&EPA
United a*K
Envirainwnlal Protection
Agency
Welfare Risk and Exposure Assessment for
Ozone
Final
Appendices
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EPA-452/R-14-005b
August 2014
Welfare Risk and Exposure Assessment for Ozone
Final
Appendices
U.S. Environmental Protection Agency
Office of Air and Radiation
Office of Air Quality Planning and Standards
Health and Environmental Impacts Division
Risk and Benefits Group
Research Triangle Park, North Carolina 27711
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DISCLAIMER
This document has been prepared by staff from the Risk and Benefits Group, Health and
Environmental Impacts Division, Office of Air Quality Planning and Standards, U.S. Environmental
Protection Agency. Any findings and conclusions are those of the authors and do not necessarily
reflect the views of the Agency.
Questions on this document should be addressed to Dr. Bryan Hubbell at the U.S.
Environmental Protection Agency, Office of Air Quality Planning and Standards, 109 TW Alexander
Drive, C504-02, Research Triangle Park, North Carolina 27711 or email — hubbell.bryan@epa.gov.
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APPENDIX 4A:
SPATIAL FIELDS FOR THE W126 METRIC
4A-1
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Appendix 4A
Spatial Fields for the W126 Metric
of
4A.1 Overview 4A-4
4A.2 Air Quality Spatial Field Techniques 4A-5
4A.2.1 Voronoi Neighbor Averaging (VNA) 4A-5
4A.2.2 Community Multi-scale Air Quality (CMAQ) Model 4A-7
4A.2.3 Enhanced Voronoi Neighbor Averaging (eVNA) 4A-7
4A.2.4 Downscaler (DS) 4A-8
4A.3 Evaluation of Spatial Field Techniques for the W126 Metric 4A-10
4A.3.1 Recent Air Quality Data 4A-10
4A.3.2 Adjusted Air Quality Data 4A-13
4A.4 Air Quality Inputs to the Welfare Risk and Exposure Assessment 4A-20
4A.4.1 Air Quality Inputs to the Biomass Loss Analyses (Chapter 6) 4A-21
4A.4.2 Air Quality Inputs to the Foliar Injury Analyses (Chapter 7) 4A-24
4A.5 References 4A-28
4A-2
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LIST OF FIGURES
FIGURE 4A-1 NUMERICAL EXAMPLE OF THE VORONOI NEIGHBOR AVERAGING (VNA) TECHNIQUE APPLIED TO A
MODEL GRID DOMAIN 4A-6
FIGURE 4A-2 NUMERICAL EXAMPLE OF THE ENHANCED VORONOI NEIGHBOR AVERAGING (EVNA) TECHNIQUE
APPLIED TO A MODEL GRID DOMAIN 4A-8
FIGURE 4A-3 EXAMPLE OF THE "4-FOLD" CROSS-VALIDATION SCHEME USED IN THE EVALUATION OF THE AIR
QUALITY SPATIAL FIELD TECHNIQUES FOR THE SOUTHERN LAKE MICHIGAN AREA 4A-11
FIGURE 4A-4 CROSS-VALIDATION RESULTS FOR THE 2007 ANNUAL W126 CONCENTRATIONS 4A-12
FIGURE 4A-5 NOAA CLIMATE REGIONS USED IN THE MODEL-BASED AIR QUALITY ADJUSTMENTS 4A-14
FIGURE 4A-6 MONITORED 2006-2008 AVERAGE W126 CONCENTRATIONS ADJUSTED TO MEET THE EXISTING
STANDARD (TOP LEFT), AND THE ALTERNATIVE W126-BASED STANDARDS OF 15 PPM-HRS (TOP RIGHT), 11 PPM-
HRS (BOTTOM LEFT), AND 7 PPM-HRS (BOTTOM RIGHT) 4A-15
FIGURE 4A-7 MAPS SHOWING VNA ESTIMATES OF OBSERVED 2006-2008 AVERAGE W126 VALUES (TOP), AND
BASE CMAQ ESTIMATES OF 2007 ANNUAL W126 VALUES (BOTTOM) 4A-18
FIGURE 4A-8 CMAQ MODEL SURFACES OF W126 ADJUSTED USING HDDM BASED ON EMISSIONS REDUCTIONS
USED TO MEET THE EXISTING STANDARD (TOP LEFT), AND THE ALTERNATIVE W126-BASED STANDARDS OF 15
PPM-HRS (TOP RIGHT), 11 PPM-HRS (BOTTOM LEFT), AND 7 PPM-HRS (BOTTOM RIGHT) AT ALL MONITORING
LOCATIONS 4A-19
FIGURE 4A-9 VNA SPATIAL FIELDS OF 2006-2008 AVERAGE W126 CONCENTRATIONS ADJUSTED TO MEET THE
EXISTING STANDARD (TOP LEFT), AND THE ALTERNATIVE W126-BASED STANDARDS OF 15 PPM-HRS (TOP
RIGHT), 11 PPM-HRS (BOTTOM LEFT), AND 7 PPM-HRS (BOTTOM RIGHT) 4A-22
FIGURE 4A-10 VNA SPATIAL FIELDS OF 2006-2008 AVERAGE W126 CONCENTRATIONS ADJUSTED TO MEET THE
EXISTING STANDARD (TOP LEFT), AND THEN FURTHER ADJUSTED TO MEET THE ALTERNATIVE W126-BASED
STANDARDS OF 15 PPM-HRS (TOP RIGHT), 11 PPM-HRS (BOTTOM LEFT), AND 7 PPM-HRS (BOTTOM RIGHT) .. 4A-23
FIGURE 4A-11 VNA SPATIAL FIELD OF ANNUAL W126 CONCENTRATIONS FOR 2006 4A-24
FIGURE 4A-12 VNA SPATIAL FIELD OF ANNUAL W126 CONCENTRATIONS FOR 2007 4A-25
FIGURE 4A-13 VNA SPATIAL FIELD OF ANNUAL W126 CONCENTRATIONS FOR 2008 4A-25
FIGURE 4A-14 VNA SPATIAL FIELD OF ANNUAL W126 CONCENTRATIONS FOR 2009 4A-26
FIGURE 4A-15 VNA SPATIAL FIELD OF ANNUAL W126 CONCENTRATIONS FOR 2010 4A-26
FIGURE 4A-16 EMPIRICAL DISTRIBUTIONS OF THE OBSERVED ANNUAL W126 CONCENTRATIONS FOR 2006-2010
BASED ON THE VNA SPATIAL FIELDS 4A-27
LIST OF TABLES
TABLE 4A-1 SUMMARY OF THE CROSS-VALIDATION PERFORMANCE METRICS FOR W126 4A-12
TABLE 4A-2 PERCENT REDUCTIONS IN U.S. ANTHROPOGENIC NOx EMISSIONS USED TO REACH EXISTING AND
ALTERNATIVE STANDARD THE IN NINE CLIMATE REGIONS 4A-17
4A-3
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4A.1 OVERVIEW
EPA focused the analyses in the welfare risk and exposure assessments on the W126 Os
exposure metric. The W126 metric is a seasonal aggregate of hourly Os concentrations, designed
to measure the cumulative effects of Os exposure on vulnerable plant and tree species, with units
in parts per million-hours (ppm-hrs). The metric uses a logistic weighting function to place less
emphasis on exposure to low hourly Os concentrations and more emphasis on exposure to high
hourly Os concentrations (Lefohn et al, 1988).
The first step in calculating W126 concentrations was to sum the weighted hourly Os
concentrations within each calendar month, resulting in monthly index values. Since plant and
tree species are not photosynthetically active during nighttime hours, only Os concentrations
observed during daytime hours (defined as 8:00 AM to 8:00 PM local time) were included in the
summations. The monthly W126 index values were calculated as follows:
Monthly W126 = ^=lZ»8__^L__ (Equation 1}
where TV is the number of days in the month,
d is the day of the month (d = 1, 2, ..., N),
h is the hour of the day (h = 0, 1, ..., 23), and
Cdh is the Os concentration observed on day d, hour /z, in parts per million.
Next, the monthly W126 index values were adjusted for missing data. IfNm is defined as
the number of daytime Os concentrations observed during month m (i.e. the number of terms in
the monthly index summation), then the monthly data completeness rate is Vm = Nm /12 * N. The
monthly index values were adjusted by dividing them by their respective Vm. Monthly index values
were not computed if the monthly data completeness rate was less than 75% (Vm < 0.75).
Finally, the annual W126 index values were computed as the maximum sum of their
respective adjusted monthly index values occurring in three consecutive months (i.e., January-
March, February-April, etc.). Three-month periods spanning across two years (i.e., November-
January, December-February) were not considered, because the seasonal nature of Os makes it
unlikely for the maximum values to occur at that time of year. The annual W126 concentrations
were considered valid if the data met the annual data completeness requirements for the existing
standard.
The various assessments in the welfare REA have a need for complete spatial coverage of
W126 index values. For example, the Forest and Agricultural Sector Optimization Model
(FASOM) estimates economic changes in national agricultural and timber markets due to relative
changes in spatially varying air pollution fields. Direct measurement of concentrations is the
preferred method for generating such data, but prohibitive logistics and costs limit the possible
spatial coverage and temporal resolution of such a database. Numerical methods that extend the
4A-4
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spatial coverage of existing air pollution networks with a high degree of confidence have thus been
a long-standing topic of investigation by researchers.
Appendix 4c of the 2nd draft Os Health REA describes the methodology of four different
techniques for predicting air quality concentrations across space, and presents the results of an
evaluation to determine which is the most appropriate for generating national-scale air quality
spatial fields as inputs to those assessments. The four methods are: 1) Voronoi Neighbor
Averaging (VNA; interpolating the monitoring data), 2) the Community Multi-scale Air Quality
model (CMAQ; using modeled air quality concentrations), 3) enhanced Voronoi Neighbor
Averaging (eVNA), and 4) Downscaler (DS). These last two methods combine, or "fuse" the air
quality monitoring data with the modeled concentrations from CMAQ. In this appendix, we
extend the evaluations of these four methods in the Health REA to the W126 metric, in order to
determine which method is most appropriate for generating national air quality spatial fields for
W126 under recent air quality conditions, air quality adjusted to just meet the existing Os standard,
and air quality adjusted to meet three potential alternative secondary Os standards with forms of
W126 and levels of 15 ppm-hrs, 11 ppm-hrs, and 7 ppm-hrs.
4A.2 AIR QUALITY SPATIAL FIELD TECHNIQUES
This section briefly describes the methodology of the four techniques considered for
generating air quality spatial fields for W126, which are used as inputs to the biomass loss analyses
presented in Chapter 6, and the foliar injury analyses presented in Chapter 7.
4A.2.1 VORONOI NEIGHBOR AVERAGING (VNA)
The Voronoi Neighbor Averaging (VNA; Gold, 1997; Chen et al, 2004) interpolation
technique uses inverse distance squared weighted averages of the concentrations from a set of
nearest neighboring monitors to estimate the concentration at a specified location (in this case a
gridded field with 12km resolution covering the contiguous U.S.). VNA identifies the nearest
neighboring monitors for each grid cell using a Delaunay triangulation algorithm, then takes the
inverse distance squared weighted average of the concentrations from each neighboring monitor
to estimate a concentration value for the grid cell. The following paragraphs provide a numerical
example of the VNA technique applied to a model grid domain.
The first step in VNA is to identify the set of nearest monitors for each grid cell in the
domain. The left-hand panel of Figure 4A-1 below presents a numerical example with nine model
grid cells and seven monitoring sites, with the focus on identifying the set of nearest neighboring
sites to grid cell "E", the center cell. The Delaunay triangulation algorithm identifies the set of
nearest neighboring monitors by drawing a set of polygons called the "Voronoi diagram" around
4A-5
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the center of grid cell "E" and each of the monitoring sites. Voronoi diagrams have the special
property that the each edge of the polygons are the same distance from the two closest points, as
shown in the right-hand panel below.
A
D
Monitor: *
80 ppb
10 miles
G
*
B
Monitor:
90 ppb ^
15 miles
"/E
-#
/ H
*
Monitor:
100 ppb
20 miles
C
*
F
*
Monitor:
60 ppb
15 miles
I
*
15 miles
*
10 miles * • - #
* 15 miles
#= Center Grid-Cell "E"
*
= Air Pollution Monitor
*
20 miles
# = Center Grid-Cell "E"
= Air Pollution Monitor
Figure 4A-1 Numerical example of the Voronoi Neighbor Averaging (VNA) technique
applied to a model grid domain
VNA then chooses the monitoring sites that share a boundary with the center of grid cell
"E". These are the nearest neighboring sites, which are used to estimate the concentration value
for grid cell "E". The VNA estimate of the concentration value in grid cell "E" is the inverse
distance squared weighted average of the four monitored concentrations. The further the monitor
is from grid cell "E", the smaller the weight.
For example, the weight for the monitor in grid cell "D" 10 miles from the center of grid
cell "E" is calculated as follows:
1/102
1/102 + 1/152 + 1/152 + 1/202
= 0.4675 (Equation!)
The weights for the other monitors are calculated in a similar fashion. The final VNA
estimate for grid cell "E" is calculated as follows:
VNA(E} = 0.4675 * 80 + 0.2078 * 90 + 0.2078 * 60 + 0.1169 * 100 = 80.3 ppb
(Equation 3)
4A-6
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4A.2.2 COMMUNITY MULTI-SCALE AIR QUALITY (CMAQ) MODEL
For more than a decade, the Community Multi-scale Air Quality (CMAQ) model has been
a powerful computational tool used by EPA and states for air quality management. The CMAQ
system simultaneously models multiple air pollutants, including ozone, particulate matter, and a
variety of air toxics to help regulators determine the best air quality management scenarios for
their communities, states, and countries. CMAQ is also used by states to assess implementation
actions needed to attain National Ambient Air Quality Standards.
The CMAQ system includes emissions, meteorology, and photochemical modeling
components. Research continues in all of these areas to reduce biases and uncertainties in model
simulations. CMAQ is a multi-scale system that has been applied over hemispheric, national,
regional, and urban modeling domains with progressively finer resolution in a series of nested
grids. The CMAQ modeling community includes researchers, regulators, and forecasters in
academia, government, and the private sector with thousands of users worldwide.
Modeled air quality concentrations from CMAQ simulations have a twofold purpose in the
generation and analysis of air quality spatial fields. First, the modeled concentrations are "fused"
with the ambient measurement data using the eVNA and DS techniques. Second, the original
modeled concentrations are evaluated against the resulting concentration estimates from the other
spatial field techniques, to ensure that those techniques successfully reduce biases in the modeled
air quality fields.
4A.2.3 ENHANCED VORONOI NEIGHBOR AVERAGING (EVNA)
Enhanced Voronoi Neighbor Averaging (eVNA; Timin et al, 2010) is a direct extension of
VNA used to combine monitored and modeled air quality concentration data. Continuing from
the previous numerical example for VNA, suppose the model grid cells containing monitors are
associated with modeled concentrations as shown in Figure 4A-2 below. The modeled
concentrations are used to weight the VNA estimates relative to the modeled concentration
gradient:
eVNA(E} = Ej!^ Weighti * Monitori * Mo e E (Equation 4)
7w OCLGL/
where Monitor* represents the monitored concentration for a nearest neighboring monitor,
Weighti represents the inverse distance squared weight for Monitor*,
Models represents the modeled concentration for grid cell "E", and
Modeli represents the modeled concentration in the grid cell containing Monitors
4A-7
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A
Model: D
95 ppb
Monitor: *
80 ppb
10 miles
G
*
Model: g
100 ppb
Monitor:
90 ppb
15 miles
Model: TE
85 ppb /
^ -u ^
/
Model: H
120 ppb
*
Monitor:
100 ppb
20 miles
C
*
Model: F
80 ppb
Monitor:
60 ppb
15 miles
I
*
#- Center Grid-Cell "E"
*
= Air Pollution Monitor
Figure 4A-2 Numerical example of the Enhanced Voronoi Neighbor Averaging (eVNA)
technique applied to a model grid domain
Based on the values shown in Figure 4A-2, the eVNA estimate for grid cell "E" is
calculated as follows:
eVNA(E) = fo.4675 * 80 * —} + fo.2078 * 90 * —} + fo.2078 * 60 * —} + fo.1169 * 100 * —} = 70.9 ppb
^ ' \ 95j \ WOj \ 80j \ 120/ FF
(Equation 5)
In this example, eVNA adjusts the modeled concentration in grid cell "E" downward to
reflect the tendency for the model to over-predict the monitored concentrations. In general, the
eVNA method attempts to use the monitored concentrations to adjust for model biases, while
preserving local gradients in the modeled concentration fields. The computations for VNA and
eVNA were executed using the R statistical computing program (R, 2012), with the Delaunay
triangulation algorithm implemented in the "deldir" package (Turner, 2012).
4A.2.4 DOWNSCALER (DS)
The Downscaler (DS) model is EPA's most recently developed method for spatially
predicting air pollution concentrations. DS essentially operates by calibrating CMAQ data to the
observational data, and then uses the resulting relationship to predict "observed" concentrations at
new spatial points in the domain. Although similar in principle to a linear regression, spatial
4A-8
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modeling aspects have been incorporated for improving the model fit, and a Bayesian1 approaching
to fitting is used to generate an uncertainty value associated with each concentration prediction.
The uncertainties that DS produces are a major distinguishing feature from earlier fusion methods
previously used by EPA such as the "Hierarchical Bayesian" (HB) model (McMillan et al, 2009).
The term "downscaler" refers to the fact that DS takes grid-averaged data (CMAQ) for input and
produces point-based estimates, thus "scaling down" the area of data representation. Although this
allows air pollution concentration estimates to be made at points where no observations exist,
caution is needed when interpreting any within-grid cell spatial gradients generated by DS since
they may not exist in the input datasets. The theory, development, and initial evaluation of DS can
be found in the earlier papers of Berrocal, Gelfand, and Holland (2009, 2010, and 2011).
DS develops a relationship between observed and modeled concentrations, and then uses
that relationship to spatially predict what measurements would be at new locations in the spatial
domain based on the input data. This process is separately applied for each time step (daily in this
work) of data, and for each of the pollutants under study (ozone and PM2.5). In its most general
form, the model can be expressed in an equation similar to that of linear regression:
Y(s, t) = ~0o (s, t) + 0! (t) * ~x(s, t) + e(s, t) (Equation 6)
where:
Y(s,t) is the observed concentration at point s and time t.
~x(s,t) is the CMAQ concentration at time t. This value is a weighted average of both the
grid cell containing the monitor and neighboring grid cells.
~fio(s,t) is the intercept, and is composed of both a global and a local component.
fti(t) is the global slope; local components of the slope are contained in the ~x(s,t) term.
s(s,t) is the model error.
DS has additional properties that differentiate it from linear regression:
1) Rather than just finding a single optimal solution to Equation 1, DS uses a Bayesian
approach so that uncertainties can be generated along with each concentration prediction. This
involves drawing random samples of model parameters from built-in "prior" distributions and
assessing their fit on the data on the order of thousands of times. After each iteration, properties
of the prior distributions are adjusted to try to improve the fit of the next iteration. The resulting
collection of ~fio and fii values at each space-time point are the "posterior" distributions, and the
means and standard distributions of these are used to predict concentrations and associated
uncertainties at new spatial points.
1 Bayesian statistical modeling refers to methods that are based on Bayes' theorem, and model the world in
terms of probabilities based on previously acquired knowledge.
4A-9
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2) The model is "heirarchical" in structure, meaning that the top level parameters in
Equation 1 (ie ~fio(s,t), fiift), ~x(s,t)) are actually defined in terms of further parameters and sub-
parameters in the DS code. For example, the overall slope and intercept is defined to be the sum
of a global (one value for the entire spatial domain) and local (values specific to each spatial point)
component. This gives more flexibility in fitting a model to the data to optimize the fit (i.e.
minimize e(s,t)~).
4A.3 EVALUATION OF SPATIAL FIELD TECHNIQUES FOR THE W126
METRIC
The four air quality spatial field techniques were evaluated to determine which method was
most appropriate for generating spatial fields of W126 for recent air quality data, air quality data
adjusted to meet the existing Os standard, and air quality data further adjusted to meet the potential
alternative W126 standards of 15 ppm-hrs, 11 ppm-hrs, and 7 ppm-hrs. Section 3.1 describes the
evaluation of these techniques for recent air quality data, and section 3.2 describes the evaluation
of these techniques for the various adjusted air quality scenarios.
4A.3.1 RECENT AIR QUALITY DATA
The evaluation based on recent air quality data was designed to assess the relative ability
of each spatial field technique to reproduce monitored W126 concentrations. For the ambient
monitoring data, the W126 metric was calculated for all monitors in the contiguous U.S. with
complete data for 2007 based on the initial dataset and the data completeness criteria described in
Appendix 4a of the 2nd draft Health REA. For the photochemical modeling data, the W126 metric
was calculated from hourly Os concentrations based on a CMAQ simulation with a 12 km gridded
domain covering the contiguous U.S., and 2007 emissions and meteorology inputs (EPA, 2012b).
Cross-validation is a method commonly used to evaluate the ability of statistical models to
make accurate predictions. In a cross-validation analysis, the data are split into two subsets, the
"calibration" subset, and the "validation" subset. The calibration subset is used to "fit" the model,
usually by estimating parameters which establish a relationship between the variable of interest
and one or more dependent variables. The resulting model fit is then applied to the dependent
variable(s) in the validation subset, and the predictive ability of the model is assessed by how
accurately it is able to reproduce the variable of interest in the validation subset.
The evaluation used a systematic "4-fold" cross-validation scheme based on the CMAQ
model grid. The CMAQ model grid was divided into four groups, or "folds", so that each 2x2
block of 12 km grid cells had one member in each fold. Figure 4A-3 shows an example of the
resulting four folds with Os monitor locations for the area surrounding southern Lake Michigan.
4 A-10
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Four cross-validations were performed using VNA, eVNA, and DS to predict W126 values at
monitored locations. The calibration subset in the first cross-validation consisted of the monitors
in folds 2, 3, and 4 as shown in Figure 4A-3 (blue dots), while the validation subset consisted of
the monitors in fold 1 (red dots). The remaining cross-validations were performed in a similar
manner, with three of the four folds used as the calibration subset and the final fold used as the
validation subset. Thus, each monitor was included in the validation subset exactly once, resulting
in a validation dataset with observed W126 values paired with VNA, eVNA, and DS predictions
of those values at the monitor locations. The CMAQ predictions were simply the modeled W126
values for the 12 km grid cells containing Os monitors.
4 Fold Validation
• Fold 1 for validation
• Folds 2; 3: 4 for calibaration
Figure 4A-3 Example of the "4-fold" cross-validation scheme used in the evaluation of the
air quality spatial field techniques for the southern Lake Michigan area
The cross-validation predictions based on the four air quality spatial field techniques were
compared with the observed W126 values based on the ambient data. The comparison focused on
three performance metrics: 1) the root mean squared error (RMSE), 2) the coefficient of variation
4 A-11
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(R2), and 3) the mean bias (MB). The results of these comparisons are shown in Figure 4A-4, and
Table 4A-1 contains a summary of the three performance metrics for each technique.
Validation Results - 2007 Annual W126 Index (ppm-hours)
CMAQ
RMSE = 6.57
RA2 = 0.6
MB = 3.57
S
CD
CO
r RMSE = 3.97
RA2 = 0.721
MB = -0.15
eVNA
RA2 = 0.709
MB =-0.04
16
VNA
0 4 16 36 64 0 4 16 36
Figure 4A-4 Cross-validation results for the 2007 annual W126 concentrations
Performance
Metric
RMSE
R2
MB
VNA
3.97
0.721
-0.15
CMAQ
6.57
0.600
3.57
eVNA
4.09
0.709
-0.04
DS
3.80
0.746
-0.25
Table 4A-1 Summary of the cross-validation performance metrics for W126
4 A-12
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The cross-validation results clearly showed that VNA, eVNA, and DS more accurately
predict monitored W126 concentrations than the CMAQ model. The scatter plots and the mean
bias statistics indicated that both eVNA and DS were effective at reducing the amount of bias
present in the modeled concentrations. The differences between VNA, eVNA, and DS were much
smaller. Although the performance metrics indicated that DS had the highest R2 and the lowest
RMSE of those three techniques, DS also had the largest absolute mean bias, and none of the
differences were statistically significant.
4A.3.2 ADJUSTED AIR QUALITY DATA
The model-based adjustment technique was developed to adjust hourly Os concentrations
in monitored locations. However, gridded W126 concentrations are required as inputs to the
welfare analyses conducted in the WREA. So in this appendix we investigated the appropriateness
of two methods for creating those fields: 1) creating a "fused" surface of adjusted model and
monitor W126 values and 2) using VNA to interpolate adjusted monitor values.
