Supplementary Information
Air Pollutant Emissions Inventory of Large Scale Production of Selected
Biofuel Feedstocks in 2022
Authors:
Yimin Zhang, Garvin Heath, Alberta Carpenter, Noah Fisher
Table of Contents
Table of Contents .......................................................................................................................................... 1
Table of Tables ............................................................................................................................................. 3
Table of Figures ............................................................................................................................................ 4
1
Biomass Production .............................................................................................................................. 5
1.1
Background on the Billion Ton Study Update ................................................................................. 5
1.2
Justification of the Selection of Cellulosic Feedstock Price Target Examined................................ 8
1.3 Comparison of Estimated Total Cellulosic Feedstock Availability in the BTS2 to That
Considered in Our Study at the Selected Price Target .............................................................................. 9
1.4 Allocation of Cellulosic Feedstocks Considered to Meet the Cellulosic Biofuel Mandate in the
RFS2 in 2022 .......................................................................................................................................... 12
1.5
2
Key Assumptions Used to Estimate County-Level Feedstock Availability .................................. 13
1.5.1
Agricultural residue............................................................................................................. 13
1.5.2
Switchgrass ......................................................................................................................... 14
1.5.3
County-Level Yield Comparison Among CG, WS and SG ................................................ 15
1.5.4
Forest residue ...................................................................................................................... 16
1.6
Number of Counties that Have Projected Production for Each Feedstock in 2022 ....................... 16
1.7
Corn Grain Data Limitations ......................................................................................................... 17
Emissions From Equipment Operation ............................................................................................... 17
2.1
NONROAD Model ........................................................................................................................ 17
2.1.1
2.1.1.1
NH3 Emissions ............................................................................................................ 19
2.1.1.2
PM2.5 Emissions........................................................................................................... 19
2.1.1.3
VOC Emissions ........................................................................................................... 19
2.1.2
2.2
Combustion Emissions Calculation .................................................................................... 18
Age distributions for equipment populations ...................................................................... 20
Equipment Lists ............................................................................................................................. 20
2.2.1
Corn Grain Non-Harvest Activity ....................................................................................... 20
2.2.1.1
3
Corn Grain Irrigation .................................................................................................. 22
2.2.2
Corn Stover and Wheat Straw Harvest ............................................................................... 24
2.2.3
Switchgrass ......................................................................................................................... 26
2.2.4
Forest Residue ..................................................................................................................... 27
Fertilizer and Pesticide Application .................................................................................................... 28
3.1
Nitrogen Fertilizer.......................................................................................................................... 28
1
3.2
Pesticide ......................................................................................................................................... 30
4
Fugitive Dust Emissions ..................................................................................................................... 32
5
Metrics Evaluated ............................................................................................................................... 33
5.1
Emission per gallon of ethanol (Figure 1 in the main text)............................................................ 33
5.2
Contribution Analysis (Figure 2 in the main text) ......................................................................... 34
5.3 Comparison to National Emissions Inventory (NEI) For Cellulosic Feedstocks (Figure 3 in the
main text) ................................................................................................................................................ 35
5.4 NEI Ratio Thresholds Compared to Attainment Status for National Ambient Air Quality
Standards (NAAQS) (Figure 4 in the main text) .................................................................................... 36
6
Additional Maps and Figures .............................................................................................................. 37
6.1
Biomass Production Density .......................................................................................................... 37
6.2
Emissions Density.......................................................................................................................... 38
6.2.1
6.3
Cellulosic ............................................................................................................................ 39
Individual Feedstock Comparison to NEI ...................................................................................... 47
6.4 Counties with R exceeding thresholds compared to NAAQS Nonattainment Areas ..................... 50
7
Comparison To Other Studies ............................................................................................................. 51
8
References ........................................................................................................................................... 53
9
List of Acronyms...................................................................................................................................61
2
Table of Tables
Table S1. Comparison of estimated 2022 production volumes for all cellulosic feedstocks in the
BTS2 to those considered in our scenario..................................................................................... 11
Table S2, Switchgrass fertilizer application rates. ........................................................................ 15
Table S3. Number of counties projected to produce each cellulosic feedstock............................ 17
Table S4. Conversion factors for default NONROAD output ...................................................... 20
Table S5. Corn grain conventional till, non-harvest equipment operations ................................. 21
Table S6. Corn grain reduced till, non-harvest equipment operations ......................................... 21
Table S7. Corn grain no-till non-harvest equipment operations ................................................... 21
Table S8. Irrigation equipment as modeled by the NONROAD program .................................... 24
Table S9. Corn stover and wheat straw harvest and transport equipment .................................... 26
Table S10. Switchgrass non-harvest equipment ........................................................................... 26
Table S11. Switchgrass harvest equipment .................................................................................. 27
Table S12. Forest residue equipment ............................................................................................ 27
Table S13. Emission factors for five commonly used nitrogen fertilizers ................................... 30
Table S14. Switchgrass pesticide application schedule (P, lb active ingredient/ac) .................... 32
Table S15, Fugitive dust emission factors (lb of PM10/ac)1.......................................................... 32
Table S16. Constants used to estimate fugitive PM emissions from transportation ..................... 33
Table S17. Ethanol Yields ............................................................................................................ 34
Table S18. NAAQS criteria air pollutant and paired air pollutants or precursors ........................ 36
Table S19. Switchgrass comparison between our estimates and GREET Version 1_2012 ......... 52
Table S20. Corn stover comparison between our estimates and GREET Version 1_2012 .......... 52
Table S21. Forest residue comparison between our estimates and GREET Version 1_2012 ...... 53
Table S22. Forest residue comparison between our estimates and GREET Version 1_2012…...53
3
Table of Figures
Figure S1. POLYSYS core modules (UT 2012) ............................................................................. 7
Figure S2. Biofuel quantities produced from total cellulosic feedstocks at selected farm-gate
price targets in the BTS2 baseline scenario . ................................................................................ 10
Figure S3. Percentage of respective feedstock among the cellulosic feedstocks considered in this
study at $55/dt price target. ........................................................................................................... 12
Figure S4. Cellulosic biofuel (represented by ethanol) production (16 bgy) using 52% of
cellulosic feedstocks considered in this study .............................................................................. 13
Figure S5. Distribution of county-level yields (dry tons/acre) for corn stover, switchgrass, and
wheat straw. .................................................................................................................................. 16
Figure S6. Single pass combine collection rates (Jacobson 2012) ............................................... 24
Figure S7. County-level distribution of nitrogen application rate ranges for conventional till and
no-till corn grain production (no-till and reduced till acres are assumed to have the same nitrogen
application rate). ........................................................................................................................... 29
Figure S8. Share of nitrogen fertilizer applied to CG, CS and WS .............................................. 30
Figure S9. Production density for all feedstocks (dry metric ton/square kilometer) .................... 38
Figure S10. Cellulosic emissions density maps (kg of pollutant per km2 county area)................ 41
Figure S11. Corn grain emissions density maps (kg of pollutant per km2 county area)............... 42
Figure S12. Corn stover emissions density maps (kg of pollutant per km2 county area) ............. 43
Figure S13. Wheat straw emissions density maps (kg of pollutant per km2 county area) ............ 44
Figure S14. Switchgrass emissions density maps (kg of pollutant per km2 county area) ............ 45
Figure S15. Forest residue emissions density maps (kg of pollutant per km2 county area) ......... 46
Figure S16. Ratio of switchgrass emissions to NEI non-point and non-road emissions .............. 47
Figure S17. Ratio of corn stover emissions to NEI non-point and non-road emissions. ............. 48
Figure S18. Ratio of wheat straw emissions to NEI non-point and non-road emissions.............. 49
Figure S19. Ratio of forest residue production emissions to NEI non-point and non-road
emissions. ...................................................................................................................................... 50
4
1 Biomass Production
The selected feedstocks considered for producing cellulosic biofuels in this study are corn stover
(CS), wheat straw (WS), forest residue (FR) (logging residues from non-federal forests, in
particular), and switchgrass (SG) (a perennial dedicated energy crop). All assumptions in this
section are derived from the Billion Ton Study Update (BTS2), which provides a strategic
assessment of potential biomass supply for many major feedstocks within the contiguous United
States (DOE 2011). SG is assumed to be grown on rain-fed land east of the 100th Meridian. This
meridian matches the western boundary of Oklahoma (excluding the panhandle) and bisects
North Dakota, South Dakota, Nebraska, Kansas, and Texas. CS and WS are only harvested from
acreage where reduced tillage or no till is practiced; collection of crop residues is prohibited
from conventionally-tilled acres. All crops and emissions are modeled on an annual basis.
Because SG is a perennial crop, details are presented in Section S.3.2.3 to describe how annual
emissions are calculated.
This section provides additional information on feedstock production in the Methods (Section 2)
of the main text.
1.1
Background on the Billion Ton Study Update
The first Billion Ton Study (BTS) report (Perlack et al 2005) estimated the “potential” biomass
within the contiguous US considering a number of assumptions about current and future
inventory and production capacity, availability, and technologies. The first BTS was a nationallevel study based on 2005 US Department of Agriculture (USDA) agricultural projections, 2000
forestry Resources Planning Act (RPA) and Timber Products Output (TPO).
The BTS2 (DOE 2011) updated the estimates in the first BTS and addressed a number of
shortcomings. One particular update, which is critically important to our air quality analysis, is
that the BTS2 estimates provide county-by-county inventory of each individual feedstock
examined in this study. The BTS2 evaluated two scenarios—baseline and high-yield. The
baseline scenario assumes a continuation of the USDA 10-year forecast for the major food and
forage crops and a continuation in trends toward no-till and reduced cultivation. It also extends
the study an additional 10 years to 2030. Energy crop yield reflects learning or experience in
5
planting energy crops and limited gains that can be made through breeding and selection of
better varieties. The high-yield scenario is more closely aligned to the assumptions in the 2005
BTS; the projected increase in corn yield averages almost 2% annually over the 20-year
simulation period while the energy crop productivity increases are modeled at three levels—2%,
3%, and 4% annually. These gains are due not only to experience in planting energy crops, but
also to more aggressive implementation of breeding and selection programs. We assume the
baseline scenario for analyses of this study.
The BTS2 used the Policy Analysis System (POLYSYS) model (UT 2012) to estimate
production of agricultural biomass. POLYSYS is a modeling framework developed to simulate
changes in policy, economic, or resource conditions and to estimate the resulting impacts on the
US agricultural sector (UT 2012). It is structured as a system of interdependent modules
simulating crop supply for 305 production regions, national crop demand and prices, national
livestock supply and demand, and agricultural income (UT 2012). The modules also include crop
rotation and management practices, environmental impacts, and production of energy crops on
multi-year production cycles. POLYSYS uses as anchors for its analyses national baseline
