 Maple-Beech-Birch 0.063 Hardwoods 0.064 Oak-Hickory 0.068 Lodgepole Pine 0.098 Oak-Pine 0.069 Other Conifer 0.060 Nonstocked 0.026 Pinyon-Juniper 0.030

PSW Douglas-fir 0.091 Ponderosa Pine 0.082 Fir-Spruce 0.109 Nonstocked 0.020 Hardwoods 0.042

SC Bottomland Hardwood 0.063 Other Conifer 0.100 Misc. Conifer 0.068 Pinyon-Juniper 0.031 Natural Pine 0.068 Redwood 0.108 Oak-Pine 0.072 Nonstocked 0.022 Planted Pine 0.077

PWE Douglas-fir 0.103 Upland Hardwood 0.067 Fir-Spruce 0.106 Nonstocked 0.013 Hardwoods 0.027 SE Bottomland Hardwood 0.064

### A-273 Lodgepole Pine 0.093 Misc. Conifer 0.081 Pinyon-Juniper 0.032 Natural Pine 0.081 Ponderosa Pine 0.103 Oak-Pine 0.063 Nonstocked 0.024 Planted Pine 0.075 Upland Hardwood 0.059 Nonstocked 0.012

a Regions and types as defi ned in Smith et al. (2003).



### Table A-222: Coefficients for estimating lo gging residue component of down dead wood.

Region a Forest Type Group b (softwood/hardwood) Initial Carbon Density (Mg/ha) Decay Coefficient Alaska hardwood 6.9 12.1 Alaska softwood 8.6 32.3
NE hardwood 13.9 12.1
NE softwood 12.1 17.9
NLS hardwood 9.1 12.1
NLS softwood 7.2 17.9
NPS hardwood 9.6 12.1
NPS softwood 6.4 17.9
PSW hardwood 9.8 12.1
PSW softwood 17.5 32.3
PWE hardwood 3.3 12.1
PWE softwood 9.5 32.3
PWW hardwood 18.1 12.1
PWW softwood 23.6 32.3
RMN hardwood 7.2 43.5
RMN softwood 9.0 18.1
RMS hardwood 5.1 43.5
RMS softwood 3.7 18.1
SC hardwood 4.2 8.9
SC softwood 5.5 17.9
SE hardwood 6.4 8.9
SE softwood 7.3 17.9

a Regions are defined in Smith et al. ( 2003) with the addition of coastal Alaska.

b Forest types are according to ma jority hardwood or softwood species.



### Litter carbon
C of the litter layer is currently sampled on a subset of th e FIA plots. However, these data are not yet available electronically for general application to all inventories in Table A-1. Litter C is the pool of organic C (including material known as duff, humus, and fine woody debris) above the mineral soil and includes woody fragments with diameters of up to 7.5 cm. Estimates therefore continue to be based on equati ons of Smith and Heath (2002) and applied at the plot level. The equations describe processes for decay or loss of forest floor following harvest and the net accumulation of new forest floor material following stand growth. For example, total forest floor C at a given number of years after a clearcut harvest for aspen-birch forests in the North is:
Total forest floor C (Mg C/ha) = ( 18.4×years)/(53.7+years) + 10.2× e (-years/9.2)



### See Table 4 of Smith and Heath (2002) for the complete set of coefficients. Note that these are direct estimates of C density; the 0.5 conversion does not apply to litter.

Soil organic carbon
Soil organic carbon (SOC) is currently sampled to a 20 cm depth on subsets of FIA plots, however, these data are not available for the entire United States. Thus, estimates of SOC are based on the national STATSGO spatial database (USDA 1991), and the general approach described by Amichev and Galbraith (2004). In their procedure, SOC was calculated for the conterminous United States using the STATSGO database, and data gaps were filled by representative values from similar soils. Links to region and forest type groups were developed with the assistance of the USDA Forest Service FIA Geospatial Service Center by overlaying FIA fore st inventory plots on the soil C map. The average SOC densities are provided in Table A-216.

### A-274 Inventory of U.S. Greenhous e Gas Emissions and Sinks: 1990-2009 Carbon in Harvested Wood Products
Estimates of the harvested wood product (HWP) contribution to forest C sinks and emissions (hereafter called “HWP Contribution”) are based on methods described in Skog (2008) using the WOODCARB II model. These methods are based on IPCC (2006) guidance for estimating HWP carbon. The 2006 IPCC Guidelines provide methods that allow Parties to report HWP Contribution using one of several different accounting approaches: production, stock change, and atmospheric flow, as well as a default method. The various approaches are described below. The approaches differ in how HWP Contribution is allocated based on production or consumption as well as what processes (atmospheric fluxes or stock changes) are emphasized.

• Production approach : Accounts for the net changes in carbon stocks in forests and in the wood products pool, but attributes both to the producing country.

• Stock change approach : Accounts for changes in the product pool within the boundaries of the consuming country.

• Atmospheric flow approach : Accounts for net emissions or removals of carbon to and from the atmosphere within national boundaries. C removal due to forest growth is accounted for in the producing country while C emissions to the atmosphere from oxidation of wood products are accounted for in the consuming country.

• Default approach : Assumes no change in C stocks in HWP. IPCC (2006) requests that such an assumption be justified if this is how a Party is choosing to report.
The United States uses the production accounting approach (as in previous years) to report HWP Contribution (Table A223). Though reported U.S. HWP estimates are based on the pr oduction approach, estimates resulting from use of the two alternative approaches—the stock change and atmospheric flow approaches—are also presented for comparison (see Table A-224). Annual estimates of change are calculated by tracking the additions to and removals from the pool of products held in end uses (i.e., products in use such as housing or publ ications) and the pool of products held in solid waste disposal sites (SWDS).
Estimates of five HWP variables that can be used to calculate HWP contribution for the stock change and atmospheric flow approaches for imports and exports are prov ided in Table A-223. The HWP variables estimated are:
(1A) annual change of C in wood and paper products in use in the United States,
(1B) annual change of C in wood and paper products in SWDS in the United States,
(2A) annual change of C in wood and paper products in use in the United States and other countries where the wood came from trees harvested in the United States,
(2B) annual change of C in wood and paper products in SWDS in the United States and other countries where the wood came from trees harvested in the United States,
(3) C in imports of wood, pulp, and paper to the United States,
(4) C in exports of wood, pulp and paper from the United States, and
(5) C in annual harvest of wood from forests in the United States.A-275Table A-223: Harvested wood products from wood harvested in United States—Annual additions of carbon to stocks and total stocks1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010Net carbon additions per year (Tg C per year)Total Harvestedwood carbon (35.9) (33.8) (33.8) (32.9) (33.4) (32.3) (30.6) (32.0) (31.1) (32.5) (30.8) (25.5) (26.8) (25.9) (28.7) (28.7) (29.6) (28.1) (22.4) (14.8)Products in use (17.7) (14.9) (16.3) (15.0) (15.9) (15.1) (14.1) (14.7) (13.4) (14.1) (12.8) (8.7) (9.6) (9.7) (12.4) (12.4) (12.3) (10.7) (5.2) 1.9Solid woodproducts (14.4) (11.9) (12.6) (12.2) (12.1)(11.2) (11.5) (11.8) (11.4) (12.1)(11.9) (10.1) (10.7) (10.1) (11.6) (11.9) (10.6) (8.7) (4.4) 0.6Paper products (3.3) (3.1) (3.7) (2.8) (3.8) (3.8) (2.6) (3.0) (2.0) (2.0) (1.0) 1.4 1.1 0.4 (0.8) (0.5) (1.7) (2.0) (0.8) 1.3Products inSWDS (18.3) (18.8) (17.4) (17.9) (17.5) (17.2) (16.5) (17.3) (17.7) (18.4) (18.0) (16.8) (17.2) (16.2) (16.3) (16.3) (17.3) (17.4) (17.2) (16.7)Solid woodproducts (9.9) (11.1) (9.5) (9.7) (9.8) (10.7)(10.6) (10.3) (10.2) (10.6) (10.7) (10.7) (11.1) (11.1) (11.3) (11.5) (11.6) (11.7) (11.5) (11.2)Paper products (8.3) (7.7) (7.9) (8.3) (7.7) (6.5) (6.0) (6.9) (7.5) (7.8) (7.3) (6.0) (6.1) (5.1) (5.0) (4.8) (5.7) (5.7) (5.7) (5.4)Total Carbon stocks (Tg C)Total Harvested woodcarbon 1,859 1,895 1,929 1,963 1,996 2,029 2,061 2,092 2,124 2,155 2,1872,218 2,244 2,271 2,296 2,3252,354 2,383 2,412 2,434 2,449Products in use 1,231 1,249 1,264 1,2801,295 1,311 1,326 1,340 1,355 1,3681,382 1,395 1,404 1,413 1,423 1,436 1,448 1,460 1,471 1,476 1,474Products in SWDS 628 646 665 683 701 718 735 752 769 787 805 823 840 857 873 890 906 923 941 958 974Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).Table A-224: Comparison of Net Annual Change in Harvested WoodProducts Carbon Stocks Using Alternative Accounting ApproachesHWP Contribution to LULUCFEmissions/ removals (Tg CO2Eq.)InventoryYearStock ChangeApproachAtmospheric FlowApproachProductionApproach1990 (129.6) (138.4) (131.8)1991 (116.3) (131.4) (123.8)1992 (120.0) (131.6) (123.8)1993 (126.8) (127.8) (120.7)1994 (130.0) (129.9) (122.5)1995 (126.0) (128.0) (118.4)1996 (122.3) (122.5) (112.2)1997 (131.4) (127.4) (117.3)1998 (139.8) (122.7) (114.1)1999 (149.4) (127.3) (119.1)2000 (143.2) (120.3) (112.9)2001 (128.3) (100.3) (93.4)2002 (135.6) (103.1) (98.2)2003 (134.6) (99.2) (94.8)2004 (163.0) (109.1) (105.3)2005 (161.4) (109.0) (105.4)2006 (138.6) (114.2) (108.6)2007 (115.4) (112.1) (103.0)A-276 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–20092008 (78.4) (94.3) (82.1)2009 (41.9) (68.3) (54.3)Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).Table A-225: Harvested Wood Products Sectoral Background Data for LULUCF—United States (production approach)1A 1B 2A 2B 3 4 5 6 7 8InventoryyearAnnualChange instock of HWPin use fromconsumptionAnnualChange instock of HWPin SWDS fromconsumptionAnnualChange instock ofHWP inuseproducedfromdomesticharvestAnnualChange instock of HWPin SWDSproducedfromdomesticharvestAnnualImports ofwood, andpaperproducts +wood fuel,pulp,recoveredpaper,roundwood/chipsAnnualExports ofwood, andpaperproducts +wood fuel,pulp,recoveredpaper,roundwood/chipsAnnualDomesticHarvestAnnual releaseof carbon totheatmospherefrom HWPconsumption(fromfuelwood &products inuse andproducts inSWDS)Annual releaseof carbon to theatmosphere fromHWP (includingfirewood) wherewood came fromdomestic harvest(from products inuse and productsin SWDS )HWPContributionto AFOLUCO2emissions/removals∆CHWP IU DC∆CHWP SWDS DC∆CHWP IUDH∆CHWP SWDSDHPIMPEXH↑CHWP DC↑CHWP DHGg C /yr Gg CO2/yr1990 17,044 18,308 17,659 18,278 12,68015,078 142,297 104,547 106,359 (131,772)1991 13,129 18,602 14,940 18,812 11,55215,667 144,435 108,588 110,682 (123,758)1992 15,718 17,006 16,334 17,427 12,85616,032 139,389 103,489 105,627 (123,791)1993 16,957 17,627 14,971 17,949 14,51214,788 134,554 99,694 101,633 (120,708)1994 18,221 17,221 15,930 17,479 15,68515,665 134,750 99,328 101,342 (122,498)1995 17,307 17,051 15,065 17,229 16,71217,266 137,027 102,115 104,733 (118,411)1996 17,018 16,348 14,092 16,513 16,69116,733 134,477 101,069 103,872 (112,219)1997 18,756 17,090 14,740 17,263 17,98316,877 135,439 100,699 103,436 (117,344)1998 20,311 17,818 13,376 17,735 19,72215,057 135,021 101,558 103,911 (114,071)1999 22,035 18,714 14,123 18,353 21,26615,245 134,939 100,211 102,464 (119,078)2000 20,491 18,560 12,828 17,962 22,42616,185 134,458 101,648 103,667 (112,898)2001 17,295 17,691 8,711 16,774 22,97515,336 128,621 101,274 103,136 (93,447)2002 18,629 18,357 9,570 17,207 24,60415,744 127,567 99,441 100,791 (98,179)2003 19,180 17,532 9,676 16,186 25,96216,303 124,949 97,896 99,086 (94,828)2004 26,384 18,077 12,429 16,298 31,65016,948 130,460 100,700 101,733 (105,332)2005 25,777 18,249 12,394 16,347 31,71417,423 131,711 101,976 102,971 (105,382)2006 19,010 18,780 12,308 17,302 25,48518,836 127,064 95,922 97,454 (108,567)2007 12,999 18,497 10,673 17,409 21,60320,670 120,922 90,360 92,840 (102,967)2008 3,589 17,786 5,203 17,188 16,82221,156 108,339 82,630 85,948 (82,101)2009 (5,401) 16,825 (1,857) 16,652 13,28120,485 95,130 76,501 80,334 (54,250)Note:↑CHWP DC= H + PIM– PEX-∆CHWP IU DC-∆CHWP SWDS DCAND↑CHWP DH= H -∆CHWP IU DH-∆CHWP SWDS DH. Parentheses indicate net C sequestration (i.e., a net removal of C from theatmosphere).

### A-277 Annual estimates of variables 1A, 1B, 2A and 2B were calculated by tracking the additions to and removals from the pool of products held in end uses (e.g., products in uses such as housing or publications) and the pool of products held in SWDS. In the case of variables 2A and 2B, the pools include products exported and held in other countries and the pools in the United States exclude products made from wood harvested in other countries. Solidwood products added to pools include lumber and panels. End-use categories for so lidwood include single and multifamily housing, alteration and repair of housing, and other end uses. There is one product cat egory and one end-use category for paper. Additions to and removals from pools are tracked beginning in 1900, with the ex ception that additions of softwood lumber to housing begins in 1800. Solidwood and paper product production and trade data are from USDA Forest Service and other sources (Hair and Ulrich 1963; Hair 1958; USDC Bureau of Census 1976; Ulrich, 1985, 1989; Steer 1948; AF&PA 2006a, 2006b; Howard 2003).
The rate of removals from products in use and the rate of decay of products in SWDS are specified by first order (exponential) decay curves with given half-lives (time at which half of amount placed in use will have been discarded from use). Half-lives for products in us e, determined after calibration of the model to meet two criteria, are shown in Table A-226. The first criterion is that the WOODCARB II model estimate of C in houses standing in 2001 needed to match an independent estimate of C in housing based on U.S. Census and USDA Forest Service survey data. The second criterion is that the WOODCARB II model estimate of wood and paper being discarded to SWDS needed to match EPA estimates of discards over the period 1990 to 2000. This calibra tion strongly influences the estimate of variable 1A, and to a lesser extent variable 2A. The calibration also determines the amounts going to SWDS. In addition ,WOODCARB II landfill decay rates have been validated by making sure that estimates of methane emissions from landfills based on EPA data are reasonable in comparison to methane estimates based on WOODCARB II landfill decay rates.
Decay parameters for products in SWDS are shown in Ta ble A-227. Estimates of 1B and 2B also reflect the change over time in the fraction of products discarded to SW DS (versus burning or recycling) and the fraction of SWDS that are sanitary landfills versus dumps.
Variables 2A and 2B are used to estimate HWP contribution under the production accounting approach. A key assumption for estimating these variables is that products exported from the United States and held in pools in other countries have the same half lives for products in use, the same percentage of discarded products going to SWDS, and the same decay rates in SWDS. Summaries of net fluxes and stocks for harvested wood in products and SWDS are in Table A-213 and Table A-214. The decline in net additions to HWP carbon stocks continued though 2009 from the recent high point in 2006. This is due to sharp declines in U.S. pr oduction of solidwood and paper products in 2009 primarily due to the decline in housing construction. The low level of gross additions to solidwood and paper products in use in 2009 was exceeded by discards from uses. The result is a net reduction in the amount of HWP carbon that is held in products in use during 2009. For 2009 additions to landfills still exceeded emi ssions from landfills and the net additions to landfills have remained relatively stable. Overall, there were net carbon additions to HWP in use and in landfills combined.

Table A-226: Half-life of solidwood and paper products in end uses

Parameter Value Units Half life of wood in single family housing 1920 and before 78.0 Years Half life of wood in single family housing 1920–1939 78.0 Years Half life of wood in single family housing 1940–1959 80.0 Years Half life of wood in single family housing 1960–1979 81.9 Years Half life of wood in single family housing 1980 + 83.9 Years Ratio of multifamily half live to single family half life 0.61 Ratio of repair and alterations half life to single family half life 0.30 Half life for other solidwood product in end uses 38.0 Years Half life of paper in end uses 2.54 Years Source: Skog, K.E. (2008) “Sequestration of carbon in harvested wood products for the United States.” Forest Products Journal 58:56-72.