As described in Chapter 4, the air quality monitoring data were adjusted using HDDM
based on domain-wide reductions in U.S. anthropogenic NOx emissions, so that in each of the 9
NOAA climate regions, the highest monitor just met the existing standard, and the alternative
W126-based standards of 15 ppm-hrs, 11 ppm-hrs, and 7 ppm-hrs. Figure 4A-5 shows a map of
the 9 NOAA climate regions for reference. This was accomplished by applying model-based
hourly Os sensitivities (response to emissions reductions) to observed hourly Os data. The adjusted
hourly Os concentrations at monitor locations could then be aggregated to the W126 metric. The
adjustment in each region was based on the minimum reduction in US anthropogenic NOx
emissions required to reduce the W126 value at all monitors within the region to the standard being
assessed. Figure 4A-6 shows the 2006-2008 average W126 values in monitored locations based
on the air quality monitoring data adjusted to meet the existing standard and the alternative W126-
based standards with levels of 15 ppm-hrs, 11 ppm-hrs, and 7 ppm-hrs, which serve as an input
for both method 1 and method 2.
4 A-13
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Legend
Central
East North Central
• Northeast
Northwest
• South
• Southeast
• Southwest
West
• West North Central
Figure 4A-5 NOAA climate regions used in the model-based air quality adjustments
4 A-14
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Monitor W126 Values - 75 ppb
Monitor W126 Values -15 ppm-hours
7
Monitor W126 Values -11 ppm-hours
7 11
Monitor W126 Values - 7 ppm-hours
11
15
20+ 0
11
15
204
Figure 4A-6 Monitored 2006-2008 average W126 concentrations adjusted to meet the existing standard (top left), and the alternative W126-
based standards of 15 ppm-hrs (top right), 11 ppm-hrs (bottom left), and 7 ppm-hrs (bottom right)
4 A-15
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We first investigated the feasibility of method 1, creating "fused" surface of adjusted model
and monitor W126 value. This could be accomplished with either eVNA or DS, both of which
require gridded modeled air quality concentrations. Thus, for the adjusted air quality scenarios
there was a need to create adjusted model W126 fields to "fuse" with the adjusted monitor W126
values. To accomplish this, the modeled hourly Os concentrations were adjusted based on the
grid- and hour-specific HDDM sensitivities combined with the percent emissions perturbations
shown in Table 4A-2 (note that the NOx reductions listed in Table 4A-2 were determined to meet
the targeted standards at monitor locations only and would not guarantee levels below the standard
in unmonitored areas). This resulted in gridded spatial fields of adjusted hourly Os concentrations
for April-October 2007, which were then aggregated to create gridded spatial fields of W126 index
values. The method used to adjust the modeled concentrations differed from the procedure used
to adjust the monitored concentrations in three important respects:
1) The starting (unadjusted) concentrations were different. As shown in Figure 4A-7, modest
differences in hourly measured and modeled Os concentration data can lead to substantial
differences in the W126 metric. In addition, the different time periods represented by the
monitoring data (2006-2008) and the modeled data (2007) added to the differences in the
starting concentrations. Finally, unlike the monitoring data, the modeled concentrations
do not have any missing values. Thus, while adjustments were made to the observed W126
index values to account for time periods with missing data, the model estimates are based
on complete, continuous hourly Os concentration data.
2) For the adjustments to the monitored concentrations, relationships were derived between
the HDDM sensitivities and the monitored Os concentrations for each monitoring site,
hour-of-the-day, and season. The sensitivities were then applied to the observed hourly Os
concentrations at each monitoring site based on the linear relationship between the HDDM
sensitivities and monitored Os concentrations. This was meant to account for differences
in monitored and modeled concentrations at specific times and locations, and to allow
application of HDDM sensitivities to monitored concentrations from unmodeled years
(e.g., 2006 and 2008). For the adjustments to the modeled concentration data, the model-
predicted HDDM sensitivities were applied directly to the modeled concentrations by
pairing them spatially and temporally (i.e., on a grid-cell and hour-specific basis). In cases
where the monitored hourly Os concentrations and their respective modeled values were
quite different, or where ozone response was atypical, the sensitivities applied to the
monitored concentrations may differ substantially from the sensitivities applied to the
modeled concentrations.
3) For the adjustments to the monitored concentrations, floors based on the 5th percentile
values were applied to the regression relationships for each specific monitor, hour, and
4 A-16
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season. This prevented estimates of negative sensitivities that were derived from modeled
conditions with low hourly Os due to NOx titration from being inaccurately applied to
situations where the monitored concentrations were not titrated (e.g., different years).
Since the adjusted model concentrations were derived directly from the HDDM
sensitivities, and not values based on regression relationships, there was no need to apply
floor values to those sensitivities.
Figure 4A-8 shows the resulting adjusted CMAQ model surfaces for W126 based on the
HDDM adjustments for the existing standard, and the alternative W126-based standards with
levels of 15 ppm-hrs, 11 ppm-hrs, and 7 ppm-hrs.
Region
Central
East North Central
Northeast
Northwest
South
Southeast
Southwest
West
West North Central
75ppb
48%
65%
96%
51%
54%
64%
55%
90%
23%
15 ppm-hrs
14%
0%
36%
0%
44%
14%
67%
91%
0%
11 ppm-hrs
58%
23%
51%
0%
56%
38%
85%
93%
6%
7 ppm-hrs
70%
61%
81%
0%
66%
58%
90%
95%
39%
Table 4A-2 Percent reductions in U.S. anthropogenic NOx emissions used to reach existing
and alternative standard the in nine climate regions.
4 A-17
-------
10
15
20
30+
0 5 10 15 20 25 30+
Figure 4A-7 Maps showing VNA estimates of observed 2006-2008 average W126 values
(top), and base CMAQ estimates of 2007 annual W126 values (bottom)
4 A-18
-------
CM AQ W126 Values - 75 ppb
CMAQ W126 Values -15 ppm-hours
7 i i 15
CMAQ W126 Values -11 ppm-hours
11 15
CMAQ W126 Values - 7 ppm-hours
11
15
20+ 0
11
15
204
Figure 4A-8 CMAQ model surfaces of W126 adjusted using HDDM based on emissions reductions used to meet the existing standard (top
left), and the alternative W126-based standards of 15 ppm-hrs (top right), 11 ppm-hrs (bottom left), and 7 ppm-hrs (bottom
right) at all monitoring locations.
4 A-19
-------
As shown in Figure 4A-8, the large amount of variability in the emissions reductions used to meet
the various standards across the nine regions resulted in very sharp spatial gradients of W126 along
regional boundaries in the adjusted model surfaces in some cases. For example, a 67% reduction
in U.S. anthropogenic NOx emissions was used to meet a 15 ppm-hr standard in the Southwest
region, while no adjustments were used in the West North Central region, which resulted in a sharp
change in W126 concentrations near the Colorado/Wyoming border. These disparities were much
less apparent in the adjusted monitor W126 values in Figure 4A-6 due in part to the scarcity of
monitors in those locations in contrast to a continuous modeled surface. In addition, the unadjusted
modeled W126 concentrations were substantially higher than the corresponding unadjusted
monitored values in many locations (see Figure 4A-7), resulting in adjusted model surfaces having
W126 values that were much higher than the respective adjusted monitor W126 values. The
differences in adjusted W126 concentration were partially due to the different time periods
represented by the monitored and modeled concentrations, and partially due to the differences in
the adjustment methodology as explained above. Thus, we determined that in this situation, it was
not appropriate to apply data fusion methods due to the magnitude of the differences in the
"monitored" and "modeled" W126 values.
The second potential method was to use VNA interpolation on the adjusted monitored
values shown in Figure 4A-6. This methodology is described in more detail below in section
4A.4.1 and the alternate resulting surfaces are shown in Figure 4A-9 and Figure 4A-10. These
figures both show much more nationally consistent surfaces for the purposes of providing inputs
to the biomass loss assessment, which incorporates effects of Os across the country on crop
switching at individual locations. On this basis, we determined that VNA was the most appropriate
method to use for the adjusted W126 spatial fields since it is the only technique which relies on
only monitored concentrations. We also determined that VNA was the most appropriate method
to use for the W126 spatial fields based on recent air quality, based on the comparable performance
of VNA to the other techniques in the cross-validation, and for the purpose of eliminating any
uncertainties associated with comparing risk results based on air quality inputs created using
different techniques.
4A.4 AIR QUALITY INPUTS TO THE WELFARE RISK AND EXPOSURE
ASSESSMENT
This section presents the set of spatial fields used as air quality inputs to the Welfare Risk
and Exposure Assessments. For the biomass loss analyses presented in Chapter 6, the air quality
inputs were VNA spatial fields of 2006-2008 average W126 concentrations for observed air
quality, air quality adjusted to meet the existing Os standard, and air quality adjusted to meet
4A-20
-------
potential alternative W126-based standards of 15 ppm-hrs, 11 ppm-hrs, and 7 ppm-hrs. For the
foliar injury analyses presented in Chapter 7, the air quality inputs were VNA spatial fields of the
observed annual W126 values for 2006-2010. All VNA fields were evaluated over a 12 km x 12
km gridded CMAQ domain covering the continental U.S., with estimates taken at the center of
each grid cell.
4A.4.1 AIR QUALITY INPUTS TO THE BIOMASS LOSS ANALYSES (CHAPTER 6)
Figure 4A-9 shows the VNA surfaces of the 2006-2008 average W126 based on air quality
data adjusted to meet the existing standard, and the alternative W126-based standards of 15 ppm-
hrs, 11 ppm-hrs, and 7 ppm-hrs. Recall from Chapter 4 that in order to assess the changes in
welfare-related impacts that could result from meeting a W126-based standard in addition to the
existing standard, the final VNA spatial fields for the alternative W126-based standards were
created using air quality adjusted to meet the existing standard as a starting point. The adjusted
monitoring data used as inputs to these spatial fields were spliced together by region, based on
which standard (either the existing standard or the relevant W126-based standard) was the
"controlling" standard in each region (i.e., which standard used a greater reduction in U.S.
anthropogenic NOx emissions in order to meet it). For example, the final VNA surface for meeting
the existing standard AND 15 ppm-hrs used monitoring data adjusted to meet the 15 ppm-hr
standard in the Southwest and West regions, and monitoring data adjusted to meet the existing
standard in all other regions. A national VNA surface was created using the spliced-together
monitor values resulting in more gentle gradients than would have been produced if VNA surfaces
were first created by region and then spliced together. Figure 4A-10 shows maps of the final VNA
surfaces used in the biomass loss analyses in Chapter 6. Note that Figure 4A-9 shows air quality
which does not first adjust to meet the existing standard before adjusting to meet the alternative
standards while Figure 4A-10 shows air quality adjusted using the approach described above to
meet the alternative W126-based standards after first meeting the current standard. The top left
panels showing W126 values based on air quality adjusted to meet the current standard are
identical in the two figures. When air quality data were adjusted to meet the existing standard,
there were only 5 monitors in the U.S. with W126 values above 15 ppm-hrs, and only 17 monitors
with W126 values above 11 ppm-hrs. All of these monitors were located in urban areas.
4A-21
-------
VNAW126Values-75ppb
VNA W126 Values -15 ppm-hours
7 i
VNA W126 Values -11 ppm-hours
7
VNA W126 Values - 7 ppm-hours
11
15
20+ 0
11
15
20+
Figure 4A-9 VNA spatial fields of 2006-2008 average W126 concentrations adjusted to meet the existing standard (top left), and the
alternative W126-based standards of 15 ppm-hrs (top right), 11 ppm-hrs (bottom left), and 7 ppm-hrs (bottom right)
4A-22
-------
VNAW126Values-75ppb
VNA W126 Values - 75 ppb AND 15 ppm-hours
7 11 15
VNA W126 Values - 75 ppb AND 11 ppm-hours
7 11 15
VNA W126 Values - 75 ppb AND 7 ppm-hours
11
15
20+ 0
11
15
20+
Figure 4A-10 VNA spatial fields of 2006-2008 average W126 concentrations adjusted to meet the existing standard (top left), and then further
adjusted to meet the alternative W126-based standards of 15 ppm-hrs (top right), 11 ppm-hrs (bottom left), and 7 ppm-hrs
(bottom right)
4A-23
-------
4A.4.2 AIR QUALITY INPUTS TO THE FOLIAR INJURY ANALYSES (CHAPTER 7)
For the foliar injury analyses presented in Chapter 7, we used VNA to create spatial fields
of the observed annual W126 values for 2006-2010, which are shown in Figure 4A-11 through
Figure 4A-15. Figure 4A-16 shows the empirical distributions of these spatial fields. There was
a substantial amount of inter-annual variability in the W126 spatial fields, with median VNA
estimates ranging from about 5.5 ppm-hrs in 2009 to about 11 ppm-hrs in 2006.
VNA Spatial Field of Annual W126 Concentrations for 2006
03 7 11 15 20
Figure 4A-11 VNA spatial field of annual W126 concentrations for 2006
25+
4A-24
-------
VNA Spatial Field of Annual W126 Concentrations for 2007
03 7 11 15 20
Figure 4A-12 VNA spatial field of annual W126 concentrations for 2007
VNA Spatial Field of Annual W126 Concentrations for 2008
25+
03 7 11 15 20
Figure 4A-13 VNA spatial field of annual W126 concentrations for 2008
25+
4A-25
-------
VNA Spatial Field of Annual W126 Concentrations for 2009
03 7 11 15 20
Figure 4A-14 VNA spatial field of annual W126 concentrations for 2009
VNA Spatial Field of Annual W126 Concentrations for 2010
25+
03 7 11 15 20
Figure 4A-15 VNA spatial field of annual W126 concentrations for 2010
25+
4A-26
-------
Empirical Distributions of VNA Spatial Fields for Annual W126, 2006- 2010
2006
2007
2008
2009
2010
20
40 60
Percentile
80
100
Figure 4A-16 Empirical distributions of the observed annual W126 concentrations for
2006-2010 based on the VNA spatial fields
4A-27
-------
4A.5 REFERENCES
Berrocal, V.J., Gelfand, A.E., Holland, D.M. (2009). A Spatio-Temporal Downscaler for
Output from Numerical Models. Journal of Agricultural, Biological, and Environmental
Statistics, 15(2), 176-197.
Berrocal, V.J., Gelfand, A.E., Holland, D.M. (2010). ABivariate Space-Time Downscaler
Under Space and Time Misalignment. Annals of Applied Statistics, 4(4), 1942-1975.
Berrocal, V.J., Gelfand, A.E., Holland, D.M. (2011). Space-Time Data Fusion Under Error in
Computer Model Output: An Application to Modeling Air Quality. Biometrics, 68(3),
837-848.
Chen, J., Zhao, R., Li, Z. (2004). Voronoi-based k-order neighbor relations for spatial analysis.
ISPRS J Photogrammetry Remote Sensing, 59(1-2), 60-72.
Gold, C. (1997). Voronoi methods in GIS. In: Algorithmic Foundation of Geographic
Information Systems (va Kereveld M., Nievergelt, J., Roos, T., Widmayer, P., eds).
Lecture Notes in Computer Sicnece, Vol 1340. Berlin: Springer-Verlag, 21-35.
Hall, E., Eyth, A., Phillips, S. (2012). Hierarchical Bayesian Model (HBM)-Derived Estimates
of Air Quality for 2007: Annual Report. EPA/600/R-12/538. Available on the Internet
at: http://www.epa.gov/heasd/sources/projects/CDC/AnnualReports/2007 HBM.pdf
McMillan, N.J., Holland, D.M., Morara, M., Feng, J. (2009). Combining Numerical Model
Output and Particulate Data using Bayesian Space-Time Modeling. Environmetrics, Vol.
21,48-65.
R Core Team (2012). R: A language and environment for statistical computing. R Foundation
for Statistical Computing, Vienna, Austria, http://www.R-project.org/.
Timin B, Wesson K, Thurman J. (2010). Application of Model and Ambient Data Fusion
Techniques to Predict Current and Future Year PM2.5 Concentrations in Unmonitored
Areas. Pp. 175-179 in SteynDG, Rao St (eds). Air Pollution Modeling and Its
Application XX. Netherlands: Springer.
Turner, R. (2012). deldir: Delaunay Triangulation and Dirichlet (Voronoi) Tessellation. R
package version 0.0-19. http://CRAN.R-project.org/package=deldir
4A-28
-------
U.S. Environmental Protection Agency. (2012a). Health Risk and Exposure Assessment for
Ozone, First External Review Draft. Available on the Internet at:
http://www.epa.gOv/ttn/naaqs/standards/ozone/s o3 2008 isa.html
U.S. Environmental Protection Agency. (2012b). Air Quality Modeling Technical Support
Document for the Regulatory Impact Analysis for the Revisions to the National Ambient
Air Quality Standards for Particulate Matter. Available on the Internet at:
http://www.epa.gov/ttn/naaqs/standards/pm/data/201212aqm. pdf
Wells, B., Wesson, K., Jenkins, S. (2012). Analysis of Recent U.S. Ozone Air Quality Data to
Support the O3 NAAQS Review and Quadratic Rollback Simulations to Support the First
Draft of the Risk and Exposure Assessment. Available on the Internet at:
http://www.epa.gOv/ttn/naaqs/standards/ozone/s o3 td.html
4A-29
-------
APPENDIX 5A: LARGER MAPS OF
FIRE THREAT AND BASAL AREA LOSS
5A-1
-------
Levels at Recent Conditions in Areas where Fire Threat is Moderate to Severe
•• v.
RedWjod.
T
Whis,feytown-Sfiasta-Trinjty. National Recreation Area
tassen Volcanic National Park
5A-2
-------
Levels after Adjusting Air Quality to Just Meeting the Existing Standard in Areas
where Fire Threat is Moderate to Severe
W126 Levels
| | 0-7
| 7-11
11-15
5A-3
-------
Levels after Adjusting Air Quality to Just Meeting an Alternate W126 Standard of
15 ppm-hrs in Areas where Fire Threat is Moderate to Severe
y~5^=i=i><
Muir Woods Nattojiffll Monument
W126 Levels
| | 0-7
7-11
5A-4
-------
Levels after Adjusting Air Quality to Just Meeting Alternate W126 Standards of
11 and 7 ppm-hrs in Areas where Fire Threat is Moderate to Severe
Sequoia National Park Death Valle
W126 Levels
1 0-7
5A-5
-------
Levels at Recent Conditions in Areas Considered 'At Risk' of High Basal Area Loss
(>25% Loss)
5A-6
-------
Levels after Adjusting Air Quality to Just Meeting the Existing Standard in Areas
Considered 'At Risk' of High Basal Area Loss (>25% Loss)
5A-7
-------
Levels after Adjusting Air Quality to Just Meeting an Alternate W126 Standard of
15 ppm-hrs in Areas Considered 'At Risk' of High Basal Area Loss (>25% Loss)
5A-8
-------
Os Levels after Adjusting Air Quality to Just Meeting Alternate W126 Standards of
11 and 7 ppm-hrs in Areas Considered 'At Risk' of High Basal Area Loss (>25% Loss)
5A-9
-------
Levels at Recent Conditions in Areas Considered 'At Risk'
of High Basal Area Loss (>25% Loss)
5A-10
-------
Levels after Adjusting Air Quality to Just Meeting the Existing Standard and Alternate
W126 Standards in Areas Considered 'At Risk' of High Basal Area Loss
5A-11
-------
APPENDIX 6A:
MAPS OF INDIVIDUAL TREE SPECIES
6A.1 Discussion
This appendix includes summary maps and figures of relative biomass loss (RBL) as discussed in
Section 6.2.1.3 for each of the 12 tree species included in Chapter 6. Data are included for Red Maple (Acer
rubrum), Sugar Maple (Acer saccharum}, Red Alder (Alnus rubra), Tulip Poplar (Liriodendron tulipifera),
Ponderosa Pine (Pinus Ponderosd), Eastern White Pine (Pinus strobus), Loblolly Pine (Pinus taeda),
Virginia Pine (Pinus virginiana), Eastern Cottonwood (Populus deltoides}, Quaking Aspen (Populus
tremuloides}, Black Cherry (Prusnus serotina), and Douglas Fir (Pseudotsuga menzeiesii).
For each species there are five maps of RBL. The first is under recent O3 conditions (2006 to 2008)
and the following four show RBL under four additional air quality scenarios (75 ppb, 15 ppm-hrs, 11 ppm-
hrs, and 7 ppm-hrs). In addition to the maps, we include histograms showing the RBL distribution under
recent O3 conditions and under the four additional air quality scenarios and the proportion RBL (relative to
the 75 ppb scenario). Note that in the final panel of histograms, the 75 ppb scenario is by definition 1, so it
is not included. For the eastern species, the 75 ppb scenario was controlling below 15 ppm-hrs (i.e., the O3
levels are the same for those two air quality scenarios in the eastern U.S.), so for those species, the 15 ppm-
hrs scenario is not included because it is also by definition 1.
-------
Red Maple (Acer rubrum) (Recent Conditions)
RBL
0.001210-0.003900
0.003901 - 0.006450
0.006451 - 0.008750
0.008751 -0.010930
0.010931 -0.013320
0.013321 -0.016170
0.016171 -0.020350
0.020351 - 0.034880
-------
Red Maple (Acer rubrum) (Current Standard)
RBL
< 0.000001
0.000001 - 0.000640
0.000641 -0.001080
0.001081 -0.001550
0.001551 -0.002020
0.002021 - 0.002560
0.002561 - 0,003330
0.003331 - 0.007750
-------
Red Maple (Acer rubrum) (15 ppm-hrs)
RBL
< 0.000001
0.000001 - 0.000640
0.000641 -0.001080
0.001081 -0.001550
0.001551 -0.002020
0.002021 - 0.002560
0.002561 - 0,003330
0.003331 - 0.007750
-------
Red Maple (Acer rubrum) (11 ppm-hrs)
RBL
< 0.000001
0.000001 -0.000600
0.000601 -0.000950
0.000951 -0.001280
0.001281 -0.001620
0.001621 -0.002050
0.002051 - 0.002700
0.002701 - 0.007020
-------
Red Maple (Acer rubrum) (7 ppm-hrs)
RBL
< 0.000001
0.000001 - 0.000420
0.000421 -0.000620
0.000621 - 0.000840
0.000841 -0.001110
0.001111 -0.001510
0.001511 -0.002340
0.002341 - 0.003890
-------
Recent Conditions
0.000
0.005 0.010 0.015
I I
0.020 0.025 0.030 0.035
75 |)|)l) Scenario
0.000
0.002
0.004 0.006
0.008
0.000
15 |i|un In Scenario
0.002
0.004 0.006
0.008
11 |>|>in -lii SceiKiiio
0.000
0.002
1 1
0.004 0.006
I
0.008
7 |>|>m-lii Scenario
0.000 0.002 0.004 0.006
Relative Biomass Loss, Red maple
0.008
-------
Recent Conditions
O
o
o
\
\
200 400 600 800
I I
1000 1200
11 ppm -hr Scenario
requency
D 4000
[ i i
_rTTTfTfn-v,^ _J"ftl-h_
j_ 1 i i i
0.2 0.4 0.6 0.8 1.
7 |.|.IM-|II Scenario
o
o _
o
o
o _
o
(N _
0.2 0.4 0.6 0.8 1.0
Proportion of 75 ppb Scenario, Red maple
-------
Sugar maple (Acer saccharum) (Recent Conditions)
RBL
<0.000001
0.000001 -0.001861
0.001862-0.003499
0.003500-0.005619
0.005620 - 0.008647
0.008648-0.013390
0.013391 -0.022518
0.022519-0.039575
-------
Sugar maple (Acer saccharum) (Current Standard)
RBL
< 0.000001
0.000001 -0.000008
0.000009 - 0.000020
0.000021 - 0.000046
0.000047 - 0.000095
0.000096 - 0.000230
0.000231 - 0.000408
0.000409 - 0.000667
-------
Sugar maple (Acer saccharum) (15 ppm-hrs)
RBL
< 0.000001
0.000001 -0.000008
0.000009 - 0.000020
0.000021 - 0.000046
0.000047 - 0.000095
0.000096 - 0.000230
0.000231 - 0.000408
0.000409 - 0.000667
-------
Sugar maple (Acer saccharum) (11 ppm-hrs)
RBL
< 0.000001
0.000001 -0.000002
0.000003 - 0.000006
0.000007-0.000012
0.000013-0.000021
0.000022 - 0.000044
0.000045 - 0.000080
0.000081 -0.000138
-------
Sugar maple (Acer saccharum) (7 ppm-hrs)
RBL
<0.000001
0.000001
0.000002
0.000003
0.000004
0.000005
0.000006 - 0.000007
0.000008 - 0.000009
-------
Recent Conditions
0.00
0.01
0.02
0.03
0.04
75 |)|)l) Scenario
Oe+OO 2e-04 4e-04 6e-04
8e-04
0)
15 |)|)m-hr Scenario
I I I I
Oe+OO 2e-04 4e-04 6e-04
11 |>|>m -In Scenario
8e-04
Oe+OO 2e-04 4e-04 6e-04
8e-04
7 |i|iin-lii Scenario
Oe+OO 2e-04 4e-04 6e-04
Relative Biomass Loss, Sugar maple
8e-04
14
-------
Recent Conditions
D
00
O
O -
5000
I
10000
I
15000
o
o
o
CM
11 ppm -In Scenario
requency
0 1 000 2500
i i i i i i
J_
0.