projections and related assumptions from the USDA, Food and Agriculture Policy Research
Institute (FAPRI) and the US Congressional Budget Office (CBO). The land base for agricultural
resources in the BTS2 covered 311 million acres of cropland and 140 million acres as cropland
pasture and permanent pasture (DOE 2011, Section 5.2.1). Sustainability criteria were used in
determining the availability of crop residues. The limiting factors for residue removal include
soil organic carbon, wind and water erosion, plant nutrient balance, soil water and temperature
dynamics, soil compaction, and offsite environmental factors.
6
Figure S1. POLYSYS core modules (UT 2012)
The non-federal forest biomass and wood waste resources considered in this BTS2 (DOE 2011)
include: forest residues from integrated forest operations of timberland, other removal residue,
thinnings from other forestland, unused primary and secondary mill processing residues, urban
wood wastes, and conventionally sourced wood. Estimation of forest biomass in the BTS2 was
derived from various data sources, including an annual survey of industrial users of roundwood
by USDA Forest Service, Resource Planning Act (RPA) projections and forest inventory and
analysis data of the USDA Forest Service on logging residues, thinnings, and other removals.
The BTS2 considered 504 million acres of non-federal timberland and 91 million acres of other
forestland in assessing the forestland resources. Forestry resources considered in BTS2 are only
those that are not currently utilized. In all cases, practices for harvesting forest biomass were
expected to have the goals of protecting ecological functions and minimizing negative
7
environmental impacts (DOE 2011). The forest estimates in BTS2 consider residue access,
recovery and merchantability, and other requirements for resource sustainability.
BTS2 (DOE 2011) is used in this study for estimating county-level feedstock quantities required
to meet the cellulosic biofuel mandate in the updated Renewable Fuel Standard (RFS2) for 2022.
While other data sources (such as the USDA National Agricultural Statistics Service (NASS))
are available for estimating biomass feedstock production, the BTS2 data are selected because
they provided consolidated county-level estimates with an extensive feedstock coverage with
projections through 2030, and allowed for selecting feedstock price targets to model potential
feedstock availability by different feedstock uses. The price selected here is used solely for
estimating air emissions from supplying this amount of biomass to meet anticipated demand
from biopower and cellulosic biofuel production in 2022, and likely will change in the future due
to factors such as inflations. The price and quantities utilized in this assessment reflect estimates
when the BTS2 data was officially released to the public.
1.2
Selection of Cellulosic Feedstock Production Scenario
The quantity of biomass feedstock which could be made available for biofuel production varies
by the price the biofuel producers are willing to pay. Biofuel producers compete with other
biomass users such as biopower generators in the market. Hence, the market price of biomass
will be dictated by the combined demand of the major users. To understand the minimum price
required to meet demand from two potentially largest biomass uses in the near future, i.e.,
advanced biofuels and biopower, Langholtz et al (2012) employed POLYSYS to estimate the
minimum prices (in 2011 dollars) under price-run and demand-run scenarios, respectively. The
price-run scenario mimicked long-term contracting conditions, where the biomass users lock a
fixed price for an extended period while the demand-run scenario assumed a gradual price
change due to varying demand over time. The combined demand modeled by Langholtz et al
(2012) took into account biomass quantity required to meet additional 96.6 billion kWh of
biopower and 21 billion gallons of advanced biofuels required by the RFS2 by 2022. The
estimated minimum farm-gate price is $53/dry short ton ($58.43/ dry metric ton) and $62/dry
short ton ($68.36/dry metric ton), respectively, under the price-run and demand-run scenarios by
2022. County-level cellulosic feedstock availability used in this study is based on a $55/dry short
8
ton ($60.64/dry metric ton) farm-gate price, which is slightly lower than the estimated price
under the demand-run scenario by Langholtz et al (2012) because we only consider the cellulosic
biofuel mandate of the RFS2, which is 16 billion gallons by 2022. We assume some advanced
biofuels will be produced from non-lignocellulosic feedstocks such as algae feedstock.
1.3
Comparison of Estimated Total Cellulosic Feedstock Availability in the BTS2 to That
Considered in Our Study at the Selected Price Target
Although the BTS2 assessed a large number of cellulosic feedstocks (Table S1), our study
focuses on three agricultural feedstocks (i.e., CS, WS, and SG) and one forest feedstock (i.e.,
FR). CS and WS are the two largest agricultural residues among all agricultural residue
resources. SG is selected because it is expected to be the major contributor to biomass from
dedicated energy crops. Logging residues (from non-federal lands) we examine account for 46%
of forest biomass. Other feedstocks (not considered here) can be evaluated in the future when
data are available. In addition, some feedstocks (such as manure) might be more appropriate for
biopower or biomethane production (vs. liquid biofuels assessed here). As noted in the previous
section, these quantities in Table S1 are subject to change in the future.
Figure S2 shows the percentages of feedstocks we examine among total cellulosic feedstock
availability estimated for the baseline scenario by BTS2. The figure also shows the amount of
biofuels, which can be produced from the biomass estimated for the baseline scenario of the
BTS2.
9
Billion gallons produced
70
60
50
40
50%
30
20
53%
68%
54%
10
0
$45
$50
$55
Farmgate Price Target ($/dry ton)
Scenario feedstocks
$60
All BTS2 feestocks
Figure S2. Biofuel (represented by ethanol) quantities produced from total cellulosic feedstocks at selected
farm-gate price target in the BTS2 baseline scenario (BLY+EC1_BLT). The percentages located above
‘Scenario feedstocks’ bars represent the percentages of feedstocks in our scenario compared to all BTS2
cellulosic feedstocks.
Table S1 shows the production volume for all cellulosic feedstocks in the baseline scenario of
BTS2, as well as cellulosic feedstocks considered in our study at $55/dry short ton ($60.64/dry
metric ton). As shown in Table S1, cellulosic feedstocks considered in this study account for
53% of total cellulosic annual production estimated by BTS2 for the baseline scenario (DOE
2011).
Figure S3 shows the distribution of mass of each cellulosic feedstock considered at $55/dry short
ton ($60.64/dry metric ton) by 2022. SG is estimated to account for 43% of the selected
cellulosic feedstocks, followed by corn stover (34%).
10
Table S1. Comparison of estimated 2022 production volumes for all cellulosic feedstocks in the BTS2 to those
considered in our scenario.
BTS2
CELLULOSIC FEEDSTOCK
DESCRIPTION
Agricultural Biomass/Residuesa
Barley straw
Corn stover
Cotton residue
Hay
Oat straw
Orchard and vineyard prunings
Rice hulls
Rice straw
Sorghum stubble
Sugarcane trash
Wheat dust
Wheat straw
Subtotal
Dedicated Energy Cropsb
Perennial grassesc
Switchgrassd
High-yield sorghume
Subtotal
Forest Biomass
Conventional wood
Coppice and non-coppice woody crops
Integrated operations (LOGRLOGT)f
Dry short
tons/year
Scenario
% of total
BTS2
1,932,300
114,697,500
5,875,600
133,766,600
19,100
5,541,300
1,668,700
7,361,500
622,100
1,121,200
580,100
28,875,800
302,061,800
0.3%
18.2%
0.9%
21.3%
0.0%
0.9%
0.3%
1.2%
0.1%
0.2%
0.1%
4.6%
48.0%
144,495,800
23.0%
8,915,000
153,410,800
1.4%
24.4%
933,950
61,105,600
34,626,800
0.1%
9.7%
5.5%
Dry short
tons/year
% of total
BTS2
114,694,378
18.2%
28,872,601
143,566,979
4.6%
22.8%
144,498,509
23.0%
144,498,509
23.0%
44,992,000
7.2%
44,992,000
7.2%
Logging residues (LOGR)g
Treatment thinnings, other forest lands
Subtotal
906,250
97,572,600
0.1%
15.5%
Waste Biomass
Manure
Urban wood waste, C&D
Urban wood waste, municipal solid waste
Subtotal
41,913,350
23,388,100
10,754,900
76,056,350
6.7%
3.7%
1.7%
12.1%
-
629,101,550
100%
333,057,488
TOTAL
52.9%
a
Agriculture biomass and residues includes feedstocks currently being produced with production quantities available
as bioenergy feedstocks.
b
Dedicated energy crops are feedstocks that are not currently in production but are projected to be available as
bioenergy feedstocks.
c
Perrenial grasses include switchgrass (Panicum virgatum), big bluestem (Andropogon gerardii), and indian grass
(Sorghastrum nutans)(DOE 2011).
d
The BTS2 total for perennial grass production is the sum of county production estimates each independently
rounded to the 100’s prior to summation. The scenario total for switchgrass is from raw data (Eaton, 2013), which
explains the difference between the two estimates.
11
Called “annual energy crop” in BTS2 (DOE 2011).
The integrated operations production numbers reflect 50% of available logging residues (tops and limbs, LOGR)
and 50% of available forest thinnings (LOGT).
g
Logging residues (LOGR) for the scenario reflects 100% of available logging residues from non-federal lands only.
e
f
WHEAT
STRAW
9%
SWITCHGRASS
43%
CORN STOVER
34%
FOREST
RESIDUES
14%
Figure S3. Percentage of respective feedstock among the cellulosic feedstocks considered in this study at
$55/dt price ($60.64/dry metric ton) target.
1.4
Allocation of Cellulosic Feedstocks Considered to Meet the Cellulosic Biofuel
Mandate in the RFS2 in 2022
One of the goals of this study is to understand the impact of additional air emissions due to the
production of 16 billion gallons of cellulosic biofuels for meeting the mandate in RFS2 by 2022.
To estimate the percentage of selected cellulosic biomass needed for producing 16 billion gallons
of cellulosic biofuels, we use a generic biofuel (represented by ethanol) yield of 89.9 gallons/dry
short ton (99.1 gallons/dry metric ton) for agricultural cellulosic feedstocks (CS, WS, and SG)
and a yield of 75.7 gallons/dry short ton (83.5 gallons/dry metric ton) for FR as per Hsu et al
(2010). Based on Equation S1, feedstock quantity in Table S1 and assumed ethanol yields, we
estimate that about 52% of the BTS2 projected biomass production for the four feedstocks we
examine will be required to meet RFS2’s cellulosic biofuel mandate in 2022.
% 𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑑 =
𝑅𝐹𝑆2 𝑣𝑜𝑙𝑢𝑚𝑒𝑟𝑖𝑐 𝑚𝑎𝑛𝑑𝑎𝑡𝑒 𝑓𝑜𝑟 𝑐𝑒𝑙𝑙𝑢𝑙𝑜𝑠𝑖𝑐 𝑏𝑖𝑜𝑓𝑢𝑒𝑙 𝑖𝑛 2022
𝐸𝑡ℎ𝑎𝑛𝑜𝑙 𝑓𝑟𝑜𝑚 𝐵𝑇𝑆2 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑣𝑜𝑙𝑢𝑚𝑒𝑠 𝑓𝑜𝑟 𝑐𝑒𝑙𝑙. 𝑓𝑒𝑒𝑑𝑠𝑡𝑜𝑐𝑘𝑠 𝑒𝑥𝑎𝑚𝑖𝑛𝑒𝑑
Equation S1
12
Allocation of feedstocks to different end uses (e.g., biofuel, biopower) is not established in the
BTS2 (DOE 2011). Consequently, we apply this estimated national average (i.e., 52%) equally to
all counties and all cellulosic feedstocks considered (as discussed in Section 2.3.1 of the main
text). Figure S4 shows the amount of biofuels (represented by ethanol), which can be produced
from each feedstock examined (after accounting for the 52% share) based on the BTS2 results.
8
7.1
7
5.7
Billion Gallons
6
5
4
3
1.9
2
1.4
1
0
SG
CS
FR
WS
Figure S4. Cellulosic biofuel (represented by ethanol) production using 52% of the BTS2 production of
cellulosic feedstocks considered in this study (total equals 16 bgy).
1.5
Key Assumptions Used to Estimate County-Level Feedstock Availability
1.5.1 Agricultural residue
The amount of agricultural residues generated as a by-product of grain production can be
estimated from harvest index (HI), which is defined as the mass ratio of grain to total biomass
(DOE 2011, Text Box 4.3). For example, if a HI is 0.5, the stover to grain ratio is 1:1. The BTS2
assumes a static harvest index of 0.5 for CS and WS for all counties in the baseline scenario
modeled in this study.
To protect soil quality, BTS2 prohibits residue collection from acres using conventional tillage.
The amount of agricultural residue that can sustainably be removed from agricultural cropland is
subject to two constraints; 1) removals cannot exceed the tolerable soil loss limit as
recommended by the USDA’s Natural Resource Conservation Service (NRCS) and 2) removals
13
cannot result in long-term loss of soil organic matter as estimated by the Revised Universal Soil
Loss Equation (RUSLE2) and the Wind Erosion Prediction System (DOE 2011). These two
constraints were taken into consideration for estimating county-level sustainable residue removal
rate in the BTS2.
Based on data from existing literature, the BTS2 (DOE 2011) estimated an average nutrient
composition of removed CS. Nutrient values used were 14.8 pounds (lb) of nitrogen, 5.1 lb P2O5
(phosphate), and 27.2 lb K2O (potassium) per dry short ton of corn stover (16.3, 5.6, and 30.0
lb/dry metric ton of CS for N, P2O5 and K2O respectively). Similarly, average nutrient values for
wheat straw were 15.4 lb nitrogen, 2.7 lb P2O5 and 25.4 lb K2O for each dry short ton removed
(17.0, 3.0 and 28.0 lb/dry metric ton of WS for N, P2O5 and K2O respectively). We assume the
equivalent nutrients will need to be compensated during corn and wheat production by applying
additional fertilizers (i.e., in addition to what is needed without residue removal).
Because agricultural residues are a by-product from crop production, we chose the productpurpose allocation approach (Wang et al. 2011) for estimating emissions associated with residue
harvesting. In other words, only additional inputs exclusively attributable to residue removal
(e.g., additional fertilizer application) are allocated to residues. Because we model a scenario for
2022, a single pass residue collection system (i.e., grain and residue collected simultaneously)
designed by INL (Jacobson 2012) is assumed in our study.
1.5.2 Switchgrass
The BTS2 assumes SG is grown only on rain-fed lands, which constrains growth to counties east
of the 100th meridian except for one county in the Pacific Northwest (DOE 2011). The BTS2
also assumes that to grow SG, the returns must be greater than the pasture rent plus additional
establishment and maintenance costs (DOE 2011). Best management practices for establishment,
cultivation, maintenance, and harvesting are assumed when determining price-point availability
(DOE 2011). The total amount of cropland in any given county that can be converted to SG is
limited to 25% (DOE 2011).
SG stands are expected to last at least 10 years, after which time the stands could be renovated
and replaced with new, higher-yielding cultivars (DOE 2011). The assumed stand life in BTS2 is
14
10 years. No-till planting is assumed for SG establishment (1st year) as it has significant cost and
environmental benefits (DOE 2011). After the establishment year, well-established SG stands
require limited herbicides. Nitrogen fertilizer is not recommended during the planting year since
nitrogen encourages weed growth. Data on fertilizer and herbicide usage for both establishment
and maintenance years were provided by Turhollow (2011). A summary of this information is
shown in Table S2.
Table S2, Switchgrass fertilizer application rates.
Year Applied
N
P2O5
Establishment Year (Year 1)
None
40 lb/ac1
Maintenance Years (Years 2-10) 13 lb N/dry short ton harvested (14.3
40 lb/ac1
lb N/dry metric ton)
1
Apply in the Northeast, Lake States, Corn Belt, Appalachia, Southeast, Delta States
K2O
80 lb/ac1
80 lb/ac1
Because activities associated with establishment year and maintenance years differ, we assume
1/10th of SG acres in any given county are in each year of the production cycle (i.e., 1/10th in 1st
year, 1/10th in 2nd year, so on so forth). For a given year (e.g., 2022), county-level emissions are
summed up over acres in each year of the production cycle using Equation S2.
10
𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 = ∑
𝑖=1
𝐴 × 𝐸𝑖
10
Equation S2
Where:

Emissions = Emissions from SG production in a county in a given year

A = Total SG acres in a county in a given year

Ei = Sum of all activity emissions from 1 acre of SG production in a given year (i) of the
10-year cycle (Ei varies by year due to different activities and chemical requirements)
1.5.3 County-Level Yield Comparison Among CG, WS and SG
Figure S5 displays a histogram of yields for WS, CS, and SG. SG has the highest yields, with CS
having comparatively higher yields than WS.
15
180
160
Number of counties
140
120
100
CS
80
WS
SG
60
40
20
0.20
0.60
1.00
1.40
1.80
2.20
2.60
3.00
3.40
3.80
4.20
4.60
5.00
5.40
5.80
6.20
6.60
7.00
7.40
7.80
8.20
8.60
9.00
9.40
9.80
0
Yield (dry tons/acre)
Figure S5. Distribution of county-level yields (dry short tons/acre) for corn stover (CS), switchgrass (SG), and
wheat straw (WS).
1.5.4 Forest residue
The FR scenarios were based on the integrated operations (LOGR) feedstock category of the
BTS2 harvested from non-federal lands. In the BTS2, integrated operations (LOGRLOGT)
combine both residues and thinnings with quantities reflecting 50% of each (50% LOGR, 50%
LOGT). ORNL performed a custom run of POLYSYS to provide results for 100% residues for
use in this study. The data provided by ORNL included the Federal Information Processing
Standard (FIPS) county code and the production quantity per county in dry tons (Eaton 2012).
1.6
Number of Counties that Have Projected Production for Each Feedstock in 2022
Table S3 shows the number of counties that have projected production of a certain feedstock and
the number of counties which have production of at least one cellulosic feedstock for 2022 based
on the BTS2 data (DOE 2011). The total number of counties in the contiguous US is also shown
16
alongside for comparison. These numbers can help interpret the results of Figure 3 in the main
text.
Table S3. Number of counties projected to produce each cellulosic feedstock
Corn
Stover
1104
1.7
Forest
Residue
2169
Switch
grass
1183
Wheat
Straw
1169
Cellulosic feedstock
(at least one)
2841
Number of Counties
in Contiguous US
3109
Corn Grain Data Limitations
Corn grain (CG) crop budgets are based on data between 1996 and 2007 and are on a state level
(not a county level). Irrigation data is also based on state averages. We assume the crop budgets
and irrigation requirements are identical among all counties within a state.
The existing crop budget database shows significant discontinuity between regions in their inputs
and practices. To correct for this, University of Tennessee (UT) and ORNL have developed a
new methodology that uses fewer, but more reliable, budgets to smooth the transition between
regions (Hellwinckel 2012).
2 Emissions From Equipment Operation
This section provides additional information on NONROAD2008, which is used for modeling
emissions from operating agricultural machinery for non-harvest, harvest and transport activities.
2.1
NONROAD Model
The EPA’s NONROAD2008a (available at http://www.epa.gov/oms/nonrdmdl.htm#model)
model is the primary tool used in this study to compute air pollutant emissions from machinery
operation for field activities because the model generates emission inventories from individual
counties to the entire nation, covers all the major air pollutants of interest (CO, NOX, SOX, PM10,
total hydrocarbon [THC]) except for NH3 (which is calculated separately based on fuel
consumption and emission factors), and takes into account emission controls required by
regulations over time (from 1970 to 2050) (EPA 2005b). In particular, the NONROAD model is
designed to account for the effect of the federal emissions regulations. However, it does not
cover any California emissions standards or any proposed federal emissions standards.
17
The model includes more than 80 basic and 260 specific types of non-road equipment, and it
further stratifies equipment types by horsepower rating. Fuel types include gasoline, diesel,
liquefied petroleum gas (LPG), and compressed natural gas (CNG).
NONROAD2008a supersedes all previous versions of the model. Compared to the original
NONROAD2005 model, the main revision of NONROAD2008a consists of new exhaust and
evaporative emission controls; the new version predicts lower THC, CO, NOX, and PM
emissions than the previous version (EPA2005a) with comparable scenario inputs owing to new
emission controls based on regulations promulgated since the original version. Further model
changes include (but are not limited to) the following:
 Added ability to model effects of ethanol blends
 Revised emission factors for small spark ignition engines
 Revised fuel consumption for some engines.
The NONROAD2008a model will hereafter be referred to simply as the NONROAD model.
2.1.1 Combustion Emissions Calculation
The NONROAD model uses the Equation S3 (EPA 2010b) to calculate combustion exhaust
emissions.
Emissions = (POP) ∗ (Power) ∗ (LF) ∗ (A) ∗ (EF)
Equation S3
where:

Emissions = the total emissions (g);

POP = the equipment population in a given county (#);

Power = the average horsepower of the machinery (hp);

LF = the load factor, the fraction of available power (%);

A = the activity of the equipment (hours/year);