### Table A-227: Parameters determining decay of wood and paper in SWDS

Parameter Value Units Percentage of wood and paper in dumps that is subject to decay 100% Percentage of wood in landfills that is subject to decay 23% Percentage of paper in landfills that is subject to decay 56% Half life of wood in landfills / dumps (portion subject to decay) 29 Years Half life of paper in landfills/ dumps (portion subject to decay) 14.5 Years Source: Skog, K.E. (2008) “Sequestration of carbon in harvested wood products for the United States.” Forest Products Journal 58:56-72

### A-278 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 Uncertainty Analysis
The uncertainty analyses for total net flux of forest C (see uncertainty table in LULUCF chapter) are consistent with the IPCC-recommended Tier 2 methodology (IPCC 2006). Separate analyses are produced for forest ecosystem and HWP flux. The uncertainty estimates are from Monte Carlo simu lations of the respective models and input data. Methods generally follow those described in Heath and Smith (2000), Smith and Heath (2000), and Skog et al. (2004). Uncertainties surrounding input data or model processes are quantif ied as probability distribution functions (PDFs), so that a series of sample values can be randomly selected from the distributions. Model simulations are repeated a large number of times to numerically simulate the effect of the random PD F selections on estimated total C flux. The separate results from the ecosystem and HWP simulations are pooled for total uncertainty (see uncertainty table in LULUCF chapter).
Uncertainty surrounding current net C flux in forest ecosystems is based on the value for 2007 as obtained from the Monte Carlo simulation. C stocks are based on forest condition level (plot-level) calculations, and, therefore, uncertainty analysis starts probabilistic sampling at the plot level. Uncertainty surrounding C density (Mg/ha) is defined for each of six C pools for each inventory plot. Live trees are generally assigned normal PDFs, which are defined according to variability information in Jenkins et al. (2003) and the species and number of trees measured on each FIA plot. Plot-level live tree C estimates from RPA data are based on volume; these PDFs also include an additional level of uncertainty based on their respective regression equations. Similarly, the normally-distributed PDFs for standing dead trees are based on both volume regression and the individual-tree uncertainties related to the Jenkins et al. (2003) based estimates. Definitions of these normal distributions, which centered on expected values, depend on region, type, and specific plot information. Where data gaps—tree or volume—are identified for older inventory data, corresponding averages from later data are applied for live and standing dead tree C densities. Uniform PDFs with a range of ±90 percent of the average are used for these plots.
Distributions for the remaining C pools are triangular or uniform, which partly reflects the lower level of information available about these estimates. Down dead wood, understory, and litter are assigned triangular distributions with the mean at the expected value for each plot and the minimum and mode at 10 percent of the expected value. The use of these PDFs skewed to the right reflects the assumption that a small proportion of plots will have relatively high C densities. Joint sampling of PDFs is specified for two pairs of samples: understory PDF sampling is slightly negatively correlated with live tree PDF sampling, and down dead wood sampling is slightly positively correlated with live tree sampling. This also reflects the structure of the estimates, wh ich are dependent on live tree C. Soil organic C is defined as a uniform PDF at ±50 percent of the mean. Sub-state or st ate total carbon stocks associated with each survey are the cumulative sum of random samples from the plot-level PDFs, which are then appropriately expanded to population estimates. These expected values for each carbon pool include uncertainty associated with sampling, which is also incorporated in the Monte Carlo simulation. Sampling errors are determined according to methods described for the FIADB ( 

### Bechtold and Patterson 2005 ), are normally distributed, and are assigned a slight positive correlation between successive surveys for Monte Carlo sampling. More recent annual inventories are assigned higher sampling correlation between successive surveys based on the proportion of plot data jointly included in each. Errors for older inventory data are not available, and these surveys are assigned values consistent with those obtained from the FIADB.
Uncertainty about net C flux in HWP is based on Skog et al. (2004) and Skog (2008). Latin hypercube sampling is the basis for the HWP Monte Carlo simulation. Estimates of the HWP variables and HWP Contribution under the production approach are subject to many sources of uncertainty. An estimate of uncertainty is provided that evaluated the effect of uncertainty in 13 sources, including production and trade data and parameters used to make the estimate. Uncertain data and parameters include data on production and trade and factors to convert them to C, the Census-based estimate of C in housing in 2001, the EPA estimate of wood and paper discarded to SWDS for 1990 to 2000, the limits on decay of wood and paper in SWDS, the decay rate (half-life) of wood and paper in SWDS, the proportion of products produced in the United States made with wood harvested in the United States, and the rate of storage of wood and paper C in other countries that came from United States harv est, compared to storage in the United States.
A total of ten thousand samples are drawn from the PDF input to separately determine uncertainties about forest ecosystem and HWP flux before they are combined for a quantitative estimate of total forest carbon uncertainty (see uncertainty table in LULUCF chapter). Again this year, true Monte Carlo sampling is used for the forest ecosystem estimates (in contrast to Latin hypercube sampling, which was used in some previous estimates), and a part of the QA/QC process includes verifying that the PDFs are adequately sampled.

### A-279 Emissions from fires

CO 2 

### As stated in other sections, the forest inventory approach implicitly accounts for emissions due to disturbances. Net C stock change is estimated by subtracting consecutive C stock estimates. A disturbance removes C from the forest. The inventory data, on which net C stock estimates are based, already reflects the C loss because only C remaining in the forest is estimated. Estimating the CO 2 

### emissions from a disturbance such as fire and adding those emissions to the net CO 2 

### change in forests would result in double-counting the loss from fire because the inventory data already reflect the loss. There is interest, however, in the size of the CO 2 

### emissions from disturbances such as fire. The IPCC (2003) methodology and IPCC (2006) default combustion factor for wildfire were employed to estimate emissions from forest fires.
The same methodology was used to estimate emissions from both wildfires and prescribed fires occurring in the lower 48 states. Wildfire area statistics are available, but th ey include non-forest land, such as shrublands and grasslands. It was thus necessary to develop a rudimentary estimate of th e percent of area burned in forest by multiplying the reported area burned by a ratio of total forest land area to the total area considered to be under protection from fire. Data on total area of forest land were obtained from FIA (USDA Forest Service 2010b ) . Data on “total area considered to be under protection from fire” were available at the state level and obtained for the year 1990 from 1984-1990 Wildfire Statistics prepared by the U.S. Forest Service (USDA Forest Se rvice 1992). Data for years 1998, 2002, 2004, and 2006 were obtained from the National Association of State Foresters (NASF 2008, 2007a, 2007b, 2007c). For states where data were available for all four years, the 1990 value was assumed for years 1990 to 1994, values for 1998 were assumed for years 1995 to 1998, values for 2002 were assumed for years 1999 to 2002, values for 2004 were assumed for years 2003 and 2004, and values for 2006 were assumed for years 2005 to 2009. For states where data were available for all years except 2002, 2004 data were assumed for years 1999 to 2004. For states where data were available for all years except 2004, 2006 data were assumed for 2003 through 2008. For years where data were available for all years except 2006, 2004 data were assumed for years 2003 to 2008. Since no 1998 value was available for Alaska, the 1990 value was assumed for years 1990 to 1997, the 2002 value was assumed for years 1998 to 2002, and the 2004 value was assumed for years 2003 and 2005 to 2009. Data for 1990, 1998, and 2006 were available for New Mexico, so the 1990 value was assumed for years 1990-1995, while the 1998 value was assumed for year 1996 through 2001, and 2006 data were assumed for all remaining years. Data for 1990, 1998, and 2002 were available for Illinois, so the 1990 value was assumed for years 1990-1995, while the 1998 value was assumed for years 1995 through 2001, and the 2002 value was assumed for all remaining years. Total forestland area for the lower 48 states was divided by total area considered to be under protection from wildfire for the lower 48 states across the 1990 to 2009 time series to create ratios that were then applied to reported area burned to estimate the area of forestland burned for the lower 48 states. The ratio was applied to area burned from wildland fires and prescribed fires occurring in the lower 48 states. Reported area burned data for prescribed fires was available from 1998 to 2009 from the National Interagency Fire Center (NIFC 2010 ) . Data for the year 1998 was assumed for years 1990 to 1997.
Forest area burned data for Alaska are from the Alaska Department of Natural Resources (Alaska Department of Natural Resources 2008) or the Alaska Interagency Coordina tion Center (Alaska Interagency Coordination Center 2010). Data are acres of land which experienced fire activity on forest service land. Based on personal communication with USFS, forest areas under the protection of USFS serve as a proxy for coastal areas, which is where the majority of forest fires in Alaska occur (Heath 2008). According to expert judgme nt, the coastal area of Alaska included in this Inventory is mostly temperate rainforest and, therefore, there is little cal l for prescribed burns (Smith 2008a). It was, thus, assumed that reported area burned for prescribed fires cove rs only prescribed fires in the lower 48 states.
The average C density in the lower 48 states for aboveground biomass C, dead wood C, and litter layer is 91 Mg/ha, according to data from FIA. A default value of 0.45 from IPCC (2006) was assumed for the amount of biomass burned by wildfire (combustion factor value). Thus, approximate ly 41.0 Mg C/ha is estimated to be emitted by wildfire. For Alaska, an average C density of 179 Mg/ha was used based on data from FIA; this translates into 80.6 Mg C/ha emitted. Based on data from the U.S. Forest Service, a value of 30 Mg/ha was used as the average C density for prescribed fires, where the average C density is adjusted to reflect the fuel load included in dead wood and litter only and the thought that prescribed fires only occur in the lower 48 stat es (Smith 2008a). Thus, prescribed fires are estimated to emit 13.5 Mg C/ha. Estimates for Mg C/ha were multiplied by estimates of forest area burned by year; the resulting estimates are displayed in Table A-228. C estimates were multiplied by 92.8 percent to account for the proportion of carbon emitted as CO 2 

### and by 3.67 to yield CO 2 

### units. Total CO 2 

### emissions for wildfires and prescribed fires in the lower 48 states and wildfires in Alaska in 2009 were estimated to be 127.0 Tg/yr.

### A-280 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 Table A-228: Areas (hectares) fr om wildfire statistics and corres ponding estimates of carbon and CO 2 

### (Tg/yr) emissions for wildfires and prescribed fires in the lower 48 states and wildfires in Alaska 1

Lower 48 States Alaska Wildfires Prescribed Fires Wildfires

Year Reported area burned 2 (ha) Forest area burned 3 (ha) Carbon emitted (Tg/yr) CO 2 emitted (Tg/yr) Reported

area burned 2

(ha) Forest

area burned 3

(ha) Carbon emitted (Tg/yr) CO 2 emitted (Tg/yr) Forest

area burned 4 (acres) Forest

area burned

(ha) Carbon emitted (Tg/yr) CO 2 emitted (Tg/yr) 1990 579,589 302,372 12 42 355,432 185,429 3 9 8 3 0.0003 0.0009 1991 486,807 254,618 10 35 355,432 185,904 3 9 557 225 0.0182 0.0618 1992 785,892 412,111 17 57 355,432 186,384 3 9 47 19 0.0015 0.0052 1993 438,865 230,704 9 32 355,432 186,845 3 9 110 45 0.0036 0.0122 1994 1,540,987 811,927 33 113 355, 432 187,273 3 9 23 9 0.0007 0.0025 1995 727,051 418,819 17 58 355,432 204,747 3 9 7 3 0.0002 0.0008 1996 2,212,309 1,313,377 54 183 355,432 211,008 3 10 103 42 0.0034 0.0115 1997 335,914 199,827 8 28 355,432 211,437 3 10 33 13 0.0011 0.0036 1998 489,246 291,647 12 41 355,432 211,878 3 10 2 1 0.0001 0.0002 1999 1,869,918 1,123,023 46 156 806,780 484,530 7 22 7 3 0.0002 0.0007 2000 2,685,981 1,615,551 66 225 77,789 46,788 1 2 1 1 0.0000 0.0001 2001 1,356,830 817,280 33 114 667,42 8 402,022 5 18 2,078 841 0.0677 0.2305 2002 2,023,976 1,217,243 50 170 1,086,503 653,436 9 30 28 11 0.0009 0.0031 2003 1,358,986 718,172 29 100 1, 147,695 606,513 8 28 17 7 0.0006 0.0019 2004 637,258 342,999 14 48 996,453 536,332 7 25 23 9 0.0008 0.0026 2005 1,629,067 940,471 39 131 934, 965 539,761 7 25 353 143 0.0115 0.0392 2006 3,888,011 2,250,696 92 314 1,100,966 637,328 9 29 8 3 0.0003 0.0009 2007 3,512,122 2,038,630 83 284 1, 274,383 739,723 10 34 2 1 0.0001 0.0002 2008 2,099,842 1,212,446 50 169 783,068 452,142 6 21 1 0 0.0000 0.0001 2009 1,201,996 711,766 29 99 1, 024,306 606,547 8 28 22 9 0.0007 0.0024

1 Note that these emissions have already been accounted for in the estimates of ne t annual changes in carbon stocks, which accou nts for the amount sequestered minus any emissions, including the assumpti on that combusted wood may continue to decay through time.

2 National Interagency Fire Center (2010).

3 Ratios calculated using forest land area estimates from FIA (U SDA Forest Service 2010b) and wildland area under protection esti mates from USDA Forest Service (1992) and the Nationa l Association of State Foresters (2007).

4 1990–2007 Alaskan forest fires data are from the Alaska Departme nt of Natural Resources (2008). 2008 data are from Alaska Int eragency Coordination Center (2009).



### Non-CO 2


### Emissions of non-CO 2 

### gases from forest fires were estimated using the default IPCC (2003) methodology, IPCC (2006) emission ratios, and default IPCC (2006) combustion factor for wildfires. Emissions estimates for CH 4 

### and N 2 

### O are calculated by multiplying the total estimated CO 2 

### emitted from forest burned by gas-specific emissions ratios and conversion factors. The equations used are:

CH 4 

### Emissions = (CO 2 

### released) × 92.8% × (44/12) × (CH 4 

### to CO 2 

### emission ratio)

N 2 

### O Emissions = (CO 2 

### released) × 92.8% × (44/12) × (N 2 

### O to CO 2 

### emission ratio)
The resulting estimates are presented in Table A-229.

Table A-229: Estimated carbon re leased and estimates of non-CO 2 

### emissions (Tg/yr) for U.S. forests 1
Year C emitted (Tg/yr) CH 4 emitted (Tg/yr) N 2 O (Tg/yr) 1990 14.886 0.152 0.008 1991 12.954 0.132 0.007 1992 19.394 0.198 0.011 1993 11.973 0.122 0.007 1994 35.777 0.365 0.020 1995 19.915 0.203 0.011 1996 56.635 0.577 0.032 1997 11.038 0.113 0.006 1998 14.803 0.151 0.008 1999 52.529 0.535 0.030 2000 66.789 0.681 0.038 2001 38.963 0.397 0.022 2002 58.668 0.598 0.033 2003 37.598 0.383 0.021

### A-281 2004 21.287 0.217 0.012 2005 45.811 0.467 0.026 2006 100.770 1.027 0.057 2007 93.468 0.953 0.053 2008 55.754 0.568 0.031 2009 37.336 0.381 0.021
1 Calculated based on C emission estimates in 

### Table A-228 and default factors in IPCC (2003, 2006)



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### A-285 

# 3.13. Methodology for Estimating Net Chan ges in Carbon Stocks in Mineral and Organic Soils on Cropland and Grassland

This sub-annex describes the methodologies used to calculate annual carbon (C) stock changes from mineral and organic soils under agricultural management, including Cropland Remaining Cropland

, Land Converted to Cropland

, Grassland Remaining Grassland , and Land Converted to Grassland . Three types of methodologies were applied: (1) a Tier 3 approach, employing the Century simulation model, (2) Tier 2 methods with country-specific stock change and emission factors; and (3) Tier 2 methods for estimating additional changes in mineral soil C stocks due to sewage sludge additions to soils and enrollment changes in the Conservation Reserve Program (CRP) after 2003.
The Inventory uses a Tier 3 approach to estimate soil C stock changes for the majority of agricultural lands. This approach has several advantages over the IPCC Tier 1 or 2 approaches:



### • It utilizes actual weather data at county scales, rath er than a broad climate region classification, enabling quantification of inter-annual variability in C fluxes at finer spatial scales;



### • The model uses a more detailed characterization of spatially-mapped soil properties that influence soil C dynamics, as opposed to the broad soil taxonomic classifications of the IPCC methodology;



### • The simulation approach provides a more detailed representation of management influences and their interactions than are represented by a discrete fact or-based approach in the Tier 1 and 2 methods; and



### • Soil C changes are estimated on a more continuous basis (monthly) as a function of the interaction of climate, soil, and land management, compared with the linear change between the start and end of the inventory that is used with the Tier 1 and 2 methods.

The Century model was chosen as an appropriate tool for a Tier 3 approach based on several criteria:

• 

### The model was developed in the United States and has been extensively tested and verified for U.S. conditions. In addition, the model has been widely used by researchers and agencies in many other parts of the world for simulating soil C dynamics at local, regional and national scales (e.g., Brazil, Canada, India, Jordan, Kenya, Mexico).

• 

### The model is capable of simulating cropland, grassl and, forest, and savanna ecosystems, and land-use transitions between these different la nd uses. It is, thus, well suited to model land-use change effects.

• 

### The model was designed to simulate all major types of management practices that influence soil C dynamics, with the exception of cultivated organic soils and a few crops that have not been parameterized for Century simulations (e.g., rice, perennial/horticultural crops, and tobacco). For these latter cases, an IPCC Tier 2 method has been used.

• 

### Much of the data needed for the model was obtainable from existing national databases. The exceptions are CRP enrollment after 2003 and sewage sludge amendments to soils, which are not known at a sufficient resolution to use the Tier 3 model. Soil C stock changes associated with these practices are addressed with a Tier 2 method.

Century Model Description
The Century model simulates C (and also N, P, and S) dynamics, soil temperature, and water dynamics for cropland, grassland, forest, and savanna (mixed forest-grassla nd) systems. For this analysis, only C and N dynamics have been included for several reasons: to simplify the analysis and reduce data requirements, and because P and S interactions are less important as determinants of land-use- and management-induced changes in soil C stocks for U.S. agricultural systems. The model has four main components: (1) soil organic matter and nutrient dynamics; (2) plant growth processes; (3) water and temperature dynamics; and (4) management practices. The model was designed to work with readily available input data: monthly weather data (e.g., temperature and precipitation); soil physical properties (e.g., soil texture, drainage condition, rooting depth); and information about la nd use/land cover (e.g., vegetation attributes) and management activities (see below). The model operates on a monthly time step (with weekly time steps used for soil water dynamics).
Dynamics of organic C and N (Figure A-13) are simulated for the surface and subsurface litter pools and the top 20 cm of the soil profile; mineral N dynamics are simulate d through the whole soil profile. Organic C and N stocks are represented by two plant litter pools (labelled metabolic and structural) and three soil organic matter (SOM) pools (labelled active, slow, and passive). The metabolic litter pool represents the easily decomposable constituents of plant residues, while the structural litter pool is composed of more recalcitrant, ligno-cellulose plant materials. The three SOM pools represent a gradient in decomposability, from active SOM (representing microbial biomass and associated

### A-286 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 metabolites) having a rapid turnover (months to years), to passive SOM (representing highly processed, humified, condensed decomposition products), which is highly recalcitrant, with mean residence times on the order of several hundred years. The slow pool represents decomposition products of intermediate stability, having a mean residence time on the order of decades and is the fraction that tends to change the most in terms of C content in response to changes in land use and management. Soil texture influences turnover rates of the slow and passive pools The clay and silt-sized mineral fraction of the soil provides physical protection from microbial attack, leading to slower decomposition and greater SOM stabilization in finely textured soils. Soil temp erature and moisture, tillage disturbance, aeration, and other factors influence the decomposition and loss of C from the soil organic matter pools.