I I I I I
0 0.2 0.4 0.6 0.8 1.0
7 ppm-hr Scenario
0.0 0.2 0.4 0.6 0.8 1.0
Proportion of 75 ppb Scenario, Sugar maple
15
-------
Red Alder (Alnus rubra) (Recent Conditions)
RBL
0.001203-0.004751
0.004752 - 0.007059
0.007060 - 0.009245
0.009246-0.012857
0.012858-0.017742
0.017743-0.025639
0.025640-0.051862
0.051863-0.101038
-------
Red Alder (Alnus rubra) (Current Standard)
RBL
0.000337-0.001301
0.001302-0.002002
0.002003 - 0.002625
0.002626 - 0.003277
0.003278 - 0.003892
0.003893 - 0.004564
0.004565 - 0.005396
0.005397 - 0.007844
-------
Red Alder (Alnus rubra) (15 ppm-hrs)
RBL
0.000328-0.001203
0.001204-0.001947
0.001948-0.002603
0.002604 - 0.003248
0.003249 - 0.003835
0.003836 - 0.004493
0.004494-0.005331
0.005332 - 0.007844
-------
Red Alder (Alnus rubra) (11 ppm-hrs)
RBL
0.000316-0.001209
0.001210-0.001931
0.001932-0.002605
0.002606 - 0.003227
0.003228 - 0.003797
0.003798 - 0.004469
0.004470 - 0.005333
0.005334 - 0.007844
-------
Red Alder (Alnus rubra) (7 ppm-hrs)
RBL
0.000301 -0.001186
0.001187-0.001910
0.001911 -0.002596
0.002597 - 0.003232
0.003233 - 0.003795
0.003796 - 0.004449
0.004450 - 0.005346
0.005347 - 0.007844
-------
Recent Conditions
Trn-h-
0.00
0.02
0.04
0.06
0.08
0.10
0.000
0.002
75 |)|)l) Scenario
0.004
0.006
0.008
0.010
15 |)|)m-hr Scenario
0.000
0.002
I I
0.004 0.006
0.008
0.010
11 |>|>m -In Scenario
0.000
0.002
0.004
0.006
0.008
0.010
7 |i|iin-lii Scenario
0.000 0.002 0.004 0.006 0.008
Relative Biomass Loss. Red alder
0.010
21
-------
Recent Conditions
o
o -
CO
I-
1 1
0 10
I I
20 30
15 ppm-hr Scenario
I—
>, 0.0
O
c
0)
0)
o -
o -
~~l - 1 - 1 - 1
0.2 0.4 0.6 0.8
11 ppm -lii Scenario
1.0
i
0.0
0.2
i i i i
0.4 0.6 0.8 1.0
7 ppm-hr Scenario
o -
o -
\ I I I
0.0 0.2 0.4 0.6 0.8 1.0
Proportion of 75 ppb Scenario, Red alder
22
-------
Tulip Poplar (Liriodendron tulipifera) (Recent Conditions)
RBL
0.012398-0.034442
0.034443-0.046128
0.046129-0.057918
0.057919-0.070426
0.070427 - 0.086542
0.086543-0.109240
0.109241 -0.146150
0.146151 -0.246795
-------
Tulip Poplar (Liriodendron tulipifera) (Current Standard)
RBL
0.000001 - 0.000758
0.000759-0.001822
0.001823-0.003022
0.003023 - 0.004290
0.004291 - 0.005791
0.005792 - 0.007926
0.007927-0.012118
0.012119-0.027862
-------
Tulip Poplar (Liriodendron tulipifera) (15 ppm-hrs)
RBL
0.000001 - 0.000758
0.000759-0.001822
0.001823-0.003022
0.003023 - 0.004290
0.004291 - 0.005791
0.005792 - 0.007926
0.007927-0.012118
0.012119-0.027862
-------
Tulip Poplar (Liriodendron tulipifera) (11 ppm-hrs)
RBL
0.000001 - 0.000463
0.000464-0.001045
0.001046-0.001663
0.001664-0.002378
0.002379 - 0.003290
0.003291 - 0.004741
0.004742-0.007121
0.007122-0.024015
-------
Tulip Poplar (Liriodendron tulipifera) (7 ppm-hrs)
RBL
0.000001 -0.000210
0.000211 -0.000484
0.000485 - 0.000764
0.000765-0.001077
0.001078-0.001540
0.001541 -0.002376
0.002377 - 0.004559
0.004560 - 0.009276
-------
Recent Conditions
0.05 0.10 0.15 0.20 0.25
75 |)|)l) Scenario
0.000 0.005
0.010
0.015 0.020
CT
0)
15 |>|>m-hi Scenario
0.000 0.005 0.010 0.015
0.020
11 |)|)in -hi Scenario
0.000
I I I
0.005 0.010 0.015
I
0.020
7 nnni-hr Scenario
0.000 0.005 0.010 0.015
Relative Biomass Loss, Tulip poplar
0.020
28
-------
Recent Conditions
D
00
O
O -
0.05
0.10
0.15
0.20
0.25
O
D
ID
O
O -
11 ppm -In Scenario
•equenc]
] 500 1500
[ i i
^dl^. ^f
trf
I i i i i i
0.0 0.2 0.4 0.6 0.8 1.
7 ppm-hr Scenario
0.0 0.2 0.4 0.6 0.8 1.0
Proportion of 75 ppb Scenario, Tulip poplar
29
-------
Ponderosa Pine (Pinus ponderosa) (Recent Conditions)
RBL
0.006660 - 0.020314
0.020315-0.032036
0.032037 - 0.045889
0.045890 - 0.062250
0.062251 - 0.082266
0.082267-0.113164
0.113165-0.164812
0.164813-0.243435
-------
Ponderosa Pine (Pinus ponderosa) (Current Standard)
RBL
0.000767 - 0.003953
0.003954 - 0.005914
0.005915-0.008482
0.008483-0.011989
0.011990-0.015976
0.015977-0.021390
0.021391 -0.028521
0.028522 - 0.040549
-------
Ponderosa Pine (Pinus ponderosa) (15 ppm-hrs)
RBL
0.000729-0.003815
0.003816-0.005624
0.005625-0.007219
0.007220 - 0.009488
0.009489-0.012511
0.012512-0.015953
0.015954-0.020754
0.020755 - 0.032525
-------
Ponderosa Pine (Pinus ponderosa) (11 ppm-hrs)
RBL
0.000687 - 0.002635
0.002636 - 0.004073
0.004074 - 0.005543
0.005544-0.007192
0.007193-0.009559
0.009560-0.012992
0.012993-0.019336
0.019337-0.032525
-------
Ponderosa Pine (Pinus ponderosa) (7 ppm-hrs)
RBL
0.000643 - 0.002341
0.002342 - 0.003536
0.003537 - 0.004690
0.004691 - 0.005921
0.005922 - 0.007591
0.007592-0.010159
0.010160-0.014979
0.014980-0.024900
-------
Recent Conditions
0.00
I I I
0.05 0.10 0.15 0.20 0.25
75 |)|)l) Scenario
rlflf
"UTTTT-n-i-!
0.00 0.01 0.02 0.03 0.04 0.05
15 |>|>m-hi Sceiunio
0)
0.00 0.01 0.02 0.03 0.04 0.05
11 |)|)in -hi Sceiunio
I I I I
0.00 0.01 0.02 0.03 0.04 0.05
7 nnni-hr Scenario
0.00 0.01 0.02 0.03 0.04 0.05
Relative Biomass Loss, Ponderosa pine
35
-------
Recent Conditions
o
o
-
1
I
1 1 1 1 1 1 1
0 10 20 30 40 50 60
o
o _
o ^
o
c
0)
^
cr
0)
o
o
o
15 ppm-hr Scenario
11 ppm -In Scenario
I I I I
0.0 0.2 0.4 0.6 0.8 1.0
J rL_ _rTTriTTTt-n
I I I I I I
0.0 0.2 0.4 0.6 0.8 1.0
o
o -
7 ppm-hr Scenario
riTTTTfl
Tl 1111 i-TI
0.0 0.2 0.4 0.6 0.8 1.0
Proportion of 75 ppb Scenario, Ponderosa pine
36
-------
Eastern White Pine (Pinus strobus) (Recent Conditions)
RBL
0.004446-0.017143
0.017144-0.029225
0.029226-0.040919
0.040920 - 0.052006
0.052007 - 0.063550
0.063551 - 0.077593
0.077594 - 0.097656
0.097657-0.146993
-------
Eastern White Pine (Pinus strobus) (Current Standard)
RBL
0.000001 - 0.000650
0.000651 -0.001885
0.001886-0.003686
0.003687 - 0.005661
0.005662 - 0.007670
0.007671 -0.010602
0.010603-0.015442
0.015443-0.026582
-------
Eastern White Pine (Pinus strobus) (15 ppm-hrs)
RBL
0.000001 - 0.000650
0.000651 -0.001885
0.001886-0.003686
0.003687 - 0.005661
0.005662 - 0.007670
0.007671 -0.010602
0.010603-0.015442
0.015443-0.026582
-------
Eastern White Pine (Pinus strobus) (11 ppm-hrs)
RBL
0.000001 - 0.000548
0.000549-0.001407
0.001408-0.002482
0.002483 - 0.003725
0.003726-0.005181
0.005182-0.007047
0.007048-0.010662
0.010663-0.020536
-------
Eastern White Pine (Pinus strobus) (7 ppm-hrs)
RBL
0.000001 - 0.000478
0.000479-0.001136
0.001137-0.001829
0.001830-0.002714
0.002715-0.004102
0.004103-0.006297
0.006298 - 0.009789
0.009790-0.016009
-------
Recent Conditions
rll-
0.00
0.05
0.10
0.15
75 |)|)l) Scenario
rh-n-v.
0.000 0.005 0.010 0.015 0.020 0.025 0.030
15 |>|>m-hi Scenario
o
C 8
|>in-lir Scenario
0.000 0.005 0.010 0.015 0.020 0.025 0.030
Relative Biomass Loss, Eastern white pine
42
-------
Recent Conditions
o
D
O
(O _
O
O _
O
Cl -
5000
I I I I
10000 15000 20000 25000
11 ppm -In Scenario
5*8-
requenc]
II 2000 50
i i i [ [ [
I i i i i i
0.0 0.2 0.4 0.6 0.8 1.
7 ppm-hr Scenario
o
o
o
-
o
o
o
CM
0.0 0.2 0.4 0.6 0.8 1.0
Proportion of 75 ppb Scenario, Eastern white pint
43
-------
Loblolly Pine (Pinus taeda) (Recent Conditions)
RBL
0.001453-0.002310
0.002311 -0.002688
0.002689 - 0.003063
0.003064 - 0.003443
0.003444 - 0.003842
0.003843 - 0.004320
0.004321 - 0.005064
0.005065 - 0.007059
-------
Loblolly Pine (Pinus taeda) (Current Standard)
RBL
0.000021 - 0.000286
0.000287 - 0.000398
0.000399-0.000516
0.000517-0.000648
0.000649 - 0.000799
0.000800 - 0.000967
0.000968-0.001169
0.001170-0.001669
-------
Loblolly Pine (Pinus taeda) (15 ppm-hrs)
RBL
0.000021 - 0.000286
0.000287 - 0.000398
0.000399-0.000516
0.000517-0.000648
0.000649 - 0.000799
0.000800 - 0.000967
0.000968-0.001169
0.001170-0.001669
-------
Loblolly Pine (Pinus taeda) (11 ppm-hrs)
RBL
0.000020 - 0.000262
0.000263 - 0.000365
0.000366 - 0.000469
0.000470 - 0.000573
0.000574 - 0.000689
0.000690 - 0.000823
0.000824-0.001022
0.001023-0.001450
-------
Loblolly Pine (Pinus taeda) (7 ppm-hrs)
RBL
0.000015-0.000210
0.000211 -0.000285
0.000286 - 0.000350
0.000351 -0.000417
0.000418-0.000486
0.000487 - 0.000566
0.000567 - 0.000674
0.000675-0.000912
-------
Recent Conditions
i
0.002 0.003 0.004 0.005 0.006 0.007
75 |)|)l) Scenario
0.0000
0.0005
0.0010
0.001 £
0.0020
>-.
o
C o
(D §
3
CT °
0)
0.0000
15 |>|>m-hi Scenario
0.0005 0.0010 0.0015 0.0020
11 |)|)in -hi Scenario
I
0.0000
I I I
0.0005 0.0010 0.0015 0.0020
S -
CD _
7 nnni-hr Scenario
0.0000 0.0005 0.0010 0.0015
Relative Biomass Loss, Loblolly pine
0.0020
49
-------
Recent Conditions
I
1
1 1 1
0 20 40
1 I I I
60 30 100 120
o
o
o
o
o
11 ppm -In Scenario
O o
C (N
-------
Virginia Pine (Pinus virginiana) (Recent Conditions)
RBL
0.003681 - 0.005463
0.005464 - 0.006326
0.006327-0.007158
0.007159-0.008109
0.008110-0.009125
0.009126-0.010367
0.010368-0.012238
0.012239-0.016253
-------
Virginia Pine (Pinus virginiana) (Current Standard)
RBL
0.000083-0.000516
0.000517-0.000937
0.000938-0.001286
0.001287-0.001633
0.001634-0.001971
0.001972-0.002357
0.002358 - 0.002853
0.002854 - 0.005420
-------
Virginia Pine (Pinus virginiana) (15 ppm-hrs)
RBL
0.000083-0.000516
0.000517-0.000937
0.000938-0.001286
0.001287-0.001633
0.001634-0.001971
0.001972-0.002357
0.002358 - 0.002853
0.002854 - 0.005420
-------
Virginia Pine (Pinus virginiana) (11 ppm-hrs)
RBL
0.000083 - 0.000474
0.000475 - 0.000847
0.000848-0.001100
0.001101 -0.001339
0.001340-0.001603
0.001604-0.001905
0.001906-0.002410
0.002411 -0.005044
-------
Virginia Pine (Pinus virginiana) (7 ppm-hrs)
RBL
0.000083-0.000341
0.000342 - 0.000542
0.000543 - 0.000682
0.000683 - 0.000820
0.000821 - 0.000975
0.000976-0.001155
0.001156-0.001473
0.001474-0.003190
-------
Recent Conditions
1 i
0.004 0.006 0.003 0.010 0.012 0.014 0.016
75 |)|)l) Scenario
0.000 0.001 0.002 0.003 0.004 0.005 0.006
15 |>|>m-hi Scenario
0.002 0.003 0.004 0.005 0.006
11 |)|)in -hi Scenario
0.000 0.001
I I I I
0.002 0.003 0.004 0.005 0.006
7 |>|>in-lir Scenario
0.000 0.001 0.002 0.003 0.004 0.005
Relative Biomass Loss, Virginia pine
0.006
56
-------
Recent Conditions
>^
o
c
-------
Eastern Cottonwood (Populus deltoides) (Recent Conditions)
RBL
0.087889-0.271708
0.271709-0.364673
0.364674 - 0.462637
0.462638 - 0.562405
0.562406 - 0.658580
0.658581 -0.748207
0.748208 - 0,840292
0.840293-0.997918
-------
Eastern Cottonwood (Populus deltoides) (Current Standard)
RBL
0.000005 - 0.028261
0.028262 - 0.062809
0.062810-0.100336
0.100337-0.140979
0.140980-0.186795
0.186796-0.247215
0.247216-0.352241
0.352242-0.658952
-------
Eastern Cottonwood (Populus deltoides) (15 ppm-hrs)
RBL
0.000005 - 0.027686
0.027687 - 0.059359
0.059360 - 0.093489
0.093490-0.132832
0.132833-0.179363
0.179364-0.241344
0.241345-0.348124
0.348125-0.658952
-------
Eastern Cottonwood (Populus deltoides) (11 ppm-hrs)
RBL
0.000005 - 0.023964
0.023965 - 0.048387
0.048388 - 0.072335
0.072336-0.100454
0.100455-0.140448
0.140449-0.203203
0.203204 - 0.304293
0.304294 - 0.533328
-------
Eastern Cottonwood (Populus deltoides) (7 ppm-hrs)
RBL
0.000005-0.014223
0.014224-0.028587
0.028588 - 0.042000
0.042001 -0.056845
0.056846 - 0.078964
0.078965-0.117139
0.117140-0.185575
0.185576-0.352875
-------
Recent Conditions
75 |)|)l) Scenario
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
O
C O
|>m-hi Scenario
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
8 I
CD _
0.0 0.1
11 |)|)in -hi Scenario
i i i i
0.2 0.3 0.4 0.5
i i
0.6 0.7
7 |>|>in-lir Scenario
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
Relative Biomass Loss, Eastern cottonwood
63
-------
Recent Conditions
g -
o
I I I I I I
10000 20000 30000 40000 50000 60000
g
o
CD
O
c
0)
3
CT
-------
Quaking Aspen (Populus tremuloides) (Recent Conditions)
RBL
0.009388-0.023140
0.023141 -0.032551
0.032552 - 0.043532
0.043533 - 0.058477
0.058478 - 0.077339
0.077340 - 0.099686
0.099687-0.147910
0.147911 -0.275059
-------
Quaking Aspen (Populus tremuloides) (Current Standard)
RBL
0.000012-0.002129
0.002130-0.005341
0.005342 - 0.008802
0.008803-0.012436
0.012437-0.017255
0.017256-0.023316
0.023317-0.031971
0.031972-0.058540
-------
Quaking Aspen (Populus tremuloides) (15 ppm-hrs)
RBL
0.000012-0.001829
0.001830-0.004058
0.004059 - 0.006820
0.006821 -0.009893
0.009894-0.013414
0.013415-0.017867
0.017868 - 0.023837
0.023838-0.042177
-------
Quaking Aspen (Populus tremuloides) (11 ppm-hrs)
RBL
0.000012-0.001826
0.001827-0.004005
0.004006 - 0.006294
0.006295 - 0.008577
0.008578-0.010976
0.010977-0.014782
0.014783 - 0.022628
0.022629 - 0.038947
-------
Quaking Aspen (Populus tremuloides) (7 ppm-hrs)
RBL
0.000012-0.001656
0.001657-0.003328
0.003329 - 0.004884
0.004885 - 0.006534
0.006535 - 0.008390
0.008391 -0.011383
0.011384-0.017413
0.017414-0.030261
-------
g
g
g
3
Recent Conditions
\ \ i r
\
0.00 0.05 0.10 0.15 0.20 0.25
75 |)|)l) Scenario
0.00 0.01 0.02 0.03 0.04 0.05 0.06
15 |>|>m-hr Sceiunio
cr
0)
0.00 0.01 0.02 0.03 0.04 0.05 0.06
g
g
0.00
I
0.01
11 |)|)in -hi Sceiunio
i i i
0.02 0.03 0.04
i i
0.05 0.06
S
g
7 nnni-hr Scenario
0.00 0.01 0.02 0.03 0.04 0.05
Relative Biomass Loss, Aspen
0.06
70
-------
Recent Conditions
o _
o
(O -
I I I I
500 1000 1500 2000
2500
O _
o
o I
15 ppm-hr Scenario
i i i r
>> 0.0 0.2 0.4 0.6 0.8 1.0
O
c
0)
11 ppm -Im Scenario
o _
o _
o
I I I I I I
0.0 0.2 0.4 0.6 0.8 1.0
7 ppm-hr Scenario
o
o
o
i i i r
\ \
0.0 0.2 0.4 0.6 0.8 1.0
Proportion of 75 ppb Scenario, Aspen
71
-------
Black Cherry (Prunus serotina) (Recent Conditions)
RBL
0.061483-0.139138
0.139139-0.186794
0.186795-0.216040
0.216041 -0.233987
0.233988 - 0.263233
0.263234-0.310889
0.310890-0.388544
0.388545-0.515083
-------
Black Cherry (Prunus serotina) (Current Standard)
RBL
0.000274-0.016480
0.016481 -0.032894
0.032895 - 0.048072
0.048073 - 0.064388
0.064389-0.081323
0.081324-0.098929
0.098930-0.123403
0.123404-0.239047
-------
Black Cherry (Prunus serotina) (15 ppm-hrs)
RBL
0.000274-0.016480
0.016481 -0.032868
0.032869 - 0.048072
0.048073 - 0.064388
0.064389-0.081323
0.081324-0.098929
0.098930-0.123403
0.123404-0.239047
-------
Black Cherry (Prunus serotina) (11 ppm-hrs)
RBL
0.000274-0.015392
0.015393-0.029034
0.029035 - 0.040957
0.040958 - 0.053077
0.053078 - 0.064753
0.064754 - 0.077805
0.077806-0.099140
0.099141 -0.194187
-------
Black Cherry (Prunus serotina) (7 ppm-hrs)
RBL
0.000274-0.014431
0.014432-0.025206
0.025207 - 0.032843
0.032844-0.040104
0.040105-0.047947
0.047948 - 0.058666
0.058667 - 0.076533
0.076534-0.136828
-------
Recent Conditions
75 |)|)l) Scenario
0.00 0.05 0.10 0.15 0.20 0.25
CT
0)
15 |>|>m-hr Scenario
0.00 0.05 0.10 0.15 0.20 0.25
11 |)|)in -hi Sceiuirio
0.00 0.05 0.10 0.15 0.20 0.25
7 |>|>in-lir Scenario
0.00 0.05 0.10 0.15 0.20
Relative Biomass Loss, Black cherry
0.25
77
-------
o
o
o
o
o
Recent Conditions
i i i i
100 200 300 400
500
o
o
o
to _
o -
o
o
tn _
11 ppm -In Scenario
requency
D 4000
ill
^-i-lTlTn-i, , ,, rlh-
I i i i i i
0.0 0.2 0.4 0.6 0.8 1.