EF = the emission factor (g/(hp*hr)).
18
The NONROAD program uses source classification codes (SCC) to distinguish the different
engine types and horsepower (hp) ranges to further classify an engine. It is important to
understand that the program does not model specific pieces of equipment, but engines of varying
power ranges (EPA 2005a). For example, a 135-hp tractor is modeled in a 100–175 hp range.
More information on how the NONROAD model calculates emissions and the default values
used in the program may be found in the model’s technical documentation (EPA 2010b).
2.1.1.1 NH3 Emissions
Ammonia emissions are calculated based on fuel consumption. First, the fuel consumption,
reported in gallons by NONROAD, is converted to Btu using the lower heating value. After the
conversion, a fuel-specific emission factor per Btu of fuel is applied, as shown in Table S4.
2.1.1.2 PM2.5 Emissions
The size distribution of the particulate matter is given in the NONROAD model’s technical
documentation (EPA 2010d, EPA 2010e). As shown in Table S4 below, PM2.5 emissions are
derived from PM10 and are distinguished by fuel type (EPA 2010c).
2.1.1.3 VOC Emissions
Hydrocarbon emissions can be reported using several metrics depending on the desired use of the
emission estimates and the measurement technique used in the underlying data. Most
hydrocarbon emissions data from mobile sources are measured as total hydrocarbon (THC,
compounds existing entirely of hydrogen and carbon) (EPA 2005a). The NONROAD program
adds THC to oxygenated compounds (alcohols and aldehydes commonly found in engine
exhaust) then subtracts the methane and ethane components to get VOC (EPA 2010a). The
definition of VOC excludes methane, ethane, acetone, and compounds not commonly found in
large quantities in engine exhaust, like chlorohydrocarbons. Although acetone is not subtracted,
it is present in smaller quantities compared to methane and ethane, and will have a negligible
effect on the results (EPA 2010a, EPA 2010g). The THC to VOC conversion factors are shown
below in Table S4.
19
Table S4. Conversion factors for default NONROAD output
Diesel
Gasoline
LPG
CNG
PM2.5
LHV (Btu/gallon)
0.97*PM101
0.92*PM102
1.0*PM102
1.0*PM102
128,4503
116,0903
84,9503
20,2683
NH3
(g/MMBtu)
0.684
1.014
(not reported)
(not reported)
VOC
1.053*THC5
0.933*THC5
0.995*THC5
0.004*THC5
1
EPA 2010d
EPA 2010e
3
DOE 2012
4
EPA 2011h
5
EPA 2010a
2
2.1.2 Age distributions for equipment populations
The NONROAD model calculates age distributions for equipment populations for given
equipment types and scenario years. This calculation is necessary for the model to account for
several factors which affect emissions over time, including emissions deterioration, new
emissions standards, technology changes, changes in equipment sales and total equipment
population, and scrappage programs. Further information on the details may be found in the
NONROAD model’s technical documentation (EPA 2005b, EPA 2005c, EPA 2004, EPA
2010f).
2.2
Equipment Lists
2.2.1 Corn Grain Non-Harvest Activity
We only model CG under current practices (for year 2011) due to data limitations (e.g., no
reliable projected crop budget for 2022). Three tillage types were considered for CG:
conventional till, reduced till, and no-till (based on conventional tillage with moldboard plow,
limited till and no-till crop budget data (APAC 2007)). Conventional till requires the most
frequent use of farm machinery among all tillage types. Tables S5-S7 show the CG equipment
usage for each tillage type.
20
Table S5. Corn grain conventional till, non-harvest equipment operations
Field Activity
Dry Fertilizer Spreader (trailer mounted)1
Chemical Applicator GE30ft (trailer mounted)1
Chemical Applicator GE30ft (trailer mounted)1
Fertilizer Applicator (attached to implement)1
8 Row Planter (regular)1
Field Cultivator GE15ft1
Tandem Disk (regular) 14-18ft1
Moldboard Plow (regular) 4-6b1
1
Tractor Used (hp)
Hours/Acre
205
205
205
205
205
205
205
205
0.0873
0.0391
0.0391
0.2619
0.2619
0.063
0.1611
0.342
APAC 2007
Table S6. Corn grain reduced till, non-harvest equipment operations
Field Activity
Dry Fertilizer Spreader (trailer mounted)1
Dry Fertilizer Spreader (trailer mounted)1
Row Cultivator GE15ft1
8 Row Planter (regular)1
Chemical Applicator GE30ft (trailer mounted)1
Tandem Disk (regular) 14-18 ft1
Offset Disk/Light duty 14-18 ft1
1
Tractor Used (hp)
Hours/Acre
100
100
135
100
100
135
135
0.0873
0.0873
0.1281
0.1
0.0391
0.1611
0.1384
APAC 2007
Table S7. Corn grain no-till non-harvest equipment operations
Field Activity
Dry Fertilizer Spreader (trailer mounted)1
Dry Fertilizer Spreader (trailer mounted)1
Chemical Applicator GE30ft (trailer mounted)1
Dry Fertilizer Spreader (trailer mounted)1
Chemical Applicator GE30ft (trailer mounted)1
7 Row No-till Planter1
1
Tractor Used (hp)
Hours/Acre
160
160
160
160
160
160
0.0873
0.0873
0.0391
0.0873
0.0873
0.2001
APAC 2007
21
2.2.1.1 Corn Grain Irrigation
The USDA Farm and Ranch Irrigation Survey is conducted every five years to collect US onfarm irrigation data (USDA 2007). The survey covers all farms that produce $1,000 or more of
agricultural products. US farms and ranches utilize gasoline, diesel, liquefied petroleum gas
(LPG), compressed natural gas (CNG), and electricity for their irrigation systems. This applies to
both well and surface water sources as well as pressure and gravity irrigation systems. Although
the dominant energy sources for the irrigation systems are electricity (60%) and diesel (27%)
(USDA 2007), the energy mix can vary by state.
The following tables from the 2008 survey were utilized to understand irrigation pumping
requirements for CG (USDA 2007). The survey provides data on a state level. County-level data
are derived from the state level based on CG county-level farm acreage.

Table 15 - Irrigation Wells Used on Farms: 2008 and 2003

Table 16 - Characteristics for Irrigation Wells Used on Farms: 2008 and 2003

Table 18 - Irrigation Pumps on Farms for Wells: 2008 and 2003

Table 19 - Irrigation Pumps on Farms Other Than for Wells: 2008 and 2003

Table 20 - Energy Expenses for On-Farm Pumping of Irrigation Water by Water Source and
Type of Energy: 2008 and 2003

Table 28 - Estimated Quantity of Water Applied and Primary Method of Distribution by
Selected Crops Harvested: 2008 and 2003.
For each state the following data were extracted:

Crop (corn only)

State

Irrigation method (well, non-well, discharge, reservoir, and boost)

Irrigated acres

Amount of water used for irrigation per acre (uH2O, acre-ft/acre)

Fuel type (gasoline, diesel, LPG, natural gas, electricity)

Percentage of acres by fuel type and irrigation method
22

Average flow (q, gpm)

Static water depth (d, ft)

Load factor of engine (lf, %)

Pump efficiency (pe, %)

Gear drive efficiency (gde, %)

System pressure (p, lb/in2)

Friction head (FH, ft)

Velocity head (VH, ft)

Pressure head (PH, ft)
Once the data were extracted from the USDA irrigation survey, time required for irrigation
(hours/acre) and the required horsepower (hp) are calculated using Equations S4 and S5 (CARB
2006) and fed into the NONROAD model to determine irrigation equipment emissions.
𝑔𝑎𝑙 𝐻2 𝑂
ℎ𝑜𝑢𝑟𝑠 𝑢𝐻2𝑂 32581 𝑎𝑐𝑟𝑒 − 𝑓𝑡
=
∗
𝑎𝑐𝑟𝑒
𝑞
60 𝑚𝑖𝑛/ℎ𝑜𝑢𝑟
Equation S4
ℎ𝑝 =
𝑞 ∗ (𝑑 + 𝑃𝐻 + 𝐹𝐻 + 𝑉𝐻)
1
1
∗
∗
3961.80
𝑔𝑑𝑒 ∗ 𝑝𝑒 𝑙𝑓
Equation S5
where:

3,961.8 ft/min-gal H2O= (55 ft-lb/sec) * (60 sec/min) / (8.34 lb/gal H2O)