Figure A-13: Flow diagram of Carbon submodel (A) and Nitrogen submodel (B)

The plant-growth submodel simulates C assimilation through photosynthesis, N uptake, dry matter production, partitioning of C within the crop or forage, senescence, and mortality. The primary f unction of the growth submodel is to estimate the amount, type, and timing of organic matter inputs to soil and to represent the influence of the plant on soil water, temperature, and N balance. Yield and removal of ha rvested biomass are also simulated. Separate submodels are designed to simulate herbaceous plants (i.e., agricultural cr ops and grasses) and woody vegetation (i.e., trees and scrub). Only the herbaceous plant submodel is currently used in the Inventory. Maximum monthly net primary production (NPP) rate (a parameter of crop and forage speci es/variety, specified in the model input files) is modified by air temperature and available water to estimate a potential monthly NPP, which is then further subject to nutrient limitations in order to estimate actual NPP and biomass allocation.
The soil-water balance submodel calculates water balance components and changes in soil water availability, which influences both plant growth and decomposition/nutrient cycling processes. The moisture content of soils are simulated through a multi-layer profile based on precipitati on, snow accumulation and melting, interception, soil and canopy evaporation, transpiration, soil water movement, runoff, and drainage.
The final main component of the model is the management submodel, which includes options for specifying crop type, crop sequence (e.g., rotation), tillage, fertilization, or ganic matter addition (e.g., manure amendments), harvest (with variable residue removal), drainage, irrigation, burning, and grazi ng intensity. An input “schedule” file is used to simulate the timing of management activities and temporal trends; schedules can be organized into discrete time blocks to define a repeated sequence of events (e.g., a crop rotation or a frequency of disturbance such as a burning cycle for perennial grassland). Management options can be specified for any month of a year within a scheduling block, where management codes point to operation-specific parameter files (referred to as *.100 files), which contain the information used to simulate management effects within the model process algorit hms. User-specified management activities can be defined by adding to or editing the contents of the *.100 files. Additional details of the model formulation are given in Parton et al. (1987, 1988, 1994) and Metherell et al. (1993), and arch ived copies of the model source code are available.
The model has been tested for application in U.S. agricultural lands and has been shown to capture the general trends in C storage across approximately 870 field plots fro m 47 experimental sites (Figure A-14). Some biases and imprecision were found in predictions of soil organic C, whic h is reflected in the uncertainty associated with Century model results as described in Step 2b of this sub-annex. Additional discussion is provided in Ogle et al. (2007, 2010)

Figure A-14: Comparison of Measured Soil Organic C from Expe rimental Sites to Modeled Soil Organic C Using the Century Model

IPCC Tier 2 Method Description
The IPCC Tier 2 method has been developed to estimate C stock changes and CO 2 

### fluxes between soils and the atmosphere based on land-use and management activity (IPCC 2003, 2006; Ogle et al. 2003). For mineral soils (i.e., all soil orders from the USDA taxonomic classification except Histosols), the Tier 2 method uses reference C values to establish baseline C stocks that are modified based on agricultural activities using land-use change, tillage, and input factors. The standard IPCC approach was modified to use ag ricultural SOC stocks as the reference condition, rather than uncultivated soils under native vegetation. This modification was needed because soil measurements under agricultural

### A-287 management are much more common and easily identified in the National Soil Survey Characterization Database (NRCS 1997). Measurements of soils under native vegetation are uncommo n in the major agricultural regions of the United States because most of the area has been converted into cropland. In addition, country-specific factors were derived for land-use change, tillage, and input factors.
Organic soils used for agricultural production are treated in a separate calculation. These soils are made up of deep (greater than 30 cm) layers of organic material that can decompose at a steady rate over several decades following drainage for crop production or grazing (IPCC 2006). The IPCC ap proach uses an emission factor to estimate annual losses of CO 2 

### from cultivated organic soils, rather than an explicit stock change approach.

Methodological Steps for Derivation of Soil Organic C Stock Change Estimates
The inventory of soil C stock changes in U.S. agricultural land combines Tier 2 and 3 approaches. A simulationbased Tier 3 approach was used to estimate soil C changes for most agricultural land (approximately 90 percent of total cropland and grassland) comprising the dominant cropping and grazing systems in the United States, for which the model has been well-tested. Estimates for the remaining area, comprising less-common crop systems (e.g., horticultural, vegetable, tobacco, rice), land converted between non-agricultur al and agricultural uses, and all agricultural land occurring on drained organic soils, were developed using the Tier 2 approach. Tier 2 methods were also used to estimate additional changes in mineral soil C stocks due to sewage sludge additions to soils, and enrollment changes in the Conservation Reserve Program after 2003. Most of the activity data sour ces were common to the Tier 2 and Tier 3 approaches, and, hence, they are described in an integrated manner below. Additional activity data required for the methods are described in adjoining sections, followed by the computation steps.

Step 1: Derive Activity Data
Activity data were compiled for the Tier 3 Century biogeochemical model and Tier 2 IPCC methods, includingclimate data, soil characteristics, and land-use/management activity data. The first step was to obtain landuse/management activity data, and determine the land base for areas under agricultural management. The areas modeled with Century and those estimated with the Tier 2 IPCC method were also subdivided. Finally, additional data, specific to each method, were collected on other key management activities (e.g., tillage management, fertilizer and manure addition rates) and environmental conditions (e .g., climate and soil characteristics).

Step 1a: Determine the Land Base and Classify Management Systems

Land Base —The National Resources Inventory (NRI) provided the basis for identifying the U.S. agricultural land base on non-federal lands, and classifying parcels into Cropland Remaining Cropland

, Land Converted to Cropland

, Grassland Remaining Grassland , and Land Converted to Grassland (USDA-NRCS 2000). Note that the Inventory does not include estimates of C stock changes for grasslands and a minor amout of croplands on federal lands, even though these areas are part of the managed land base for the United States. C stock changes on federal croplands and grasslands will be further evaluated and included in future inventories. The NRI has a stratified multi-stage sampling design, where primary sample units are stratified on the basis of county and township boundaries defined by the U.S. Public Land Survey (Nusser and Goebel 1997). Within a primary sample unit, typically a 160-acre (64.75 ha) square quarter-section, three sample points are selected according to a restricted randomizati on procedure. Each point in the survey is assigned an area weight (expansion factor) based on other known areas and land-use information (Nusser and Goebel 1997). In principle, the expansion factors represent the amount of area with the land use and land use change history that is the same as the point location. It is important to note that the NRI is a sa mpling of land use, and therefore there is some uncertainty associated with scaling the point data to a region or the count ry using the expansion factors. In general, those uncertainties decline at larger scales, such as states compared to smaller county units, because of a larger sample size. An extensive amount of soils, land-use, and land management data have been collected through the survey, which occurs every five years (Nusser et al. 1998). 65 

### Primary sources for data include aerial photography and remote sensing imagery as well as field visits and county office records. The annual NRI data product provides crop data for most years between 1979 and 2003, with the exception of 1983, 1988, and 1993. These years were gap-filled using an automated set of rules so that cropping sequences were filled with the most likely crop type given the historical cropping pattern at each NRI point location. Grassland data were reported on 5-year increments prior to 1998, but it was assumed that the land use was also grassland between the years of data collection (see Easter et al. 2008 for more information).

65 In the current Inventory, NRI data only provide land-use and management statistics through 2003, but additional data will be incorporated in the future to extend the time series of land use and management data.

### A-288 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 NRI points were included in the land base for the agricultural soil C inventory if they were identified as cropland or grassland 66 

### between 1990 and 2003 (Table A-230). The most recent national-level data available for NRI were for 2003; and so the designation for 2003 was extended to 2009 in order to provide C stock changes over the entire time series. An additional modification was made to the time series from 2004 to 2009 for Grassland Remaining Grassland and Land Converted to Grassland associated with the modification of NRI data with the Forest Inventory and Analysis Dataset. Overall, more than 260,000 NRI points were included in the inventory calculations, and the total agricultural land base varied from 370 to 367 million hectares from 1990 through 2009. Each NRI point represents a specific land parcel based upon the weighted expansion factors.
For each year, land parcels were subdivided into Cropland Remaining Cropland

, Land Converted to Cropland

, Grassland Remaining Grassland , and Land Converted to Grassland . Land parcels under cropping management in a specific year were classified as Cropland Remaining Cropland if they had been cropland for at least 20 years. Similarly land parcels under grassland management in a specific year of the inventory were classified as Grassland Remaining Grassland if they had been designated as grassland for at least 20 years. 67 

### Otherwise, land parcels were classified as Land Converted to Cropland or Land Converted to Grassland based on the most recent use in the inventory time period. Lands are retained in the land-use change categories (i.e., Land Converted to Cropland and Land Converted to Grassland ) for 20 years as recommended by the IPCC guidelines (IPCC 2006).
Table A-230: Total Land Areas for the Agricultural Soil C Inventory, Subdivided by Land Use Categories (Million Hectares) Land Areas (10 6 ha) Category 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Mineral Soils Cropland Remaining Cropland 166.38 166.14 165.89 163.32 162.27 161.95 161. 61 161.26 158.40 158.67 Land Converted to Cropland 14.01 14.24 14.41 14.23 15.71 15.88 16. 24 16.37 17.79 17.43 Grassland Remaining Grassland 176.03 175. 67 175.38 172.75 171.52 171.43 171. 20 171.12 169.79 169.70 Land Converted to Grassland 10.10 10. 23 10.44 9.94 10.51 10.77 10. 91 11.27 13.73 13.90 Non-Agricultural Uses a 2.46 2.46 2.46 8.08 8.08 8.08 8.08 8.08 8.08 8.08 Organic Soils Cropland Remaining Cropland 0.70 0.70 0.70 0.72 0.72 0.72 0. 72 0.72 0.72 0.72 Land Converted to Cropland 0.07 0.07 0.07 0.03 0.03 0.03 0. 03 0.03 0.03 0.03 Grassland Remaining Grassland 0.50 0. 50 0.50 0.48 0.48 0.48 0. 48 0.48 0.48 0.48 Land Converted to Grassland 0.04 0. 04 0.04 0.07 0.07 0.07 0. 07 0.07 0.07 0.07 Non-Agricultural Uses a 0.04 0.04 0.04 0.05 0.05 0.05 0.05 0.05 0.05 0.05 Total 370.32 370.10 369.93 369.66 369. 43 369.45 369.40 369.44 369.13 369.12

Category 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Mineral Soils Cropland Remaining Cropland 158.83 158.87 159.56 160.72 160.72 160.72 160. 72 160.72 160.72 160.72 Land Converted to Cropland 17.09 16.83 16.09 14.87 14.87 14.87 14. 87 14.87 14.87 14.87 Grassland Remaining Grassland 169.65 169. 50 169.97 170.26 170.04 169.78 169. 52 169.25 168.99 168.73 Land Converted to Grassland 14.24 14. 64 14.28 13.98 13.91 13.83 13. 74 13.66 13.58 13.50 Non-Agricultural Uses a 8.08 8.08 8.08 8.08 8.08 8.08 8.08 8.08 8.08 8.08 Organic Soils Cropland Remaining Cropland 0.72 0.72 0.72 0.72 0.72 0.72 0. 72 0.72 0.72 0.72 Land Converted to Cropland 0.03 0.03 0.03 0.03 0.03 0.03 0. 03 0.03 0.03 0.03 Grassland Remaining Grassland 0.48 0. 48 0.48 0.48 0.48 0.48 0. 48 0.48 0.48 0.48 Land Converted to Grassland 0.07 0. 07 0.07 0.07 0.07 0.07 0. 07 0.07 0.07 0.07 Non-Agricultural Uses a 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 Total 369.24 369.27 369.32 369.25 368. 95 368.62 368.27 367.93 367.58 367.23

a The non-agricultural uses were converted to or from cropland or gra ssland between 1990 and 2003.



### Subdivide Land Base for Tier 2 and 3 Inventory Approaches –The Tier 3 method based on application of the Century model was used to model NRI points on most mineral soils. Parcels of land that were not simulated with Century were allocated to the Tier 2 approach, including (1) land parcels occurring on organic soils; (2) land parcels that included non-agricultural uses such as forest and federal lands in one or more years of the inventory;68 (3) land parcels on mineral

66 Includes non-federal lands only, because federal lands are not cla ssified into land uses as part of the NRI survey (i.e, they are only designated as federal lands). 67 NRI points were classified according to land-use history records starting in 1982 when the NRI survey began, and consequently the classifcations were based on less than 20 years from 1990 to 2001.

### A-289 soils that were very gravelly, cobbly, or shaley (i.e., classi fied as soils that have greater than 35 percent of soil volume comprised of gravel, cobbles, or shale); or (4) land parcels th at were used to produce vegetables, perennial/horticultural crops, tobacco or rice, which was either grown continuously or in rotation with other crops. Century has not been fully tested for non-major crops, horticultural or perennial crops, rice and agricultural use of organic soils. In addition, Century has not been adequately tested for soils with a high gravel, cobble, or shale content, or fully tested for the transitions between agricultural and non-agricultural uses.

Table A-231: Total Land Area Estimated with Tier 2 a 

### and 3 Inventory Approaches (Million Hectares) Land Areas (10 6 ha) Year Tier 2* Tier 3 Total 1990 47.27 323.05 370.32 1991 47.27 322.83 370.10 1992 47.27 322.66 369.93 1993 47.25 322.41 369.66 1994 47.25 322.17 369.43 1995 47.25 322.19 369.45 1996 47.25 322.14 369.40 1997 47.25 322.19 369.44 1998 47.25 321.88 369.13 1999 47.25 321.87 369.12 2000 47.25 321.98 369.24 2001 47.25 322.01 369.27 2002 47.25 322.07 369.32 2003 47.25 321.99 369.25 2004 47.25 321.70 368.95 2005 47.25 321.36 368.62 2006 47.25 321.02 368.27 2007 47.25 320.67 367.93 2008 47.25 320.32 367.58 2009 47.25 319.98 367.23

a Land use data for 1998-2003 are based on the Revised 1997 NRI data product for the Tier 2 method. Consequently, area data est imates in this table are not used for the Tier 2 portion of the Inventory.


### Management System Classification —NRI points on mineral soils were classified into specific crop rotations, continuous pasture/rangeland, and other non-agricultural uses fo r the Tier 2 inventory analysis based on the survey data (Table A-232). NRI points were assigned to IPCC input categories (low, medium, high, and high with organic amendments) according to the classification provided in IPCC (2006). In addition, NRI differentiates between improved and unimproved grassland, where improvements include irrigati on and interseeding of legumes. In order to estimate uncertainties, PDFs for the NRI land-use data were constructed as multivariate normal based on the total area estimates for each land-use/management category and associated covariance matrix. Through this approach, dependencies in land use were taken into account resulting from the likelihood that current use is correlated with past use.
For the Tier 3 inventory estimates, the actual cropping and grassland histories were simulated with the Century model so it was not necessary to classify NRI points into mana gement systems. Uncertainty in the areas associated with each management system was determined from the estimated sampling variance from the NRI survey (Nusser and Goebel 1997). See Step 2b for additional discussion.
Table A-232: Total Land Areas by Land-Use and Managem ent System for the Tier 2 Approach (Million Hectares) Land Areas (10 6 ha) Land-Use/Management System 1990-92 (Tier 2) 1993-2009 (Tier 2) Cropland Systems 31.53 29.25

Irrigated Crops 7.27 6.91 Continuous Row Crops 4.12 3.63 Continuous Small Grains 1.25 1.04 Continuous Row Crops and Small Grains 2.30 1.95 Row Crops in Rotation with Hay and/or Pasture 0.30 0.23 Small Grains in Rotation with Hay and/or Pasture 0.06 0.06 Row Crops and Small Grains in Rotation with Hay and/or Pasture 0.03 0.04

68 Federal land is treated as forest or nominal grassland for purposes of these cal culations, although the specific use is not id entified in the NRI survey (USDA-NRCS 2000). Future inventories will include C estimation for th e disaggegrated land use and land use chan ge categories on federal lands.

### A-290 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 Vegetable Crops 2.90 3.16 Low Residue Annual Crops (e.g., Tobacco or Cotton) 0.87 1.03 Small Grains with Fallow 2.01 1.31 Row Crops and Small Grains with Fallow 1.72 1.80 Row Crops with Fallow 0.52 0.34 Miscellaneous Crop Rotations 0.54 0.43 Continuous Rice 0.34 0.31 Rice in Rotation with other crops 1.78 1.91 Continuous Perennial or Horticultural Crops 2.57 2.50 Continuous Hay 0.59 0.50 Continuous Hay with Legumes or Irrigation 1.31 1.12 CRP 1.03 0.96 Aquaculture 0.01 0.01 Grassland Systems 12.02 8.68

Rangeland 5.98 5.16 Continuous Pasture 3.76 2.49 Continuous Pasture with Legumes or Irri gation (i.e., improved) 2.25 1.03 CRP 0.02 0.00 Non-Agricultural Systems 2.46 8.08

Forest 1.53 3.95 Federal 0.01 0.05 Water 0.11 0.25 Settlements 0.04 2.46 Miscellaneous 0.77 1.36 Total 46.01 46.01



### Organic soils are also categorized into land-use syst ems based on drainage (IPCC 2006). Undrained soils are treated as having no loss of organic C. Drained soils are su bdivided into those used for cultivated cropland, which are assumed to have high drainage and greater losses of C, an d those used for managed pasture, which are assumed to have less drainage and smaller losses of C. Overall, the area of organic soils drained for cropland and grassland has remained relatively stable since 1992 (see Table A-233).

Table A-233: Total Land Areas for Dr ained Organic Soils By Land Management Category and Climate Region (Million Hectares) Land Areas (10 6 ha)

Cold Temperate Warm Temperate Tropical
IPCC Land-Use Category for Organic Soils 1992 1997 1992 1997 1992 1997 Undrained 0.07 0.06 0.0020 0.0017 0.12 0.09 Managed Pasture (Low Drainage) 0. 42 0.42 0.0136 0.0119 0.07 0.08 Cultivated Cropland (High Drainage) 0.33 0.34 0.0971 0.0974 0.19 0.20 Other Land Uses a 0.02 0.01 0.0002 0.0017 0.00 0.02 Total 0.84 0.84 0.11 0.11 0.39 0.39

a Urban, water, and miscellaneous non-cropla nd, which are part of the agricultural land base because these areas were converted f rom or into agricultural land uses during the 1990s.