7 ppm-hr Scenario
0.0 0.2 0.4 0.6 0.8 1.0
Proportion of 75 ppb Scenario, Black cherry
78
-------
Douglas Fir (Pseudotsuga menziesii) (Recent Conditions)
RBL
^Bi 0.0000-15.7446
^^| 15.7447-64.0292
64.0293-165.9609
165.9610-382.3406
382.3407-781.0116
| 781.0117-1410.1246
^Hl 1410.1247-3177.5261
^^| 3177.5262-4575.9793
Note: Values have been multiplied by 1,000,000
-------
Douglas Fir (Pseudotsuga menziesii) (Current Standard)
RBL
^Bi 0.00000-0.10269
^^| 0.10270-0.37442
0.37443 - 0.77249
0.77250-1.31747
1.31748-2.13943
2.13944-3.69894
^Hl 3.69895-7.41207
^^| 7.41208-12.86961
Note: Values have been multiplied by 10,000,000
-------
Douglas Fir (Pseudotsuga menziesii) (15 ppm-hrs)
RBL
^Bi 0.000000-0.016844
^Bl 0.016845-0.059532
0.059533-0.134777
0.134778-0.250422
0.250423 - 0.438084
0.438085-0.721538
^Hl 0.721539-1.459698
^^| 1.459699-2.485387
Note: Values have been multiplied by 10,000,000
-------
Douglas Fir (Pseudotsuga menziesii) (11 ppm-hrs)
RBL
^Bi 0.00000-0.00277
^^| 0.00278-0.01037
0.01038-0.03224
0.03225-0.12153
0.12154-0.25333
0.25334 - 0.52447
^Hl 0.52448-0.94606
^^| 0.94607 - 1.45970
Note: Values have been multiplied by 10,000,000
-------
Douglas Fir (Pseudotsuga menziesii) (7 ppm-hrs)
RBL
^Bi 0.000000-0.000880
^^| 0.000881 -0.003161
0.003162-0.010351
0.010352-0.041669
0.041670-0.083566
0.083567-0.168655
^Hl 0.168656-0.310380
^^| 0.310381 -0.475834
Note: Values have been multiplied by 10,000,000
-------
Recent Conditions
I
0.000 0.001
I I
0.002 0.003
I
0.004
75 |)|)l) Scenario
Oe+00 2e-04 4e-04 6e-04 Be-04 1e-03
O
C o
0) B
0)
15 |>|>m-hi Scenario
Oe+00 2e-04
1 1 1
4e-04 6e-04 3e-04
1e-03
11 |)|)in -hi Sceiunio
I I I I I
Oe+00 2e-04 4e-04 6e-04 3e-04 1e-03
I
Oe+00
7 |>|>ni-ln Scenario
2e-04 4e-04 6e-04 3e-04
Relative Biomass Loss, Douglas fir
1e-03
84
-------
Recent Conditions
fN -
O -
1
1
50
On
i
100
n
i
150
nnn
15 ppm-hr Scenario
:L
I I I I I
>> 0.0 0.2 0.4 0.6 0.8 1.0
o
c
0)
o-
1_
J_
11 ppm -In Scenario
I I I I I
0.0 0.2 0.4 0.6 0.8 1.0
o _
7 ppm-hr Scenario
i 1 1 1 1 1
0.0 0.2 0.4 0.6 0.8 1.0
Proportion of 75 ppb Scenario, Douglas fir
85
-------
July 2013
Assessment of the Impacts of
Alternative Ozone Concentrations
on the U.S. Forest and Agriculture
Revised Draft Report
Prepared for
Christine Davis
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
Prepared by
Robert Beach, PhD
Yuquan Zhang, PhD
Viola Glenn, MS
Ross Loomis, MA
RTI International
3040 Cornwallis Road
Research Triangle Park, NC 27709
RTI Project Number 0212979.002.016
HRTI
INTERNATIONAL
-------
RTI Project Number
0212979.002.016
Assessment of the Impacts of
Alternative Ozone Concentrations
on the U.S. Forest and Agriculture
Revised Draft Report
July 2013
Prepared for
Christine Davis
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
Prepared by
Robert Beach, PhD
Yuquan Zhang, PhD
Viola Glenn, MS
Ross Loomis, MA
RTI International
3040 Cornwallis Road
Research Triangle Park, NC 27709
RTI International is a trade name of Research Triangle Institute.
-------
CONTENTS
Section Page
1 Introduction 1-1
2 Overview of the FASOMGHG Model 2-1
2.1 Brief History and Previous Applications 2-2
2.2 Commodities 2-5
2.2.1 Primary Commodities 2-5
2.2.2 Secondary Commodities 2-10
2.2.3 Bioenergy Products 2-14
2.2.4 Blended Livestock Feeds 2-14
2.3 Inputs to Production 2-15
2.4 U.S. Regional Disaggregation 2-18
2.5 Land Use Categories 2-20
2.5.1 Agricultural Land 2-24
2.5.2 Forestland 2-27
2.5.3 Developed Land 2-29
2.5.4 Land Allocation 2-29
2.6 Market Modeling 2-30
2.7 International Trade 2-32
2.7.1 Forestry 2-32
2.7.2 Agriculture 2-32
2.8 GHG Accounts 2-37
2.8.1 Forest GHG Accounts 2-40
2.8.2 Agricultural GHG Accounts 2-41
2.8.3 Bioenergy GHG Accounts 2-41
2.8.4 Developed Land GHG 2-42
2.9 Other Environmental Impacts 2-42
in
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3 Methods Used to Develop Estimates of Ozone Effects on Crop and Forest
Productivity 3-1
3.1 Ambient Ozone Concentration Data 3-1
3.2 Calculation of Relative Yield Loss 3-2
3.2.1 Relative Yield Loss for Crops 3-3
3.2.2 Relative Yield Loss for Trees 3-4
3.3 Calculation of Relative Yield Gain 3-5
3.4 Conducting Model Scenarios in FASOMGHG 3-5
4 Data Inputs 4-1
4.1 Ambient Ozone Concentration Data 4-1
4.2 Changes in Crop and Forest Yields with Respect to 75 ppb Scenario 4-5
5 Model Results 5-1
5.1 Agricultural Sector 5-1
5.1.1 Production and Prices 5-1
5.1.2 Crop Acreage 5-4
5.2 Forestry Sector 5-6
5.2.1 Production and Prices 5-7
5.2.2 Forest Acres Harvested 5-10
5.2.3 Forest Inventory 5-11
5.3 Cross-Sectoral Policy Impacts 5-12
5.3.1 Land Use 5-13
5.3.2 Welfare 5-15
5.3.3 Greenhouse Mitigation Potential 5-17
5.4 Summary 5-20
IV
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6 County-Level Agricultural Welfare 6-1
6.1 Calculating County-Level Agricultural Welfare 6-1
6.2 Changes in County-Level Agricultural Welfare: Alternative Scenarios
versus Current Standard (75 ppb) 6-4
6.3 Summary 6-13
7 References 7-1
Appendixes
A Data Inputs: Focusing on Comparison of Alternative Ozone Standards with
Current Conditions A-l
B Model Results: Focusing on Comparison with Current Conditions B-l
C County-Level Agricultural Welfare Analysis: Focusing on Comparison with
Current Conditions C-l
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LIST OF FIGURES
Number
2-1. Map of the FASOMGHG Regions 2-19
2-2. Baseline FASOMGHG U.S. Land Base by Land Use Category (million
acres) 2-24
3-1. FASOMGHG Modeling Flowchart 3-7
4-1. Ozone Reductions with Respect to 75 ppb under Alternative Scenarios 4-5
4-2. Percentage Changes in Corn RYGs with Respect to the 75 ppb Scenario 4-6
4-3. Percentage Changes in Cotton RYGs with Respect to the 75 ppb Scenario 4-7
4-4. Percentage Changes in Potato RYGs with Respect to the 75 ppb Scenario 4-8
4-5. Percentage Changes in Sorghum RYGs with Respect to the 75 ppb Scenario 4-9
4-6. Percentage Changes in Soybean RYGs with Respect to the 75 ppb Scenario 4-10
4-7. Percentage Changes in Winter Wheat RYGs with Respect to the 75 ppb
Scenario 4-11
4-8. Percentage Changes in Softwood RYGs with Respect to the 75 ppb Scenario 4-12
4-9. Percentage Changes in Hardwood RYGs with Respect to the 75 ppb Scenario.... 4-13
5-1. Carbon Storage in Forestry Sector, MMtCO2e 5-18
6-1. Relationship between Select Crops Used for Analysis and Total Agricultural
Production 6-2
6-2. County Share of Total FASOMGHG Subregion Production Is Used to
Calculate County Gross Revenue and Production 6-2
6-3. Area of Producer Surplus 6-3
6-4. Changes in Crop Producer Welfare under Alternative Standards with Respect
to 75 ppb, 2010, Thousand 2010 U.S. Dollars 6-6
6-5. Changes in Crop Producer Welfare under Alternative Standards with Respect
to 75 ppb, 2020, Thousand 2010 U.S. Dollars 6-8
6-6. Changes in Crop Producer Welfare under Alternative Standards with Respect
to 75 ppb, 2030, Thousand 2010 U.S. Dollars 6-9
6-7. Changes in Crop Producer Welfare under Alternative Standards with Respect
to 75 ppb, 2040, Thousand 2010 U.S. Dollars 6-12
VI
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LIST OF TABLES
Number
2-1 Primary Commodities 2-6
2-2 Secondary (Processed) Commodities 2-10
2-2 Secondary (Processed) Commodities 2-11
2-2 Secondary (Processed) Commodities 2-12
2-2 Secondary (Processed) Commodities 2-13
2-3 Bioenergy Products 2-14
2-4. Blended Livestock Feeds 2-15
2-5 Major Categories Included in Crop Budgets in Quantities 2-17
2-6 Major Categories Included in Livestock Budgets in Quantities 2-17
2-7 Definition of F ASOMGHG Production Regions and Market Regions 2-19
2-8. Aggregated U.S. Regions 2-20
2-9. Forest Management Intensity Classes (regions of application in parentheses) 2-28
2-10. Explicitly Traded Commodities and Countries/Regions Trading with the
United States 2-34
2-11. Commodities with Only ROW Export or Import Possibilities 2-35
2-12. Commodities without International Trade Possibilities Modeled 2-36
2-13 Categories of GHG Sources and Sinks in F ASOMGHG 2-38
3-1 Parameter Values Used for Crops and Tree Species 3-2
3-2 Mapping of Ozone Impacts on Crops toFASOMGHG Crops 3-3
3-3 Mapping of Ozone Impacts on Forests to F ASOMGHG Forest Types 3-4
4-1 Forestland W126 Ozone Values under Alternative Scenarios 4-1
4-2 Cropland W126 Ozone Values under Modeled Scenarios 4-2
5-1 Agricultural Production Fisher Indices (Current conditions =100) 5-2
5-2 Agricultural Price Fisher Indices (Current Conditions = 100) 5-3
5-3 Major Crop Acreage, Million Acres 5-5
5-4 Forest Products Production, Million Cubic Feet 5-8
5-5 Forest Product Prices, U.S. Dollars per Cubic Foot 5-8
5-6 Forest Product Prices and Percentage Change, U.S. Dollars per Cubic Foot 5-9
5-7 Forest Acres Harvested, Thousand Acres 5-10
5-8 Existing and New Forest Inventory, Million Cubic Feet 5-12
5-9 Land Use by Major Category, Thousand Acres 5-13
5-10 Consumer and Producer Surplus in Agriculture, Million 2010 U.S. Dollars 5-15
5-11 Consumer and Producer Surplus in Forestry, Million 2010 U.S. Dollars 5-16
5-12 Annualized Changes in Consumer and Producer Surplus in Agriculture and
Forestry, 2010-2044, Million 2010 U.S. Dollars (4% Discount Rate) 5-17
5-13 Carbon Storage, MMtCO2e 5-19
vn
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5-13 Forestry Carbon Sequestration, MMtCO2e 5-19
6-1 Mapping of USDA Crops to FASOMGHG Crops 6-4
6-2 Driving Factors Behind County-Level Welfare Changes 6-13
Vlll
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SECTION 1
INTRODUCTION
Ground-level ozone has a number of negative impacts on human health and ecosystems
that increase with ambient ozone concentrations. One important category of impacts is damage to
plants that results in reduced growth rates, leading to lower productivity for agricultural crops
and for the trees used to produce forestry products. As one component of the risk and ecosystem
services impacts assessment of the effects of ozone, RTI International is working with the U.S.
Environmental Protection Agency (EPA) to examine the potential impacts on agriculture and
forests. We are examining the potential forest and agricultural market responses under alternative
ambient ozone concentrations, as well as the associated effects on consumer and producer
welfare. To adequately investigate the dynamic effects of policies affecting the forestry and
agricultural sectors, we need an analytical framework that can simulate the time path of market
and environmental impacts. The model we are using to simulate market outcomes under
alternative ozone concentrations is the Forest and Agricultural Sector Optimization Model with
Greenhouse Gases (FASOMGHG).
FASOMGHG is a dynamic, nonlinear programming model of the U.S. forest and
agricultural sectors. Although public timberland is not explicitly modeled (the focus of the model
is on private decision-maker responses to changing incentives), FASOMGHG includes an
exogenous timber supply from public forestlands. Harvests from public forestlands are included
in the model but are treated as exogenously determined by the government. The model solves a
constrained dynamic optimization problem that maximizes the net present value of the sum of
producer and consumer surplus across the two sectors over time. The model is constrained such
that total production is equal to total consumption, technical input/output relationships hold, and
total land use must remain constant. FASOMGHG simulates the allocation of land over time to
competing activities in both the forest and agricultural sectors and the associated impacts on
commodity markets. In addition, the model simulates environmental impacts resulting from
changing land allocation and production practices, including detailed accounting for changes in
net greenhouse gas (GHG) emissions. The model was developed to evaluate the welfare and
market impacts of policies that influence land allocation and alter production activities within
these sectors. FASOMGHG has been used in numerous studies to examine such issues as the
potential impacts of GHG mitigation policy, climate change, timber harvest policy on public
lands, federal farm programs, bioenergy production, changes in ozone levels, and other policies
affecting the forest and agricultural sectors.
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The comprehensive sectoral coverage provided by FASOMGHG offers several
advantages for analysis of policies affecting the forest and agricultural sectors. Because the
model accounts for land competition among forestry, crop production, and livestock production
(pasture) and landowner responses to changing relative prices, FASOMGHG provides a more
complete assessment of the net market impacts associated with a policy than models that focus
only on direct policy impacts on an individual commodity or subset of alternative land uses.
Using FASOMGHG enables determination of secondary impacts, such as crop switching,
movements between cropland and pasture, movements between forestland and agricultural land,
and changes in equilibrium quantities of forest and agricultural commodities due to changes in
relative commodity prices. FASOMGHG also captures changes in the livestock market due to
changes in feed costs and pasture rents, as well as changes in U.S. exports and imports of major
agricultural commodities. In addition, the model accounts for changes in the primary agricultural
GHGs (carbon dioxide [CO2], methane [CH4], and nitrous oxide [TSbO]) from the majority of
emitting agricultural activities and tracks carbon sequestration and carbon losses over time. The
intertemporal dynamics of the economic and biophysical systems allow for an accounting of
environmental impacts over time and by region. This approach allows for a more complete
quantification of net impacts, providing additional insights into the important environmental and
economic impacts in these sectors.
FASOMGHG simulates a dynamic baseline and changes from that baseline in response to
changes in public policy or other factors affecting these sectors. For instance, the model is often
used to evaluate the joint economic and biophysical effects of GHG mitigation and bioenergy
scenarios in U.S. forestry and agriculture. The model has also been used for previous studies of
ozone and climate impacts on forests and agriculture. The primary data required for simulations
of the impacts of changing ambient ozone concentrations are regionally disaggregated
productivity effects of these concentrations for each crop and forest type included within
FASOMGHG. These values are incorporated as shifts in the model production functions.
Because of changes in the relative returns available for alternative land uses, landowners will
alter their land use, crop mix, production practices, and other factors, moving to a new
equilibrium.
In the remainder of this report, we provide an overview of FASOMGHG (Section 2),
describe the methodology we used to calculate productivity effects associated with alternative
ozone concentrations (Section 3), present the model inputs used to represent ozone impacts in
our scenarios (Section 4), summarize the results of our analyses (Section 5), and show the
distribution of welfare effects on agricultural producers (Section 6). The calculations of impacts
1-2
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in the main body of the report are based on comparison with current primary standards for
ambient ozone concentrations. Appendixes A, B, and C present the ozone impacts on crop yields,
model results, and welfare impacts, respectively, for our scenarios relative to current conditions
for ambient ozone concentrations.
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SECTION 2
OVERVIEW OF THE FASOMGHG MODEL
FASOMGHG1 combines component models of agricultural crop and livestock
production, renewable fuels production, livestock feeding, agricultural processing, log
production, forest processing, carbon sequestration, GHG emissions, wood product markets,
agricultural markets, GHG payments, and land use to systematically capture the rich mix of
biophysical and economic processes that will determine the technical, economic, and
environmental implications of changes in policies. FASOMGHG covers private timberlands
(along with an exogenously determined timber supply from public forestlands2) and all
agricultural activity across the conterminous ("lower 48") United States, broken into 11 market
regions. Finally, FASOMGHG tracks approximately 80 forest product categories and more than
2,000 production possibilities for field crops, livestock, and renewable energy feedstocks.
FASOMGHG assumes intertemporal optimizing behavior by economic agents. For
instance, the decision to continue growing a stand of timber rather than harvesting it now is
based on a comparison of the net present value of timber harvest from a future period versus the
net present value of harvesting now and replanting (or not replanting and shifting the land to
agricultural use). Similarly, landowners make a decision to keep their land in agriculture versus
afforestation based on a comparison of the net present value of returns in agriculture and
forestry. Land can also move between cropland and pasture, depending on relative returns. This
process establishes a land price equilibrium across the sectors (reflecting productivity in
alternative uses and land conversion costs) and, given the land base interaction, a link between
contemporaneous commodity prices in the two sectors as well.
The model solution portrays simultaneous multiperiod, multicommodity, multifactor
market equilibria, typically over 60 to 100 years on a 5-year time-step basis, when running the
combined forest-agriculture version of the model. Results yield a dynamic simulation of prices,
production, management, consumption, GHG effects, and other environmental and economic
indicators within these sectors under each scenario defined in the model run.
The key endogenous variables in FASOMGHG include
1 See Adams et al. (2005), Beach et al. (2010), and Beach and McCarl (2010) for more detailed documentation of
FASOMGHG.
2 In the scenarios modeled for this draft report, we assumed that timber supply from public forestlands remains
constant under all scenarios. However, we may revisit this assumption in the future to examine the potential
effects of reduced ozone concentrations on public forests and timber supply from public lands.
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• commodity and factor prices;
• production, consumption, and export and import quantities;
• land use allocations between sectors;
• management strategy adoption;
• resource use;
• economic welfare measures;
• producer and consumer surplus;
• transfer payments;
• net welfare effects; and
• environmental impact indicators, such as
— GHG emission/sequestration of CCh, CH4, and N2O and
- total nitrogen and phosphorous applications.
Additional details on the model and key characteristics are provided in the following
subsections.
Brief History and Previous Applications
The current version of FASOMGHG reflects numerous model enhancements that have
been made over time, dating back to the first version of the Agricultural Sector Model (ASM)
(Baumes, 1978). Since the initial version of ASM, the model has undergone many changes,
including improvements for pesticide analysis by Burton (1982), as reported in Burton and
Martin (1987), and a number of model additions to enable more detailed environmental and
resource analyses. ASM has been used for analyses of renewable fuels dating back to the late
1970s and 1980s (Tyner et al., 1979; Chattin, 1982; Hickenbotham, 1987). In addition, ASM was
applied to study ozone impacts (Hamilton, 1985; Adams, Hamilton, and McCarl, 1984), acid rain
(Adams, Callaway, and McCarl, 1986), soil conservation policy (Chang et al., 1994), global
climate change impacts (Adams et al., 1988, 1990, 1999, 2001; McCarl, 1999; Reilly et al., 2000,
2002), and GHG mitigation (Adams et al., 1993; McCarl and Schneider, 2001).
One of the drivers behind integrating ASM with forest-sector models to create the initial
Forest and Agricultural Sector Optimization Model (FASOM) was an ASM study examining
issues regarding joint forestry and agricultural GHG mitigation (Adams et al., 1993). Attempts to
reconcile forestry production possibilities with the static single-year equilibrium representation
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in ASM led to the recognition that the model did not adequately reflect the dynamic issues
associated with land allocation between forestry and agriculture. Thus, FASOM was constructed
to address these limitations by linking a simple intertemporal model of the forest sector with a
version of the ASM in a dynamic framework, allowing some portion of the land base in each
sector to be shifted to the alternative use. Land could transfer between sectors based on its
marginal profitability in all alternative forest and agricultural uses over the time horizon of the
model. Management investment decisions in both sectors, including harvest timing in forestry,
were made endogenous, so they too would be based on the expected profitability of an additional
dollar spent on expanding future output (both timber and carbon, if valued monetarily).
The basic structure of the forest sector was based on the family of models developed to
support the timber assessment component of the U.S. Forest Service's decennial Forest and
Rangeland Renewable Resources Planning Act (RPA) assessment process3: TAMM (Timber
Assessment Market Model) (Adams and Haynes, 1980, 1996; Haynes, 2003), NAPAP (North
American Pulp and Paper model) (Ince, 1994; Zhang, Buongiorno, and Ince, 1993, 1996),
ATLAS (Aggregate Timberland Assessment System) (Mills and Kincaid, 1992), and
AREACHANGE (Alig et al., 2003, 2010a; Alig, Kline, and Lichtenstein, 2004; Alig and
Plantinga, 2007). Timber inventory data and estimates of current and future timber yields were
taken in large part from the ATLAS inputs used for the 2000 RPA Timber Assessment (Haynes,
2003) (these data have since been updated with information from the 2005 RPA Update
assessment, as described later in this report). The AREACHANGE models provide timberland
area and forest type allocations to the ATLAS model. TAMM and NAPAP are "myopic" market
projection models (they project ahead one period at a time) of the solid wood and fiber products
sectors in the United States and Canada. In ATLAS, harvested lands are regenerated (grown)
according to exogenous assumptions about the intensity of management and associated yield
volume changes. The timberland base is adjusted for gains and losses projected over time by the
AREACHANGE models, including afforestation of the area moving from agriculture into
forestry. Product demand relations were extracted directly from the latest versions of TAMM and
NAPAP, as were product supply relations for the solid wood products and all product conversion
coefficients for both solid wood and fiber commodities. Trade between the United States and
Canada in all major classes of wood products is endogenous and subject to the full array of
potential trade barriers and exchange rates. Timber supply also uses nearly the full set of
management intensity options available in ATLAS (e.g., for the South, seven planted pine
1 Adams and Haynes (2007) give a complete description of the full modeling system.
2-3
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management intensity classes directly from ATLAS), and the selection of management intensity
is endogenous.
In addition, detailed GHG accounting for CCh and major non-CCh GHGs was added to
FASOM to create the model denoted as FASOMGHG, as described in the following paragraphs.
The forest carbon accounting component of FASOMGHG is largely derived from the U.S. Forest
Service's Forestry Carbon (FORCARB) modeling system, which is an empirical model of forest
carbon budgets simulated across regions, forest types, land classes, forest age classes, ownership
groups, and carbon pools. The U.S. Forest Service uses FORCARB, in conjunction with its
economic forest-sector models (e.g., TAMM, NAPAP, ATLAS, AREACHANGE), to estimate
the total amount of carbon stored in U.S. forests over time as part of the Forest Service's ongoing
assessment of forest resources in general (i.e., pursuant to the RPA) and forest carbon
sequestration potential in particular (Joyce, 1995; Joyce and Birdsey, 2000). Basing the model's
forest carbon accounting structure on FORCARB ensures that forest carbon estimates from
FASOMGHG can be compared with ongoing efforts by the Forest Service to estimate and
project national forest carbon sequestration.4 It also enables FASOMGHG to be updated over
time as the FORCARB system evolves to incorporate the latest science.
After the inclusion of forest carbon accounting and some limited coverage of soil carbon
changes associated with land use change, work began to widen the coverage of agricultural GHG
sources and management possibilities for mitigating GHG. Schneider (2000) and McCarl and
Schneider (2001) expanded ASM to account for numerous categories of GHGs and to include a
detailed set of agricultural-related GHG management possibilities. That work expanded ASM to
include changes in tillage, land use exchange between pasture and crops, afforestation, nitrogen
fertilization alternatives, enteric fermentation, manure management, renewable fuel offsets, fossil
fuel use reduction, and changes in rice cultivation. The resulting model was labeled ASMGHG.
Given the dynamic modeling and forest carbon sequestration coverage included in
FASOM and the agricultural coverage in ASMGHG, it was decided to merge the agricultural
alternatives into the FASOM structure. This change was manifest in the first version of
FASOMGHG that was built in the context of Lee (2002). In that work, the agricultural model
was expanded to have all the GHG management alternatives in ASMGHG with the additional
coverage of dynamics. More recently, model modifications have been made to enhance
4 Note that FASOMGHG forest carbon accounting currently reflects sequestration on private timberland. Because
public forest acreage is held constant and public timber supply is exogenous, the model has assumed no change
in carbon storage across scenarios. We anticipate revisiting this assumption in future modeling of ozone impacts,
though, because the effects of ozone on growth rates of public forests would be expected to affect carbon
sequestration on those lands.
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FASOM's ability to provide detailed analyses of the agricultural and environmental impacts of
bioenergy production (both liquid transportation fuels and bioelectricity) from forest and
agricultural feedstocks.
In the following subsections, we provide an overview of the scope of FASOMGHG in
terms of the commodities included and commodity flows between primary and secondary
(processed) products, inputs used in production, U.S. regional disaggregation, land categories
and allocation, market modeling, treatment of international trade, GHG accounts tracked, and
other environmental impacts calculated.
Commodities
FASOMGHG includes several major groupings of agricultural and forest commodities,
depending on the sector and whether they are primary commodities, processed, used for
bioenergy, or mixed for livestock feed. These commodity groups are
• raw crop, livestock, forestry, and renewable fuel feedstock primary commodities
grown on the land;
• processed, secondary commodities made from the raw crop, livestock, and wood
products;
• bioenergy products made from renewable fuel feedstocks; and
• blended feeds for livestock consumption.