FH = 2.54 ft

VH = usually negligible

PH = p * 2.31 in2/lb
Irrigation equipment is categorized into four NONROAD runs according to the fuel type:
gasoline, diesel, LPG, and CNG (see Table S8).
23
Table S8. Irrigation equipment as modeled by the NONROAD program
Field Activity
Size of Equipment (hp)
Hours/Acre
Gasoline Irrigation Equipment
Diesel Irrigation Equipment
LPG Irrigation Equipment
CNG Irrigation Equipment
3-100
6-300
100-175
25-300
Varies By State
Varies By State
Varies By State
Varies By State
2.2.2 Corn Stover and Wheat Straw Harvest
Currently, there is no large-scale collection of agricultural residues for industrial applications. A
single-pass methodology is used to model the harvest of CS and WS, which are harvested from
reduced and no-till acres only. In this system, a combine collects the grain (corn or wheat) while
towing a baler, which collects the residue. The residue feeds directly into the baler, making its
collection highly efficient (Jacobson 2012). The maximum operating speed of the combine is
found to occur when the yield is less than 1 dry short ton/acre (Jacobson 2012). As the yield
increases, the harvest time per acre also increases (see Figure S6).
(Hr/acre)
Hours Per Acre vs. Yield
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
Maximum
operating speed
0
1
2
3
4
5
Yield
(dry short ton/acre)
Figure S6. Single pass combine collection rates (Jacobson 2012)
Based on Idaho National Laboratory’s recent research, it was determined that a 540-hp combine
would be required for the single pass methodology to collect both grain and residue without
24
significantly slowing down grain harvest as in the normal harvesting system whereas only grain
is harvested (Jacobson 2012).
Compared to harvesting only the grain (corn or wheat), harvesting both residues and grain
significantly reduces the speed of the combine. Based on estimates (Jacobson 2012), we allocate
about 380-hp out of the 540-hp to residue harvest while 160-hp is allocated to grain harvest. This
calculation is shown in the Equations S6 and S7:
𝐸𝑐𝑜𝑟𝑛 𝑠𝑡𝑜𝑣𝑒𝑟 = 𝐸𝑁𝑜𝑛𝑅𝑜𝑎𝑑 ∗ (
380
)
540
Equation S6
Or for WS
𝐸𝑊ℎ𝑒𝑎𝑡 𝑆𝑡𝑟𝑎𝑤 = 𝐸𝑁𝑜𝑛𝑅𝑜𝑎𝑑 ∗ (
380
)
540
Equation S7
Table S9 shows the equipment for the harvest and on-farm transport for CS and WS. Because
harvest of crop residues is not currently practiced at a large-scale for biofuel production,
alternative harvest and processing systems may be developed and implemented using different
equipment compared to that modeled in our analysis. Readers are encouraged to refer to Hess et
al. (2009) for more information about likely future crop residue harvest and processing systems.
25
Table S9. Corn stover and wheat straw harvest and transport equipment
Field Activity
Machinery Used
(hp)
Hours/Acre
Corn stover, wheat straw harvest Combine
Corn stover and wheat straw on- farm transport
540
100-175
varies by yield
13 dry short tons/hr max
throughput
2.2.3 Switchgrass
SG is produced in a 10-year rotation, and different equipment is used for establishment and
maintenance years. As stated previously, because of the perennial nature of SG, our analysis
assumes that 1/10 of the total SG acres in 2022 is in the first, second, third, fourth, fifth, sixth,
seventh, eighth, ninth, and tenth year of the rotation, respectively. The equipment list for all nonharvest operations for each year of the cycle is shown in Table S10.
Table S10. Switchgrass non-harvest equipment
Machinery Complement
Times Over Field
Tractor Used
(hp)
Hours/
Acre
Establishment - Year 1
Offset disk, 12 ft .
2
1
Fertilizer and lime spreader, 4T, 40ft
2
Boom sprayer, 50ft1
3
1
No-till drill, 15 ft
1
Maintenance - Years 2-10
130
60
60
130
0.286
0.084
0.117
0.183
Reseeding (year 2 only)1
1
130
0.046
Fertilizer and lime spreader, 4T, 40ft1
Boom sprayer, 50ft (year 5 only)1
Tractor 2,4-D application (year 5 only)1
1
1
1
60
60
60
0.042
0.039
0.089
1
1
Turhollow 2011
For SG harvest, the year 1 yields are 1/3 and year 2 yields are 2/3 of mature yields (years 3–10);
only one harvest per year is assumed. The harvest equipment list is shown in Table S11. Similar
to crop residues, switchgrass is not currently produced and harvested at a commercial scale for
biofuel production, alternative harvest and processing systems may be developed and
implemented using different equipment compared to that modeled in our analysis. Readers are
encouraged to refer to Hess et al. (2009) for more information about likely future harvest and
processing systems, advanced for switchgrass harvest and processing.
26
Table S11. Switchgrass harvest equipment
Machinery
Complement
Tractor Used (hp)
1
Mower - conditioner
Rake1
Baler1
Bale mover1
1
130
60
130
130
Hours/Acre
Max Throughput
(dry short tons/hour)
Years 1 - 10
0.138
0.286
0.169
0.117
20
13
Turhollow 2011
2.2.4 Forest Residue
FR collection occurs only where other logging operations are the driving economic force, or in
other words, where there are pre-existing logging operations (DOE 2011). For this reason, the
equipment list for FR consists only of a chipper and a loader as the FR is already transported to
the forest landing. A product-purpose allocation is applied to FR thus only activities at the
landing are considered in our inventory; all activities to get the FR to the landing is attributed to
the harvested logs (not considered in this study). The equipment specifics of chipper and loader
are based on Leinonen (2004) (See Table S12). Because NONROAD estimates emissions from
forestry equipment operations based on volumes processed, we converted mass to volumes by
assuming a conversion factor of 30 pound (lb) per cubic feet (ft3) for forest residue.
Logging residues are sometimes piled and open burned on location. This practice varies
depending on a number of factors including ownership, location, type, regeneration and forest
productivity. Without access to spatial data for how to represent current residue management,
this analysis did not include offsetting of open burning, which result in conservative estimates of
emissions.
Table S12. Forest residue equipment
Field Activity
Loader1
Chipper1
Tractor Used (hp)
124.5
414
ft3/hr
18,878
1,905
1
Leinonen 2004.
27
3 Fertilizer and Pesticide Application
Production of biomass often requires use of fertilizers and pesticides. Nitrogen (N), phosphorous
(P), and potassium (K) fertilizers are commonly applied to CG and SG during their production
(DOE 2011). Harvesting CS and WS remotes nutrients, which could be available for next crops
if residues are left in the field. As such, additional N, P, K fertilizers are assumed to be applied to
compensate for the loss of nutrients due to residue removal. Of fertilizers, only NOx and NH3
emissions from N fertilizer application are estimated; we were unable to locate EFs for P and K
fertilizers for the air pollutants examined.
A pesticide is used to control weeds (herbicides), insects (insecticides), fungi (fungicides), and
rodents (rodenticides). Pesticides are made of active ingredients (i.e., the pest-killing material)
and inert ingredients (i.e., solvents used as carriers for the pest-killing materials). Both active and
inert ingredients may potentially release VOC emission either during application or as a result of
evaporation (ERG 2012).
3.1
Nitrogen Fertilizer
N-application rate for corn grain varies significantly by locations. A distribution of county-level
N-application rate (used in our modeling) for conventional till and no till is shown in Figure S7.
Reduced till N-application rate is assumed to be the same as conventional till N-application rate.
28
Figure S7. County-level distribution of nitrogen application rate ranges for conventional till and no-till corn
grain production (no-till and reduced till acres are assumed to have the same nitrogen application rate).
There are many N fertilizers, with prices varying considerably among these sources. Data from
the USDA Economic Research Service indicates the nitrogen fertilizers with highest
consumption in 2010 are anhydrous ammonia, ammonium nitrate, urea, ammonium sulfate, and
nitrogen solutions (USDA 2012). We assume these five N fertilizers are used for CG as well as
for CS and WS (to compensate for the loss of nutrients due to residue removal). Because each N
fertilizer type emits different levels of NOx and NH3, we assume the share of each N fertilizer
among total N usage is identical to that in 2010 (Figure S8). For SG, nitrogen solutions will
likely be the primary fertilizer used in the model year (Turhollow 2011) and are assumed as the
only N fertilizer applied to SG in this analysis.
29
Urea
25%
Anhydrous
Ammonia
34%
Nitrogen solutions
35%
Ammonium Nitrate
3%
Ammonium Sulfate
3%
Figure S8. Share of nitrogen fertilizer applied to CG, CS and WS (USDA 2010a)
Table S13 shows the emission factors for NH3 and NOx for the N fertilizers based on existing
literature.
Table S13. Emission factors for five commonly used nitrogen fertilizers
N fertilizer type
Anhydrous ammonia
Ammonium nitrate (33.5% N)
Urea (44%-46% N)
Ammonium sulfate
Nitrogen Solutions
NO a, b
(% of N in Nitrogen Fertilizer]
0.79
3.8
0.9
3.5
0.79
NH3
(% N volatized as NH3)c,d
1.0
2.0
15.0
8.0
8.0
a
FOA (2001)
GREET (ANL 2010)
c
Goebes et al 2003
d
Davidson et al 2004
b
3.2
Pesticide
The estimation of emissions from pesticides is challenging due to the wide range of formulations
(e.g., emulsifiable concentrate, aerosol, solution, flowable, granule), application equipment, and
application type (e.g., band, broadcast, serial, spot).
Presently, the most widely accepted guidance for the estimation of emissions from pesticide
application is Emission Inventory Improvement Program (EIIP) guidance (EPA 2001). The
preferred methodology uses the vapor pressure of the active ingredient to determine the
30
appropriate emission factor, the amount of pesticide applied to an area, and the percent of the
active ingredient in the pesticide applied. Although this methodology is preferred, it typically
requires a significant amount of data regarding the fraction of active ingredient in the pesticide
applied and the type of formulation by which the active ingredient is applied. For a given active
ingredient, there may be dozens of pesticide labels with various formulation types and active
ingredient fractions. In addition, the list of pesticides in the EIIP guidance is far from
comprehensive (ERG 2012, EPA 2012a). Given the lack of information necessary to estimate
emission factor using the preferred method, we used an alternative approach from the EIIP
guidance.
This alternative methodology uses Equation S8, shown below.
𝐸 = 𝑅 ∗ 𝐼 ∗ 𝐸𝑅 ∗ 𝐶𝑉𝑂𝐶
Equation S8
Where:

E=
Total emissions (lb VOC/acre)

R=
Lb of pesticide applied per year per harvested acre

I=
Lb of active ingredient per lb of pesticide

ER =
Evaporation rate (ratio) (default value = 0.9)