### Step 1b: Obtain Additional Management Activity Data for the Tier 3 Century Model
Tillage Practices —Tillage practices were estimated for each cropping system based on data compiled by the Conservation Technology Information Center (CTIC 1998). CT IC compiles data on cropland area under five tillage classes by major crop species and year for each county. Beca use the surveys involve county-level aggregate area, they do not fully characterize tillage practices as they are applied within a management sequence (e.g., crop rotation). This is particularly true for area estimates of cropland under no-till, which include a relatively high proportion of “intermittent” no-till, where no-till in one year may be followed by tillage in a subsequent year. For example, a common practice in maize-soybean rotations is to use tillage in the maize crop while no-till is used for soybean, such that no-till practices are not continuous in time. Estimates of the area under continuous no-till were provided by experts at CTIC to account for intermittent tillage activity and its impact on soil C (Towery 2001).
Tillage practices were grouped into 3 categories: full, reduced, and no-tillage. Full tillage was defined as multiple tillage operations every year, including significant soil inversion (e.g., plowing, deep disking) and low surface residue coverage. This definition corresponds to the intensive tillag e and “reduced” tillage systems as defined by CTIC (1998). No-till was defined as not disturbing the soil except through the us e of fertilizer and seed drills and where no-till is applie d to all crops in the rotation. Reduced tillage made up the remainder of the cultivated area, including mulch tillage and ridge

### A-291 tillage as defined by CTIC and intermittent no-till. The specific tillage implements and applications used for different crops, rotations, and regions to represent the three tillage classes were derived from the 1995 Cropping Practices Survey by the Economic Research Service (ERS 1997).
Tillage data were further processed to construct proba bility distribution functions (PDFs) using CTIC tillage data. Transitions between tillage systems were based on observed county-level changes in the frequency distribution of the area under full, reduced, and no-till from the 1980s through 2004. Generally, the fraction of full tillage decreased during this time span, with concomitant increases in reduced till and no-till management. Transitions that were modeled and applied to NRI points occurring within a county were full tillage to reduced and no-till, and reduced tillage to no-till. The remaining amount of cropland was assumed to have no change in tillage (e.g., full tillage remained in full tillage). Transition matrices were constructed from CTIC data to represent tillage changes for three time periods, 1980-1989, 1990- 1999, 2000-2009. Areas in each of the three tillage classes— full till (FT), reduced till (RT), no-till (NT)—in 1989 (the first year the CTIC data were available) were used for the first time period, data from 1997 were used for the second time period, and data from 2004 were used for the last time period. Percentage areas of cropland in each county were calculated for each possible transition (e.g., FT → FT, FT → RT, FT → NT, RT → RT, RT → NT) to obtain a probability for each tillage transition at an NRI point. Since continuous NT c onstituted < 1 percent of total cropland prior to 1990, there were no transitions for NT → FT or NT → NT. Uniform probability distributions we re established for each tillage scenario in the county. For example, a particular crop rotation had 80 percent chance of remaining in full tillage over the two decades, a 15 percent chance of a transition from full to reduced tillage and a 5 percent chance of a transition from full to no-till. The uniform distribution was subdivided into three segments with random draws in the Monte Carlo simulation (discussed in Step 2b) leading to full tillage over the entire time period if the value was greater than or equal to 0 and less than 80, a transition from full to reduced till if the random dr aw was equal to or greater than 80 and less than 95, or a transition from full to no-till if the draw was greater than or equal to 95. See step 2b for additional discussion of the uncertainty analysis.
Mineral Fertilizer Application —Data on nitrogen fertilizer rates were obtained primarily from USDA’s Economic Research Service’s 1995 Cropping Practices Survey (ERS 1997). In this survey, data on inorganic nitrogen fertilization rates were collected for major crops (corn, cotton, soybeans, potatoes, winter wheat, durum wheat, and other spring wheat) in the key crop producing states. Note that all wheat data were combined into one category and assumed to represent small grains in general. Estimates for sorghum fe rtilizer rates were derived from corn rates using a ratio of national average corn fertilizer rates to national average sorghum fertilizer rates derived from additional publications (NASS 2004, 1999, 1992; ERS 1988; Grant and Krenz 1985; USDA 1954, 1957, 1966).
The ERS survey parameter “TOT N” (total amount of N applied per acre), with a small number of records deleted as outliers, was used in determining the fraction of crop acres receiving fertilizer and the average fertilizer rates f or a region. Mean fertilizer rates and standard deviations for irrigated and rainfed crops were produced for each state at the finest resolution available. State-level data were produced for surveyed states if a minimum of 15 data points existed for each of the two categories (irrigated and rainfed). If a state was not surveyed for a particular crop or if fewer than 15 data points existed for one of the categories, then data at the Farm Production Region level were substituted. If Farm Production Region data were not available, then U.S.-level estimates (all major states surveyed) were used in the simulation for that particular crop in the state lacking sufficient data. Note that standard deviations for fertilizer rates o n log scale were used to construct PDFs on a log-normal scale, in order to address uncertainties in application rates (see Step 2b for discussion of uncertainty methods).
Manure Application —County-level manure N addition estimates were obtained from the Natural Resources Conservation Service (Edmonds et al. 2003). Working with the farm-level crop and animal data from the 1997 Census of Agriculture, NRCS has coupled estimates of manure nitrogen produced with estimates of manure nitrogen recoverability by animal waste management system to produce county-level estimates of manure nitrogen applied to cropland and pasture. Edmonds et al. (2003) defined a hierarchy of land us e systems to which manure is applied, that included 24 crops, cropland used as pasture, and permanent pasture. They estimated the area amended with manure and manure nitrogen application rates in 1997 for both manure-producing farms and manure-receiving farms within a county, for two scenarios—before implementation of Comprehensive Nutrient Management Plans (baseline) and after implementation. The application rates for the baseline scenario were used in the inventory under the assumption that Comprehensive Nutrient Management Plans have not been fully implemented.
In order to derive estimates of manure application rates over time, the availability of managed manure N for application to soils (which are available annually) was used to adjust the amount of area amended with manure on a county scale (Note: Edmonds et al. (2003) only provide information on application rates for 1997). Specifically, the estimated available managed manure N in another year was divided by the managed manure N available in 1997. The amendment area in a county for 1997 was then multiplied by the ratio to reflect the probability of manure amendments based on the variation in available manure N across time. If more managed manure N was available in a given year for a county

### A-292 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 relative to the amount available in 1997 (ratio > 1), it was assumed that there was a higher probability of a manure amendment. In contrast, if less managed manure N was availa ble (ratio < 1), the probability of an amendment declined in comparison to 1997. A detailed description of the derivation of the managed manure N availability data is provided in the Manure Management section (Section 6.2) and Annex (Annex 3.10). Managed manure N availability in the 1980s was based on USDA estimates (Kellogg et al. 2000) after adjusting for relative differences in manure N production between the USDA dataset and estimates derived from the method described in Annex 3.10. Unmanaged manure classified as pasture/range/paddock manure was assumed to have negligible impacts on soil C stocks because of the tradeoff between reduced litterfall C versus C ingested by livestock and deposited on soils in manure.
For Century simulations, the amended areas were aver aged for three time periods (1980-1989, 1990-1999, and 2000-2009) similar to the tillage transitions. Rates for manure-producing farms and manure-receiving farms have been area-weighted and combined to produce a manure nitrogen application rate for each crop in a county. Several of the crops in Edmonds et al. (2003) have been area-weighted and combined into broader crop categories. For example, all small grain crops have been combined into one category. In order to address uncertainty, uniform probability distributions were constructed based on the proportion of land receiving manure versus the amount not receiving manure for each crop type and pasture. For example, if the 20 percent of land producing corn in a county was amended with manure, randomly drawing a value equal to or greater than 0 and less than 20 would lead to simulation with a manure amendment, while drawing a value greater than or equal to 20 and less than 100 would lead to no amendment in the simulation (see Step 2b for further discussion of uncertainty methods).
To estimate the C inputs associated with the manure N application rates (from Edmonds et al. 2003), C:N ratios for various manure types (based on animal species and manure management system) were estimated from data in the Agricultural Waste Management Field Handbook (USDA 1996) and the On-Farm Composting Handbook (NRAES 1992). Weighted county-average C:N ratios for total manure applied were then calculated based on the C:N ratio and the manure N production rate for each manure type reported in the county. Manure C addition rates were then calculated by multiplying the county-average manure C:N ratio by the manure N application rates.
To account for the common practice of reducing inorganic nitrogen fertilizer inputs when manure is added to a cropland soil, a set of crop-specific reduc tion factors were derived from mineral fe rtilization data for land amended with manure versus land not amended with manure in the ERS 1995 Cropping Practices Survey (ERS 1997). In the simulations, mineral N fertilization rates were reduced for crops receiving manure nitrogen based on a fraction of the amount of manure nitrogen applied, depending on the crop and whether it was irrigated or a rainfed system. The reduction factors were selected from PDFs with normal densities in order to address uncertainties in this dependence between manure amendments and mineral fertilizer application.
Irrigation —NRI differentiates between irrigated and non-irrigated land but does not provide more detailed information on the type and intensity of irrigation. Hence, irrigation was modeled by assuming that applied water was sufficient to meet full crop demand (i.e., irrigation plus precipitation equaled potential evapotranspiration during the growing season).

Step 1c—Obtain Additional Management Activity Data for Tier 2 IPCC Method
Tillage Practices —PDFs were constructed for the CTIC tillage data , as bivariate normal on a log-ratio scale toreflect negative dependence among tillage classes. This structure ensured that simulated tillage percentages were nonnegative and summed to 100 percent. CTIC data do not diffe rentiate between continuous and intermittent use of notillage, which is important for estimating SOC storage. Thus , regionally based estimates for continuous no-tillage (defined as 5 or more years of continuous use) were modified based on consultation with CTIC experts, as discussed in Step 1a (downward adjustment of total no-tillage acres reported, Towery 2001).

Manure Amendments —Manure management is also a key practice in agricultural lands, with organic amendments leading to significant increases in SOC storage. USDA provides information on the amount of land amended with manure for 1997 based on manure production data and field-scale surveys detailing application rates that had been collected in the Census of Agriculture (Edmonds et al. 2003). Similar to the Century model discussion in Step1b, the amount of land receiving manure was based on the estimates provided by Edmonds et al. (2003), as a proportion of crop and grassland amended with manure within individual climate regions. The resulting proportions were used to re-classify a portion of crop and grassland into a new management category. Specifically, a portion of medium input cropping systems was re-classified as high input, and a portion of the high input systems was re-classified as high input with amendment. In grassland systems, the estimated proportions for land amended with manure were used to re-classify a portion of nominally-managed grassland as improved, and a por tion of improved grassland as improved with high input. These classification approaches are consistent with the IPCC inventory methodology (IPCC 2003, 2006). Uncertainties in

### A-293 the amount of land amended with manure were based on the sample variance at the climate region scale, assuming normal density PDFs (i.e., variance of the climate region estima tes, which were derived from county-scale proportions).

Wetland Reserve —Wetlands enrolled in the Conservation Reserve Program have been restored in the Northern Prairie Pothole Region through the Partners for Wildlife Program funded by the U.S. Fish and Wildlife Service. The area of restored wetlands was estimated from contract agreemen ts (Euliss and Gleason 2002). While the contracts provide reasonable estimates of the amount of land restored in th e region, they do not provide the information necessary to estimate uncertainty. Consequently, a ±50 percent range was used to construct the PDFs for the uncertainty analysis.

Step 1d—Obtain Management Activity Data to Compute Additional Changes in Soil Organic C Stocks in Mineral Soils Due to Sewage Sludge Applications and CRP Enrollment after 2003
Two additional influences on soil organic C stocks in mineral soils were estimated using a Tier 2 method, including: sewage sludge additions to agricultural soils and changes in enrollment for the Conservation Reserve Program after 2003.
Total sewage sludge generation data for 1988, 1996, and 1998, in dry mass units, were obtained from an EPA report (EPA 1999) and estimates for 2004 were obtained from an independent national biosolids survey (NEBRA 2007). These values were linearly interpolated to estimate values for the intervening years. Sewage sludge generation data are not available for 2005 onwards (Bastian 2007), so the 1990 through 2004 data were linearly extrapolated for the most recent years. The total sludge generation estimates were then converted to units of N by applying an average N content of 3.9 percent (McFarland 2001), and disaggregated into use and di sposal practices using historical data in EPA (1993) and NEBRA (2007). The use and disposal practices were agricultural land application, other land application, surface disposal, incineration, landfilling, ocean dumping (ended in 1992), and other disposal. Sewage sludge N was assumed to be applied at the assimilative capacity provided in Kellogg et al. (2000), which is the amount of nutrients taken up by a crop and removed at harvest, representing the recommended application rate for manure amendments. This capacity varies from year to year, because it is based on specific crop yields dur ing the respective year (Kellogg et al. 2000). Total sewage sludge N available for application was divided by the assimilative capacity to estimate the total land area over which sewage sludge had been applied. The resulting estimates were used for the estimation of soil C stock change.
The change in enrollment for the Conservation Reserve Program after 2003 was based on the amount of land under active contracts from 2004 through 2009 relative to 2003 (USDA-FSA 2010).

Step 1e: Obtain Climate and Soils Data
Tier 3 Century Model —Monthly weather data (temperature and precipitation) from the PRISM database (Parameter-elevation Regressions on Independent Slopes Model) (Daly et al. 1994) were used as an input to the Century model simulations for the period 1895 through 2003. PRISM is based on observed weather data from the National Weather Service network database and statistical models for interpolation and orographic corrections. The primary database consists of approximately 4×4 km grid cells. Thes e data were averaged (weighted by area) for each county in the United States, so that counties are the finest spatial scale represented in the Century simulations.
Soil texture and natural drainage capacity (i.e., hydric vs. non-hydric soil characterization) were the main soil variables used as input to the Century model. Other soil ch aracteristics needed in the simulation, such as field capacity and wilting-point water contents, were estimated from soil text ure data using pedo-transfer functions available in the model. Soil input data are derived from the NRI database, which contain descriptions for the soil type at each NRI point (used to specify land-use and management time series-see below). The data are based on field measurements collected as part of soil survey and mapping. Soils are classified according to “soil-series,” which is the most detailed taxonomic level used for soil mapping in the United States. Surface soil texture and hydric condition were obtained from the soil attribute table in the NRI database. Texture is one of the main controls on soil C turnover and stabilization in the Century model, which uses particle size fractions of sand (50-2,000 μ m), silt (2-50 μ m), and clay (< 2 μ m) as inputs. NRI points were assigned to one of twelve texture classes for the simulations. Hydric condition specifies whether soils are poorly-drained, and hence prone to water-logging, or moderately to well-drained (non-hydric), in their native (pre-cultivation) condition. 69 

### Poorly drained soils can be subject to anaerobic (lack of o xygen) conditions if water inputs (precipitation and irrigation) exceed water losses from drainage and evapotranspiration. Depending on moisture conditions, hydric soils can range from being fully aerobic to completely anaerobic, varying over the year . Decomposition rates are modified according to a linear

69 Artificial drainage (e.g., ditch- or tile-drain age) is simulated as a management variable.

### A-294 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 function that varies from 0.3 under completely anaerobic conditions to 1.0 under fully aerobic conditions (default parameters in Century). 70



### IPCC Tier 2 Method —The IPCC inventory methodology for agricultural soils divides climate into eight distinct zones based upon average annual temperature, average annual precipitation, and the length of the dry season (IPCC 2006) (Table A-234). Six of these climate zones occur in the cont erminous United States and Hawaii (Eve et al. 2001).

Table A-234: Characteristics of the IPCC Climate Zones that Occur in the United States

Climate Zone Annual Average Temperature (  ̊ C) Average Annual Precipitation (mm) Length of Dry Season (months) Cold Temperate, Dry < 10 < Potential Evapotranspiration NA Cold Temperate, Moist < 10 ≥ Potential Evapotranspiration NA Warm Temperate, Dry 10 – 20 < 600 NA Warm Temperate, Moist 10 – 20 ≥ Potential Evapotranspiration NA Sub-Tropical, Dry* > 20 < 1,000 Usually long Sub-Tropical, Moist (w/short dry season) a > 20 1,000 – 2,000 < 5

a The climate characteristics listed in the table for these zone s are those that correspond to the tropical dry and tropical moi st zones of the IPCC. They have been renamed “sub-tropical” here.


### Mean climate (1961-1990) variables from the PRISM data set (Daly et al. 1994) were used to classify climate zones. Mean annual precipitation and annual temperature data were averaged (weighted by area) for each of the 4×4 km grid cells occurring within a MLRA region. These averages were used to assign a climate zone to each MLRA according to the IPCC climate classification (Figure A-15). MLRAs represent geographic units with relatively similar soils, climate, water resources, and land uses; and there are approximately 180 MLRAs in the United States (NRCS 1981).

Figure A-15: Major Land Resource Areas by IPCC Climate Zone

Soils were classified into one of seven classes based upon texture, morphology, and ability to store organic matter (IPCC 2006). Six of the categories are mineral types and one is organic (i.e., Histosol). Reference C stocks, representing estimates from conventionally managed cropland, were computed for each of the mineral soil types across the various climate zones, based on pedon (i.e., soil) data from the National Soil Survey Characterization Database (NRCS 1997) (Table A-235). These stocks are used in conjunction with management factors to compute the change in SOC stocks that result from management and land-use activity. PDFs, which represent the variability in the stock estimates, were constructed as normal densities based on the mean and variance from the pedon data. Pedon locations were clumped in various parts of the country, which reduces the statistical independence of individual pedon estimates. To account for this lack of independence, samples from each climate by soil zone were tested for spatial autocorrelation using the Moran’s I test, and variance terms were inflated by 10 percent for all zones with significant p-values.
Table A-235: U.S. Soil Groupings Based on the IPCC Cate gories and Dominant Taxonomic Soil, and Reference Carbon Stocks (Metric Tons C/ha)

IPCC Inventory Soil Categories USDA Taxonomic Soil Orders Reference Carbon Stock in Climate Regions Cold Temperate, Dry Cold Temperate, Moist Warm Temperate, Dry Warm Temperate, Moist SubTropical, Dry SubTropical, Moist High Clay Activity

Mineral Soils Vertisols, Mollisols, Inceptisols, Aridisols, and high base status Alfisols 42 (n = 133) 65 (n = 526) 37 (n = 203) 51 (n = 424) 42 (n = 26) 57 (n = 12)

Low Clay Activity

Mineral Soils Ultisols, Oxisols, acidic Alfisols, and many Entisols 45 (n = 37) 52 (n = 113) 25 (n = 86) 40 (n = 300) 39 (n = 13) 47 (n = 7)

Sandy Soils Any soils with greater than 70 percent sand and less than 8 percent clay (often Entisols) 24 (n = 5) 40 (n = 43) 16 (n = 19) 30 (n = 102) 33 (n = 186) 50 (n = 18)

Volcanic Soils Andisols 124 (n = 12) 114 (n = 2) 124 (n = 12) 124 (n = 12) 124 (n = 12) 128 (n = 9) Spodic Soils Spodosols 86 (n=20) 74 (n = 13) 86 (n=20) 107 (n = 7) 86 (n=20) 86 (n=20) Aquic Soils Soils with Aquic suborder 86 (n = 4) 89 (n = 161) 48 (n = 26) 51 (n = 300) 63 (n = 503) 48 (n = 12)

70 Hydric soils are primarily subject to anaerobic conditions outsi de the plant growing season (i.e., in the absence of active pl ant water uptake). Soils that are water-logged during much of the year are typically classified as organi c soils (e.g., peat), which are not simulated with the Century model.