Agricultural commodities are frequently substitutable in demand. For example, sorghum
is a close substitute for corn on a calorie-for-calorie basis in many uses, and beet sugar is
essentially a perfect substitute for sugar derived from sugarcane. In addition, a number of feed
grains are substitutes in terms of livestock feeding. Similarly, many forestry products are
substitutes for one another, such as sawtimber or pulpwood derived from alternative hardwood
and softwood species groups. In addition, bioenergy feedstocks derived from individual
agricultural and forestry commodities are substitutes for one another (e.g., ethanol can be
produced using either crop residues or logging residues, among other potential feedstocks). Thus,
the mix of commodities that will be produced in a given model run depends on interactions
between many related markets.
2.1.1 Primary Commodities
Primary commodity production is derived from allocation decisions that reflect the set of
production possibilities for field crops, livestock, and biofuels. The allocation decisions are
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based on optimizing across the budgets associated with each production possibility, given prices
for outputs and inputs. Budgets are based on using inputs to produce a given level of outputs.
In the model, primary commodities can be used directly or converted to secondary
products via processing activities with associated costs (e.g., soybean crushing to meal and oil,
livestock to meat and dairy). Primary commodities can go to livestock use, feed mixing,
processing, domestic consumption, or exports. A mixture of primary commodities and processed
products is supplied to meet national-level demands in each market. Table 2-1 summarizes the
primary commodities currently included within FASOMGHG and their units. There are 40
primary crop products (including multiple subcategories of crops, such as grapefruit, oranges,
and tomatoes), 25 primary livestock products, 12 categories of forest and agricultural residues,
and 32 categories of public and private domestic and imported logs.
Table 2-1. Primary Commodities
Commodities
Units
Crop Products
Barley
Canola
Corn
Cotton
Grapefruit, fresh (67 Ib box)
Grapefruit, fresh (80 Ib box)
Grapefruit, fresh (85 Ib box)
Grapefruit, processing (67 Ib box)
Grapefruit, processing (80 Ib box)
Grapefruit, processing (85 Ib box)
Hay
Hybrid poplar
Miscanthus
Oats
Orange, fresh (75 Ib box)
Orange, fresh (85 Ib box)
Orange, fresh (90 Ib box)
Bushels
Hundredweight (cwt)
Bushels
480 Ib bales
1,000 boxes (CA,AZ)
1,000 boxes (TX)
1,000 boxes (FL)
1,000 boxes (CA,AZ)
1,000 boxes (TX)
1,000 boxes (FL)
U.S. tons
U.S. tons
U.S. tons
Bushels
1,000 boxes (CA, AZ)
1,000 boxes (TX)
1,000 boxes (FL)
(continued)
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Table 2-1. Primary Commodities (continued)
Commodities
Units
Orange, processing (75 Ib box)
Orange, processing (85 Ib box)
Orange, processing (90 Ib box)
Potatoes
Rice
Rye
Silage
Sorghum, energy
Sorghum, grain
Sorghum, sweet
Sorghum, sweet (ratooned)
Soybeans
Sugar beets
Sugarcane
Switchgrass
Tomatoes, fresh
Tomatoes, processing
Wheat, durum
Wheat, hard red spring
Wheat, hard red winter
Wheat, soft red winter
Wheat, soft white
Willow
Livestock Products
Beef cows, culled
Beef slaughter, feedlot
Beef slaughter, nonfed
Broilers
Calves, dairy
Calves for slaughter
Calves, heifer
Calves, steer
Calves, stocked
1,000 boxes (CA, AZ)
1,000 boxes (TX)
1,000 boxes (FL)
cwt
cwt
Bushels
U.S. tons
Dry metric tons
cwt
U.S. tons
U.S. tons
Bushels
U.S. tons
U.S. tons
U.S. tons
cwt
U.S. tons
Bushels
Bushels
Bushels
Bushels
Bushels
U.S. tons
100 Ib (liveweight)
100 Ib (liveweight)
100 Ib (liveweight)
100 Ib (liveweight)
100 Ib (liveweight)
100 Ib (liveweight)
100 Ib (liveweight)
100 Ib (liveweight)
100 Ib (liveweight)
(continued)
2-7
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Table 2-1. Primary Commodities (continued)
Commodities
Units
Calves, stocked heifer
Calves, stocked steer
Dairy cows, culled
Eggs
Ewes, culled
Hogs for slaughter
Horses and mules
Lamb slaughter
Milk
Pigs, feeder
Sows, culled
Turkeys
Wool, raw
Yearlings, stocked
Yearlings, stocked heifer
Yearlings, stocked steer
Forest and Agricultural Residues
Barley residues
Biomanure, beef
Biomanure, dairy
Corn residues
Logging residues, hardwood
Logging residues, softwood
Milling residues, hardwood
Milling residues, softwood
Oats residues
Rice residues
Sorghum residues
Wheat residues
100 Ib (liveweight)
100 Ib (liveweight)
100 Ib (liveweight)
Dozens at farm level
100 Ib (liveweight)
100 Ib (liveweight)
Number of head
100 Ib (liveweight)
100 Ib
100 Ib (liveweight)
100 Ib (liveweight)
100 Ib (liveweight)
Pounds
100 Ib (liveweight)
100 Ib (liveweight)
100 Ib (liveweight)
U.S. tons
U.S. tons
U.S. tons
U.S. tons
U.S. tons
U.S. tons
U.S. tons
U.S. tons
U.S. tons
U.S. tons
U.S. tons
U.S. tons
(continued)
2-8
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Table 2-1. Primary Commodities (continued)
Commodities
Units
Logs From Timber Harvest
Fuel lo|
Fuel lo|
Fuel lo|
Fuel lo|
Fuel lo|
Fuel lo|
Pulp loj
Pulp loj
Pulp loj
Pulp loj
Pulp loj
Pulp loj
Saw loj
Saw loj
Saw loj
Saw loj
Saw loj
Saw loj
Fuel loj
Fuel loj
Fuel loj
Fuel loj
Fuel loj
Fuel loj
Pulp loj
Pulp loj
Pulp loj
Pulp loj
Saw loj
Saw loj
Saw loj
Saw loj
I, hardwood privately produced
I, hardwood publicly produced
I, imported hardwood
I, imported softwood
I, softwood privately produced
I, softwood publicly produced
I, hardwood privately produced
I, hardwood publicly produced
I, softwood privately produced
I, softwood publicly produced
I, imported softwood
I, imported hardwood
;, hardwood privately produced
;, hardwood publicly produced
;, imported hardwood
;, imported softwood
;, softwood privately produced
;, softwood publicly produced
I, imported softwood
I, imported hardwood
I, hardwood privately produced
I, hardwood publicly produced
I, softwood privately produced
I, softwood publicly produced
1, imported hardwood
I, imported softwood
I, hardwood publicly produced
I, softwood publicly produced
;, imported hardwood
;, imported softwood
;, hardwood publicly produced
;, softwood publicly produced
1,000 cu ft in the woods
1,000 cu ft in the woods
1,000 cu ft in the woods
1,000 cu ft in the woods
1,000 cu ft in the woods
1,000 cu ft in the woods
1,000 cu ft in the woods
1,000 cu ft in the woods
1,000 cu ft in the woods
1,000 cu ft in the woods
1,000 cu ft in the woods
1,000 cu ft in the woods
1,000 cu ft in the woods
1,000 cu ft in the woods
1,000 cu ft in the woods
1,000 cu ft in the woods
1,000 cu ft in the woods
1,000 cu ft in the woods
1,000 cu ft delivered to the mill
1,000 cu ft delivered to the mill
1,000 cu ft delivered to the mill
1,000 cu ft delivered to the mill
1,000 cu ft delivered to the mill
1,000 cu ft delivered to the mill
1,000 cu ft delivered to the mill
1,000 cu ft delivered to the mill
1,000 cu ft delivered to the mill
1,000 cu ft delivered to the mill
1,000 cu ft delivered to the mill
1,000 cu ft delivered to the mill
1,000 cu ft delivered to the mill
1,000 cu ft delivered to the mill
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2.1.2 Secondary Commodities
As shown in Table 2-2, FASOMGHG contains a set of processing activities that make
secondary commodities using primary commodities and other inputs (included as a processing
cost). Secondary commodities are generally included in the model either to represent substitution
or to depict demand for components of products. For example, processing possibilities for
soybeans are included depicting soybeans being crushed into soybean meal and soybean oil
because these secondary commodities frequently flow into different markets. Similar
possibilities exist in the forest sector. For instance, paper can be made from pulp logs or from
logging residues. Thus, the model reflects a large degree of demand substitution. It includes 27
crop products, 17 livestock products, 10 processing byproducts, and 40 forestry products as
secondary commodities.
Table 2-2. Secondary (Processed) Commodities
Secondary Products
Units
Crop Products
Baked goods, sweetened
Beverages, sweetened
Canned goods, sweetened
Canola meal
Canola oil
Confectionaries, sweetened
Corn starch
Corn oil
Corn oil, nonfood, from dried distillers grains extraction
Corn syrup
Distillers grains, corn
Distillers grains, corn fractionation
Distillers grains, export
Distillers grains, noncorn
Dextrose
Gluten meal
Gluten feed
Grapefruit juice
High-fructose corn syrup
Orange juice
1,000 Ib
1,000 gal
1,000 gal
U.S. tons
100 gal
1,000 Ib
1,000 Ib
100 gal
100 gal
1,000 gal
1,000 Ib
1,000 Ib
1,000 Ib
1,000 Ib
1,000 Ib
1,000 Ib
1,000 Ib
1,000 gal at single-strength equivalent
1,000 gal
1,000 gal at 42 brix
(continued)
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Table 2-2. Secondary (Processed) Commodities
Secondary Products
Potatoes, chipped
Potatoes, dried
Potatoes, frozen
Soybean meal
Soybean meal equivalent, produced using feedstocks other than
soybeans
Soybean oil
Sugar, refined
Units
100 Ib
100 Ib
100 Ib
U.S. tons
U.S. tons
1,000 Ib
U.S. tons
Livestock Products
American cheese
Beef, grain-fed
Beef, grass-fed (nonfed)
Butter
Chicken
Cottage cheese
Cream
Evaporated condensed milk
Fluid milk, low-fat
Fluid milk, skim
Fluid milk, whole
Ice cream
Nonfat dry milk
Other cheese
Pork
Turkey
Wool, clean
Processing Byproducts
Lard from swine slaughter
Lignin produced from nonwood cellulosic ethanol processes
Poultry fat from chicken and turkey slaughter
Sugarcane bagasse
Sweet sorghum pulp
Pounds
100 Ib (carcass weight)
100 Ib (carcass weight)
Pounds
100 Ib on ready-to-cook basis
Pounds
Pounds
Pounds
Pounds
Pounds
100 Ib
Pounds
Pounds
Pounds
100 Ib after dressing
100 Ib on ready-to-cook basis
Pounds
U.S. tons
U.S. tons
Pounds
U.S. tons
U.S. tons
(continued)
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Table 2-2. Secondary (Processed) Commodities
Secondary Products
Units
Tallow, edible, from beef cattle slaughter
Tallow, nonedible, from beef cattle slaughter
Yellow grease (waste cooking oil)
Wood Products
Agrifiber, long fiber
Agrifiber, short fiber
Chemi-thermomechanical market pulp
Coated free sheet
Coated roundwood
Construction paper and board
Corrugated medium
Dissolving pulp
Hardwood kraft market pulp
Hardwood lumber
Hardwood miscellaneous products
Hardwood plywood
Hardwood pulp
Hardwood pulp, moved to agricultural component of model for use in
cellulosic ethanol production
Hardwood residues
Hardwood used in non-OSB reconstituted panel
High-grade deinking
Kraft packaging
Linerboard
Mixed wastepaper
Newsprint
Old corrugated paper
Old newspapers
Oriented strand board (OSB)
Pulp substitutes
Recycled board
Recycled market pulp
Softwood kraft market pulp
Pounds
Pounds
Pounds
U.S. tons
U.S. tons
Million metric tons
Million metric tons
Million metric tons
Million metric tons
Million metric tons
Million metric tons
Million metric tons
Million board feet, lumber tally
Million cubic feet
Million square feet, 3/8"
Million cubic meters
U.S. tons
Million cubic meters
Million square feet, 3/8"
Million metric tons
Million metric tons
Million metric tons
Million metric tons
Million metric tons
Million metric tons
Million metric tons
Million square feet, 3/8"
Million metric tons
Million metric tons
Million metric tons
Million metric tons
(continued)
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Table 2-2. Secondary (Processed) Commodities
Secondary Products
Units
Solid blended board
Specialty packaging
Softwood lumber
Softwood miscellaneous products
Softwood plywood
Softwood pulp
Softwood pulp, moved to agricultural component of model for use in
cellulosic ethanol production
Softwood residues
Softwood used in non-OSB reconstituted panel
Tissue and sanitary
Uncoated free sheet
Uncoated roundwood
Million metric tons
Million metric tons
Million board feet, lumber tally
Million cubic feet
Million square feet, 3/8"
Million cubic meters
U.S. tons
Million cubic meters
Million square feet, 3/8"
Million metric tons
Million metric tons
Million metric tons
Primary agricultural and forestry products are converted into processed products using
processing budgets. These budgets are generally reflective of a somewhat simplified view of the
resources used in processing, where the primary factors in the budgets are the use of primary
commodities as inputs, the yield of secondary products, and processing costs to convert primary
products into processed products. Processing costs for agricultural products are usually assumed
to equal the observed price differential between the value of the outputs and the value of the
inputs based on U.S. Department of Agriculture (USDA) Agricultural Statistics.5 On the forestry
side, the nonwood input supply curve provides the cost of processing wood.
The processing budgets for wood products are regionalized for all forest products with
different data in the nine domestic forest production regions and the Canadian regions.
Agricultural processing is regionalized for renewable fuels production, soybean crushing, wet
milling, and bioelectricity generation. Processing budgets for other agricultural products are
defined at a national level.
5U.S. Department of Agriculture, National Agricultural Statistics Service. Various years. USDA Agricultural
Statistics (1990-2002). Available at http://www.nass.usda.gov/Publications/Ag_Statistics/.
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2.1.3 Bioenergy Products
Another category of processed product that can be evaluated in FASOMGHG using a subset of
primary and secondary commodities is bioenergy. In addition to the category totals shown in
Table 2-3, the model tracks the quantity of each bioenergy product produced using each
individual feedstock. The bioenergy sector is an important component of the FASOMGHG
specification that has received a great deal of enhancement since the last major model update.
Given recent policy interest and promulgation of rules greatly expanding renewable energy
production and consumption, as well as the sizable potential role for bioenergy in GHG
mitigation, we have been engaged in a major effort to update this component of the model in
recent years. These changes have included updates to data and parameters, as well as
incorporation of additional feedstocks.
Table 2-3. Bioenergy Products
Bioenergy Products Units
Crop ethanol 1,000 gal
Cellulosic ethanol 1,000 gal
Biodiesel 1,000 gal
Bioenergy inputs to electricity production Trillion British thermal units (Btus)
2.1.4 Blended Livestock Feeds
In addition to using the primary and secondary commodities directly as livestock feed,
FASOMGHG allows for blending of livestock feeds from a number of alternative formulas.
Table 2-4 summarizes the categories of blended livestock feeds that can be used to meet
livestock feed demand. These blends are defined to meet nutritional requirements of the
individual livestock types, but each blend can be made using a variety of mixtures of primary
and secondary commodities to deliver the appropriate nutrient levels. These alternative mixtures
are defined by feed and feed blending alternative and vary by market region. The actual mixtures
that will be used in the market equilibrium will depend on relative prices and availability, as well
as nutrient requirements. The resultant feeds are supplied for consumption by each livestock type
included in the model.
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Table 2-4. Blended Livestock Feeds
Feed Item Units
Protein feed for stackers 100 Ib (cwt)
Blend of grains for cattle 100 Ib (cwt)
Protein feed for cattle 100 Ib (cwt)
Blend of grains for cow calf operations 100 Ib (cwt)
Protein feed for cow calf operations 100 Ib (cwt)
Blend of grains for pig finishing 100 Ib (cwt)
Protein feed for pig finishing 100 Ib (cwt)
Blend of grains for farrowing operations 100 Ib (cwt)
Protein feed for farrowing operations 100 Ib (cwt)
Blend of grains for feeder pigs 100 Ib (cwt)
Protein feed for feeder pigs 100 Ib (cwt)
Blend of grains for dairy operations 100 Ib (cwt)
Blend of grains for broilers 100 Ib (cwt)
Protein feed for broilers 100 Ib (cwt)
Blend of grains for turkeys 100 Ib (cwt)
Protein feed for turkeys 100 Ib (cwt)
Blend of grains for eggs 100 Ib (cwt)
Protein feed for eggs 100 Ib (cwt)
Blend of grains for sheep 100 Ib (cwt)
Protein feed for sheep 100 Ib (cwt)
Inputs to Production
The production component of FASOMGHG includes agricultural crop and livestock
operations, as well as forest industry (FI) and nonindustrial private forests (NIPF) forestry
operations. FASOMGHG contains an agricultural production model for each of the primary
commodities identified previously. Production of traditional agricultural crops, bioenergy crops,
livestock, and forestry results in competition for suitable land. In addition to land, FASOMGHG
depicts the factor supply of other resources (such as water, labor, and other agricultural inputs) in
agriculture, as well as nonwood inputs in the forest sector.
In agricultural production, water and labor availability are specified on a regional basis.
Supply curves for both items have a fixed-price component and an upward-sloping component,
representing rising marginal costs of higher supply quantities. For water, the fixed price is
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available to a maximum quantity of federally provided agricultural water, whereas pumped water
has an upward-sloping supply curve and is subject to maximum availability. Many other inputs
(e.g., fossil fuels, capital) are assumed to be infinitely available at a fixed price (i.e., the
agricultural sector is a price taker in these markets).
On the forestry side, nonwood inputs are available on an upward-sloping basis and
include hauling, harvesting, and product processing costs. Other forest inputs are assumed to be
infinitely available at a fixed price.
Budgets are included for all crops in the model based on data drawn from a variety of
USD A and agricultural extension sources. Table 2-5 summarizes major categories of inputs
included within the crop budgets that are defined and tracked in terms of quantities, typically
because those quantities provide information on key energy, natural resource, GHG emissions,
and other environmental impacts under a policy scenario (not all inputs are included in all crop
budgets). The remainder of budget items are defined only in terms of dollars and largely
aggregated for the purposes of the model. For each traditional crop, production budgets are
differentiated by region, tillage choice (three choices: conventional tillage, conservation tillage,
or no-till), and irrigated or dryland. The differentiation included results in thousands of cropping
production possibilities (budgets) representing agricultural production in each 5-year period.
Energy crop production possibilities are similar, except that irrigation is not an available option
in the current FASOMGHG production possibilities; all energy crops are assumed to be
produced under nonirrigated conditions and do not compete for irrigation water.
Table 2-6 summarizes the inputs included in FASOMGHG livestock production budgets
in terms of quantities (not all inputs are included in all livestock budgets). A number of
categories track manure management systems because they are a key source of emissions for
livestock. As for crops, the remainder of the inputs identified in available livestock budgets are
included only in dollar terms and aggregated for model purposes. For livestock production,
budgets are included that are defined by region, animal type, enteric fermentation management
alternative, manure management alternative, and feeding alternative. Hundreds of livestock
production possibilities (budgets) represent agricultural production in each 5-year period.
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Table 2-5. Major Categories Included in Crop Budgets in Quantities
Carbon—fertilizer production
Carbon—fuel use
Carbon—grain drying
Carbon—irrigation water pumping
Carbon—pesticide production
Crop residue
Crop yield
Diesel fuel
Electricity
Fungicide
Gasoline
Herbicide
Insecticide
Irrigation water
Labor
Land
Lime and gypsum
Methane—residue burning
Methane—rice cultivation
Natural gas
Nitrogen
Nitrous oxide—fertilizer
Nitrous oxide—histosol
Nitrous oxide—leaching
Nitrous oxide—residue burning
Nitrous oxide—volatilization
Phosphorus
Potassium
Table 2-6. Major Categories Included in Livestock Budgets in Quantities
Barley
Biomanure
Blended feed requirements
Corn
Hay
Head in liquid systems
Labor
Liquid volatile solids volume
Livestock head
Livestock product output
Managed manure fraction
Methane—enteric fermentation
Methane—manure
Nitrous oxide—manure
Oats
Pasture
Silage
Soybean meal
Volatile solids in manure
Wheat
Supply curves for agricultural products are generated implicitly within the system as the
outcome of competitive market forces and market adjustments. This method is in contrast to
supply curves that are estimated from observed, historical data. The approach is useful here in
part because FASOMGHG is often used to simulate conditions that fall well outside the range of
historical observation (such as large-scale tree-planting programs or implementation of
mandatory GHG mitigation policies).
The forest production component of FASOMGHG depicts the use of existing private
timberland, as well as the reforestation decision on harvested land. The forest sector relies on a
series of forest growth and yield values to grow the forest inventory over time and to convert
harvested area into forest products. In addition, forest carbon sequestration is calculated over
time based on the inventory characteristics. Timberland is differentiated by region, the age
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cohort of trees,6 ownership class, forest type, site condition, management regime, and suitability
of the land for agricultural use. Decisions pertaining to timber management investment are
endogenous. Actions on the inventory are depicted in a framework that allows timberland owners
to institute management activities that alter the inventory consistent with maximizing the net
present value of the returns from the activities. The key decision for existing timber stands
involves selecting the harvest age. Lands that are harvested and subsequently reforested or lands
that are converted from agriculture to forestry (afforested) introduce decisions involving the
choice of forest type, management regime, and future harvest age.
U.S. Regional Disaggregation
FASOMGHG includes all states in the conterminous United States, broken into 63
subregions for agricultural production and 11 market regions (see Table 2-7) (forestry production
is disaggregated into the 11 market regions, but not into the 63 subregions). These regions are
graphically displayed in Figure 2-1. The 11 market regions provide a consolidation of regional
definitions that would otherwise differ if the forest and agricultural sectors were treated
separately. Forestry production is included in 9 of the market regions (all but Great Plains and
Southwest), whereas agricultural production is included in 10 of the market regions (all but
Pacific Northwest—West side). The Great Plains and Southwest regions are kept separate
because they reflect important differences in agricultural characteristics. Likewise, there are
important differences in the two Pacific Northwest regions (PNWW, PNWE) for forestry
production, and the PNWE region is considered a significant producer of agricultural
commodities tracked in the model, whereas PNWW is not. Thus, the two model regions that
make up the Pacific Northwest are tracked separately. Each of the production regions is uniquely
mapped to one of the 11 larger market regions. The majority of production regions are defined at
the state level. However, for selected major production areas with significant differences in
production conditions within states, the states are broken into subregions.
5 Timberlands are grouped in 21 5-year cohorts, 0 to 4 years, 5 to 9, etc., up to 100+ years. Harvesting is assumed to
occur at the midyear of the cohort.
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Table 2-7. Definition of FASOMGHG Production Regions and Market Regions
Key
Market Region
Production Region (States/Subregions)
NE Northeast
LS Lake States
CB Corn Belt
GP Great Plains
(agriculture only)
SE Southeast
SC South Central
SW Southwest
(agriculture only)
RM Rocky Mountains
PSW Pacific Southwest
PNWE Pacific Northwest-
East side
PNWW Pacific Northwest-
West side (forestry
only)
Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire,
New Jersey, New York, Pennsylvania, Rhode Island, Vermont, West Virginia
Michigan, Minnesota, Wisconsin
All regions in Illinois, Indiana, Iowa, Missouri, Ohio (IllinoisN, IllinoisS,
IndianaN, IndianaS, lowaW, lowaCent, lowaNE, lowaS, OhioNW, OhioS,
OhioNE)
Kansas, Nebraska, North Dakota, South Dakota
Virginia, North Carolina, South Carolina, Georgia, Florida
Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Tennessee, Eastern
Texas
Oklahoma, all of Texas except the eastern portion included in the SC region
(Texas High Plains, Texas Rolling Plains, Texas Central Blacklands, Texas
Edwards Plateau, Texas Coastal Bend, Texas South, Texas Trans Pecos)
Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, Wyoming
All regions in California (CaliforniaN, CaliforniaS)
Oregon and Washington, east of the Cascade mountain range
Oregon and Washington, west of the Cascade mountain range
Pacific i
Northwest
West East
Pacific
Southwest
Rocky Mountains
South
West
I »
Lake States
Northeast
Corn Belt
Southeast
South Central I
ast/
Figure 2-1. Map of the FASOMGHG Regions
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When running the model, one can choose whether to keep the 63 regions or collapse to
11 regions to reduce run time. It is also possible to model agriculture explicitly in all 63 regions
for an initial time period to provide maximum regional detail for the near to intermediate term
and then collapse to 11 regions at a specified future time period for model size control purposes.