CVOC = VOC content (default value = 0.835) (lb VOC/lb active ingredient).
This method was also used to estimate VOC emissions from pesticide in the 2008 National Emission
Inventory (NEI) (Huntley 2012). In the NEI, the default evaporation rate is 0.9 and the default VOC
content is 0.835 lb VOC/lb of active ingredient. These default values are used in our analysis.
Data on pesticide use for CG vary by state and are derived from USDA (2010b). The same
methodology is applied to SG to estimate VOC emissions from herbicide applications. During
the establishment year (year 1) of SG production, herbicides are applied to eliminate the
competition of weeds. During the maintenance years (years 2-10), only one treatment is
assumed to be needed (Turhollow 2011). Herbicide application rate and schedule are
summarized in Table S14. The pesticide application data is from scenario modeling since
switchgrass does not have an established crop budget and therefore is continually being refined
and updated. The data used in this manuscript reflects the model as of 2011.
31
Table S14. Switchgrass pesticide application schedule (P, lb active ingredient/ac)
Application Time
Establishment (year 1)
Year 5
2,4-D-amine
1.0 lb/ac
1.5 lb/ac
Atazine
1.0 lb/ac
Quinclorac
0.5 lb/ac
4 Fugitive Dust Emissions
Fugitive dust sources may be separated into two broad categories: process sources and open dust
sources (WRAP 2006). Agricultural tilling and harvesting fall under open dust sources and apply
to all feedstocks considered in this study except FR, for which product-purpose allocation
obviates the need to estimate fugitive dust. The agricultural tilling source includes airborne soil
particulate matter (PM) emissions produced during the preparation of agricultural lands for
planting, harvest and other activities. Dust emissions are produced by the mechanical disturbance
of the soil by the implement used and the tractor pulling it (WRAP 2006). PM emissions vary by
activities. Depending on the type of activity performed and the number of passes required for
performing the activity (e.g., discing, weeding), the PM emission factors are computed for nonharvest and harvest activity categories for each feedstock on a basis of per acre (Table S15).
Emission factors of PM10 specific to each activity are taken from WRAP (2006) and Gaffney and
Yu (2003). The PM2.5/PM10 ratio is assumed to be 0.20 for fugitive dust emissions based on MRI
(2006). FR chipping is likely to generate fugitive emissions, however, no EF was identified for
the operation.
Table S15, Fugitive dust emission factors (lb of PM10/ac)1
Non-Harvest
Harvest
1All
Corn Grain
Conventional
Reduced
Till
Till
8
7.2
2.4
2.4
CS
WS
SG
1.8
1.8
1.6
2.4
No-Till
5.2
2.4
values calculated based on WRAP (2006) and Gaffney and Yu (2003), and the crop budget for each feedstock.
Fugitive dust emissions from non-paved roads are estimated using Equation S9. These emissions
arise from vehicles traveling on unpaved roads that pulverize the surface material. These
particles can get lifted into the air and become PM emissions (EPA 1998). The constants are
listed in table S16 for specific sizes (EPA 1998, table 13.2.2-2).
32
𝐸=
𝑠 𝑎 𝑊 𝑏
𝑘( ) ( ) 𝑣
12
3
𝑀 𝑐
( )
0.2
Equation S9
Where:
E=
size specific emissions (lb PM2.5 or PM10)
s=
Surface material silt content (%)
W=
Mean vehicle weight (short tons)
M=
Surface material moisture content (%)
v=
Vehicle miles traveled (miles)
k=
Constant (lb/VMT)
a=
Constant
b=
Constant
c=
Constant
Table S16. Constants used to estimate fugitive PM emissions from transportation
k (lb/VMT)
a
b
c
PM2.5
0.38
0.8
0.4
0.3
PM10
2.6
0.8
0.4
0.3
5 Metrics Evaluated
This section provides additional information on construction of Figures 1-4 in the main text.
5.1
Emission per gallon of ethanol (Figure 1 in the main text)
County-level air emissions are calculated for each feedstock, and are divided by the total amount
of ethanol, which can be potentially produced from a given feedstock to estimate emissions on
the basis of 1 gallon of ethanol produced for comparison across feedstocks (Equation S10).
Ethanol yields for the feedstocks examined are shown in Table S17.
33
𝐸𝑝𝑒𝑟𝑔𝑎𝑙𝑙𝑜𝑛 =
∑𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 𝑓𝑟𝑜𝑚 𝑎𝑙𝑙 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑖𝑒𝑠 𝑓𝑜𝑟 𝑎 𝑔𝑖𝑣𝑒𝑛 𝑓𝑒𝑒𝑑𝑠𝑡𝑜𝑐𝑘
𝐶𝑜𝑢𝑛𝑡𝑦 𝑓𝑒𝑒𝑑𝑠𝑡𝑜𝑐𝑘 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 ∗ 𝐸𝑡𝑂𝐻𝑦𝑖𝑒𝑙𝑑
Equation S10
Table S17. Ethanol Yields
Feedstock
Corn Grain
Corn Stover, Wheat Straw, Switchgrass
Forest Residue
1
2
Ethanol yield
2.76 gallons/bushel1
89.9 gallons/dry ton2
75.7 gallons/dry ton2
Average corn ethanol yield in 2011 (EPA 2011)
Hsu et al 2010
5.2
Contribution Analysis (Figure 2 in the main text)
The contribution analysis estimates the contribution of emissions from one of the following five
activity categories: non-harvest, N fertilizer, pesticide, harvest, and transport. The contributions
from each feedstock are summarized below.
Non-Harvest Category:
-
Emissions from irrigation, machinery operations associated with fertilizer and
chemical application, and machinery operation for field preparation (e.g., cultivating,
discing, plowing).
N Fertilizer Category:
-
NO and NH3 emissions from N fertilizer application (e.g., through nitrification and
volatilization).
Chemical Category:
-
VOC emissions from pesticide application
Harvest Category:
34
-
Emissions from machinery operations associated with feedstock harvesting. For
example, SG harvesting includes machinery operations such as mower, rake, and
baler. Forest residue harvesting includes chipper and loader.
Transport Category:
-
Combustion emissions from tractor used to transport feedstock to temporary on-farm
storage facility.
To calculate the contribution from a specific activity category in a county, the sum of emissions
from that category is calculated and then divided by the total emission from all activities from
the production of a feedstock in the county (Equation S11). Figure 2 in the manuscript displays
the distribution of contribution calculated per county. The ranges in Figure 2 are attributed to the
spatial variation of the data inputs. Note that for fertilizer and chemical applications, the
combustion emissions associated with applying the fertilizers/chemicals are accounted for in the
non-harvest activity category.
𝐶𝑜𝑛𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦 =
5.3
∑ 𝑝𝑜𝑙𝑙𝑢𝑡𝑎𝑛𝑡 𝑏𝑦 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦
∑ 𝑎𝑐𝑟𝑜𝑠𝑠 𝑎𝑙𝑙 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑖𝑒𝑠
Equation S11
Comparison to National Emissions Inventory (NEI) For Cellulosic Feedstocks (Figure
3 in the main text)
Equation S12 and S13 describe the methods used to compare emissions from cellulosic feedstock
to the baseline 2008 NEI.
𝑅𝑛𝑟+𝑛𝑝 =
𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 𝑓𝑟𝑜𝑚 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝑐𝑒𝑙𝑙𝑢𝑙𝑜𝑠𝑖𝑐 𝑏𝑖𝑜𝑓𝑢𝑒𝑙 𝑓𝑒𝑒𝑑𝑠𝑡𝑜𝑐𝑘
𝑁𝑜𝑛𝑟𝑜𝑎𝑑 + 𝑁𝑜𝑛𝑝𝑜𝑖𝑛𝑡 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 𝑓𝑟𝑜𝑚 2008 𝑁𝐸𝐼
Equation S12
𝑅𝑁𝐸𝐼 𝑡𝑜𝑡𝑎𝑙 =
𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 𝑓𝑟𝑜𝑚 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝑐𝑒𝑙𝑙𝑢𝑙𝑜𝑠𝑖𝑐 𝑏𝑖𝑜𝑓𝑢𝑒𝑙 𝑓𝑒𝑒𝑑𝑠𝑡𝑜𝑐𝑘
𝑇𝑜𝑡𝑎𝑙 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 𝑓𝑟𝑜𝑚 2008 𝑁𝐸𝐼
35
Equation S13
The Rs (Rnr+np and RNEI total) are the calculated ratio of the modeled emissions to 2008 NEI
emissions. Emissions from production of cellulosic biofuel feedstock are the sum of emissions in
a given county for all relevant cellulosic feedstocks examined. Nonroad (NR) + Nonpoint (NP)
emissions from NEI are the sum of the nonroad and nonpoint category emissions from the NEI
(EPA 2008). Total emissions from 2008 NEI are a sum of the nonroad, nonpoint, onroad and
source point category emissions from the 2008 NEI. R is calculated for every county where there
are emissions from cellulosic feedstock production. The R analysis described in the manuscript
refers to Rnr+np.
5.4
NEI Ratio Thresholds Compared to Attainment Status for National Ambient Air
Quality Standards (NAAQS) (Figure 4 in the main text)
To inform consideration of the potential effect cellulosic biofuel feedstock production might
have on a county’s NAAQS non-attainment status (EPA 2014), the NEI comparison results are
visualized along with locations of counties in non-attainment for ozone or PM2.5 NAAQS. The
ratio thresholds are set at 5%, 10% and 20%, which were thought to be potentially significant.
Overlap in the counties exceeding the ratio thresholds and non-attainment might indicate
potential challenges to meet the NAAQS. Counties exceeding these thresholds for the precursor
pollutants are compared to counties that were in non-attainment for the NAAQS for the relevant
pollutants (ozone, PM2.5 and, SO2). For pollutants with multiple precursors (see Table S18), the
county was included in the comparison if any one of the precursors exceeded a threshold level.
No counties are in non-attainment for NO2 and CO NAAQS. The maps are generated by
overlaying layers exhibiting counties in non-attainment and counties exceeding the different
thresholds.
Table S18. NAAQS criteria air pollutant and paired air pollutants or precursors
NAAQS Criteria
Pollutant
Air pollutant or
precursor
Ozone
PM2.5 and PM10
SO2
NO2
CO
NOX, VOC
NOX, VOC, SO2,
PM2.5 or PM10, NH3
SO2
NO2
CO
36
6 Additional Maps and Figures
6.1
Biomass Production Density
Maps displaying the density of biomass production are created based on Equation S14 (note:
county area is different than harvested area, or planted area).
𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 =
𝑏𝑖𝑜𝑚𝑎𝑠𝑠 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 (𝑚𝑎𝑠𝑠)
𝑡𝑜𝑡𝑎𝑙 𝑐𝑜𝑢𝑛𝑡𝑦 𝑎𝑟𝑒𝑎
Equation S14
The production density maps (Figure S9) are useful for gaining perspective on where emissions
might concentrate due to the large production of several cellulosic feedstocks. The map labeled
cellulosic takes into account the sum of production from CS, FR, SG, and WS. There is a high
level of overlap in production of SG, WS, and CS in some counties in Kansas, North Carolina,
Virginia, and Maryland, making these areas more likely to see higher increase in emissions than
counties producing only one feedstock. As previously stated, SG production is limited to
counties west of the 100th meridian because SG is assumed to be grown on rain-fed lands only
(DOE 2011). The 100th meridian (roughly) runs along the eastern border of Montana, Wyoming,
Colorado and New Mexico; there is no production west of this border, except for one county in
the Pacific Northwest. FR is assumed to be collected from non-federal lands and has high
production in the Midwestern, Northwestern, and Eastern states.
Numeric data on county-level biomass production density related to the projected production of
cellulosic feedstock (individual feedstock and aggregated for all cellulosic feedstock) examined
in this analysis can be obtained at http://en.openei.org/datasets/dataset/air-pollutant-emissionsinventory-supporting-data.
37
CELLULOSIC
CG
CS
FR
SG
WS
Figure S9. Production density for all feedstocks (dry metric ton/square kilometer)
6.2
Emissions Density
The following maps (Figures S10-S15) display the emissions of each pollutant per county area.
Generally speaking, the high production density areas (see Figure S9) are also high emissions
density areas (Figures S10-S15). A few common patterns are briefly described here to facilitate
38
understanding of the results. PM10 and PM2.5 always follow the same spatial trend because all
PM2.5 emissions are modeled as a fraction of PM10 emissions. In general, CO and SOX, which
occur only from fuel combustion emissions, share the same spatial patterns. NOX and NH3 follow