### A-295 Organic Soils a Histosols NA NA NA NA NA NA

a C stocks are not needed for organic soils. Notes: C stocks are for the top 30 cm of the soil profile, and were estimated from pedon data available in the National Soil Su rvey Characterization database (NRCS 1997); samp le size provided in parentheses (i.e., ‘n’ values refer to sample size).



### Step 2: Estimate Organic C Stock Changes for Agricultural Lands on Mineral Soils Simulated with the Tier 3 Century Model
This methodology description is divided into two sub-steps. First, the model was used to establish the initial conditions and C stocks for 1979, which wa s the last year before the NRI survey was initiated. In the second sub-step, Century was used to estimate changes in soil organic C stocks based on the land-use and management histories recorded in the NRI (USDA-NRCS 2000), including the reporting period starting in 1990.

Step 2a: Simulate Initial Conditions (Pre-NRI Conditions)
Century model initialization involves two steps, with the goal of estimating the most accurate stock for the preNRI history, and the distribution of organic C among the pools represented in the model (e.g., Structural, Metabolic, Active, Slow, Passive). Each pool has a different turnover ra te (representing the heterogeneous nature of soil organic matter), and the amount of C in each pool at any point in time influences the forward trajectory of the total soil organic C storage. There is currently no national set of soil C measurem ents that can be used for establishing initial conditions in the model. Sensitivity analysis of the Century model showed that the rate of change of soil organic matter is relatively insensitive to the amount of total soil organic C but is highly sensitive to the relative distribution of C among different pools (Parton et al. 1987). By simulating the historical land us e prior to the inventory period, initial pool distributions ar e estimated in an unbiased way.
The first step involves running the model to a steady-state condition (e.g., equilibrium) under native vegetation, with long-term mean climate based on 30-yr averages of th e PRISM data (1960-1990), and the soil physical attributes for the NRI points. Native vegetation is represented at the MLRA level for pre-settlement time periods in the United States. The model was run for 7,000 years to represent a pre-se ttlement era and achieve a steady-state condition.
The second step is to run the model for the period of time from settlement to the beginning of the NRI survey, representing the influence of historic land-use change and mana gement, particularly the conversion of native vegetation to agricultural uses. This encompasses a varying time peri od from land conversion (depending on historical settlement patterns) to 1979. The information on historical cropping practices used for Century simulations was gathered from a variety of sources, ranging from the historical accounts of farmi ng practices reported in the literature (e.g., Miner 1998) to national level databases (e.g., NASS 2004). A detailed description of the data sources and assumptions used in constructing the base history scenarios of agricultural practices can be found in Williams and Paustian (2005).

Step 2b—Estimate Soil Organic C Stock Changes and Uncertainties
After estimating model initialization, the model is used to simulate the NRI land use and management histories from 1979 through 2003. 71 

### The simulation system incorporates a dedicated MySQL database server and a 24-node parallel processing computer cluster. Input/output operation s are managed by a set of run executive programs written in PERL. The assessment framework for this analysis is illustrated in Figure A-16.

Figure A-16: Uncertai nty in Data Inputs

Evaluating uncertainty was an integral part of the analysis, and included three components: (1) uncertainty in the main activity data inputs affecting soil C balance (input uncertainty); (2) uncertainty in the model formulation and parameterization (structural uncertainty); and (3) uncertainty in the land-use and management system areas (scaling uncertainty) (Ogle et al. 2010). For component 1, input uncertainty was evaluated for fertilization management, manure applications, and tillage, which are the primary management activity data that were supplemental to the NRI observations and have significant influence on soil C dynamics. As described in Step 1b, PDFs were derived from surveys at the county scale in most cases. To represent uncertainty in thes e inputs, a Monte-Carlo Analysis was used with 100 iterations for each NRI cluster-point in which random draws were made from PDFs for fertilizer, manure application, and tillage.

71 The estimated soil C stock change in 2003 is currently assumed to represent the changes between 2004 and 2009. New estimates w ill be available in the future to extend the time series of land use and management data.

### A-296 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 As described above, an adjustment factor was also selected from PDFs with normal densities to represent the dependence between manure amendments and N fertilizer application rates. The total number of Century simulations was over 12 million for the Monte Carlo Analysis with 100 iterations.
The second component dealt with uncertainty inherent in model formulation and parameterization. An empirically-based procedure was employed to develop a structural uncertainty estimator from the relationship between modeled results and field measurements from agricultural experiments (Ogle et al. 2007). The Century model was initialized for 45 long-term field experiments with over 800 treatments in which soil C was measured under a variety of management conditions (e.g., variation in crop rotation, tilla ge, fertilization rates, manure amendments). These studies were obtained from an extensive search of published studies . All studies located in North America that met minimum criteria of having sufficient site-level information and experimental designs were used, including C stock estimates, texture data, experimental designs with control plots, and land-use and management records for the experimental time period and pre-experiment condition. The inputs to the model were essentially known in the simulations for the long-term experiments, and, therefore, the analysis was designed to evaluate uncertainties associated with the model structure (i.e., model algorithms and parameterization).
The relationship between modeled soil C stocks and field measurements was statistically analyzed using linearmixed effect modeling techniques. Additional fixed effects were included in the mixed effect model if they explained significant variation in the relationship between modeled and measured stocks (i.e., if they met an alpha level of 0.05 for significance). Several variables were tested including: land-use class; type of tillage; cropping system; geographic location; climate; soil texture; time since the management change; original land cover (i.e., forest or grassland); grain harvest as predicted by the model compared to the experimental values; and variation in fertilizer and residue management. The final model included variables for organic matt er amendments, fertilizer rates, inclusion of hay/pasture in cropping rotations, use of no-till, and inclusion of bare fallo w in the rotation, which were significant at an alpha level o f 0.05. These fixed effects were used to make an adjustment to modeled values due to biases that were creating significant mismatches between the modeled and measured stock values. Random effects captured the statistical dependence (i.e., the data are not fully independent) in time series and data collected from the same long-term experimental site. Accounting for this statistical dependency is needed to estimate a ppropriate standard deviations for parameter coefficients.
A Monte Carlo approach was used to apply the uncertainty estimator (Ogle et al. 2010). Parameter values for the statistical equation (i.e., fixed effects) were selected from their joint probability distribution, as well as random errorassociated with fine-scale estimates at NRI points, and the residual or unexplained error associated with the linear mixedeffect model. The stock estimate and associated management information was then used as input into the equation, and adjusted stock values were computed for each C stock estimate produced in the evaluation of input uncertainty for Cropland Remaining Cropland (Component 1 of the uncertainty analysis). Note that the uncertainty estimator needs further development for application to Grassland Remaining Grassland and the land-use change categories. This development is a planned improvement for the soil C inventory. The variance of the adjusted C stock estimates were computed from the 100 simulated values from the Monte Carlo analysis.
The third element was the uncertainty associated with scaling the Century results for each NRI point to the entire land base, using the expansion factors provided with the NRI database. The expansion factors represent the number of hectares associated with the land-use and management history for a particular point. This uncertainty was determined by computing the variances of the expanded estimates, account ing for the two-stage sampling design of the NRI.
For the land base that was simulated with the Century model, soil organic C stocks ranged from losses of 4.25 Tg CO 2 

### Eq. to gains of 68.05 Tg CO 2 

### Eq. annually, depending on the land-use/land-use change category and inventory time period. Estimates and uncertainties are provided in Table A-236.
Table A-236: Annual Change in Soil Org anic Carbon Stocks (95% Confidence Inte rval) for the Land Base Simulated with the Tier 3 Century Model-Based Approach (Tg CO 2 

### Eq.)

Year Cropland Remaining Cropland Land Converted to Cropland Grassland Remaining Grassland Land Converted to Grassland

Estimate 95% CI Estimate 95% CI Estimate 95% CI Estimate 95% CI 1990 (55.19) (111.72) to 1.33 (4.43) (5.06) to (3.80) (55.10) (57.18) to (53.01) (15.75) (18.43) to (13.07)

1991 (57.50) (90.49) to (24.51) (4.30) (4.93) to (3.68) (28.04) (29.87) to (26.21) (15.06) (17.58) to (12.54) 1992 (68.05) (99.81) to (36.28) (4.75) (5.38) to (4.13) (9.84) (10.82) to (8.85) (13.83) (16.23) to (11.44) 1993 (64.25) (95.93) to (32.56) (8.66) (9.34) to (7.98) (1.48) (3.29) to 0.33 (13.52) (16.09) to (10.94)

1994 (62.38) (94.48) to (30.29) (12.61) (13.42) to (11.80) (66.74) (68.28) to (65.20) (17.87) (20.62) to (15.12) 1995 (47.58) (83.91) to (11.25) (3.84) (4.51) to (3.17) (29.45) (30.78) to (28.13) (18.21) (20.80) to (15.61)

### A-297 1996 (54.99) (83.65) to (26.32) (4.55) (5.24) to (3.8 5) 4.25 3.00 to 5.50 (15.19) (17.78) to (12.60) 1997 (54.08) (81.53) to (26.63) (4.21) (4.94) to (3.48) (20.44) (21.73) to (19.14) (19.90) (22.64) to (17.16)

1998 (44.34) (77.06) to (11.61) (10.77) (11.60) to (9. 94) 1.46 2.49 to 0.43 (17.33) (20.33) to (14.33)

1999 (29.72) (58.21) to (1.23) (3.30) (4.02) to (2.59) (18.92) (19.78) to (18.06) (23.95) (26.64) to (21.26)

2000 (54.83) (85.86) to (23.80) (4.41) (5.15) to (3.67) (55.15) (56.04) to (54.26) (23.11) (26.26) to (19.97)

2001 (37.57) (68.05) to (7.08) (2.53) (3.24) to (1.83) (27.34) (28.17) to (26.51) (24.03) (27.01) to (21.05)

2002 (36.14) (67.43) to (4.85) (1.97) (2.68) to (1.25) (46.81) (47.62) to (46.00) (22.72) (25.70) to (19.73)

2003 (42.32) (69.67) to (14.98) (0.84) (1.54) to (0.13) (11.65) (12.50) to (10.79) (20.69) (23.50) to (17.89)

2004 (42.32) (69.67) to (14.98) (0.84) (1.54) to (0.13) (11.49) (12.34) to (10.64) (20.51) (23.31) to (17.70)

2005 (42.32) (69.67) to (14.98) (0.84) (1.54) to (0.13) (11.32) (12.17) to (10.47) (20.31) (23.11) to (17.50)

2006 (42.32) (69.67) to (14.98) (0.84) (1.54) to (0.13) (11.14) (11.99) to (10.29) (20.10) (22.90) to (17.30)

2007 (42.32) (69.67) to (14.98) (0.84) (1.54) to (0.13) (10.97) (11.82) to (10.12) (19.90) (22.70) to (17.09) 2008 (42.32) (69.67) to (14.98) (0.84) (1.54) to (0.13) (10.79) (11.65) to (9.94) (19.69) (22.49) to (16.89) 2009 (42.32) (69.67) to (14.98) (0.84) (1.54) to (0.13) (10.62) (11.47) to (9.77) (19.48) (22.29) to (16.68) Note: Does not include the change in stor age resulting from the annual application of sewage sludge, or the additional Conserva tion Reserve Program enrollment.



### Step 3: Estimate C Stock Changes in Agricultural Lands on Mineral Soils Approximated with the Tier 2 Approach, in Addition to CO 2 

### Emissions from Agricultural Lands on Drained Organic Soils
Mineral and organic soil calculations were made for each climate by soil zone across the United States. Mineral stock values were derived for non-major crop rotations and land converted from non-agricultural uses to cropland in 1982, 1992, and 1997 based on the land-use and management activity data in conjunction with appropriate reference C stocks, land-use change, tillage, input, and wetland restoration factors. C losses from organic soils were computed based on 1992 and 1997 land use and management in conjunction with the appropriate C loss rate. Each input to the inventory calculations for the Tier 2 approach had some level of uncertainty that was quantified in PDFs, including the land-use and management activity data, reference C stocks, and management factors. A Monte Carlo Analysis was used to quantify uncertainty in SOC change for the inventory period based on uncertainty in the inputs. Input values were randomly selected from PDFs in an iterative process to estimate SOC change for 50,000 times and produce a 95 percent confidence interval for the inventory results.

Step 3a: Derive Mineral Soil Stock Change and Organic Soil Emission Factors
Stock change factors representative of U.S. conditions were estimated from published studies (Ogle et al. 2003, Ogle et al. 2006). The numerical factors quantify the impact of changing land use and management on SOC storage in mineral soils, including tillage practices, cropping rotation or intensification, and land conversions between cultivated and native conditions (including set-asides in the Conservation Re serve Program), as well as the net loss of SOC from organic soils attributed to agricultural production on drained soils. St udies from the United States and Canada were used in this analysis under the assumption that they would best represent management impacts for the Inventory.
For mineral soils, studies had to report SOC stocks (or information to compute stocks), depth of sampling, and the number of years since a management change to be include d in the analysis. The data were analyzed using linear mixed-effect modeling, accounting for both fixed and random effect s. Fixed effects included de pth, number of years since a management change, climate, and the type of management change (e.g., reduced tillage vs. no-till). For depth increments, the data were not aggregated for the C stock measurements; each depth increment (e.g., 0-5 cm, 5-10 cm, and 10-30 cm) was included as a separate point in the dataset. Similarly, time series data were not aggregated in these datasets. Consequently, random effects were needed to account for the dependence in time series data and the dependence among data points representing different depth increments from the same study. Factors were estimated for the effect of management practices at 20 years for the top 30 cm of the soil (Table A-237). Variance was calculated for each of the U.S. factor values, and used to construct PDFs with a norma l density. In the IPCC method, specific factor values are given for improved grassland, high input cropland with organic amendments, and for wetland rice, each of which

### A-298 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 influences the C balance of soils. Specifically, higher stocks are associated with increased productivity and C inputs (relative to native grassland) on improved grassland with both medium and high input. 72 

### Organic amendments in annual cropping systems also increase SOC stocks due to greater C inputs, while high SOC stocks in rice cultivation are associated with reduced decomposition due to periodic flooding. There were insufficient field studies to derive factor values for these systems from the published literature, an d, thus, estimates from IPCC (2003) were used under the assumption that they would best approximate the impacts, given th e lack of sufficient data to derive U.S.-specific factors. A measure of uncertainty was provided for these factor s in IPCC (2003), which was used to construct PDFs.
Table A-237: Soil Organic Carbon Stock Change Factors for the United States and the IPCC De fault Values Associated with Management Impacts on Mineral Soils
U.S. Factor IPCC default Warm Moist Climate Warm Dry Climate Cool Moist Climate Cool Dry Climate Land-Use Change Factors Cultivated a 1 1 1 1 1 General Uncult. a, b (n=251) 1.4 1.42±0.06 1.37± 0.05 1.24±0.06 1.20±0.06 Set-Aside a (n=142) 1.25 1.31±0.06 1.26± 0.04 1.14±0.06 1.10±0.05 Improved Grassland Factors c Medium Input 1.1 1.14±0.06 1.14±0.06 1.14±0.06 1.14±0.06 High Input Na 1.11±0.04 1. 11±0.04 1.11±0.04 1.11±0.04 Wetland Rice Production Factor c 1.1 1.1 1.1 1.1 1.1 Tillage Factors Conv. Till 1 1 1 1 1 Red. Till (n=93) 1.05 1.08±0. 03 1.01±0.03 1.08± 0.03 1.01±0.03 No-till (n=212) 1.1 1.13±0.02 1.05±0.03 1.13±0.02 1.05±0.03 Cropland Input Factors Low (n=85) 0.9 0.94±0.01 0.94±0.01 0.94±0.01 0.94±0.01 Medium 1 1 1 1 1 High (n=22) 1.1 1.07±0.02 1.07±0.02 1.07±0.02 1.07±0.02 High with amendment c 1.2 1.38±0.06 1.34±0.08 1.38±0.06 1.34±0.08 Note: The “n” values refer to sample size.

a Factors in the IPCC documentation (IPCC 2006) were converted to re present changes in SOC storage from a cultivated condition ra ther than a native condition.

b Default factor was higher for aquic soils at 1.7. The U.S. an alysis showed no significant diff erences between aquic and non-aqu ic soils, so a single U.S. factor was estimated for all soil types.

c U.S.-specific factors were not estimated for land improvements, ri ce production, or high input with amendment because of few st udies addressing the impact of legume mixtures, irrigation, or manure applications for crop and grassland in the United States, or th e impact of wetland rice production in the US. Factors provided in IPCC (2003) we re used as the best estimates of these impacts.



### Wetland restoration management also influences SOC storage in mineral soils, because restoration leads to higher water tables and inundation of the soil for at least part of the year. A stock change factor was estimated assessing the difference in SOC storage between restored and unrestored wetlands enrolled in the Conservation Reserve Program (Euliss and Gleason 2002), which represents an initial increase of C in the restored soils over the first 10 years (Table A238). A PDF with a normal density was constructed from these data based on results from a linear regression model. Following the initial increase of C, natural erosion and deposition leads to additional accretion of C in these wetlands. The mass accumulation rate of organic C was estimated using annual sedimentation rates (cm/yr) in combination with percent organic C, and soil bulk density (g/cm 3 

### ) (Euliss and Gleason 2002). Procedures for calculation of mass accumulation rate are described in Dean and Gorham (1998); the resulting rate and variance were used to construct a PDF with a normal density (Table A-238).
Table A-238: Factor Estimate for the Initial and Subsequent Increase in Organic Soil C Following Wetland Restoration of Conservation Reserve Program
Variable Value Factor (Initial Increase—First 10 Years) 1.22±0.18 Mass Accumulation (After Initial 10 Years) 0.79±0.05 Mg C/ha-yr Note: Mass accumulation rate represents additional gains in C fo r mineral soils after the first 10 years (Euliss and Gleason 20 02).


### In addition, C loss rates were estimated for cultivated organic soils based on subsidence studies in the United States and Canada (Table A-239). PDFs were constructed as normal densities based on the mean C loss rates and associated variances.

72 Improved grasslands are identified in the 1997 National Resources Inventory as grasslands that were irrigated or seeded with legumes, in addition to those reclassified as improved with manure amendments.

### A-299 Table A-239: Carbon Loss Rates for Organic Soils Under Agri cultural Management in the United States, and IPCC Default Rates (Metric Ton C/ha-yr)
Cropland Grassland Region IPCC U.S. Revised IPCC U.S. Revised Cold Temperate, Dry & Cold Temper ate, Moist 1 11.2±2.5 0.25 2.8±0.5 a Warm Temperate, Dry & Warm Temperat e, Moist 10 14.0±2.5 2.5 3.5±0.8 a Sub-Tropical, Dry & Sub-Tropical, Moist 20 14.0±3.3 5 3.5±0.8 a

a There were not enough data available to es timate a U.S. value for C losses from gra ssland. Consequently, estimates are 25 perc ent of the values for cropland, which was an assumption used for th e IPCC default organic soil C losses on grassland.