The full FASOMGHG can also be run at the more aggregated regional definition shown
in Table 2-8, although the aggregated version of the model is more typically used for model
development and testing. In addition, the wood products production and GHG accounting
calculations employ an even more aggregated set of U.S. regions, following the regional
definition in the North American Pulp and Paper (NAPAP) model (Zhang et al., 1993, 1996;
Ince, 1994). This specification combines the Midwest and Northeast regions into a North region
and does not include the Plains region because there are no forests tracked in that region.
Table 2-8. Aggregated U.S. Regions
Region
Midwest
Northeast
Plains
PNW_West_side
SouthernJJS
WesternJJS
FASOMGHG Market Regions Included
CB,LS
NE
GP, SW
PNWW
SE, SC
PNWE, RM, and PSW
Note: CB = Corn Belt; GP = Great Plains; LS = Lake States; NE = Northeast; PNWE = Pacific Northwest—East
side; PNWW = Pacific Northwest—West side; PSW = Pacific Southwest; RM = Rocky Mountains; SC = South
Central; SE = Southeast; SW = Southwest.
Land Use Categories
Underlying the commodity production described previously and the associated
environmental impacts is the decision by landowners on how much, where, and when to allocate
land across the two sectors. The inclusion of endogenous land allocation across sectors sets
FASOMGHG apart from the majority of other forest and agricultural sector models of the United
States. The conceptual foundation for land allocation is described in Section 2.5.4.
FASOMGHG includes all cropland, pastureland, rangeland, and private timberland7
throughout the conterminous United States. The model tracks area used for production and area
7 As noted above, although public timberland is not explicitly modeled because the focus of the model is on private
decision-maker responses to changing incentives, FASOMGHG includes an exogenous timber supply from
public forestlands.
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idled (if any) within each land category. In addition, the model accounts for the movement of
forest and agricultural lands into developed uses. We recently updated our land use
categorization system to represent a more comprehensive range of categories. This process
included expanding our coverage of pasturelands to explicitly represent multiple forms of public
and private grazing lands (each with different animal unit grazing potential per unit of land). The
FASOMGHG land base was developed based on land classifications from multiple sources, with
the USD A Economic Research Service Major Land Use (MLU) database (USD A Economic
Research Services [ERS], 2012) and the Natural Resources Inventory (NRI) published by the
USDA-Natural Resource Conservation Service serving as our primary data sources.
These databases rely on different sampling methods and define land use categories in
separate ways that each have advantages and disadvantages. To maintain consistency with other
FASOMGHG input data, we rely on the ERS depiction of cropped acres to define our cropland
base. However, the ERS lacks a clear distinction between grassland pasture and rangeland, while
the NRI defines these as separate land categories, a distinction that we also wish to maintain
given differences in ownership and productivity. Therefore, we make use of both datasets and
attempt to avoid overlap between different land use categories as outlined in the following
bullets. This "hybrid" NRI-MLU land categorization system is unique, and we feel that it
provides FASOMGHG with a more realistic representation of public and private grazing lands,
as well as regional land transition possibilities between alternative uses.
Land categories included in the model are specified as follows:
• Cropland is land suitable for crop production that is being used to produce either
traditional crops (e.g., corn, soybeans) or dedicated energy crops (e.g., switchgrass).
This category includes only cropland from which one or more crops included in
FASOMGHG were harvested.8 Cropland used for livestock grazing before or after
crops were harvested is included within this category as long as crops are harvested
from the land. Data used to define cropland area are directly from the ERS-MLU
(USDA ERS, 2012).
• Cropland pasture is managed land suitable for crop production (i.e., relatively high
productivity) that is being used as pasture. The ERS-MLU database defines this area
as "used only for pasture or grazing that could have been used for crops without
additional improvement. Also included were acres of crops hogged or grazed but not
harvested prior to grazing" (USDA ERS, 2012, Glossary). Not requiring additional
improvement to be suitable for crop production is a key distinction between cropland
pasture and other forms of grassland pasture or rangeland. This land is assumed to be
! Note that FASOMGHG does not include every cropping activity conducted in the United States. For instance,
tobacco, vineyards, and most fruits and vegetables are not included in the model.
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more freely transferable with cropland than other grassland types. State totals for
cropland pasture used in the model are drawn directly from the ERS-MLU Web site.
Pasture is defined in an attempt to maintain a consistent definition with the NRI
classification of grassland pasture but to eliminate overlap with ERS cropland or
cropland pasture as defined above. For each region, we compute the initial stock of
"pasture" algebraically as the maximum of (1) (CroplandNRi + Grassland PastureNRi) -
(CroplandERs + Cropland PastureERs) or (2) zero. This procedure is necessary to avoid
double counting of pasturelands between the NRI and ERS data.
Private grazed forest is calculated based on woodland areas of farms reported in the
Agricultural Census to be used for grazing (woodland pasture).9 Woodland pasture is
defined as "all woodland used for pasture or grazing during the census year.
Woodland or forestland pastured under a per-head grazing permit was not counted as
land in farms and, therefore, was not included in woodland pastured" (USDA ERS,
2012, Glossary). These lands are not included in the private timberland areas defined
in FASOMGHG, and there are no forest products harvested from these lands in the
model. The area in this category is fixed over time and is not allowed to transfer into
forestland or other alternative uses.
Public grazed forest is computed as the difference between the ERS-MLU total
forest pasture stock and the private portion given by the Agricultural Census as
described above.
Private rangeland is defined in FASOMGHG using a combination of NRI and ERS-
MLU data. Rangeland is typically unimproved land where a significant portion of the
natural vegetation is native grasses and shrubs. The NRI database defines rangeland
as "land on which the climax or potential plant cover is composed principally of
native grasses, grass-like plants, forbs or shrubs suitable for grazing and browsing,
and introduced forage species that are managed like rangeland. This would include
areas where introduced hardy and persistent grasses, such as crested wheatgrass, are
planted and practices, such as deferred grazing, burning, chaining, and rotational
grazing, are used with little or no chemicals or fertilizer being applied. Grassland,
savannas, many wetlands, some deserts, and tundra are considered to be rangeland.
Certain low forb and shrub communities, such as mesquite, chaparral, mountain
shrub, and pinyon-juniper, are also included as rangeland" (USDA Natural Resource
Conservation Service [NRCS], 2003, p. A-6). Thus, rangeland generally has low
forage productivity and is unsuitable for cultivation, and it is assumed that rangeland
cannot be used for crop production or forestland. To calculate rangeland acres while
avoiding double counting, we first use 2003 NRI data to provide a base definition for
the rangeland class. States with no reported rangeland acres in the NRI database
(USDA NRCS, 2003) are defined to have no rangeland area in FASOMGHG to be
consistent with the NRI definition and to limit overlap between the NRI classification
of rangeland and the ERS-MLU classification of "grassland pasture and range." Then,
9 Data are available at
http://www.agcensus.usda.gov/Publications/2002A^olume_l,_Chapter_2_US_State_Level/st99_2_008_008.pdf.
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to determine the state totals of private rangeland, we use USDA ERS (2012) data
defining regional totals of privately held grazing land by type. These regional
proportions are multiplied by corresponding state-level totals to define the private
rangeland stock by state. For example, the private rangeland stock in Wyoming is
calculated by multiplying the total ERS estimate for Wyoming by the proportion of
private to total rangeland for the "Mountain" region in which Wyoming is located. In
solving for the private rangeland area used in FASOMGHG, it is important to
maintain the relationship between all grazing lands for consistency. The ERS defines
all privately owned grazing lands to be equal to the sum of cropland pasture, grazed
forest, and grassland pasture and range and reports a total of approximately 488
million acres. Following all of our adjustments to develop a consistent land use
definition based on both NRI and ERS-MLU data, the total private grazing land base
in the baseline is approximately 484 million acres.
Public rangeland is calculated using the proportions described above under private
rangeland and totals about 182 million acres. The total includes federal, state, and
local sources.
Forestland in FASOMGHG refers to private timberland, with a number of
subcategories (e.g., different levels of productivity, management practices, age
classes) tracked (see Section 2.5.2 for additional details). The model also reports the
number of acres of private forestland existing at the starting point of the model that
remains in standing forests (i.e., have not yet been harvested), the number of acres
harvested, the number of harvested acres that have been reforested, and the area
converted from other land uses (afforested). Public forestland area is not explicitly
tracked because it is assumed to remain constant over time. Regional timberland
stocks, as well as timber demand, inventory, and additional forestry sector
information, are drawn from the 2005 RPA Timber Assessment (Adams and Haynes,
2007).
Developed (urban) land is assumed to increase over time at an exogenous rate for
each region based on projected changes in population and economic growth. It is
assumed that the land value for use in development is sufficiently high that the
movement of forest and agricultural land into developed land will not vary between
the policy cases analyzed. All private land uses (except Conservation Research
Program [CRP] land and grazed forest) are able to convert to developed land,
decreasing the total land base available for forestry and agriculture over time. Land
transfer rates vary by land use type over time and are consistent with the national land
base assessment by Alig et al. (2010b).
CRP land is specified as land that is voluntarily taken out of crop production and
enrolled in the USDA's CRP. Land in the CRP is generally marginal cropland retired
from production and converted to vegetative cover (such as grass, trees, or woody
vegetation) to conserve soil, improve water quality, enhance wildlife habitat, or
produce other environmental benefits. State- and county-level land areas enrolled in
the CRP are obtained from the USDA Farm Service Agency (FSA; 2009).
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Figure 2-2 shows the baseline land allocation in FASOMGHG at the national level across
each of the land categories defined above. Land is allowed to move between categories over time
subject to restrictions based on productivity and land suitability. The conversion costs of moving
between land categories are set at the present value of the difference in the land rental rates
between the alternative uses based on the assumed equilibration of land markets (see
Sections 2.5.1 through 2.5.4 for additional detail on each land use category and its potential
conversion to alternative land uses).
• Cropland
• Cropland Pasture
Pasture
•CRP
• Private Rangeland
Public Rangeland
Private Grazed Forest
Public Grazed Forest
Private Timberland
Figure 2-2. Baseline FASOMGHG U.S. Land Base by Land Use Category (million acres)
2.4.1 Agricultural Land
As described previously, cropland is land that is suitable for crop production and can
potentially be used in the production of any of the crops included in FASOMGHG for the
particular production region being considered. Land in the cropland category is the most
productive land available for producing primary agricultural commodities, although cropland is
more productive in some regions than in others. Therefore, crop yields vary across regions based
on historical data. The total area of baseline cropland is based on ERS-MLU data as described
above, with baseline land in production of individual crops based on USD A National
Agricultural Statistics Service (NASS) historical data on county-level harvested acreage by crop.
Cropland enrolled in the CRP is included under the CRP land category, and cropland used as
pasture is implicitly included in the pastureland category in FASOMGHG (i.e., both of these
categories of cropland are included in other categories rather than being reported under
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cropland). The average annual areas of cropland with failed crops10 are not included in the
reported FASOMGHG cropland and are not explicitly tracked in FASOMGHG. Cropland can
potentially be converted to cropland pasture or private forestland. In addition to tracking
aggregate cropland area, cropland is tracked by crop tillage system and irrigated/dryland status,
as well as the duration of time the land has been in such a system.11 This approach allows for
tracking of sequestered soil carbon and the transition to a new soil carbon equilibrium after a
change in tillage. Also, there are differences in crop yields between irrigated and dryland
systems, as well as differences in input use, GHG emissions, and other environmental impacts.
Different tillage systems also have differences in input usage and environmental impacts in
FASOMGHG.
CRP land is cropland that has been enrolled in the Conservation Reserve Program, a
USDA program that provides payments to encourage activities with conservation and
environmental benefits. The land that farmers choose to enroll in the program is typically
marginal cropland that they have agreed to retire from production for a contracted period. The
area of CRP land in FASOMGHG in the baseline is based on 2007 data on CRP enrollment by
state available from the USDA FSA (2009). Because landowners can choose to remove their
land from the CRP program when their contract expires (or before expiration, subject to a
financial penalty), FASOMGHG also tracks the area of CRP land with expiring contracts in each
year. As CRP contracts expire, landowners will move land back into agricultural production if
the returns to agricultural production exceed the returns associated with maintaining land in the
CRP. However, based on the 2008 Farm Bill, which specifies a maximum of 32 million acres in
the CRP, and indications that USDA plans to provide sufficient funding to maintain that
maximum level of 32 million acres in the CRP, FASOMGHG model runs generally place a floor
of 32 million acres in CRP land in future years.
1 ° USDA data for planted area exceed the harvested area because there will inevitably be some fraction of planted
cropland area that is not harvested due to crop failure associated with poor weather, extreme events, or other
conditions. In that case, the cost of harvesting may exceed the value of the crop. Thus, farmers will choose not to
harvest those areas.
11 Crop tillage systems in FASOMGHG include conventional tillage, conservation tillage, and no-till. Conservation
tillage and no-till reduce the exposure of carbon in the soil to oxidation and allow larger soil aggregates to form.
These practices also leave crop residues on the soil, thereby potentially increasing carbon inputs. Tillage changes
from more intensive conventional tillage practices, such as moldboard plowing, to conservation or zero tillage
practices will generally increase levels of soil carbon over time. In addition, emission reductions may result
because less-intensive tillage typically involves less direct fossil fuel use for tractors. However, there are also
alterations in chemical usage (possibly increases in pesticide usage and alterations in rate of fertilization), which
can potentially increase emissions associated with increased manufacture and usage. FASOMGHG has the
ability to track these indirectly induced GHG effects associated with changes in tillage.
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Cropland pasture, pasture, and private and public grazed forest are all suitable for
livestock grazing (i.e., land that provides sufficient forage to support the needs of grazing
livestock within a region), but cropland pasture tends to be more productive. Because it has
sufficient quality to be used in crop production, cropland pasture can potentially be converted to
crop production within the model. It can also be converted to forestland. Pasture, which is
considered less productive, can be converted to forestland but not cropland. Private and public
grazed forest refers to land that has varying amounts of tree cover but can also be used as
pasture. Forage production on these lands tends to be relatively low, however. Neither private
nor public grazed forest can be converted to any other uses. As mentioned above, FASOMGHG
assumes that no timber is produced from private grazed forest.
Rangeland in FASOMGHG includes both public and private rangeland. Rangeland
differs from pastureland primarily in that it is assumed to be generally unimproved land where a
significant portion of the land cover is native grasses and shrubs. The productivity of rangeland
varies considerably across regions of the United States. Therefore, the area of rangeland required
per animal for a given species can be very different across regions. Overall, rangeland provides
lower forage production per acre than pastureland and is considered unsuitable for cultivation. In
addition, much of the rangeland in the United States is publicly owned. Thus, it is assumed that
rangeland cannot be used for crop production or forestland.
The area of pastureland or rangeland required per animal is calculated in FASOMGHG
for each combination of livestock type and pasture or rangeland category available in each
region. These values are based on forage requirements for each livestock species and estimated
forage productivity per acre for each category of pasture in FASOMGHG, defined on a regional
basis.12 The area of pastureland used in livestock production is limited to the pastureland
inventory by time period and region. It is possible to have idle pastureland in FASOMGHG, and
idle pastureland area and associated soil carbon sequestration are tracked in the model. In
particular, changes in livestock populations will affect pasture and rangeland used for animal
production and could increase or decrease idle land in the model. Changes in animal populations
over time and impacts of policies affecting livestock markets, including use of each of the
pasture and rangeland categories by each type of livestock, are tracked within FASOMGHG.
12 The calculation of acres of pasture required by a given type of livestock in a particular region is implicitly based
on estimates of animal unit months (AUMs) available for each category of pastureland in that region.
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2.4.2 Forestland
Timberland refers to productive forestlands able to grow at least 20 cubic feet of growing
stock per acre per year and that are not reserved for uses other than timber production (e.g.,
wilderness use). Lands under forest cover that do not produce at least 20 cubic feet per acre per
year, called unproductive forestland, and timberland that is reserved for other uses are not
considered part of the U.S. timber base (Haynes et al., 2007) and are therefore not tracked by the
model.
In FASOMGHG, endogenous land use modeling is done only for privately held parcels,
not publicly owned or publicly managed timberlands. The reason is that management of public
lands is largely dictated by government decisions on management, harvesting, and other issues
that account for multiple public uses of these lands rather than responses to market conditions.
However, an exogenous quantity of timber harvested on U.S. public lands is accounted for
within the model. Projected regional public harvest levels are drawn from the assumptions used
in the baseline case of the U.S. Forest Service's 2005 RPA Timber Assessment (Haynes et al.,
2007). Timber inventory levels for public timberlands are simulated based on these harvest
levels.
Private timberland is tracked by its quality and its transferability between forestry and
agricultural use. FASOMGHG includes three different site classes to reflect differences in
forestland productivity (these site groups were defined according to ATLAS inputs [Haynes et
al., 2007]), where yields vary substantially between groups13:
• HIGH—high site productivity group (sites that produce >85 cubic feet of live
growing stock per acre per year)
• MEDIUM—medium site productivity group (sites that produce between 50 and 85
cubic feet of live growing stock per acre per year)
• LOW—low site productivity group (sites that produce between 20 and 50 cubic feet
of live growing stock per acre per year)
FASOMGHG also tracks land ownership, including two private forest owner groups:
forest industry (FI) and nonindustrial private forests (NIPF). The traditional definitions are used
for these ownership groups: industrial timberland owners possess processing capacity for the
13 Changes in ozone concentrations affect the specific forest growth rates for each region/species/management
intensity/productivity class but are assumed not to result in movements between productivity classes. The
primary use of the productivity classes in FASOMGHG is to aid in defining potential land use between
forestland and other land uses (e.g., only high productivity forestland can be converted to cropland).
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timber, and NIPF owners do not. As a result, the NIPF group includes lands owned by timber
investment management organizations (TIMOs) and real estate investment trusts (REITs).
In addition, FASOMGHG tracks land in terms of the type of timber management
practiced, forest type (identified by dominant species), and stand age. As shown in Table 2-9,
across all regions there are 18 management intensity classes defined based on whether thinning,
partial cutting, passive management, or other management methods are used. Note that some
management intensity classes are defined only for a subset of regions (as identified by the region
codes in parentheses) based on regional data and definitions. There are also 25 forest types,
which vary by region (e.g., Douglas-fir and other species types in the West and planted pine,
natural pine, and various hardwood types in the South). Stand age is explicitly accounted for in
5-year cohorts, ranging from 0 to 4 years up to 100+ years.
Table 2-9. Forest Management Intensity Classes (regions of application in parentheses)
MIC Code
Description
AFFOR
AFFOR_CB
LO
NAT_REGEN
NAT_REGEN_PART_CUT_HI
NAT_REGEN_PART_CUT_LO
NAT_REGEN_PART_CUT_MED
NAT_REGEN_THIN
PART_CUT_HI
PART_CUT_HI+
PART_CUT_LO
PASSIVE
PLANT
PLANT_THIN
PLANT+
PLNTJH
PLNT_HI_THIN
PLNT_LO_THIN
PLNT MED
Afforestation of bottomland hardwood (SE, SC)
Afforestation of hardwood and softwood forest types (CB)
Natural regeneration (or afforestation) with low management
Natural regeneration with low management (PNWW)
Partial cutting with high level of management (PNWW)
Partial cutting with medium level of management (PNWW)
Partial cutting with low level of management (PNWW)
Natural regeneration with a commercial thin (PNWW)
Partial cutting with medium level of management (SE, SC)
Partial cutting with high level of management (SE, SC)
Partial cutting with low level of management (SE, SC)
Passive management (minimal amount of management)
Plant with no intermediate treatments (PNWW)
Plant with medium level of management (PNWW)
Plant with high level of management (PNWW)
Planted pine with high level of management (SE, SC)
Planted pine with commercial thin and high level of management (SE, SC)
Planted pine with commercial thin and no intermediate treatments (SE, SC)
Planted pine with medium level of management (SE, SC)
(continued)
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Table 2-9. Forest Management Intensity Classes (regions of application in parentheses)
(continued)
MIC Code Description
PLNT_MED_THIN Planted pine with commercial thin and medium level of management (SE,
SC)
RESERVED Reserved from harvest
SHORT_ROTSWDS Short rotation softwoods with high level of management (SE, SC)
TRAD_PLNT_PINE Planted pine with no intermediate treatments (SE, SC)
Note: CB = Corn Belt; PNWW = Pacific Northwest—West side; SC = South Central; SE = Southeast.
2.4.3 Developed Land
FASOMGHG also accounts for the movement of agricultural and forestland into
developed uses. The economic returns to developed land uses typically exceed the returns
available to agricultural or forestry land uses. Thus, FASOMGHG assumes an exogenous rate of
land conversion into developed uses by region for each of the agricultural and forestland
categories included in the model (with the exception of private and public grazed forest pasture
and CRP lands) based on projections of future U.S. population and income, with endogenous
competition between agriculture and forestry for the remaining land base available for these uses
over time. It is assumed that developed land does not convert back to other uses.
2.4.4 Land Allocation
In FASOMGHG, the initial land endowment is fixed. However, because land can move
between forests and agriculture, agricultural production faces, in effect, an endogenous excess
land supply "equation" from forestry. Forestry production, in turn, effectively faces an
endogenous excess land supply "equation" from agriculture.
The conceptual foundation for land allocation is described in the following paragraphs. In
terms of transferability between agriculture and forestry, FASOMGHG includes five land
suitability classes:
• FORONLY—includes timberland acres that cannot be converted to agricultural uses
• FORCROP—includes acres that begin in timberland but can potentially be converted
to cropland
• FORPAST—includes acres that begin in timberland but can potentially be converted
to pastureland
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• CROPFOR—includes acres that begin in cropland but can potentially be converted to
timberland
• PASTFOR—includes acres that begin in pasture but can potentially be converted to
timberland
Land can flow between the agricultural and forestry sectors or vice versa in the
FORCROP, FORPAST, CROPFOR, and PASTFOR land suitability categories. Movements
between forestry and cropland are permitted only within the high forest site productivity class.
Changes in land allocation involving pastureland occur within the medium-quality forest site
productivity class. In addition, land movements in forestry are allowed only in the NIPF owner
category, reflecting an assumption (and lengthy historical observation) that land held by the FI
ownership group will not be converted from timberland to agriculture.
As mentioned previously, the decision to move land between uses depends on the net
present value of returns to alternative uses, including the costs of land conversion. Land transfers
from forestry to agriculture take place only upon timber harvest and require an investment to
clear stumps from the land, level the land, and otherwise prepare it for planting agricultural
crops. Agricultural land can move to other uses during any of the 5-year model periods, but when
afforested it begins in the youngest age cohort of timberland.
In addition to the endogenous land allocation decision, land moves out of agricultural and
forestry uses into developed uses (e.g., shopping centers, housing, and other developed and
infrastructural uses) at an exogenous rate. Rates at which forest and agricultural land are
converted to developed uses in FASOMGHG are based on land use modeling for a national land
base assessment by the U.S. Forest Service and cooperators. Thus, although land can move
between forest, cropland, and pasture, the total land area devoted to agricultural and forestry
production is trending downward over time as more land is shifted to developed uses.
Another potential source of land is CRP land moving back into production. There are,
however, environmental benefits associated with land in CRP and indications that USDA plans
to retain 32 million acres of land in CRP. Because of this assumption that no less than 32 million
acres can be allocated to CRP land, there are only about 5 million acres of land in FASOMGHG
in the 2000 base period that can move from CRP to cropland over time.
Market Modeling
FASOMGHG uses commodity supply and demand curves for the U.S. market that are
calibrated to historic price and production data with constant differentials between regional and
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national prices for some crops. In addition, the model includes supply and demand data for major
commodities traded on world markets, such as corn, wheat, soybeans, rice, and sorghum (see
Section 2.7 for additional discussion of international trade modeling and foreign regions
included). Transportation costs clearly influence equilibrium exports, and FASOMGHG includes
data on transportation costs to all regions included within the model and between foreign regions
for those commodities where trade is explicitly modeled.