the same spatial trends because the N fertilizer application is the dominating source of these
emissions. Note that even when spatial trends are similar, magnitude of emissions can differ (as
seen in the scales).
6.2.1 Cellulosic
Figure S10 is the emission density maps for all cellulosic feedstocks combined.
The NOX and NH3 have different spatial patterns for the aggregated cellulosic figure, even
though these two pollutants have identical spatial trends for each of the individual cellulosic
feedstocks (See Figures S11-S15). For NOX, the highest emissions density areas are estimated to
be in Kansas, Oklahoma, and Iowa. SG dominates the NOX emissions in Oklahoma and Kansas
because the N fertilizer applied to SG is assumed to be nitrogen solutions, which have higher
NOX emissions than the weighted average N fertilizers used for corn grain, CS and WS.
However, nitrogen solutions have slightly lower NH3 emissions compared to the weighted
average emission factors for the N fertilizers used for corn grain, CS and WS. In addition, both
CS and WS are produced in some parts of Kansas and Oklahoma, contributing to the total
emissions. The high emissions density area in Iowa for NOX and NH3 is entirely attributable to
higher CS production density.
CO and SOX have high emissions density in Iowa, Kansas, Oklahoma, and Illinois because of the
production of multiple feedstocks in these states. The high emissions density in Iowa is primarily
attributable to high production density of CS. In Illinois, the high emission density is mainly
because of the overlapping production of WS, CS, and FR; and in Kansas, it is attributable to
overlap in production from SG, WS, and CS.
The highest PM emissions density areas are in Kansas, Oklahoma, and Texas. At all of these
locations, the emissions can be mainly attributed to SG because SG has both non-harvest and
harvest activities with high fugitive dust emissions from both categories, compared to other
cellulosic feedstocks.
39
The high VOC emissions density in Kansas, Oklahoma, and Texas are due to the application of
multiple pesticides for SG production, which emits VOC.
Numeric data on county-level air emission densities from the projected production of cellulosic
feedstock can be obtained at http://en.openei.org/datasets/dataset/air-pollutant-emissionsinventory-supporting-data.
40
NH3
NOx
CO
SOx
PM10
PM2.5
VOC
Figure S10. Cellulosic emissions density maps (kg of pollutant per km2 county area)
41
NH3
NOX
CO
SOX
PM2.5
PM10
VOC
Figure S11. Corn grain emissions density maps (kg of pollutant per km2 county area)
42
NOX
NH3
CO
SOX
PM10
PM2.5
VOC
3
Figure S12. Corn stover emissions density maps (kg of pollutant per km2 county area)
43
NOX
NH3
CO
SOX
PM10
PM2.5
VOC
Figure S13. Wheat straw emissions density maps (kg of pollutant per km2 county area)
44
NOX
NH3
CO
SOX
PM10
PM2.5
VOC
Figure S14. Switchgrass emissions density maps (kg of pollutant per km2 county area)
45
NOX
NH3
CO
SOX
PM10
PM2.5
VOC
Figure S15. Forest residue emissions density maps (kg of pollutant per km2 county area)
46
6.3
Individual Feedstock Comparison to NEI
Figures S16-S20 are individual feedstock emissions compared to the 2008 national emissions
inventory (NEI) non-point and non-road categories (Rnp+nr). NH3 and NOX emissions from SG,
CS, and WS production are the main factor to cause R to exceed the three thresholds. In addition,
PM emissions from SG production are responsible for a few exceedances.
Figure S16. Ratio (Rnpr+nr) of switchgrass emissions to 2008 NEI nonpoint and nonroad emissions
47
Figure S17. Ratio (Rnpr+nr) of corn stover emissions to 2008 NEI nonpoint and nonroad emissions.
48
Figure S18. Ratio (Rnpr+nr) of wheat straw emissions to 2008 NEI nonpoint and nonroad emissions.
49
Figure S19. Ratio (Rnpr+nr) of forest residue production emissions to 2008 NEI nonpoint and nonroad
emissions.
6.4
Counties with R exceeding thresholds compared to NAAQS Nonattainment
Areas
Counties with R exceeding selected thresholds for any pollutant potentially contributing to
ambient ozone and PM2.5 concentrations are displayed in Figure 4. Emissions of ozone and PM2.5
precursors are relevant to secondary pollutant (ozone and PM2.5) formation at regional scales
(order of 100-1000 kilometer),37 thus all counties in nonattainment areas (NAAs) for the ozone
and PM2.5 NAAQS are also displayed in Figure 4.
50
The list of counties exceeding the three thresholds for ozone precursors along with reference to
counties in nonattainment for 1997 or 2008 8-hour ozone NAAQS and the list of counties
exceeding the three thresholds for PM2.5 and its precursors along with reference to counties in
nonattainment for 1997 or 2006 PM2.5 NAAQS are available at
http://en.openei.org/datasets/dataset/air-pollutant-emissions-inventory-supporting-data.
7 Comparison To Other Studies
Tables S19-S22 show comparisons of the results of this study to estimates from the GREET
Version 1_2012 model (ANL 2012) and Tessum et al (2012) when data can be derived based on
one gallon of ethanol produced. To facilitate the comparisons, the boundary used for GREET
modeling is before feedstock is shipped to biorefinery (i.e., transportation of feedstocks is not
included). Note that our study does not include emissions from upstream activities (e.g.,
electricity generation); therefore only emissions emitted directly from feedstock production
activities are included. Yields reported in Table S17 are used to convert GREET emissions to
mass per gallon of ethanol for comparison to ours. Our results are reported as maximum, mean
and minimum while results from GREET are only reported as means.
Tessum et al (2012) used an earlier version of the GREET model (GREET 1.8d1) (ANL 2010)
and the results are expressed in g/mile. The default fuel economy for year 2010 in GREET 1.8d1
is used to convert results in Tessum et al (2012) to a comparable unit used in our study (g/gallon
of ethanol) (Tessum 2013). The system boundary used for comparison is up to the farm-gate
before feedstock is shipped to biorefinery. While Tessum et al (2012) considered co-product
credits in estimating emissions for corn production used for ethanol; the credits are excluded in
our comparisons. Table S19 shows the comparison of mean emissions per gallon of corn ethanol
from Tessum et al (2012) (S1b-corn.xls).
Since GREET does not track ammonia emissions, Tessum et al (2012) estimated NH3 emissions
based on EFs and the types of N fertilizer used. The types and shares of N fertilizer used by
Tessum et al (2012) (Hill et al 2009) are 70.7% ammonia, 21.2% urea, and 8.2% ammonium
nitrate. Once fertilizer is applied, an “industry-weighted” average of 0.038 kg of NH3 per kg N
51
applied (i.e., the average of 0.027 and 0.049) was used by Tessum et al (2012) to estimate NH3
emissions.
Observations of differences between our results and those from GREET/Tessum et al (2012) in
air pollutant emissions examined are discussed in section 3.5 of the main text, along with reasons
to potentially explain these differences.
Table S19. Corn grain comparison among our estimates, GREET Version 1_2012 and Tessum et al (2012)
Corn Grain
g/gal EtOH
VOC
CO
NOx
PM10
PM2.5
SOx
NH3
GREET
(year 2011)
Mean
0.17
0.96
4.29
0.11
0.10
0.01
N.A.
Max
1.72E+01
7.22E+01
2.22E+01
4.80E+01
1.00E+01
8.33E-02
5.99E+01
Our estimates
(year 2011)
Mean
3.60E+00
5.82E+00
5.81E+00
1.17E+01
2.52E+00
8.89E-03
1.43E+01
Min
1.00E+00
4.84E-01
2.25E+00
6.02E+00
1.33E+00
8.27E-04
5.03E+00
Tessum et al
(year 2010)
Mean
0.152
0.841
3.61
9.70E-02
8.80E-02
1.10E-02
5.48
Table S20. Switchgrass comparison between our estimates and GREET Version 1_2012
Switchgrass
g/gal EtOH
VOC
CO
NOX
GREET
(year2020)
Mean
0.04
0.17
1.46
PM10
PM2.5
SOX
2.9E-02
2.6E-02
6.9E-04
Max
9.04E-01
2.59E-01
2.89E+00
Our estimates
(year 2022)
Mean
2.86E-01
1.00E-01
2.53E+00
Min
1.67E-01
6.97E-02
2.46E+00
1.13E+01
2.29E+00
9.08E-04
3.50E+00
7.14E-01
3.70E-04
2.00E+00
4.10E-01
2.66E-04
52
Table S21. Corn stover comparison between our estimates and GREET Version 1_2012
g/gal EtOH
Corn Stover
VOC
CO
NOx
PM10
PM2.5
SOx
GREET
(year 2020)
Mean
0.07
0.29
1.83
0.05
0.04
1.2E-03
Max
4.26E-02
1.92E-01
1.99E+00
6.48E+00
1.32E+00
6.32E-04
Our estimates
(year 2022)
Mean
3.44E-02
1.53E-01
1.89E+00
3.33E+00
6.85E-01
5.11E-04
Min
3.13E-02
1.38E-01
1.85E+00
1.43E+00
3.05E-01
4.65E-04
Table S22. Forest residue comparison between our estimates and GREET Version 1_2012
Forest Residue
g/gal EtOH
VOC
CO
NOx
PM10
GREET
(year 2020)
Mean
0.10
0.41
0.9
0.07
PM2.5
SOx
0.06
1.65E-03
Max
7.47E-04
1.17E-03
2.95E-03
1.37E-04
Our estimates
(year 2022)
Mean
7.47E-04
1.17E-03
2.95E-03
1.37E-04
Min
7.47E-04
1.17E-03
2.95E-03
1.37E-04
1.33E-04
1.39E-05
1.33E-04
1.39E-05
1.33E-04
1.39E-05
8 References
Agriculture Policy Analysis Center (APAC). 2007. Economic and environmental impacts of
movement toward a more sustainable agriculture in the United States, Appendix 3: Data sources
used in developing the ABS budgets. Project No. 43-3AEK-3-80080. Knoxville, TN: University
of Tennessee.
ANL (Argonne National Laboratory). 2010. The Greenhouse Gases, Regulated Emissions, and
Energy Use in Transportation (GREET) Model version 1.8d1. Lemont, IL: Argonne National
Laboratory. [online] No longer available for download [Accessed August 2010].
53
ANL. 2012. Green House Gases, Regulated Emissions, and Energy Use in Transportation
(GREET) Model version 1 2012 revision 2. Lemont, IL: Argonne National Laboratory. [online]
Available at: http://greet.es.anl.gov/ [Accessed July 2012].
California Air Resources Board (CARB). 2006. Emission Inventory Methodology, Agricultural
Irrigation Pumps – Diesel, Appendix D. [online] Available at:
http://www.arb.ca.gov/regact/agen06/attach2.pdf [Accessed October 2013].
Davidson, C., Adams, P., Strader, R., Rinder, R., Anderson, N., Geobes, M. and Ayers, J. 2004.
CMU Ammonia Model, Version 3.6. Pittsburgh, PA: The Environmental Institute, Carnegie
Mellon University.
DOE (US Department of Energy). 2011. US Billion-Ton Update: Biomass Supply for a
Bioenergy and Bioproducts Industry. R.D. Perlack and B.J. Stokes (Leads), ORNL/TM2011/224. Oak Ridge, TN: Oak Ridge National Laboratory.
DOE. 2012. Alternative Fuels Data Center. DOE, Energy Efficiency and Renewable Energy
(EERE). [online] Available at: www.afdc.energy.gov/afdc/fuels/properties.html [Accessed
December 2012].
Eaton, L. 2012. “BTS2 projections for forest residue (tops and limbs only/LOGR) production for
2022 at $55/dry ton”. Oak Ridge National Laboratory. [Email] (Personal communication with A.
Carpenter on August 23, 2012).
Eaton, L. 2013. “Perennial grass and switchgrass production”. Oak Ridge National Laboratory.
[Email] (Personal communication with A. Carpenter on January 4, 2013).
EPA (US Environmental Protection Agency). 1995. Air Pollutant Emissions Factors, Volume 1:
Stationary Point and Area Sources (AP42), chapter 13. 5th Edition. Research Triangle Park, NC:
Office of Air Quality Planning and Standards. [online] Available at:
http://www.epa.gov/ttn/chief/ap42/ch13/index.html [Accessed January 2012].
EPA. 2001. Emission Inventory Improvement Program (EIIP). PESTICIDES - AGRICULTURAL
AND NONAGRICULTURAL. Research Triangle Park, NC: Office of Air Quality Planning and
54
Standards. [online] Available at:
http://www.epa.gov/ttnchie1/eiip/techreport/volume03/iii09_jun2001.pdf [Accessed January
2012].
EPA. 2004. Nonroad Engine Growth Estimates, NR-008c. Washington, DC: Office of
Transportation and Air Quality. [online] Available at: http://www.epa.gov/oms/nonrdmdl.htm
[Accessed January 2012].
EPA. 2005a. User’s Guide for the Final NONROAD2005 Model. Washington, DC: Office of