### Step 3b: Estimate Annual Changes in Mineral Soil Organic C Stocks and CO 2 

### Emissions from Organic Soils
In accordance with IPCC methodology, annual changes in mineral soil C were calculated by subtracting the beginning stock from the ending stock and then dividing by 20. 73 

### For this analysis, the base inventory estimate for 1990 through 1992 is the annual average of 1992 stock minus the 1982 stock. The annual average change between 1993 and 2009 is the difference between the 1997 and 1992 C stocks. Using the Monte Carlo approach, SOC stock change for mineral soils was estimated 50,000 times between 1982 and 1992, and between 1992 and 1997. From the final distribution of 50,000 values, a 95 percent confidence interval was generated based on the simulated values at the 2.5 and 97.5 percentiles in the distribution (Ogle et al. 2003).
For organic soils, annual losses of CO 2 

### were estimated for 1992 and 1997 by applying the Monte Carlo approach to 1992 and 1997 land-use data in the United States. The results for 1992 were applied to the years 1990 through 1992, and the results for 1997 were applied to the years 1993 through 2009.
Mineral soils for the land base estimated with the Tier 2 approach accumulated about 1.7 to 3.0 Tg CO 2 

### Eq. annually in Cropland Remaining Cropland , while mineral soils in Land Converted to Cropland lost C at a rate of about 4.1 to 4.2 Tg CO 2 

### Eq. annually. Minerals soils in Grassland Remaining Grassland had small gains of about 0.2 to 0.3 Tg CO 2 

### Eq. annually and sequestered from 4.6 to 5.0 Tg CO 2 

### Eq. annually in Land Converted to Grassland . Organic soils lost about 27.4 to 27.7 Tg CO 2 

### Eq. annually in Cropland Remaining Cropland and 2.4 to 2.6 Tg CO 2 

### Eq. annually in Land Converted to Cropland , as well as an additional 3.7 to 3.9 Tg CO 2 

### Eq. annually in Grassland Remaining Grassland (Table A-240) and 0.5 to 0.9 Tg CO 2 

### Eq. annually in Land Converted to Grassland . Estimates and uncertainties are provided in Table A-240. Table A-240: Annual Change in Soil Organic Carbon Stocks (9 5% Confidence Interval) for the Land Base Estimated with the Tier 2 Analysis using U.S. Factor Values, Re ference Carbon Stocks, and Carbon Loss Rates (Tg CO 2 

### Eq./yr)

Year Cropland Remaining Cropland Land Converted to Cropland Grassland Remaining Grassland* Land Converted to Grassland* Estimate 95% CI Estimate 95% CI Estimate 95% CI Estimate 95% CI Mineral Soils 1990-1992 (1.65) (2.6) to 5.8 4.18 2.5 to 6. 0 (0.33) (0.6) to (0.1) (4.55) (6.5) to (2.7) 1993-2009 (3.01) (6.9) to 0.8 4.14 2.5 to 6. 0 (0.15) (0.4) to 0.04 (4.99) (7.2) to (2.9) Organic Soils 1990-1992 27.43 18.3 to 39.4 2.42 1.4 to 3.8 3.85 1.97 to 6.4 0.47 0.22 to 0.8

1993-2009 27.68 18.5 to 39.5 2.64 1.5 to 4.0 3.69 1.9 to 6.1 0.88 0.4 to 1.5

* Preliminary estimates that will be finalized after public review period following completion of quality control measures.



### Step 4: Compute Additional Changes in Soil Organ ic C Stocks Due to Organic Amendments and CRP Enrollment after 2003
There are two additional land-use and management activities in U.S. agricultural lands that were not estimated in Steps 2 and 3. The first activity involved the application of sewage sludge to agricultural lands. Minimal data exist on where and how much sewage sludge is applied to U.S. agricu ltural soils, but national estimates of mineral soil land area receiving sewage sludge can be approximated based on sewage sludge N production data, and the assumption that amendments are applied at a rate equivalent to the assimilative capacity from Kellogg et al. (2000). It was assumed that sewage sludge for agricultural land application was applied to grassland because of the high heavy metal content and other pollutants found in human waste, which limits its application to crops. The impact of organic amendments on SOC was

73 The difference in C stocks is divided by 20 because the stock change factors represent change over a 20-year time period.

### A-300 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 calculated as 0.38 metric tonnes C/ha-yr. This rate is based on the IPCC default method and country-specific factors (see Table A- 241), by calculating the effect of converting nomina l, medium-input grassland to high input improved grassland (assuming a reference C stock of 50 metric tonnes C/ha, which represents a mid-range value for the dominant cropland soils in the United States, the land use factor for grassla nd (1.4) and the country-specific factor of 1.11 for high input improved grassland, with the change in stocks occurring over a 20 year (default value) time period; i.e., [50 × 1.4 × 1.11 – 50 × 1.4] / 20 = 0.38). From 1990 through 2009, sewage sludge applications in agricultural lands increased SOC storage from 0.6 to 1.3 Tg CO 2 

### Eq./year (Table A- 241). A nominal ±50 percent uncertainty was attached to these estimates due to limited information on application and the rate of change in soil C stock change with sewage sludge amendments.
The second activity was the change in enrollment for the Conservation Reserve Program after 2003 for mineral soils. Relative to the enrollment in 2003, the total area in the Conservation Reserve Program decreased from 2004 to 2009, leading to a reduction in enrollment of 0.16 million ha over the five-year period (USDA-FSA 2009). An average annual change in SOC of 0.5 metric tonnes C/ha-yr was used to es timate the effect of the enrollment changes. This rate is based on the IPCC default method and country-specific factor s (see Table A-237) by calculating the impact of setting aside a medium input cropping system in the Conservation Reserve Program (assuming a reference C stock of 50 metric tonnes C/ha, which represents a mid-range value for the dominant cropland soils in the United States and the average country-specific factor of 1.2 for setting-aside cropland from production, with the change in stocks occurring over a 20 yr (default value) time period; i.e., [50 × 1.2 – 50] / 20 = 0.5). While increases in enrollment from 2004 to 2009 generated additional accumulation of CO 2 

### Eq. annually, reductions in enrollment in 2009 caused emissions of 0.29 Tg CO 2 

### Eq (Table A-242). A nominal ±50 percent uncertainty was also attached to these estimates due to limited information about the enrollment trends at subregional scales, which creates uncertainty in the rate of the soil C stock change (stock change factors for set-aside lands vary by climate region).

Step 5: Compute Net CO 2 

### Emissions and Removals from Agricultural Lands

The sum of total CO 2 

### emissions and removals from the Tier 3 Century Model Approach (Step 2), Tier 2 IPCC Methods (Step 3) and additional land-use and management consid erations (Step 4) are presented in Table A-242. Overall, there was a net accumulation of 99.2 Tg CO 2 

### Eq. in 1990 for agricultural soils, and this rate had decreased by the end of the reporting period in 2009 to 43.4 Tg CO 2 

### Eq.
The total stock change (as seen in the Land Use, Land-Use Change, and Forestry chapter) as well as per hectare rate of change varies among the states (Figure A-17 and Fi gure A-18). On a per hectare basis, the highest rates of C accumulation occurred in the Northeast, Midwest, northern Great Plains, and Northwest. The states with highest total amounts of C sequestration were Iowa, Illinois, Missouri, M ontana, Oklahoma, North Dakota, and South Dakota (Table A- 243). For organic soils, emission rates were highest in th e regions that contain the majority of the drained organic soils, including California, Florida, Michigan, Minnesota, and New York. On a per hectare basis, the emission rate patterns were very similar to the total emissions in each state, with the highest rates in those regions with warmer climates and a larger proportion of the drained organic soil managed for crop production.

Figure A-17: Net C Stock Change, per Hectare, for Mineral Soils Under Agricultural Management, 2009

Figure A-18: Net C Stock Change, per Hectare, for Organic Soils Under Agricultural Management, 2009A-301Table A- 241: Assumptions and Calculations to Estimate the Contribution to Soil Organic CarbonStocks from Application of Sewage Sludge to Mineral Soils1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Sewage Sludge N Appliedto Agricultural Land (MgN)a52,198 55,658 59,250 62,977 65,966 69,00172,081 75,195 78,353 80,932 83,52386,124 88,736 91,358 93,991 98,081100,887 103,682 106,468 109,245Assimilative Capacity (MgN/ha)b0.120 0.120 0.120 0.122 0.122 0.1220.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122Area covered by AvailableSewage Sludge N (ha)c434,985 463,816 493,746 516,202 540,707 565,583 590,828 616,357 642,240 663,381 684,612 705,932 727,341 748,836 770,418 803,942 826,940 849,851 872,686 895,452Average Annual Rate of Cstorage (Mg C/ha-yr)d0.38 0.38 0.38 0.38 0.38 0.380.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38Contribution to Soil C(TgCO2/yr)e,f(0.61) (0.65) (0.69) (0.72) (0.75) (0.79) (0.82) (0.86) (0.89) (0.92)(0.95) (0.98) (1.01) (1.04) (1.07) (1.12) (1.15) (1.18) (1.22) (1.25)Values in parentheses indicate net C storage.aN applied to soils described in Step 1d.bAssimilative Capacity is the national average amount of manure-derived N that can be appliedon cropland without buildup of nutrients in the soil (Kellogg et al., 2000).cArea covered by sewage sludge N availablefor application to soils is the availableN applied at the assimilative capacity rate. The 1992 assimilative capacity rate was applied to 1990–1992 and the 1997 rate wasapplied to 1993-2009.dAnnual rate of C storage based on national average increase inC storage for grazing lands that is attributed to organic matter amendments (0.38 Mg/ha-yr)eContribution to Soil C is estimated as the product of the areacovered by the available sewagesludge N and the average annualC storage attributed to an organic matter amendment.fSome small, undetermined fraction of this applied N is probably not applied to agricultural soils, but instead is applied to forests, home gardens, and other lands.Table A-242: Annual Soil C Stock Change in Cropland Remaining Cropland(CRC), Land Converted to Cropland(LCC), Grassland Remaining Grassland(GRG), and Land

Converted to Grassland(LCG), in U.S. Agricultural Soils (Tg CO2Eq.)1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Net emissions based on Tier 3 Century-based analysis (Step 2)CRC (55.2) (57.5) (68.0) (64.2) (62.4) (47.6) (55.0) (54.1) (44.3) (29.7) (54.8) (37.6) (36.1) (42.3)(42.3) (42.3) (42.3) (42.3) (42.3)(42.3)LCC (4.4) (4.3) (4.8) (8.7) (12.6) (3.8) (4.5) (4.2) (10.8) (3.3) (4.4) (2.5) (2.0) (0.8) (0.8) (0.8) (0.8) (0.8) (0.8) (0.8)GRG (55.1) (28.0) (9.8) (1.5) (66.7)(29.5) 4.2 (20.4) 1.5 (18.9) (55.2)(27.3) (46.8) (11.7) (11.5) (11.3) (11.1) (11.0) (10.8) (10.6)LCG (15.8) (15.1) (13.8) (13.5) (17.9) (18.2) (15.2) (19.9) (17.3) (24.0) (23.1) (24.0) (22.7) (20.7)(20.5) (20.3) (20.1) (19.9) (19.7)(19.5)Net emissions based on the IPCC Tier 2 analysis (Step 3)MineralSoilsCRC (1.6) (1.6) (1.6) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0)LCC 4.2 4.2 4.2 4.1 4.1 4.14.1 4.1 4.1 4.1 4.1 4.1 4.14.1 4.1 4.1 4.1 4.1 4.1 4.1GRG (0.3) (0.3) (0.3) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2)LCG (4.5) (4.5) (4.5) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0)Organic SoilsCRC 27.4 27.4 27.4 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7LCC 2.4 2.4 2.4 2.6 2.6 2.62.6 2.6 2.6 2.6 2.6 2.6 2.62.6 2.6 2.6 2.6 2.6 2.6 2.6GRG 3.9 3.9 3.9 3.7 3.7 3.73.7 3.7 3.7 3.7 3.7 3.7 3.73.7 3.7 3.7 3.7 3.7 3.7 3.7LCG 0.5 0.5 0.5 0.9 0.9 0.90.9 0.9 0.9 0.9 0.9 0.9 0.90.9 0.9 0.9 0.9 0.9 0.9 0.9Additional changes in net emissions from mineral soils basedon application of sewage sludge to agricultural land (Step 4)GRG (0.6) (0.6) (0.7) (0.7) (0.8) (0.8) (0.8) (0.9) (0.9) (0.9) (1.0) (1.0) (1.0) (1.0) (1.1) (1.1) (1.2) (1.2) (1.2) (1.3)Additional changes in net emissions from mineral soilsbased on additional enrollment of CRP land (Step 4)CRC - - - - - - - - - - - - - - (0.4) (0.6) (1.4) (2.0) (0.4) 0.3Total Stock Changes by Land Use/Land-Use Change Category (Step 5)A-302 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009CRC (29.4) (31.7) (42.3) (39.6) (37.7)(22.9) (30.3) (29.4) (19.7) (5.1) (30.2) (12.9) (11.5) (17.7) (18.1) (18.3)(19.1) (19.7) (18.1) (17.4)LCC 2.2 2.3 1.8 (1.9) (5.8) 2.9 2.2 2.6 (4.0) 3.5 2.4 4.2 4.8 5.9 5.9 5.9 5.9 5.9 5.9 5.9GRG (52.2) (25.2) (7.0) 1.3 (64.0) (26.7) 7.0 (17.8) 4.1 (16.3) (52.6) (24.8) (44.3) (9.2) (9.0) (8.9) (8.8) (8.6) (8.5) (8.3)LCG (19.8) (19.1) (17.9) (17.6) (22.0) (22.3) (19.3) (24.0) (21.4) (28.0) (27.2) (28.1) (26.8) (24.8) (24.6)(24.4) (24.2) (24.0) (23.8) (23.6)Total * (99.2) (73.7) (65.3) (57.8) (129.5) (69.0) (40.4) (68.6) (41.0) (46.0) (107.6) (61.6) (77.8) (45.7)(45.8) (45.6) (46.1) (46.3) (44.4) (43.4)

### A-303 Table A- 243: Soil C Stock Change for Mineral and Org anic Soils during 2009 within individual states (Tg CO 2 

### Eq.)) State Mineral Soil Organic Soil Total AL (0.45) - (0.45) AR (1.09) - (1.09) AZ (0.80) - (0. 80) CA (0.16) 2.29 2.14 CO (0.54) 0.00 (0.54) CT (0.06) - (0.06) DE 0.02 - 0.02 FL 0.26 10.84 11.10 GA (0.21) - (0.21) HI (0.02) 0.25 0.24 IA (4.67) 0.75 (3.92) ID (1.53) 0.11 (1.43) IL (5.16) 0.54 (4.61) IN (1.72) 2.93 1.21 KS (2.34) - (2.34) KY (1.90) - (1.90) LA (0.93) 0.07 (0.86) MA (0.02) 0.03 0.01 MD (0.19) 0.03 (0.16) ME (0.17) - (0.17) MI (2.28) 2.72 0.43 MN (2.57) 7.30 4.73 MO (9.54) - (9.54) MS (1.19) 0.00 (1.18) MT (6.10) 0.11 (5.99) NC (0.35) 2.25 1.90 ND (6.03) - (6.03) NE (2.03) - (2.03) NH (0.04) 0.01 (0.03) NJ (0.09) 0.01 (0.08) NM (1.18) - (1.18) NV (0.23) 0.00 (0.23) NY (1.83) 0.61 (1.23) OH (2.91) 0.42 (2.49) OK (6.48) - (6.48) OR (2.13) 0.12 (2.00) PA (1.85) 0.01 (1.84) RI (0.01) 0.00 (0.00) SC 0.05 0.04 0.09 SD (5.86) - (5.86) TN (1.85) - (1.85) TX 5.46 - 5.46 UT (0.02) - (0.02) VA (0.50) 0.02 (0.48) VT (0.28) 0.00 (0.27) WA (2.19) 0.26 (1.93) WI (2.48) 2.88 0.41 WV (0.41) - (0.41) WY 0.17 0.01 0.18 Note: Parentheses indicate net C accumulation. Estimates do not in clude soil C stock change associated with CRP enrollment aft er 2003 or sewage sludge application to soils, which we re only estimated at the national scale. Th e sum of state results will not match th e national results because state results are generated in a separate programming package, the sewage sludge and CRP enrollment after 2003 are not included, and differences arise due to rounding of values in this table.

### A-304 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

# 3.14. Methodology for Estimating CH 4 

# Emissions from Landfills

Landfill gas is a mixture of substances generated when bacteria decompose the organic materials contained in solid waste. By volume, landfill gas is about half CH 4 

### and half CO 2 

### . 74 

### The amount and rate of CH 4 

### generation depends upon the quantity and composition of the landfilled material, as well as the surrounding landfill environment.

Not all CH 4 

### generated within a landfill is emitted to the atmosphere. The CH 4 

### can be extracted and either flared or utilized for energy, thus oxidizing to CO 2 

### during combustion. Of the remaining CH 4 

### , a portion oxidizes to CO 2 

### as it travels through the top layer of the landfill cover. In general, landfill-related CO 2 

### emissions are of biogenic origin and primarily result from the decomposition, either aerobic or an aerobic, of organic matter such as food or yard wastes. 75 

### To estimate the amount of CH 4 

### produced in a landfill in a given year, information is needed on the type and quantity of waste in the landfill, as well as the landfill characteristics (e.g., si ze, aridity, waste density). This information is not availab le for the majority landfills in the United St ates. Consequently, to estimate CH 4 

### generation, a methodology was developed based on the quantity of waste placed in landfills nationwide each year, the first order decay model, and model parameters from the analysis of measured CH 4 

### generation rates for U.S. landfills with gas recovery systems.
From various studies and surveys of the generation and disp osal of solid waste, estimates of the amount of waste placed in MSW and industrial landfills were developed. A database of measured CH 4 

### generation rates at landfills with gas recovery systems was compiled and analyzed. The results of this analysis and other studies were used to develop an estimate of the CH 4 

### generation potential for use in the first order decay model. In addition, the analysis and other studies provided estimates of the CH 4 

### generation rate constant as a function of precipitation. The first order decay model was applied to annual waste disposal estimates for each year and for three ranges of precipitation to estimate CH 4 

### generation rates nationwide for the years of interest. Based on the organic content of industrial wastes and the estimates of the fraction of these wastes sent to industrial landfills, CH 4 

### emissions from industrial landfills were also estimated using the first order decay model. Total CH 4 

### emissions were estimated by adding the CH 4 

### from MSW and industrial landfills and subtracting the amounts recovered for energy or flaring and the amount oxidized in the soil. The steps taken to estimate CH 4 

### emissions from U.S. landfills for the years 1990 th rough 2009 are discussed in greater detail below.
Figure A-19 presents the CH 4 

### emissions process—from waste generation to emissions—in graphical format.

Step 1: Estimate Annual Quantities of Solid Waste Placed in Landfills
For 1989 to 2009, estimates of the annual quantity of waste placed in MSW landfills were developed from a survey of State agencies as reported in BioCycle’s State of Garbage in America (BioCycle 2008), adjusted to include U.S. territories. 76 

### The BioCycle survey is the only continually updated nationwide survey of waste disposed in landfills in the United States.