The model solution requires that all markets are in equilibrium (i.e., the quantity supplied
is equal to the quantity demanded in every market modeled at the set of market prices in the
model solution). The demand and supply curves included within the model that need to be in
equilibrium in each 5-year period include
• regional product supply;
• national raw product demand;
• regional or national processed commodity demand;
• regional or national supply of processed commodities;
• regional or national (depending on commodity) export demand;
• regional or national (depending on commodity) import supply;
• regional feed supply and demand;
• regional direct livestock demand;
• interregional transport perfectly elastic supply;
• international transport perfectly elastic supply; and
• country-specific excess demand and supply of rice, sorghum, corn, soybeans, and the
five individual types of wheat modeled.
In the case of forestry products, commodities are typically produced regionally and are
then transported to meet a national demand at a fixed regional transport cost. Harvests from
public forestlands are included in the model but are treated as exogenously determined by the
government. For agricultural products, processed commodities such as soybean meal, gluten
feed, starch, and all livestock feeds are manufactured and used on the 11-market region basis but
are supplied into a single national domestic market to meet export demand.
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International Trade
FASOMGHG accounts for international trade in both forestry and agricultural products,
with the commodities included in the trade component and their treatment varying based on the
importance of trade to the U.S. market and available data.
2.6.1 Forestry
For the forest sector, trade of forest products with Canada and trade of softwood lumber
with the rest of the world are endogenous. These are the largest (by volume or weight) U.S.
forest products trade flows. All other product movements are exogenous and, in the baseline
case, follow projections derived from the Forest Service's 2005 RPA Timber Assessment Update
(Haynes et al., 2007).
Product movements from Canadian producing regions to the United States are
endogenous and subject to appropriate transport costs, exchange rates, and tariffs. Supplies of
logs in Canada derive primarily from public lands ("Crown" lands) governed by individual
provinces, with small volumes from private lands. Harvests from these lands vary over time
based on provincial policies, extraction and delivery costs, and market prices for logs. These
supplies are represented by a set of (log price sensitive) delivered log supply equations for both
sawlogs and pulpwood in each Canadian region.
Softwood lumber imports into the United States from non-Canadian sources are based on
a linear import supply function drawn from the 2005 RPA Timber Assessment Update (Haynes
et al., 2007), which shifts over time to correspond to the base scenario in the Update.
2.6.2 Agriculture
primary
Agriculture
Three types of agricultural commodity trade arrangements are represented. Agricultural
/ and secondary commodities may be portrayed
with trade occurring in explicit international markets using a Takayama and Judge
(1971) style, spatial equilibrium submodel that portrays country/region-level excess
demand on behalf of a set of foreign countries/regions, excess supply on behalf of a
set of foreign countries/regions, and interregional trade between the foreign
countries/regions themselves and with the United States;
with the United States facing a single excess supply or excess demand relationship on
behalf of the rest of world (ROW); or
without being subject to international trade.
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FASOMGHG has explicit trade functions between the United States and 29 distinct
foreign trading partners for agricultural commodities with detailed trade data available. For the
remaining commodities traded internationally, excess supply and demand functions are specified
to capture net trade flows with the rest of the world as one composite trade region. Demand
levels are parameterized based on the USDA Static World Policy Simulation Model
(SWOPSEVI) database and USDA annual statistics.
International regions are generally defined in a more simple way than domestic regions,
with individual region-level supply and demand curves specified only for the commodities with
the largest trade volumes, such as corn, wheat, soybeans, sorghum, and rice. In addition, only
certain regions are defined for exporters and importers of a given commodity. For many other
commodities (e.g., cotton, oats, barley, beef, pork, poultry), trade is modeled as total excess
import supply and export demand functions for the ROW facing the United States rather than
individual region supply and demand. In these cases, there are single curves representing the
import supply and export demand facing the United States. In addition, there are many
commodities without any explicit opportunities for international trade, such as hay, silage,
energy crops, livestock, and many processed commodities. Generally, trade is not explicitly
modeled for commodities where international trade volumes for the United States are small or
the commodity is not actively traded.
When commodities are subject to explicit spatial interregional trade with spatial
equilibrium submodels, then trading is portrayed among the 29 individual countries/foreign
regions currently included in FASOMGHG. In those countries/foreign regions that are major
importers or exporters of an explicitly traded commodity, explicit supply and demand functions
are defined. Table 2-10 presents the commodities that are traded and the countries/regions that
supply and demand them in the model. Note that when a country supplies certain commodities, it
can export them either to another explicitly defined country/foreign region or to the United
States. Similarly, demand in a country/region can be met from imports from other countries or
from the United States.
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Table 2-10. Explicitly Traded Commodities and Countries/Regions Trading with the
United States
FASOMGHG
Commodity
Canola
Canola oil
Canola meal
Corn
Exporting Countries
Canada
Canada
Canada
Argentina, Brazil, China,
Importing Countries
NA
NA
NA
Canada, Caribbean, E-Mexico, Indonesia, Japi
in, N-Africa, NC-
Rice
USSR, W-Africa
E-Medit, India, Myanmar,
N-Africa, Pakistan,
Thailand, Vietnam
Euro, Philippines, SE-Asia, S-Korea, Taiwan, W-Asia
Bangladesh, Brazil, Caribbean, China, Indonesia, Japan, N-Korea,
NC-Euro, Philippines, S-Africa, SE-Asia, Taiwan, USSR, W-
Africa, WS-America
Sorghum
Soybeans
Wheat, durum
Wheat, hard red
spring
Wheat, hard red
sinter
Wheat, soft red
winter
Wheat, soft
white
Argentina, Australia,
China
Argentina, Brazil,
Canada, Caribbean, USSR
Canada
Australia, Canada
Argentina, Australia,
Canada
Argentina, Australia,
Canada
Australia, Canada, NC-
Euro
E-Mexico, Japan, NC-Euro, S-Korea, Taiwan
China, E-Europe, E-Mexico, Indonesia, Japan, N-Africa, NC-
Euro, SE-Asia, S-Korea, Taiwan, W-Africa, W-Asia
Brazil, Indonesia, Japan, N-Africa, Philippines, SE-Asia, S-Korea,
Taiwan, USSR
Brazil, Caribbean, China, Indonesia, Japan, N-Africa, Philippines,
SE-Asia, S-Korea, Taiwan, USSR, W-Africa, W-Asia
Brazil, China, E-Mexico, Indonesia, Japan, N-Africa, Philippines,
SE-Asia, S-Korea, Taiwan, USSR, W-Africa, W-Asia
Brazil, China, E-Mexico, Indonesia, Japan, N-Africa, Philippines,
SE-Asia, S-Korea, Taiwan, USSR, W-Africa, W-Asia
Brazil, China, E-Mexico, Indonesia, Japan, N-Africa, Philippines,
SE-Asia, S-Korea, Taiwan, USSR, W-Africa, W-Asia
For commodities where trade is important to the U.S. market, but data on trade flows
with individual countries/foreign regions are more limited, U.S. trade is modeled at an aggregate
level with the ROW. When U.S. trade is included in the model with only ROW excess import
supply and export demand functions, then the curves represent the sum of ROW exports and
imports that are faced at the national U.S. market level. The commodities currently included in
the model in this way are listed in Table 2-11, identifying whether they are included in the
import supply or export demand functions.
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Table 2-11. Commodities with Only ROW Export or Import Possibilities
FASOMGHG Commodity Imported into the United States Exported from the United States
Canola Y N
Canola oil Y N
Canola meal Y N
Cotton N Y
Distillers grains N Y
Oats N N
Barley Y Y
Sugarcane N N
Potatoes Y Y
Tomatoes, fresh Y Y
Tomatoes, processed N N
Oranges, fresh (75 Ib box) Y Y
Grapefruit, fresh (85 Ib box) Y Y
Eggs Y Y
Orange juice Y Y
Grapefruit juice Y Y
Soybean meal N Y
Soybean oil N Y
High-fructose corn syrup N Y
Confection Y N
Gluten feed N Y
Frozen potatoes Y Y
Dried potatoes Y Y
Chipped potatoes N Y
Refined sugar Y Y
Fed beef N Y
(continued)
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Table 2-11. Commodities with Only ROW Export or Import Possibilities (continued)
FASOMGHG Commodity
Nonfed beef
Feedlot beef slaughter
Stocked calf
Stocked steer calf
Pork
Chicken
Turkey
Wool, clean
Evaporated condensed milk
Nonfat dry milk
Butter
American cheese
Other cheese
Imported into the United States
Y
Y
Y
Y
Y
N
N
Y
Y
Y
Y
Y
Y
Exported from the United States
N
N
N
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
Commodities without explicit trade are generally specified as such because either the
trade numbers are small or the commodity is not traded. These include the commodities listed in
Table 2-12, as well as all of the blended feeds.
Table 2-12. Commodities without International Trade Possibilities Modeled
Baking
Beverages
Biodiesel
Broilers
Calf slaughter
Canning
Corn oil
Corn starch
Corn syrup
Cottage cheese
Cream
Cull beef cows
Cull dairy cows
Feeder pigs
Fluid milk
Grapefruit, fresh (67 Ib box)
Grapefruit, fresh (80 Ib box)
Grapefruit, processing (67 Ib box)
Grapefruit, processing (80 Ib box)
Grapefruit, processing (85 Ib box)
Hay
Heifer calves
Hogs for slaughter
Horses and mules
Hybrid poplar
Ice cream
Oranges, processing (75 Ib box)
Oranges, processing (85 Ib box)
Oranges, processing (90 Ib box)
Refined sugar
Silage
Skim milk
Steer calves
Stocked heifer calves
Stocked heifer yearlings
Stocked steer yearlings
Stocked yearlings
Sugar beet
Switchgrass
(continued)
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Table 2-12. Commodities without International Trade Possibilities Modeled (continued)
Cull ewes Lamb slaughter Tbtus
Cull sow Milk Turkeys
Dairy calves Nonfed slaughter Willow
Dextrose Oranges, fresh (85 Ib box) Wool
Ethanol Oranges, fresh (90 Ib box)
Note: FASOMGHG does not explicitly include ethanol trade, but in applications forbiofuels analyses, we have
assumed that exogenous levels of mandated ethanol volumes would be provided by imports based on information
from other models.
GHG Accounts
FASOMGHG quantifies the stocks of GHGs emitted from and sequestered by agriculture
and forestry, as well as the carbon stock on lands in the model that are converted to
nonagricultural, nonforest developed usage. In addition, the model tracks GHG emission
reductions in other sectors caused by mitigation actions in the forest and agricultural sectors.
The GHGs tracked by the model include CCh, CH4, and N2O. Given the multi-GHG
impact of the agricultural and forestry sectors, there are multidimensional trade-offs between
model variables and net GHG emissions. To consider these trade-offs, all GHGs are converted to
carbon or carbon dioxide equivalent basis using 100-year global warming potential (GWP)
values for application of GHG incentives.
GWPs compare the abilities of different GHGs to trap heat in the atmosphere. They are
based on the radiative forcing (heat-absorbing ability) and decay rate of each gas relative to that
of CCh. The GWP allows one to convert emissions of various GHGs into a common measure,
which allows for aggregating the radiative impacts of various GHGs into a single measure
denominated in CCh or C equivalents. Extensive discussion of GWPs can be found in the
documents of the Intergovernmental Panel on Climate Change (IPCC). The IPCC updated its
estimates of GWPs for key GHGs in 2001 (IPCC, 2001) and again in 2007 (IPCC, 2007), but
these estimates are still under debate. As a result, the FASOMGHG model continues to use the
1996 GWPs for the GHGs covered by the model:
• CO2 = 1
• CH4 = 21
• N2O = 310
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When CCh equivalent results are converted to a C equivalent basis, a transformation is
done based on the molecular weight of C in the CCh. The CCh equivalent quantities of gas are
divided by 3.667 to compute the carbon equivalent quantities.
A list of all categories included in the model's GHG accounting appears in Table 2-13,
totaling 57 categories. Brief summaries of the major categories are presented in the following
subsections.
Table 2-13. Categories of GHG Sources and Sinks in FASOMGHG
Forest_SoilSequest
Forest_LitterUnder
Forest_ContinueTree
Forest_AfforestSoilSequest
Forest_AfforestLitterUnder
Forest_AfforestTree
ForestJJSpvtProduct
ForestJJSpubProduct
Forest_CANProduct
Forest_USExport
ForestJJSImport
ForestJJSFuelWood
Forest_USFuelResidue
ForestJJSresidProduct
Forest_CANresidProduct
Carbon_For_Fuel
Dev_Land_from_Ag
Dev_Land_from_Forest
AgSoil_CropSequest_Initial
AgSoil_CropSequest_TillChange
AgSoil_PastureSequest
Carbon_AgFuel
Carbon_Dryg
Carbon Pert
Carbon in forest soil
Carbon in litter and understory of forests that remain forests
Carbon in trees of forests that remain forests
Carbon in forest soil of afforested forests
Carbon in litter and understory of afforested forests
Carbon in trees of afforested forests
Carbon from U.S. private forests consumed producing forest products
Carbon from U.S. public forests consumed producing forest products
Carbon in U.S. consumed but Canadian produced forest products
Carbon in U.S. produced but exported forest products
Carbon in U.S. consumed but imported from non-Canadian source
Carbon in U.S. consumed fuelwood
Carbon in U.S. residue that is burned
Carbon from U.S. residues consumed producing forest products
Carbon from Canadian residues consumed producing forest products
Carbon emissions from forest use of fossil fuel
Carbon on land after it moves from agriculture into developed use
Carbon on land after it moves from forest into developed use
Carbon in cropped agricultural soil with initial tillage
Carbon in cropped agricultural soil with change in tillage
Carbon in pastureland
Carbon emissions from agricultural use of fossil fuels
Carbon emissions from grain drying
Carbon emissions from fertilizer production
(continued)
2-38
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Table 2-13. Categories of GHG Sources and Sinks in FASOMGHG (continued)
Carbon_Pest
Carbon_Irrg
Carbon_Ethl_Offset
Carbon_Ethl_Haul
Carbon_Ethl_Process
Carbon_CEth_Offset
Carbon_CEth_Haul
Carbon_CEth_Process
Carbon_BioElec_Offset
Carbon_BioElec_Haul
Carbon_BioElec_Process
Carbon_Biodiesel_Offset
Carbon_Biodiesel_Process
Methane_Liquidmanagement
Methane_EntericFerment
Methane_Manure
Methane_RiceCult
Methane_ AgRe sid_Burn
Methane_BioElec
Methane_Biodiesel
Methane_Ethl
Methane_CEth
NitrousOxide_Manure
NitrousOxide_BioElec
NitrousOxide_Biodiesel
NitrousOxide_Ethl
NitrousOxide_CEth
NitrousOxide_Fert
NitrousOxide Pasture
Carbon emissions from pesticide production
Carbon emissions from water pumping
Carbon emission offset by conventional ethanol production
Carbon emissions in hauling for conventional ethanol production
Carbon emissions in processing of conventional ethanol production
Carbon emission offset by cellulosic ethanol production
Carbon emissions in hauling for cellulosic ethanol production
Carbon emissions in processing of cellulosic ethanol production
Carbon emission offset from bioelectricity production
Carbon emissions in hauling for bioelectricity production
Carbon emissions in processing of for bioelectricity production
Carbon emission offset from biodiesel production
Carbon emissions in processing of biodiesel production
Methane from emission savings from improved manure technologies
Methane from enteric fermentation
Methane from manure management
Methane from rice cultivation
Methane from agricultural residue burning
Net change in methane emissions from bioelectricity relative to coal-fired
Net change in methane emissions from biodiesel production relative to diesel
Net change in methane emissions from ethanol production relative to gasoline
Net change in methane emissions from cellulosic ethanol production relative to
gasoline
Livestock manure practices under managed soil categories under AgSoilMgmt
Net change in nitrous oxide emissions from bioelectricity relative to coal-fired
Net change in nitrous oxide emissions from biodiesel production relative to diesel
Net change in nitrous oxide emissions from noncellulosic ethanol processing
relative to gasoline
Net change in nitrous oxide emissions from cellulosic ethanol processing relative
to gasoline
Nitrous oxide emissions from nitrogen inputs including nitrogen fertilizer
application practices, crop residue retention, and symbiotic nitrogen fixation
under managed soil categories under AgSoilMgmt
Nitrous oxide emissions from pasture
(continued)
2-39
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Table 2-13. Categories of GHG Sources and Sinks in FASOMGHG (continued)
NitrousOxide_Histosol Emissions from temperate histosol area
NitrousOxide_Volat Indirect soils volatilization
NitrousOxide_Leach Indirect soils leaching runoff
NitrousOxide_AgResid_Burn Agricultural residue burning
2.7.1 Forest GHG Accounts
As identified in Table 2-13, forest GHG accounting includes carbon sequestered, carbon
emitted, and fossil fuel-related carbon emissions avoided. Sequestration accounting
encompasses carbon in standing (live and dead) trees, forest soils, the forest understory
vegetation, forest floor including litter and large woody debris, and wood products both in use
and in landfills. The sequestration accounting involves both increases and reductions in stocks,
with changes in specific accounts to reflect land movement into forest use through afforestation,
net growth of forests not of afforestation origin, and placement of products in long-lasting uses
or landfills.14 Reductions arise when land is migrated to agriculture or development and products
decay in their current uses.
Forest-related emissions accounting includes GHGs emitted when fossil fuels are used in
forest production. Forest-related GHG accounting calculates the estimated amount of fossil fuels
(and associated GHG emissions) that are saved when wood products are combusted in place of
fossil fuels, particularly when milling residues are burned to provide energy (generally for use at
the mill). In addition, woody biomass may be used as a bioenergy feedstock.
Forest carbon accounts also include the carbon content of products imported into, or
exported out of, the United States. In particular, there is explicit accounting for products
• processed in and coming from Canada,
• imported from other countries, and
• exported to other countries.
14 In the case of wood product accounts, note that these accounts have increases in C sequestration when more
products are made, but the forest carbon accounts are simultaneously reduced to account for C reduced by
harvesting.
2-40
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These categories may or may not be included in an incentive scheme for GHG mitigation,
because they will generally be accounted for elsewhere. Nonetheless, the accounts are included
in the model in case they are needed for policy analysis.
2.7.2 Agricultural GHG Accounts
On the agricultural side, the categories tracked in the model are also listed in Table 2-13.
Agricultural emissions arise from crop and livestock production, principally from
• fossil fuel use,
• nitrogen fertilization usage,
• other nitrogen inputs to crop production,
• agricultural residue burning,
• rice production,
• enteric fermentation, and
• manure management.
In addition, changes in carbon sequestration are tracked within the model. Agricultural
sequestration involves the amount of carbon sequestered in agricultural soils, due principally to
choice of tillage, and irrigation along with changes to crop mix choice. Sequestration is also
considered in terms of grasslands versus cropland or mixed usage, where cropland can be moved
to pasture use or vice versa. The sequestration accounting can yield either positive or negative
quantities, depending on the direction of change in tillage between the three available options
(conventional, conservation, or zero tillage) and irrigation choices, along with pasture land
(grassland)/cropland conversions and movements between agriculture and forestry. With
movements from forestry to agriculture, gains in the agricultural soil carbon account are typically
more than offset by losses in the forest soil carbon account (e.g., forest soils typically store more
carbon per acre than soils in agricultural uses). When moving from agricultural land uses to
forestland, on the other hand, there are typically net increases in soil carbon sequestration.
As with forest products, certain agricultural commodities can also be used as bioenergy
feedstocks.
2.7.3 Bioenergy GHG Accounts
Selected agricultural and forestry commodities can be used as feedstocks for biofuel
production processes in FASOMGHG, possibly affecting fossil fuel usage and associated GHG
2-41
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emissions after accounting for emissions during hauling and processing of bioenergy feedstocks.
Four major forms of bioenergy production are included:
• Biodiesel: usage of canola oil, corn oil, lard, poultry fat, soybean oil, tallow, or
yellow grease in the production of biodiesel, which replaces petroleum-based diesel
fuel
• Bioelectricity: usage of bagasse, crop residues, energy sorghum, hybrid poplar, lignin,
manure, miscanthus, sweet sorghum pulp, switchgrass, willow, wood chips, logging
residues, or milling residues as inputs to electric generating power plants in place of
coal (through either cofiring or dedicated biomass plants)
• Cellulosic ethanol: usage of bagasse, crop residues, energy sorghum, hybrid poplar,
miscanthus, sweet sorghum pulp, switchgrass, willow, wood chips, logging residues,
or milling residues to produce cellulosic ethanol, which replaces gasoline
• Starch or sugar-based ethanol: usage of barley, corn, oats, rice, sorghum, sugar, sweet
sorghum, or wheat for conversion to ethanol and replacement of gasoline
In all of these cases, the GHG reduction provided by bioenergy production is equal to the
GHGs emitted from burning and producing the fossil fuel replaced less the GHG emissions of
producing, transporting, and processing the bioenergy feedstock.
2.7.4 Developed Land GHG
F ASOMGHG incorporates exogenous data that specify the rate of conversion of
agriculture and forestry lands to nonagricultural and nonforestry developed uses. Simplified
accounting is employed to estimate the carbon sequestered on these lands.
Other Environmental Impacts
F ASOMGHG considers a number of environmental indicators above and beyond the
GHG accounts. The main components are nitrogen and phosphorus application and runoff, soil
erosion, irrigation water usage, and a number of descriptions of total resource use and activity
within the agricultural and forestry sectors (e.g., total land use, total pasture use, manure load,
livestock numbers, total afforestation).
2-42
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SECTION 3
METHODS USED TO DEVELOP ESTIMATES OF OZONE EFFECTS ON CROP
AND FOREST PRODUCTIVITY
Incorporating the impacts of different ambient ozone concentration levels into
FASOMGHG requires determining crop yield and forest productivity impacts associated with
changes in concentrations. Productivity impacts are required for each crop/region and forest
type/region combination included within the model. In this section, we describe our methods for
calculating relative yield losses (RYLs) and relative yield gains (RYGs) of crops and tree species
under alternative ambient ozone concentration levels.
These data are essential for our market analysis because crop and forest yields play an
important role in determining the economic returns to agricultural and forest production
activities. Thus, they affect landowner decisions regarding land use, crop mix, forest rotation
lengths, production practices, and others. Alterations in ambient ozone concentration levels will
therefore change the supply curves of U.S. agricultural and forest commodities, resulting in new
market equilibriums. Because both the changes in ozone concentrations and the distribution of
ozone-sensitive crops and tree species vary spatially, there may be substantial differences in the
net impacts across regions. There may also be distributional impacts as commodity production
shifts between regions in response to changes in relative productivity.
Ambient Ozone Concentration Data
There are several alternative metrics used for assessing ozone concentrations (see Lehrer
et al. [2007] for more information). For this assessment, we are using the W126 metric, which is
a weighted sum of all ozone concentrations observed from 8 a.m. to 8 p.m. More specifically, we
are using W126 ozone concentration surfaces generated using enhanced Voronoi Neighbor
Averaging (eVNA). W126 concentration surfaces based on meeting the current ozone standard15
were provided by EPA to serve as the baseline for this analysis. In addition, EPA provided W126
ozone concentration surfaces for current conditions, as well as for 15 ppm-hr, 11 ppm-hr, and
7 ppm-hr W126 standards. According to information provided by EPA, the eVNA W126 ozone
surface is built from monitor data fused with Community Multiscale Air Quality (CMAQ)
model-based gradient interpolations. The spatial resolution of the ozone surface in ArcGIS
Shapefile format is 12 km.
15The current primary and secondary ozone standards are 0.075 parts per million (ppm) (or 75 parts per billion)
based on the annual fourth-highest daily maximum 8-hr concentration, averaged over 3 years. For the purposes
of calculating impacts on crop yields and forest growth rates, we used the W126 equivalent of the current
standard.
3-1
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County-level values were extracted from the eVNA W126 ozone surface using ArcGIS.
Only the ozone concentrations for the cropland and forestland portions of the W126 ozone
surface are used to derive the county-level average crop and forest W126 ozone levels,
respectively. These weighting adjustments were made to better reflect the ozone concentration
that would affect the specific portions of each county containing forested land or cropland, rather
than basing county-level exposure on the ozone concentration across the whole county. Data
from the 2006 National Land Cover Database (NLCD) are used to extract the cropland and
forestland portions from the ozone surface.
Calculation of Relative Yield Loss
The median W126 ozone concentration response (CR) functions for crops and tree
seedlings in the 2007 EPA technical report (Lehrer et al., 2007) are used to calculate the RYLs
for crops and tree species under each ambient ozone concentration scenario used in this analysis.