Transportation and Air Quality. [online] Available at: http://www.epa.gov/oms/nonrdmdl.htm
[Accessed January 2012].
EPA. 2005b. Exhaust Emission Effects of Fuel Sulfur and Oxygen on Gasoline Nonroad Engines,
NR-003c. Washington, DC: Office of Transportation and Air Quality. [online] Available at:
http://www.epa.gov/oms/nonrdmdl.htm [Accessed January 2012].
EPA. 2005c. Calculation of Age Distributions in the Nonroad Model – Growth and Scrappage,
NR-007c. Washington, DC: Office of Transportation and Air Quality. [online] Available at:
http://www.epa.gov/oms/nonrdmdl.htm [Accessed January 2012].
EPA. 2005d. Geographic Allocation of Nonroad Engine Population Data to the State and
County Level, NR-014d. Washington, DC: Office of Transportation and Air Quality. [online]
Available at: http://www.epa.gov/oms/nonrdmdl.htm [Accessed January 2012].
EPA. 2006. AP 42, Fifth Edition, Volume I Chapter 13: Miscellaneous Sources. 13.2.2 Unpaved
Roads. [online] Available at: http://www.epa.gov/ttn/chief/ap42/ch13/final/c13s0202.pdf
[Accessed August 2013].
EPA. 2008. National Emissions Inventory 2008 Data. Research Triangle Park, NC: Office of Air
Quality Planning and Standards. [online] Available at
http://www.epa.gov/ttn/chief/net/2008inventory.html [Accessed January 2012].
55
EPA. 2010a. Conversion Factors for Hydrocarbon Emissions Components NR-002d.
Washington, DC: Office of Transportation and Air Quality. [online] Available at:
http://www.epa.gov/oms/nonrdmdl.htm [Accessed January 2012].
EPA. 2010b. Median Life, Annual Activity, and Load Factor Values for Nonroad engine
Emissions Modeling, NR-005d. Washington, DC: Office of Transportation and Air Quality.
[online] Available at: http://www.epa.gov/oms/nonrdmdl.htm [Accessed January 2012].
EPA. 2010c. Nonroad Engine Population Estimates, NR-006e. Washington, DC: Office of
Transportation and Air Quality. [online] Available at: http://www.epa.gov/oms/nonrdmdl.htm
[Accessed January 2012].
EPA. 2010d. Exhaust and Crankcase Emission Factors for Nonroad Engine Modeling –
Compression Ignition, NR-009d. Washington, DC: Office of Transportation and Air Quality.
[online] Available at: http://www.epa.gov/oms/nonrdmdl.htm [Accessed January 2012].
EPA. 2010e. Exhaust Emission Factors for Nonroad Engine Modeling – spark-Ignition, NR010f. Washington, DC: Office of Transportation and Air Quality. [online] Available at:
http://www.epa.gov/oms/nonrdmdl.htm [Accessed January 2012].
EPA. 2010f. Nonroad Spark-Ignition Engine Emissions Deterioration Factors, NR-011d.
Washington, DC: Office of Transportation and Air Quality. [online] Available at:
http://www.epa.gov/oms/nonrdmdl.htm [Accessed January 2012].
EPA. 2010g. Nonroad Evaporative Emission Rates, NR-012d. Washington, DC: Office of
Transportation and Air Quality. [online] Available at: http://www.epa.gov/oms/nonrdmdl.htm
[Accessed January 2012].
EPA. 2010h. Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis.
Assessment and Standards Division Office of Transportation and Air Quality. [online] Available
at: http://www3.epa.gov/otaq/renewablefuels/420r10006.pdf [Accessed January 2015].
56
EPA. 2012a. 2008 National Emissions Inventory, v2, Technical Support Document, DRAFT.
Research Triangle Park, NC: Office of Air Quality Planning and Standards. [online] Available at
http://www.epa.gov/ttn/chief/net/2008neiv2/2008_neiv2_tsd_draft.pdf [Accessed January 2012].
EPA. 2012b. The Green Book Nonattainment Areas for Criteria Pollutants. Research Triangle
Park, NC: Office of Air Quality Planning and Standards. [online] Available at
http://www.epa.gov/oar/oaqps/greenbk/index.html [Accessed December 2012].
EPA. 2012c. “Regulatory Actions.” EPA Revises the National Ambient Air Quality Standards for
Particle Pollution. Research Triangle Park, NC: Office of Air Quality Planning and Standards.
[online] Available at http://www.epa.gov/pm/actions.html [Accessed December 2012].
EPA. 2013. Index of/EmisInventory/2011nei/doc/. Agricultural_pesticides_2011.zip. Data
modified 11/25/2013. Available at ftp://ftp.epa.gov/EmisInventory/2011nei/doc/. Accessed
January 2015.
EPA. 2014. The Green Book Nonattainment Areas for Criteria Pollutants. Available at
http://www.epa.gov/airquality/greenbook/. As of December 5, 2013. Accessed January 2014.
FOA. 2001. Global estimates of gaseous emissions of NH3, NO and N2O from agricultural land.
International Fertilizer Industry Association. Food and Agriculture Organization of the United
Nations. Available at http://www.fao.org/docrep/004/y2780e/y2780e00.HTM. Accessed January,
2014.
Gaffney, P. and Yu, H. 2003. Computing Agricultural PM10 Fugitive Dust Emissions Using
Process Specific Emission Rates and GIS. US EPA Annual Emission Inventory Conference.
[online] Available at: www.epa.gov/ttnchie1/conference/ei12/fugdust/yu.pdf [Accessed January
2012].
Goebes, M., Strader, R. and Davidson. C. 2003. An Ammonia Emission Inventory for Fertilizer
Application in the United States. Atmospheric Environment, 37(18):2539–50.
Hall, S. and Matson, P. 1996. NOX Emissions from soil: Implications for Air Quality Modeling
in Agricultural Regions. Annual Review of Energy and the Environment, 21:311–46.
57
Hellwinckel, C. 2012. “Methodology to update the corn crop budgets.” University of Tennessee,
Agriculture Policy Analysis Center. [Email] (Personal communication with A. Carpenter on July
30, 2012).
Hess, J. R., Kenney, K. L., Ovard, L. P., Searcy, E. M., & Wright, C. T. (2009). CommodityScale Production of an Infrastructure-Compatible Bulk Solid from Herbaceous Lignocellulosic
Biomass. (Idaho National Lab, Ed.). Uniform-Format Bioenergy Feedstock Supply System
Design Report Series.
Hill, J., Polasky, S., Nelson, E., Tilman, D., Huo, H., Ludwig, L., Neumann, J., Zheng, H. and
Bonta, D. 2009. Climate change and health costs of air emissions from biofuels and gasoline.
Proceedings of the National Academy of Sciences USA, 106(6):2077−82.
Huntley, R. (2012). 2008 National Emissions Inventory (NEI) methodology documentation.
Research Triangle Park, NC: US Environmental Protection Agency, Emission Inventory and
Analysis Group.
Hsu, D. D., Inman, D., Heath, G. A., Wolfrum, E. J., Mann, M. K. and Aden, A. 2010. Life
Cycle Environmental Impacts of Selected US Ethanol Production and Use Pathways in 2022.
Environmental Science & Technology, 44(13):5289-97. NREL Report No. JA-510-45915.
doi:10.1021/es100186h.
Jacobson, J. 2012. “Biomass Logistics Model Output”. Idaho National Laboratory [Email]
(Personal Communication with Yimin Zhang, January 2012).
Langholtz, M., Graham, R., Eaton, L., Perlack, R., Hellwinkel, C. and De LaTorre Ugarte, D.
2012. Price projections of feedstocks for biofuels and biopower in the US. Energy Policy,
41:484-93.
Leinonen, A. 2004. Harvesting Technology of Forest residues for fuel in the USA and Finland.
VTT Research Notes 2229. Helsinki, Finland: VTT Technical Research Center of Finland.
MRI (Midwest Research Institute). 2006. Background Document for Revisions to Fine Fraction
Ratios Used for AP-42 Fugitive Dust Emission Factors. Western Governors’ Association,
58
Western Regional Air Partnership (WRAP) Report. MRI Project No. 110397. [online] Available
at www.epa.gov/ttnchie1/ap42/ch13/bgdocs/b13s02.pdf [Accessed January 2012].
NRC. 2011. Renewable Fuel Standard: Potential Economic and Environmental Effect of US
Biofuels Policy. Washington, DC: The National Academies Press. ISBN-10: 0-309-18751-6.
Perlack R.D., Wright L.L., Turhollow A.F., Graham R.L., Stokes B.J. and Erbach D.C. 2005.
Biomass as feedstock for a bioenergy and bioproducts industry: the technical feasibility of a
billion-ton annual supply. DOE/GO-102995-2135 or ORNL/TM-2005/66. Oak Ridge, TN: Oak
Ridge National Laboratory.
Tessum, C.W., Marshall, J.D. and Hill, J.D. 2012. A Spatially and Temporally Explicit Life
Cycle Inventory of Air Pollutants from Gasoline and Ethanol in the United States. Environmental
Science & Technology, 46(20):11408−17.
Tessum, C.W. 2013. “Conversion of emissions to per gallon of ethanol produced”. University of
Minnesota. [Email] (Personal communication with Y. Zhang on January 16, 2013).
Turhollow, A., 2011. “Switchgrass crop management and harvesting equipment requirements”.
Oak Ridge National Laboratory. [Email] (Personal Communication with A. Carpenter on July
14, 2011).
USDA (US Department of Agriculture). 2007. Farm and ranch irrigation survey. USDA, Editor.
[online] Available at:
http://www.agcensus.usda.gov/Publications/2007/Online_Highlights/Farm_and_Ranch_Irrigatio
n_Survey/index.php [Accessed August 2012].
USDA, 2010a. Economic Research Service, Fertilizer Use and Price Survey, US consumption of
selected nitrogen materials (Table 4). [online] Available at http://www.ers.usda.gov/dataproducts/fertilizer-use-and-price.aspx#26720 [Accessed May 2012].
USDA. 2010b. Corn, Upland Cotton and Fall Potatoes, Pesticide Use. [online] Available at:
http://www.nass.usda.gov/Data_and_Statistics/PreDefined_Queries/2010_Corn_Upland_Cotton_Fall_Potatoes/index.asp [Accessed January 2013].
59
USDA. 2012. US Consumption of nitrogen, phosphate, and potash. [online] Available at
http://www.ers.usda.gov/data-products/fertilizer-use-and-price.aspx#26720 [Accessed January
2013].
University of Tennessee (UT). (2012). The POLYSYS Modeling Framework: A Documentation.
Knoxville, TN: Institute of Agriculture, Agricultural Policy Analysis Center. [online] Available
at http://www.agpolicy.org/polysys.html [Accessed January 2012].
Veldkamp, E., and Keller, M., 1997. Fertilizer-induced Nitric Oxide Emissions from Agricultural
Soils. Nutrient Cycling in Agroecosystems, 48(1-2):69-77.
Wang, M., Huo, H., and Arora, S. 2011. Methods of dealing with co-products of biofuels in lifecycle analysis and consequent results within the US context. Energy Policy, 39: 5726-36.
Western Regional Air Partnership (WRAP). 2006. Fugitive Dust Handbook. Prepared for
Western Governors’ Association by Countess Environmental. [online] Available at:
http://www.wrapair.org/forums/dejf/fdh/content/fdhandbook_rev_06.pdf [Accessed December
2012].
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9 List of Acronyms
AQ
BTS
BTS2
CBO
CG
CNG
CO
CS
DOE
EF
EIIP
EPA
FAPRI
FIPS
FR
GHG
GREET
HI
HP
INL
K
K2O
LB
LCA
LPG
N
NAAQS
NAAs
NASS
NEI
NH3
NONROAD
NOX
NP
NR
NRCS
ORNL
Air Quality
First Billion Ton Study
Billion Ton Study Update
US Congressional Budget Office
Corn Grain
Compressed Natural Gas
Carbon Monoxide
Corn Stover
Department of Energy
Emission Factor
Emission Inventory Improvement Program
US Environmental Protection Agency
Food and Agriculture Policy Research Institute
Federal Information Processing Standard
Forest Residue
Greenhouse Gas
Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation
Harvest Index
Horsepower
Idaho National Laboratory
Potassium
Potassium oxide
Pounds
Life Cycle Assessment
Liquefied Petroleum Gas
Nitrogen
National Ambient Air Quality Standards
Non-Attainment Areas
National Agricultural Statistics Service
National Emission Inventory
Ammonia
EPA NONROAD2008a Model
Oxides of Nitrogen
Nonpoint
Nonroad
Natural Resource Conservation Service
Oak Ridge National Laboratory
61
P
P2O5
PM
PM10
PM2.5
POLYSYS
R
RFS2
RPA
RUSLE2
SCC
SG
SI
SOX
THC
TPO
USDA
UT
VOC
WS
Phosphorous
Phosphate
Particulate Matter
Particulate Matter Size fractions less than 10 µm in diameter
Particulate Matter Size fractions less than 2.5 µm in diameter
Policy Analysis System
Ratio
Updated Renewable Fuel Standard
Resource Planning Act
Revised Universal Soil Loss Equation
Source Classification Codes
Switchgrass
Supporting Information Document
Oxides of Sulfur
Total Hydrocarbons
Timber Products Output
US Department of Agriculture
University of Tennessee
Volatile Organic Compounds
Wheat Straw
62