Table A-244 shows estimates of waste quantities contributing to CH 4 

### emissions. The table shows BioCycle estimates of total waste landfilled each year from 1990 thro ugh 2000, 2002, 2004, and 2006 adjusted for U.S. territories. A linear interpolation was used for 2001, 2003, 2005, 2007, 2008, and 2009 because there are no BioCycle surveys for those years. The next BioCycle survey will be published in 2011 representing 2008 data, at which time, the waste landfilled for 2007, 2008, and 2009 will be updated.

Figure A-19: Methane Emissions Resulting fr om Landfilling Municipal and Industrial Waste

Table A-244: Solid Waste in MSW Landfills Contributing to CH 4 

### Emissions (Tg unless otherwise noted) Description 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Total Waste Generated a 271 302 377 416 455 462 470 459 448 452 456 460 Percent of Wastes Landfilled a 77% 63% 61% 63% 66% 65% 64% 64% 65% 65% 65% 65%

74 Typically, landfill gas also contains small amounts of nitroge n, oxygen, and hydrogen, less than 1 percent nonmethane volatile organic compounds (NMVOCs), and tr ace amounts of inorganic compounds.
75 See Box 8-1 “Biogenic Emissions and Sinks of Carbon” in the Waste chapter for additional background on how biogenic emissions of landfill CO 2 are addressed in the U.S. Inventory. 76 Since the BioCycle survey does not include U.S. territories, waste landfilled in U.S. territories was estimated using populati on data for the U.S territories (U.S. Census Bureau 2010) and the per capita rate for waste landfilled from BioCycle (2008).

### A-305 Total Wastes Landfilled a 209 190 230 263 298 300 301 285 289 291 294 297 Waste in Place (30 years) b 4,674 5,075 5,400 5,488 5,608 5,759 5,909 6, 058 6,198 6,328 6,458 6,587 Waste Contributing to

Emissions c 6,815 7,796 8,828 9,092 9,390 9,690 9,991 10, 286 10,574 10,866 11,160 11,457

a Source: BioCycle (2008), adjusted for missing U.S. territories using U.S. Census Bureau (2010) population data and per capita disposal rate from BioCycle . The data, originally reported in short tons, are converted to metric tons. Estimates s hown for 2001 and 2003 are based on a n interpolation because there were no surveys in 2001 and 2003; es timates shown for 2005, 2007, 2008, and 2009 based on the incre ase in population.

b This estimate represents the waste that has been in place for 30 years or less, wh ich contributes about 90 percent of the CH 4 generation. Values are based on EPA (1993).

c This estimate represents the cumulative amount of waste that has been placed in landfills from 1940 to the year indicated and is the sum of the annual disposal rates used in the first order decay model. Values are based on EPA (1993).



### Estimates of the annual quantity of waste placed in landfills from 1960 through 1988 were developed from EPA’s 1993 Report to Congress (EPA 1993) and a 1986 survey of MSW landfills (EPA 1988). Based on the national survey and estimates of the growth of commercial, residential and other wastes, the annual quantity of waste placed in landfills averaged 127 million metric tons in the 1960s, 154 milli on metric tons in the 1970s, 190 million metric tons in the 1990s, and 285 million metric tons in the 2000’s. Estimates of waste placed in landfills in the 1940s and 1950s were developed based on U.S. population for each year and the per capital disposal rates from the 1960s.

Step 2: Estimate CH 4 

### Generation at Municipal Solid Waste Landfills

The CH 4 

### generation was estimated from the integrated form of the first order decay (FOD) model using the procedures and spreadsheets from IPCC (2006) for estimating CH 4 

### emissions from solid waste disposal. The form of the FOD model that was applied incorporates a time delay of 6 months after waste disposal before the generation of CH 4 

### begins.
The input parameters needed for the FOD model equations are the mass of waste disposed each year, which was discussed in the previous section, degradable organic carbo n (DOC), and the decay rate constant (k). The DOC is determined from the CH 4 

### generation potential (L 0 

### in m 3 

### CH 4 

### /Mg waste), which is discussed in more detail in subsequent paragraphs, and the following equation:

DOC = [L 0 

### × 6.74 × 10 -4 

### ] ÷ [F × 16/12 × DOC f 

### × MCF]

Where, DOC = degradable organic carbon (fraction, Gg C/Gg waste), L 0 

### = CH 4 

### generation potential (m 3 

### CH 4 

### /Mg waste), 6.74 × 10 -4 

### = CH 4 

### density (Mg/m 3 

### ), F = fraction of CH 4 

### by volume in generated landfill gas (equal to 0.5) 16/12 = molecular weight ratio CH 4 

### /C, DOC f 

### = fraction of DOC that can decompose in the anaerobic conditions in the landfill (fraction equal to 0.5 for MSW), and MCF = methane correction factor for year of disposal (fraction equal to 1 for anaerobic managed sites).

The DOC value used in the CH 4 

### generation estimates from MSW landfills is 0.203 based on the CH 4 

### generation potential of 100 m 3 

### CH 4 

### /Mg waste as described below. Data from a set of 52 representative landfills across the U.S. in different precipitation ranges were chosen to evaluate L o 

### , and ultimately the country-specific DOC value. The 2004 Chartwell Municipal Solid Waste Facility Directory confirmed that each of the 52 landfills chosen accepted or accepts both MSW and construction and demolition (C&D) waste (Chartwell 2004; RTI 2009).
The methane generation potential (L o 

### ) varies with the amount of organic content of the waste material. A higher L o 

### occurrs with a higher content of organic waste. Waste composition data is not collected for all landfills nationwide; thus a default value must be used. Values for L o 

### were evaluated from landfill gas recovery data for this set of 52 landfills, which resulted in a best fit value for L o 

### of 99 m 3 

### /Mg of waste (RTI 2004). This value compares favorably with a range of 50 to 162 (midrange of 106) m 3 

### /Mg presented by Peer, Thorneloe, a nd Epperson (1993); a range of 87 to 91 m 3 

### /Mg from a detailed analysis of 18 landfills sponsored by the Solid Waste Association of North America (SWANA 1998); and a value of 100 m 3 

### /Mg recommended in EPA’s compilation of emission factors (EPA 1998; EPA 2008) based on data from 21 landfills. Based on the results from these studies, a value of 100 m 3 

### /Mg appears to be a reasonable best estimate to use in the FOD model for the national inventory.

### A-306 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 The FOD model was applied to the gas recovery data for the 52 landfills to calculate the rate constant (k) directly for L 0 

### = 100 m 3 

### /Mg. The rate constant was found to increase with annual average precipitation; consequently, average values of k were developed for three ranges of precipitation, shown in Table A- 245 and recommended in EPA’s compilation of emission factors (EPA 2008). Table A- 245. Average Values for Rate Constant (k) by Precipitation Range (yr -1 

### ) Precipitation range (inches/year) k (yr -1 ) <20 0.020 20-40 0.038 >40 0.057



### These values for k show reasonable agreement with the results of other studies. For example, EPA’s compilation of emission factors (EPA 1998; EPA, 2008) recommends a value of 0.02 yr -1 

### for arid areas (less than 20 inches/year of precipitation) and 0.04 yr -1 

### for non-arid areas. The SWANA study of 18 landfills reported a range in values of k from 0.03 to 0.06 yr -1 

### based on CH 4 

### recovery data collected generally in the time frame of 1986 to 1995.
Using data collected primarily for the year 2000, the distribution of waste in place versus precipitation was developed from over 400 landfills (RTI 2004). A distributi on was also developed for population vs. precipitation for comparison. The two distributions were very similar and indicated that population in areas or regions with a given precipitation range was a reasonable proxy for waste landfilled in regions with the same range of precipitation. Using U.S. Census data and rainfall data, the distributions of population versus rainfall were developed for each Census decade from 1950 through 2000. The distributions showed that the U.S. popul ation has shifted to more arid areas over the past several decades. Consequently, the population distribution was used to apportion the waste landfilled in each decade according to the precipitation ranges developed for k, as shown in Table A-246.
Table A-246. Percent of U.S. Popula tion within Precipitation Ranges (%) Precipitation Range (inches/year) 1950 1960 1970 1980 1990 2000 <20 11 13 14 16 19 20 20-40 40 39 38 36 34 33 >40 49 48 48 48 47 47 Source: RTI (2004) using population data from the U.S. Bureau of Census and precipitation data from the National Climatic Data Center’s National Oceanic and Atmospheric Administration.


### In developing the Inventory, the proportion of waste dispos ed of in managed landfills versus open dumps prior to 1980 was re-evaluated. Based on the historical data presented by Mintz et al. (2003), a timeline was developed for the transition from the use of open dumps for solid waste disposed to the use of managed landfills. Based on this timeline, it was estimated that 6 percent of the waste that was land disposed in 1940 was disposed of in managed landfills and 94 percent was managed in open dumps. Between 1940 and 1980, the fraction of waste land disposed transitioned towards managed landfills until 100 percent of the waste was disposed of in managed landfills in 1980. For wastes disposed of in dumps, a methane correction factor (MCF) of 0.6 was us ed based on the recommended IPCC default value for uncharacterized land disposal (IPCC 2006); this MCF is equiva lent to assuming 50 percent of the open dumps are deep and 50 percent are shallow. The recommended IPCC default value for the MCF for managed landfills of 1 was used for the managed landfills (IPCC 2006).

Step 3: Estimate CH 4 

### Generation at Industrial Landfills
Industrial landfills receive waste from factories, proces sing plants, and other manufacturing activities. In national inventories prior to the 1990 through 2005 inventory, CH 4 

### generation at industrial landfills was estimated as seven percent of the total CH 4 

### generation from MSW landfills, based on a study conducted by EPA (1993). For the 1990 through 2007 and current inventories, the methodology was updated and improved by using activity factors (industrial production levels) to estimate the amount of industrial waste landfilled each year and by applying the FOD model to estimate CH 4 

### generation. A nationwide survey of industrial waste landfills found that over 99 percent of the organic waste placed in industrial landfills originated from two industries: food processing (meat, vegetables, fruits) and pulp and paper (EPA 1993). Data for annual nationwide production for the food processing and pulp and paper industries were taken from industry and government sources for recent years; estimates were developed for production for the earlier years for which data were not available. For the pulp and paper industry, production data published by the Lockwood-Post’s Directory (ERG 2010) and U.S. Department of Agriculture (2 010) were the primary sources for years 1965 through 2009. An extrapolation based on U.S. real gross domestic pr oduct was used for years 1940 through 1964. For the food processing industry, production levels were obtained or develo ped from the U.S. Department of Agriculture (2010) for the

### A-307 years 1990 through 2009 (ERG 2010). An extrapolation based on U.S. population was used for the years 1940 through 1989.
In addition to production data for the pulp and paper and food processing industries, the following inputs were needed to use the FOD model for estimating CH 4 

### generation from industrial landfills: 1) quantity of waste that is disposed in industrial landfills (as a function of production), 2) CH 4 

### generation potential (L 0 

### ) or DOC, and 3) FOD decay constant (k). Research into waste generation and disposal in landfills for the pulp and paper industry indicated that the quantity of waste landfilled was about 0.050 Mg/Mg of product compared to 0.046 Mg/Mg product for the food processing industry (Weitz and Bahner 2006). These factors were applied to esti mates of annual production to estimate annual waste disposal in landfills. Estimates for DOC were derived from available da ta (Kraft and Orender, 1993; NCASI 2008; Flores et al. 1999). The DOC value for industrial pulp and paper waste is estimated as 0.20 (L o 

### of 99 m 3 

### /Mg); the DOC value for industrial food waste is estimated as 0.26 (L o 

### of 128 m 3 

### /Mg) (Coburn 2008). Estimates for k were taken from the default values in the 2006 IPCC Guidelines; the value of k given for food waste with disposal in a wet temperate climate is 0.19
yr -1 

### , and the value given for paper waste is 0.06 yr -1 

### . A literature review was conducted for the current inventory year with the intent of updating values for Lo and k in the pulp and paper industry. Insufficient data was obtained to warrant a change for the current inventory year, but ongoing efforts may result in a U.S. industry-specific value rather than a default IPCC value in future inventory years.
As with MSW landfills, a similar trend in disposal prac tices from open dumps to managed landfills was expected for industrial landfills; therefore, the same time line that was developed for MSW landfills was applied to the industrial landfills to estimate the average MCF. That is, between 1940 and 1980, the fraction of waste land disposed transitioned from 6 percent managed landfills in 1940 and 94 percent open dumps to 100 percent managed landfills in 1980 and on. For wastes disposed of in dumps, an MCF of 0.6 was used an d for wastes disposed of in managed landfills, an MCF of 1 was used, based on the recommended IPCC default values (IPCC 2006).
The parameters discussed above were used in the integrated form of the FOD model to estimate CH 4 

### generation from industrial landfills.

Step 4: Estimate CH 4 

### Emissions Avoided

The estimate of CH 4 

### emissions avoided (e.g., combusted) was base d on landfill-specific data on landfill gas-toenergy (LFGTE) projects and flares. A destruction efficiency of 99 percent was applied to CH 4 

### recovered to estimate CH 4 

### emissions avoided. The value for efficiency was selected based on the range of efficiencies (98 to 100 percent) recommended for flares in EPA’s “AP-42 Compilation of Air Po llutant Emission Factors, Chapter 2.4," efficiencies used to establish new source performance standards (NSPS) for landfills , and in recommendations for closed flares used in the Landfill Methane Outreach Program (LMOP).

Step 4a: Estimate CH 4 

### Emissions Avoided Through Landfill Gas-to-Energy (LFGTE) Projects
The quantity of CH 4 

### avoided due to LFGTE systems was estimated based on information from two sources: (1) a database maintained by the Energy Information Administration (EIA) for the voluntary reporting of greenhouse gases (EIA 2007) and (2) a database compiled by LMOP (EPA 2009). The EIA database included location information for landfills with LFGTE projects, estimates of CH 4 

### reductions, descriptions of the projects, and information on the methodology used to determine the CH 4 

### reductions. Generally the CH 4 

### reductions for each reporting year were based on the measured amount of landfill gas collected and the percent CH 4 

### in the gas. For the LMOP database, data on landfill gas flow and energy generation (i.e., MW capacity) were used to estimate the total direct CH 4 

### emissions avoided due to the LFGTE project. Detailed information on the landfill name, owner or operator, city, and state were available for both the EIA and LMOP databases; consequently, it was straightforward to identify landfills that were in both databases. The EIA database was given priority because reductions were reported for each year and were based on direct measurements. Landfills in the LMOP database that were also in th e EIA database were dropped to avoid double counting.

Step 4b: Estimate CH 4 

### Emissions Avoided Through Flaring
The quantity of CH 4 

### flared was based on data from the EIA database and on information provided by flaring equipment vendors. To avoid double-counting, flares associ ated with landfills in the EIA and LMOP databases were excluded from the flare vendor database. As with the LFGTE projects, reductions from flaring landfill gas in the EIA database were based on measuring the volume of gas collected and the percent of CH 4 

### in the gas. The information provided by the flare vendors included information on the number of flares, flare design flow rates or flare dimensions, year of installation, and generally the city and state location of the landfill. When a range of design flare flow rates was provided by the flare vendor, the median landfill gas flow rate was used to estimate CH 4 

### recovered from each remaining

### A-308 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 flare (i.e., for each flare not associated with a landfill in the EIA or LMOP databases). Several vendors provided information on the size of the flare rather than the flare design gas flow rate. To estimate a median flare gas flow rate for flares associated with these vendors, the size of the flare was matched with the size and corresponding flow rates provided by other vendors. Some flare vendors reported the maximum capacity of the flare. An analysis of flare capacity versus measured CH 4 

### flow rates from the EIA database showed that the flares operated at 51 percent of capacity when averaged over the time series and at 72 percent of capacity for the highest flow rate for a given year. For those cases when the flare vendor supplied maximum capacity, the actual flow was estimated as 50 percent of capacity. Total CH 4 

### avoided through flaring from the flare vendor database was estimated by summing the estimates of CH 4 

### recovered by each flare for each
year.

Step 4c: Reduce CH 4 

### Emissions Avoided Through Flaring
As mentioned in Step 4b, flares in the flare vendor da tabase associated with landfills in the EIA and LMOP databases were excluded from the flare reduction estimates in the flare vendor database. If comprehensive data on flares were available, each LFGTE project in the EIA and LMOP databases would have an identified flare because it is assumed that most LFGTE projects have flares. However, given that the flare vendor data only covers approximately 50 to 75 percent of the flare population, an associated flare was not identified for all LFGTE projects. These LFGTE projects likely have flares, yet flares were unable to be identified for one of two reasons: 1) inadequate identifier information in the flare vendor data; or 2) a lack of the flare in the flare ve ndor database. For those projects for which a flare was not identified due to inadequate information, CH 4 

### avoided would be overestimated, as both the CH 4 

### avoided from flaring and the LFGTE project would be counted. To avoid overestimating emissions avoided from flaring, the CH 4 

### avoided from LFGTE projects with no identified flares was determined and the flaring estimate from the flare vendor database was reduced by this quantity (referred to as a flare correction fact or) on a state-by-state basis. This step likely underestimates CH 4 

### avoided due to flaring but was applied to be conservative in the estimates of CH 4 

### emissions avoided.
Additional effort was undertaken to improve the methodology behind the flare correction factor for the 1990- 2009 Inventory to reduce the total number of flares in the flare vendor database that were not matched (512) to landfills and/or LFGTE projects in the EIA and LMOP databases. Each flare in the flare vendor database not associated with a LFGTE project in the EIA or LMOP databases was investigated to determine if it could be matched to either a landfill in the EIA database or a LFGTE project in the LMOP database. For some unmatched flares, the location information was missing or incorrectly transferred to the flare vendor database. In other instances, the landfill names were slightly differen t between what the flare vendor provided and the actual landfill name as listed in the EIA and LMOP databases.
It was found that a large majority of the unmatched fl ares are associated with landfills in LMOP that are currently flaring, but are also considering LFGTE. These landfills projects considering a LFGTE project are labeled as candidate, potential, or construction in the LMOP database. Th e flare vendor database was improved to match flares with operational, shutdown as well as candidate, potential, and c onstruction LFGTE projects, thereby reducing the total number of unidentified flares in the flare vendor database, all of whic h are used in the flare correction factor. The results of this effort significantly decreased the number of flares used in the flare correction factor, and consequently, increased recovered flare emissions, and decreased net emissions fro m landfills for the 1990-2009 Inventory. The revised state-bystate flare correction factors were applied to the entire 1990 to 2009 time series for the current Inventory year.

Step 5: Estimate CH 4 

### Oxidation
A portion of the CH 4 

### escaping from a landfill oxidizes to CO 2 

### in the top layer of the soil. The amount of oxidation depends upon the characteristics of the soil and the e nvironment. For purposes of this analysis, it was assumed that of the CH 4 

### generated, minus the amount of gas recovered for fl aring or LFGTE projects, 10 percent was oxidized in the soil (Jensen and Pipatti 2002; Mancinelli and McKay 1985; Czepiel et al 1996). The factor of 10 percent is consistent with the value recommended in the 2006 IPCC revised guidelines for managed and covered landfills, and was therefore applied to the estimates of CH 4 

### generation minus recovery for both MSW and industrial landfills
Literature reviews were conducted in 2006 and 2010 to provide recommendations for the most appropriate oxidation rate assumptions. It was found that oxidation values are highly variable and range from zero to over 100 percent (i.e., the landfill is considered to be an atmospheric sink by virtue of the landfill gas extraction system pulling atmospheric metane down through the cover). There is considerable uncertainty and variability surrounding estimates of oxidation because it is difficult to measure and varies considerably with the thickness and type of the cover material, climate, and the presence of cracks/fissures in the cover material through whic h methane can escape. IPCC (2006) notes that test results from field and laboratory studies may lead to over-estimations of oxidation in landfill cover soils, because they largely determine oxidation using uniform and homogeneous soil layers. In addition, a number of studies note that gas escapes

### A-309 more readily through the side slopes of a landfill as co mpared to moving through the cover thus complicating the correlation between oxidation and cover type or gas recovery.
Spokas et al (2005), in particular, helps to illustrate expected patterns (e.g., seasonality of generation, effectiveness of gas recovery) associated with landfill methane production and flux through the cover system. This study also highlights the large variability in oxidation between and within sites and ultimately reports oxidation ranging between 4-50 percent. All but one of the test sites had an active gas recovery system in place. For landfills with gas collection systems, there have been studies to show that gas recove ry increases oxidation because it slows the flux of methane through the cover system. Although this may be true, there does not appear to be enough data to support the premise that landfills with gas recovery systems increase oxidation. This is demonstrated by the Spokas et al (2005) data where the oxidation rates were about the same for a landfill site with and without gas recovery. However, the site also had a thin temporary cover so the oxidation would be less than a site with a final cover system. Based on this and other studies, there does not appear to be adequate justification for increasing the default oxidation value for landfills with gas recovery.
Changing the oxidation rate from 10 percent to 20 percent has a minimal impact on the overall inventory results. Changing the oxidation rate from 10 percent to 60 percent has a more significant impact on the overall inventory results, lowering the overall inventory by as much as 15 to 20 percent.
The current default oxidation factor of 10 percent is recommended for all landfills in the Inventory until more reliable, peer-reviewed data is available about the influence of climate, cover type, and gas r ecovery is better understood.

Step 6: Estimate Total CH 4 

### Emissions

Total CH 4 

### emissions were calculated by adding emissions from MSW and industrial landfills, and subtracting CH 4 

### recovered and oxidized, as shown in Table A- 247.A-310 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009Table A- 247: CH4Emissions from Landfills (Gg)Activity 199019952000 2001 2002 2003 2004 2005 2006 2007 2008 2009MSW Generation 8,2199,1329,854 10,068 10,367 10,754 11,12711,486 11,813 12,107 12,395 12,679Industrial Generation 549611682 695 710 710 716 724 727 732 738 744Potential Emissions 8,7689,74210,536 10,763 11,069 11,464 11,84212,210 12,540 12,839 13,133 13,423Landfill Gas-to-Energy (649)(1,070)(2,352) (2,548) (2,589) (2,563) (2,673)(2,691) (2,807) (3,033) (3,189) (3,429)Flare (321)(1,298)(2,276) (2,491) (2,738) (2,886) (3,364)(3,566) (3,820) (3,918) (3,810) (3,779)Emissions Avoided (970)(2,368)(4,628) (5,039) (5,326) (5,448) (6,037)(6,257) (6,628) (6,951) (7,000) (7,208)Oxidation at MSW Landfills (725)(676)(523) (503) (504) (531) (509) (523) (518) (516) (540) (547)Oxidation at Industrial Landfills (55)(61)(68) (70) (70) (71) (72) (73) (73) (73) (74) (74)Net Emissions 7,0186,6375,317 5,152 5,169 5,414 5,2245,358 5,321 5,299 5,520 5,593Note: Totals may not sum exactly to the last significant figure due to rounding.Note: MSW generation inrepresents emissions before oxidation. In other tables throughout the text, MSW generation estimates account for oxidation.Note: Parentheses denote negative values.

### A-311 

## References BioCycle (2008) "The State of Garbage in America" By L. Arsova, R. Van Haaren, N. Goldstein, S. Kaufman, and N. Themelis. BioCycle. December 2008. Available online at < http ://www.jgpress.com/archives/_free/001782.html>
Czepiel, P., B. Mosher, P. Crill, a nd R. Harriss (1996) “Quantifying the Effect of Oxidation on Landfill Methane Emissions.” Journal of Geophysical Research , 101(D11):16721-16730.
Coburn, J. (2008) “Analysis of DOC Values for Industrial Solid Waste for the Pulp and Paper Industry and the Food Industry.” Memorandum to M. Weitz (EPA), October 13, 2008.
EIA (2007) Voluntary Greenhouse Gas Reports for EIA Fo rm 1605B (Reporting Year 2006). Available online at <ftp://ftp.eia.doe.gov/pub/oiaf/1605/cdrom/>.
Chartwell (2004) Municipal Solid Wa ste Directory. The Envirobiz Group.
EPA (1993) Anthropogenic Methane Emissions in the United States, Estimates for 1990: Report to Congress , U.S. Environmental Protection Agency, Office of Air and Radiation. Washington, DC. EPA/430-R-93-003. April 1993.
EPA (1998) Compilation of Air Pollution Emission Factors, Publication AP-42 , Section 2.4 Municipal Solid Waste Landfills. November 1998.
EPA (1988) National Survey of Solid Waste (Municipal) Landfill Facilities , U.S. Environmental Protection Agency. Washington, DC. EPA/530-SW-88-011. September 1988.
EPA (2008). Compilation of Air Pollution Emission Factors, Publication AP-42 , Draft Section 2.4 Municipal Solid Waste Landfills. October 2008.
EPA (2009) Landfill Gas-to-Energy Project Database. Landfill Methane and Outreach Program. July 2009.
ERG (2010). Production Data Supplied by ERG for 1990-2009 for Pulp and Paper, Fruits and Vegetables, and Meat. July.
Flores, R.A., C.W. Shanklin, M. Loza-Garay, S.H. Wie (19 99) “Quantification and Characterization of Food Processing Wastes/Residues.” Compost Science & Utilization , 7(1): 63-71.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Ga s Inventories. The National Greenhouse Gas Inventories Programme, The Intergovernmental Panel on Climate Change, H. S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Jensen, J.E.F., and R. Pipatti (2002) “CH 4 

### Emissions from Solid Waste Disposal.” Background paper for the Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
Kraft, D.L. and H.C. Orender (1993) “Cons iderations for Using Sludge as a Fuel.” Tappi Journal , 76(3): 175-183.
Mancinelli, R. and C. McKay (1985) “Methane-O xidizing Bacteria in Sanitary Landfills.” Proc. First Symposium on Biotechnical Advances in Processing Municipal Wastes for Fuels and Chemicals , Minneapolis, MN, 437-450. August.
Mintz C., R. Freed, and M. Walsh (2003) “Timeline of Anaer obic Land Disposal of Solid Waste.” Memorandum to T. Wirth (EPA) and K. Skog (USDA), December 31, 2003.
National Council for Air and Stream Improvement, Inc. (NCASI) (2008) “Calculations Documenting the Greenhouse Gas Emissions from the Pulp and Paper Industry.” Me morandum to R. Nicholson (RTI), May 21, 2008.
Peer, R., S. Thorneloe, and D. Epperson (1993) “A Comp arison of Methods for Estimating Global Methane Emissions from Landfills.” Chemosphere , 26(1-4):387-400.
RTI (2004) Documentation for Changes to the Methodology for the Inventory of Methane Emissions from Landfills. Memorandum to E. Scheehle (EPA), August 26, 2004.
RTI (2009) GHG Inventory Improvement – Construction & Demolition Waste DOC and Lo Value. Memorandum to R. Schmeltz, April 15, 2010.
Spokas, K.; Bogner, J.; Chant on, J.P.; Morcet, M.; Aran, C. ; Graff, C.; Moreau-Le Golvan, Y.; and I. Hebe (2006). Methane Mass Balance at Three Landfill Sites: What is the Efficiency of Capture by Gas Collection Systems? Waste Management , 26(5): 516-525.

### A-312 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 Solid Waste Association of North America (SWANA) (1998) Comparison of Models for Predicting Landfill Methane Recovery

. Publication No. GR-LG 0075. March 1998.
U.S. Bureau of Census (2010) International Data Base. August 2010. Available online at <http://www.census.gov/ipc/www/idb/>.
Weitz, K. and M. Bahner (2006) “Methane Emissions for Industrial Landfills.” Memorandum to M. Weitz (EPA), September 5, 2006.Figure A-4: Domestic Greenhouse Gas Emissions by Mode and Vehicle Type, 1990 to 2009 (Tg CO 2 Eq.)

0 500 1,000 1,500 2,000 2,50019901991199219931994199519961997199819992000200120022003200420052006200720082009Tg CO2Eq.

Passenger Cars/Motorcycles Light-Duty Trucks Medium- and Heavy-Duty Trucks Aircraft Boats/Ships, Rail, and Pipelines Mobile AC, Refrig. Transport, Lubricants Non-Transportation Mobile Sources

### Section Name X-7 Figure A-5
Effect of Soil Temperature, Water-Filled Pore Space, and pH on Nitrification Rates

0 0.2 0.4 0.6 0.8

1 1.2

0 10 20 30 40 50 60F(temp)

Soil Temperature

0 0.2 0.4 0.6 0.8

1 1.2F(WFPS)

0 0.2 0.4 0.6 0.8 1 WFPS

0 0.2 0.4 0.6 0.8

1 1.2F(pH)

3 5 4 6 7 9 8 10 pH fine

coarse

### X-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 –2006 Figure A-6

Effect of Soil Nitrite Concentration, Heterotrophic Respiration Rates, and Water-Filled Pore Space on Denitrification Rates

0 5 10 15 20 25 30 35

0 100 200 300 400N2+N2O μg N/g soil/day

NO 3 μg N/g soil

0 5 10 15 20 25 30 35 40N2+N2O μg N/g soil/day

0 20 40 60 80 100 CO 2 μg C/g soil/day

0 0.2 0.4 0.6 0.8

1

0 20 40 60 80 100Relative Denitrification

WFPS % clay-low resp clay-high resp

loam-low resp loam-high resp

### Figure A-7: DAYCENT model flow diagram 

## DEAD PLANT

MATERIAL

METABOLIC STRUCTURAL PLANT COMPONENTS

LEAVES

FINE ROOTS

BRANCHES

LARGE WOOD

LARGE ROOTS 

## NO 3 - 

## NH 4 +



### 0-1 cm
1-4 cm 4-15 cm
15-30 cm

etc. 0-15 cm



## SOM

ACTIVE 0.5-1 yr

SLOW 10-50 yr

PASSIVE 1000-5000 yr 

## H 2 

## O soil



## T soil 

### 0-1 cm
1-4 cm
4-15 cm
15-30 cm

etc. 

## N GAS

0-1 cm
1-4 cm
4-15 cm 15-30 cm

etc.



## C:N CO 2 

## S,R h



## PPT,V,L

C:N V

S C:N

CO 2 

## CO 2 

### CH 4 

### Uptake



## Decomp
R h 

## S R h


## S

S NPP 

# DAYCENT MODEL

S=soil type V=veg type L=land usey = 1.0765x - 0.6936
R

2 = 0.9587

y = 0.1688x + 3.6132
R

2 = 0.3931

0 5 10 15 20 25 30 35 40

0 1 0 2 0 3 0 4 0

N 2 O gN ha -1 day -1N2O gN ha-1day-1 DAYCENT IPCC 1-1 line Linear (DAYCENT) Linear (IPCC) Linear (1-1 line)



### Figure A-8: Comparisons of Results from DAYCENT Model and IPCC Tier 1 Method with Measurements of Soil N2O Emissions

### Major Crops, Average Annual Direct N 2 

### O Emissions Estimated Using the DAYCENT Model, 1990–2009 (Metric Tons CO 2 

### Eq./ha/year) Figure A-9

Fi gure A- 5: Major C ro ps, Av erag e Annual Direct N 2 

### O Em issions E st im ated Using the DA YC EN T Model, 1990-2009 (Mg CO 2 

### eq/ha/ yr )

Metric Tons CO 2 Eq./ha/year
< 0.4
0.4

– 0.5
0.5

– 0.6

>1 0.8

– 1 0.7 – 0.8 0.6

– 0.7

### Fi gure A- 6: Major C ro ps, Av erag e Annual N Losses Leading to I ndirec t N 2 

### O Em issions Estimated Using the DA YC EN T Model, 1990-2009 (kg N/ha/yr ) 

### Major Crops, Average Annual N losses Leading to Indirect N 2 

### O Emissions Estimated Using the DAYCENT Model, 1990-2009 (kg N/ha/year) Figure A-10

kg N/ha/year

60 to 80 40 to 60 20 to 40 10 to 20 < 10

80 to 100 > 100

### Fi gure A- 7: Grasslands

, Av er age Annual Direct N 2 

### O Em issions E st im ated Using the DA YC EN T Model, 1990-2009 (Mg C O 2 

### eq/ha/ yr ) 

### Grasslands, Average Annual Direct N 2 

### O Emissions Estimated Using the DAYCENT Model, 1990–2009 (Metric Tons CO 2 

### Eq./ha/year) Figure A-11

Metric Tons CO 2 Eq./ha/year
< 0.1
0.1

– 0.2
0.2

– 0.3

>0.6 0.5

– 0.6 0.4

– 0.5 0.3 – 0.4

### Fi gure A- 8: Grasslands

, Av er age Annual N Losses Leading to Indirect N 2 

### O Em issions Estimated Using the D AY CE NT Model, 1990-2009 (kg N/ha/yr ) 

### Figure A-12
Grasslands, Average Annual N Losses Leading to Indirect N 2 

### 0 Emissions Estimated Using the DAYCENT Model, 1990-2009 (kg N/ha/year)

kg N/ha/year

20 to 30 15 to 20 10 to 15 5 to 10 < 5

30 to 50 > 50

### X-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 –2006 Flow diagram of Carbon submodel (A) and Nitrogen submodel (B) Figure A-13 'MPXEJBHSBNPG$TVCNPEFM	"
BOE/TVCNPEFM	# 

### 'JHVSF

A

B

### Section Name X-13 Comparison of Measured Soil Organic C from Experimental Sites to Modeled Soil Organic C Using the Century Model Figure A-14

30 50 70 90

30 50 70 90

Sqrt Modeled Soil Organic Carbon (g C m -2 )Sqrt Modeled Soil Organic Carbon (gCm-2)

### X-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 –2006 Major Land Resource Areas by IPCC Climate Zone Figure A-15

### Section Name X-15 Uncertainty in Data Inputs Figure A-16 

### 6ODFSUBJOUZJO%BUB*OQVUT 'JHVSF

6ODFSUBJOUZJOEBUBJOQVUT	JFGFSUJMJ[FS
NBOVSFBOEUJMMBHFQSBDUJDFT
BSFFTUJNBUFEVTJOHB.POUF$BSMPQSPDFEVSFXJUISBOEPNESBXTGSPNJOQVU EBUBQSPCBCJMJUZEJTUSJCVUJPOT
GPSFBDI/3*QPJOUTJNVMBUF E.PEFMVODFS UBJOUZJTFTUJNBUFEUISPVHIBOFNQJSJDBMCBTFEBQQSPBDI6ODFSUBJOUZJOUIF MBOESFQSFTFOUBUJPOPG/3*JTFTUJNBUFEGSPNUIFTUBUJTUJDTDPN QJMFEGSPNUIF/3*TVS WFZTUPEFUFSNJOFUIFMBOEBSFBFYQBOTJPOGBDUPST
XIJDIBSFVTFE UPVQTDBMFEBUBUPUIFOBUJPOBMMFWFM

### Figure A-17


## Net C Stock Change, Per Hec ta re, For Mineral Soils Under Agricultural Management, 200 9

Note: Values greater than zero represent emissions, and values less than zero represent sequestration. Map accounts for fluxes associated with the Tier 2 and 3 inventory computations. See Methodology for additional details. 

### Net C Stock Change, per Hectare, for Mineral Soils Under Agricultural Management, 2009

Metric Tons CO 2 Eq./ha/year

< -0.5 -0.5 to -0.25 -0.25 to -0.1 -0.1 to -0.05 -0.05 to 0 > 0

### Net C Stock Changes, per Hectare, for Organic Soils Under Agricultural Management, 2009 Figure A-18

Note: Values greater than zero represent emissions. Metric Tons CO 2 Eq./year

0 to 0.1 0.1 to 0.25 0.25 to 0.5 0.5 to 1 > 1

No organic soils 

## Net C Stock Changes, Per H ec tare, F or Org an ic So ils Under Agricultural Management, 200 9Figure A-19: Methane Emissions Resulting fromLandfilling Municipal and Industrial WasteaBioCycle 2006 for MSW and activity factors for industrial waste.b1960 through 1988 based on EPA 1988 and EPA 1993;1989 through 2006 based on BioCycle 2006.c2006 IPCC Guidelines – First Order Decay Model.dEIA 2007 and flare vendor database.eEIA 2007 and EPA (LMOP) 2007.f2006 IPCC Guidelines; Mancinelli and McKay 1985; Czepiel et al 1996MSW andIndustrialWasteGeneratedaTotalMethaneGeneratedatLandfillscMSW andIndustrialWasteLandfilledbTotalMethaneEmittedNonrecoveredMethaneGeneratedat LandfillsCompostedRecycledIncineratedFlareddOxidizedRecoveredforEnergye10%f