Table 3-1 presents the a and ft parameters being used in the W126 ozone CR function for
different crops and tree species. The W126 ozone CR function is as follows: RYL = 1 —
Table 3-1. Parameter Values Used for Crops and Tree Species
a P
Crops
Corn
Sorghum
Soybean
Winter wheat
Potato
Cotton
Tree Species
Ponderosa
Red alder
Black cherry
Tulip poplar
Sugar maple
Eastern white
Red maple
Douglas fir
Quaking aspen
Virginia pine
98.3
205.9
110.0
53.7
99.5
94.4
159.63
179.06
38.92
51.38
36.35
63.23
318.12
106.83
109.81
1,714.64
2.973
1.963
1.367
2.391
1.242
1.572
1.1900
1.2377
0.9921
2.0889
5.7785
1.6582
1.3756
5.9631
1.2198
1.0000
3-2
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3.1.1 Relative Yield Loss for Crops
Specifically, for crops, we first calculate the FASOMGHG subregion RYLs for crops that
have W126 ozone CR functions using the subregion-level, cropland-based ozone concentration
values under each scenario. The FASOMGHG subregion-level ozone concentration values are
initially calculated for all crops as the simple averages of the county-level ozone concentration
values. For crops that do not have W126 ozone CR functions, we assign them W126 ozone CR
functions based on the crop proxy mapping shown in Table 3-2. This crop mapping was based on
the authors' judgment and previous experience.16 In addition, for oranges, rice, and tomatoes,
which have ozone CR functions that are not W126-based (they are defined based on alternative
measures of ozone levels), we directly used the median RYG values under the 13 ppm-hr ozone
level reported in Table G-7 of Lehrer et al. (2007). More details on RYG are presented in further
subsections.
Table 3-2. Mapping of Ozone Impacts on Crops to FASOMGHG Crops
Crops Used for
Estimating Ozone
Impacts FASOMGHG Crops
W126 Crops
Corn Corn
Cotton Cotton
Potatoes Potatoes
Winter wheat Soft white wheat, hard red winter wheat, soft red winter wheat, durum wheat, hard red
spring wheat, oats, barley, rye, sugar beet, grazing wheat, and improved pasture
Sorghum Sorghum, silage, hay, sugarcane, switchgrass, miscanthus, energy sorghum, and sweet
sorghum
Soybeans Soybeans and canola
Aspen (tree) Hybrid poplar, willow (FASOMGHG places short-rotation woody biomass production in
the crop sector rather than in the forest sector)
Non-W126 Crops
Oranges Orange fresh/processed, grapefruit fresh/processed
Rice Rice
Tomatoes Tomato fresh/processed
Moreover, for crops that have county-level production data and W126 ozone CR
functions (including functions based on proxy crops), we updated the RYLs with production-
weighted W126 values. The 2007 USDA Census of Agriculture (Ag Census) county-level
16 Also, note that FASOMGHG defines short-rotation woody trees such as hybrid poplar and willow as crops. Ozone
impacts on short-rotation woody trees were based on ozone RYLs for aspen.
3-3
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production data are used to derive the weighted FASOMGHG subregion RYLs, following
Formula (3.1).
wRYLik = Ozone CR FunctionkfjPr°dijk*
W126;
(3.1)
where /' denotes FASOMGHG subregion, y indicates county, and k represents crop. Ozone CR
Functions refers to the ozone concentration response function for crop k. Prod*/* represents the
county-level production level of crop k, and W126/y represents the cropland-based ozone value
for county y in subregion /'. Finally, wRYL* stands for the weighted FASOMGHG subregion
RYL for crop k. RYLs are calculated for each ozone concentration level being considered.
3.1.2 Relative Yield Loss for Trees
The ozone CR functions for tree seedlings were used to calculate RYLs for FASOMGHG
trees over their whole life span. To derive the FASOMGHG region-level RYLs for trees under
each ozone concentration scenario, we used FASOMGHG region ozone values and the mapping
in Table 3-3.
Table 3-3. Mapping of Ozone Impacts on Forests to FASOMGHG Forest Types
Tree Species Used for Estimating
Ozone Impacts
FASOMGHG Forest Type
FASOMGHG Region(s)
Black cherry, tulip poplar
Douglas fir
Eastern white pine
Ponderosa pine
Quaking aspen
Quaking aspen, black cherry, red maple,
sugar maple, tulip poplar
Red alder
Red maple
Virginia pine
Virginia pine, eastern white pine
Virginia pine, eastern white pine
Upland hardwood
Douglas fir
Softwood
Softwood
Hardwood
Hardwood
Hardwood
Bottomland hardwood
Natural pine, oak-pine, planted pine
Natural pine, oak-pine, planted pine
Softwood
SC, SE
PNWW
CB,LS
PNWE, PNWW, PSW, RM
RM
CB, LS, NE
PNWE, PNWW, PSW
SC, SE
SC
SE
NE
Note: CB = Corn Belt; LS = Lake States; NE = Northeast; PNWE = Pacific Northwest—East side; PNWW = Pacific
Northwest—West side; PSW = Pacific Southwest; RM = Rocky Mountains; SC = South Central; SE = Southeast.
Specifically, the FASOMGHG region-level RYLs are first calculated for each tree
species listed in first column of Table 3-3. Then, a simple average of RYLs for each tree species
3-4
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mapped to a FASOMGHG forest type in a given region is calculated. The mapping of tree
species to FASOMGHG forest types is based on Elbert L. Little, Jr.'s Atlas of United States
Trees (1971, 1976, 1977, 1978). Note that crop RYLs are generated at the FASOMGHG
subregion level, whereas forest RYLs are calculated at the FASOMGHG region level, consistent
with the greatest level of regional disaggregation available for these sectors within
FASOMGHG.
Calculation of Relative Yield Gain
As described by Lehrer et al. (2007), the RYL is the relative yield loss compared with the
baseline yield under a "clean air" environment. For implementation within FASOMGHG, we
calculate the RYG for crops and trees from moving between ambient ozone concentrations (i.e.,
RYG is calculated as a change in RYL when moving between scenarios).
Thus, to obtain the RYG for crops and trees under alternative ozone concentrations, we
need the RYLs under each scenario. For example, to derive RYG under the current standard
75 ppb scenario relative to current conditions "currcond," we use Formula (3.2):
n\r r 7pp 'currcon'7pp ,~. ~\
KY Ujsppfo — -- 1 — - ^J .2)
~ ~
The FASOMGHG subregion-level crop RYGs and the FASOMGHG region-level tree
RYGs for changes associated with moving from one scenario to another were calculated for
additional comparisons in the same way.
Conducting Model Scenarios in FASOMGHG
The current crop/forest budgets included in FASOMGHG are assumed to reflect
input/output relationships under current ambient ozone concentrations because these budgets are
based on historical data. To model the effects of changing ozone concentrations on the
agricultural and forest sectors, the following five scenarios were constructed and run through the
model:
1 . "current" scenario, where no RYGs of crops and trees are considered (assumed to be
consistent with current ambient ozone concentration levels);
2. 75 ppb scenario, where crop and forest yields are assumed to increase by the
percentages calculated in RYG75/>/>6, calculated relative to the current scenario;
3. 15 ppm-hr scenario, using RYG75P/™-/w, calculated relative to both current and 75 ppb
scenarios
3-5
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4. 11 ppm-hr scenario, using RYGnPpm-hr, calculated relative to both current and 75 ppb
scenarios; and
5. 7 ppm-hr scenario, using RYG7P/™-/?r, calculated relative to both current and 75 ppb
scenarios.
Our primary comparisons in this report are between the current standard and the
15 ppm-hr, 11 ppm-hr, and 7 ppm-hr cases. The results of those comparisons are included in
Sections 5 and 6 of this report. However, we also calculated comparisons between current
conditions and the current standard (75 ppb), 15 ppm-hr, 11 ppm-hr, and 7 ppm-hr scenarios that
are reported in Appendixes A, B, and C.
The time scope of the FASOMGHG model scenarios used for these analyses is 2000-
2050, solved in 5-year time steps.17 The crop and tree RYGs are introduced into the model
starting in 2010, and they remain constant at those percentage changes relative to baseline for the
rest of the modeling period.
Figure 3-1 presents the modeling process of simulating ozone scenarios using
FASOMGHG. The changes in crop and tree yield growth potentially lead to new market
equilibriums for agricultural and forestry commodities, as well as land use changes between
agricultural and forestry uses and consequent GHG emissions and sequestration changes.
By comparing the market equilibriums under different scenarios, we can calculate the
welfare, land use, and GHG impacts of alternative ozone standards on the U.S. agricultural and
forest sector, including changes in consumer and producer welfare, land use allocation, and GHG
mitigation potential over time.
17 Because of terminal period effects, the model is ran out to the 2050 time period, but only results through the 2040
model time period (representative of 2040-2044) are used in our analyses.
3-6
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Figure 3-1. FASOMGHG Modeling Flowchart
3-7
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SECTION 4
DATA INPUTS
In this section, we summarize the input data used in the FASOMGHG scenarios specified
for this assessment. Following the methods described in Section 3, we calculated W126 ozone
concentration levels by region and crop. Effects on crop yields and forest productivity were
calculated for each FASOMGHG region. We present the values used as model inputs in tabular
and map format, with a primary focus here on comparison of the more stringent scenarios to the
current standard.
Ambient Ozone Concentration Data
The county-level forested and cropland W126 ozone values were aggregated at regional
and subregional levels, respectively. Table 4-1 presents the forestland W126 ozone values for
each of the five scenarios. Comparing the 75 ppb current standard scenario with the current
conditions scenario, one can see that the Pacific Southwest, South Central, Southeast, and Rocky
Mountains regions would have the largest ground-level ozone reductions, if attained. The Corn
Belt, Southwest, and Northeast regions would also experience significant ozone reductions. The
Great Plains, Lake States, and Pacific Northwest regions are the regions least affected by
attainment of current ozone standards.
Table 4-1. Forestland W126 Ozone Values under Alternative Scenarios
FASOMGHG Region
CB
GPa
LS
NE
PNWE
PNWW
PSW
RM
SC
SE
swa
currcond
11.78
8.99
6.32
8.50
5.56
3.74
17.15
13.31
11.84
13.05
10.05
75 ppb
3.41
3.10
1.15
0.69
1.92
1.83
1.53
3.72
2.49
2.35
1.83
15 ppm-hr
3.41
3.05
1.15
0.69
1.86
1.81
1.40
3.09
2.49
2.35
1.82
11 ppm-hr
2.46
2.97
1.14
0.55
1.80
1.80
1.26
2.28
2.07
2.13
1.79
7 ppm-hr
1.61
2.52
1.12
0.42
1.76
1.79
1.13
1.96
1.53
1.41
1.78
a GP and SW are modeled as agriculture-only regions in FASOMGHG.
Note: CB = Corn Belt; GP = Great Plains; LS = Lake States; NE = Northeast; PNWE = Pacific Northwest—East
side; PNWW = Pacific Northwest—West side; PSW = Pacific Southwest; RM = Rocky Mountains; SC = South
Central; SE = Southeast; SW = Southwest.
4-1
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Comparing the most stringent 7 ppm-hr standard scenario with the 75 ppb scenario, the
Corn Belt and Rocky Mountains regions are the areas that will have the most notable further
ozone reductions.
Table 4-2 displays the agricultural W126 ozone values at the subregion level. Similar to
the forest ozone values, the Pacific Southwest, South Central, Southeast, and Rocky Mountains
regions experience the greatest agricultural ozone reductions under the 75 ppb scenario
compared with current conditions. The Corn Belt, Southwest, and Northwest regions also see
noteworthy agricultural ozone reductions.
Table 4-2. Cropland W126 Ozone Values under Modeled Scenarios
FASOMGHG
Region
Corn Belt (CB)
Great Plains (GP)
Lake States (LS)
FASOMGHG
Subregion
Illinois, Northern
Illinois, Southern
Indiana, Northern
Indiana, Southern
Iowa, Central
Iowa, Northeast
Iowa, Southern
Iowa, Western
Missouri
Ohio, Northeast
Ohio, Northwest
Ohio, Southern
Kansas
Nebraska
North Dakota
South Dakota
Michigan
Minnesota
Wisconsin
Current
Conditions
7.80
11.16
10.35
13.01
5.68
6.24
6.53
5.71
11.41
12.74
12.03
13.75
10.90
8.79
4.46
5.64
9.51
4.95
7.01
75 ppb
3.14
3.60
3.60
4.27
1.09
0.73
1.57
1.80
3.41
3.19
3.90
3.42
2.35
2.86
1.96
2.13
2.35
1.11
1.28
15 ppm-hr
3.14
3.60
3.60
4.27
1.09
0.73
1.57
1.80
3.41
3.19
3.90
3.42
2.15
2.62
1.96
2.11
2.35
1.11
1.28
11 ppm-hr
2.44
2.53
2.65
2.96
1.07
0.73
1.35
1.78
2.47
2.30
2.90
2.39
1.87
2.31
1.96
2.09
2.29
1.11
1.27
7 ppm-hr
1.73
1.54
1.72
1.75
1.04
0.73
1.14
1.66
1.64
1.46
1.93
1.45
1.72
2.00
1.70
1.81
2.22
1.04
1.25
(continued)
4-2
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Table 4-2. Cropland W126 Ozone Values under Modeled Scenarios (continued)
FASOMGHG
Region
Northeast (ME)
Pacific Northwest
East (PNWE)
Pacific Southwest
(PSW)
Rocky Mountains
(RM)
South Central
(SC)
FASOMGHG
Subregion
Connecticut
Delaware
Maine
Maryland
Massachusetts
New Hampshire
New Jersey
New York
Pennsylvania
Rhode Island
Vermont
West Virginia
Oregon
Washington
California,
Northern
California,
Southern
Arizona
Colorado
Idaho
Montana
Nevada
New Mexico
Utah
Wyoming
Alabama
Arkansas
Kentucky
Louisiana
Mississippi
Current
Conditions
11.74
17.23
3.71
17.36
9.90
5.71
16.76
8.34
11.97
11.61
5.52
10.86
6.39
4.95
21.91
20.06
13.75
16.34
12.54
6.59
15.55
12.54
18.04
14.13
13.22
12.43
13.77
9.60
11.32
75ppb
0.24
0.38
0.39
0.76
0.27
0.31
0.30
0.16
0.53
0.40
0.28
2.25
2.25
1.99
1.50
3.08
5.52
4.75
3.16
2.32
2.77
3.48
4.88
4.22
3.07
2.41
3.91
1.20
1.56
15 ppm-hr
0.24
0.38
0.39
0.76
0.27
0.31
0.30
0.16
0.53
0.40
0.28
2.25
2.16
1.93
1.31
2.72
4.27
3.77
2.72
2.31
2.47
2.78
3.90
3.76
3.07
2.41
3.91
1.20
1.56
11 ppm-hr
0.24
0.38
0.39
0.70
0.27
0.31
0.30
0.16
0.46
0.40
0.28
1.60
2.05
1.86
1.11
2.31
2.68
2.50
2.17
2.29
2.10
1.88
2.59
3.20
2.81
2.14
2.71
1.20
1.52
7 ppm-hr
0.24
0.36
0.39
0.56
0.27
0.31
0.30
0.16
0.40
0.40
0.28
0.95
2.00
1.83
0.93
2.01
2.20
1.95
1.94
1.92
1.95
1.64
2.20
2.62
1.86
1.89
1.61
1.19
1.44
(continued)
4-3
-------
Table 4-2. Cropland W126 Ozone Values under Modeled Scenarios (continued)
FASOMGHG
Region
Southeast (SE)
Southwest (SW)
FASOMGHG
Subregion
Tennessee
Texas, East
Florida
Georgia
North Carolina
South Carolina
Virginia
Oklahoma
Texas, Central
Blacklands
Texas, Coastal
Bend
Texas, Edwards
Plateau
Texas, High Plains
Texas, Rolling
Plains
Texas, South
Texas, Trans Pecos
Current
Conditions
15.53
9.92
9.24
12.86
14.28
12.88
12.57
11.50
9.31
7.28
9.14
11.76
10.71
4.38
11.78
75ppb
3.76
1.59
2.93
2.68
2.00
1.71
2.09
1.87
1.96
1.73
1.93
2.60
2.06
1.41
3.59
15 ppm-hr
3.76
1.59
2.93
2.68
2.00
1.71
2.09
1.82
1.96
1.73
1.85
2.18
1.99
1.41
3.20
11 ppm-hr
2.66
1.59
2.78
2.47
1.84
1.59
1.86
1.76
1.96
1.73
1.75
1.65
1.89
1.40
2.70
7 ppm-hr
1.62
1.59
2.08
1.64
1.20
1.07
1.22
1.74
1.96
1.73
1.73
1.52
1.87
1.40
2.57
Figure 4-1 presents the incremental ozone reductions under alternative ozone standards
with respect to the current 75 ppb standard. As the standard is tightened from the 15 ppm-hr to
7 ppm-hr scenario, the greatest ozone reductions are observed in the southern half of the Rocky
Mountains region. The southern Corn Belt and northern areas of the South Central regions, along
with selected places in the Southeast and Pacific Southwest near urban areas, also see substantial
ozone reductions. These ozone reductions would affect the production of crops and timber that
are susceptible to ground-level ozone in these regions.
4-4
-------
Legend
I I FASOM Res«fi
^J FASOM Sulmgon
Reduction from Current S
Figure 4-1. Ozone Reductions with Respect to 75 ppb under Alternative Scenarios
Changes in Crop and Forest Yields with Respect to 75 ppb Scenario
Figures 4-2 through 4-7 display major crops' RYGs under alternative ozone standards
scenarios at the FASOMGHG subregion level, with respect to the current 75 ppb standard. These
are the values that were directly incorporated into FASOMGHG to define the scenarios modeled.
See Appendix A for maps of county-level yield changes for major crops. Figures 4-8 and 4-9
display changes in forest RYGs. As discussed previously, the Rocky Mountains, Corn Belt, and
parts of the southern regions of the United States (e.g., within the Pacific Southwest, South
Central, and Southeast regions) would experience the most significant further ozone reductions.
Hence, one would expect to see the most sizable increases in RYGs for crops and trees grown in
those regions. This finding is consistent with our calculations.
4-5
-------
_egend
I FASOM Region
FASOM Sub region
o
V*
SS
ss
ss
ss
I
n
c
en
ET
'S
'S
s
'S
s
;s
s
\
Figure 4-2. Percentage Changes in Corn RYGs with Respect to the 75 ppb Scenario
4-6
-------
Legend
FASOM Region
FASOM Subregion
3
\\
Figure 4-3. Percentage Changes in Cotton RYGs with Respect to the 75 ppb Scenario
4-7
-------
_egend
FASOM Region
FASOM Subregion
ss
Figure 4-4. Percentage Changes in Potato RYGs with Respect to the 75 ppb Scenario
4-8
-------
-egend
^ FASOM Region
FASOM Subregion
%
;-
Figure 4-5. Percentage Changes in Sorghum RYGs with Respect to the 75 ppb Scenario
4-9
-------
Legend
J FASOM Region
U FASOM Subregion
\\
S'S
SS
\\
?, '
\\
"«,
Figure 4-6. Percentage Changes in Soybean RYGs with Respect to the 75 ppb Scenario
4-10
-------
Legend
^ FASOM Region
FASOM Subregion
«*
CL
O
o
o
\
Figure 4-7. Percentage Changes in Winter Wheat RYGs with Respect to the 75 ppb
Scenario
4-11
-------
Figure 4-8. Percentage Changes in Softwood RYGs with Respect to the 75 ppb Scenario
4-12
-------
Legend
FASOM Region
\\ I
Figure 4-9. Percentage Changes in Hardwood RYGs with Respect to the 75 ppb Scenario
For more information about RYL and RYG data from which the percentage changes in
RYGs for crops and forests were derived, see Appendix A.
4-13
-------
SECTION 5
MODEL RESULTS
FASOMGHG was used to estimate the projected effects of alternative ozone
concentration standards on the U.S. agricultural and forestry sectors. As introduced earlier, the
comparisons considered for this report focus on the differences between a scenario assuming
compliance with the existing 2008 standards (75 ppb) and scenarios in which three more
stringent ozone standards are met. Those three scenarios are 15 ppm-hr, 11 ppm-hr, and
7 ppm-hr W126 standards. Our analysis included changes to production, prices, forest inventory,
land use, welfare, and GHG mitigation potential associated with achieving each of the more
stringent standards.
Agricultural Sector
Ozone negatively affects growth in many plants, leading to lower crop yields. The
reductions in ozone concentrations that would be achieved under a given standard vary across
regions. In addition, some crops are more sensitive to ozone than others, so the percentage
changes in yield will vary by crop and region. However, reducing ambient ozone concentrations
would generally increase agricultural yields and total production. Our analysis began by
determining the extent to which current yield losses caused by ozone could be reversed by
reducing ozone levels. Increased crop yields lead to a greater available supply of most
agricultural crops, which in turn tends to reduce market prices. There is also an overall tendency
toward acreage shifting away from ozone-sensitive crops. In general, impacts in the agricultural
sector are relatively limited, especially when compared with the forestry sector. By and large,
more stringent standards led to increased incremental impact, but the additional impact in
moving to increasingly stringent ozone standards was relatively small.18
5.0.1 Production and Prices
Changes in U.S. agricultural production and prices were measured using Fisher indices
(sees Tables 5-1 and 5-2).19 Both primary and secondary commodity production levels are
projected to increase by 2040 as a result of heightened productivity. Agricultural production
changes were generally relatively small across products, rarely exceeding an increase of 0.50%
with respect to the current standard and often changing by 0.01% or less.
18 As shown in the appendixes, there are large impacts associated with reductions in ozone consistent with moving
from current conditions to meeting any of the four standards examined. The differences between standards are
much smaller, though.
19 The Fisher price index is known as the "ideal" price index. It is calculated as the geometric mean of an index of
current prices and an index of past prices.
5-1
-------
Table 5-1. Agricultural Production Fisher Indices (Current conditions =100)
Sector
Policy
2010
2020
2030
2040
Primary
Commodities
Crops
Livestock
Farm products3
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
101.01
101.01
101.05
101.08
100.13
100.13
100.14
100.14
100.58
100.58
100.61
100.62
100.77
100.77
100.79
100.83
100.03
100.03
100.03
100.03
100.42
100.42
100.43
100.45
100.97
100.95
100.96
100.96
100.36
100.36
100.36
100.36
100.43
100.65
100.65
100.65
100.87
100.87
100.88
100.90
100.88
100.90
100.92
100.92
100.40
100.89
100.90
100.91
Secondary
Commodities
Processed
Meats
Mixed feeds
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
100.17
100.16
100.19
100.20
100.03
100.05
100.05
100.05
100.06
100.07
100.08
100.07
100.10
100.10
100.10
100.12
100.01
100.01
100.01
100.01
100.01
100.01
100.01
100.01
100.08
100.08
100.08
100.08
100.43
100.43
100.43
100.43
100.64
100.64
100.64
100.64
100.28
100.28
100.28
100.28
100.40
100.41
100.41
100.41
100.53
100.54
100.54
100.54
' Farm Products is the composite of Crops and Livestock.
5-2
-------
Table 5-2. Agricultural Price Fisher Indices (Current Conditions = 100)
Sector
Policy
2010
2020
2030
2040
Primary
Commodities
Crops
Livestock
Farm products
Processed
Meats
Mixed feeds
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
97.51
97.49
97.49
97.46
99.78
99.25
98.97
99.33
97.51
97.49
97.49
97.46
97.60
97.58
97.58
97.56
99.86
99.86
99.86
99.94
97.48
98.53
97.52
100.05
98.35
98.36
98.33
98.31
99.31
99.07
98.97
99.50
98.35
98.36
98.33
98.31
Secondary
Commodities
98.34
98.34
98.29
98.29
100.00
100.00
100.00
100.00
98.98
96.29
99.48
97.40
99.05
99.05
99.02
98.97
100.26
100.31
99.86
100.34
99.75
99.04
99.02
98.97
98.69
98.69
98.65
98.61
99.75
99.75
99.73
99.71
99.11
98.55
100.65
99.80
98.73
98.71
98.72
98.66
98.90
99.31
98.84
99.85
99.51
98.70
98.72
98.65
98.22
98.22
98.21
98.16
99.51
99.51
99.53
99.52
98.91
99.48
98.81
99.32
Increased production led to a general decline in market prices because the equilibrium
price adjusts to higher levels of supply. This result is consistent with expectations because higher
productivity leads to greater supply, which tends to decrease market prices. Changes in price
were generally more pronounced than changes in production, with the largest decreases in the
5-3
-------
Farm Products, Livestock, and Mixed Feeds categories. Agricultural prices tend to decline by a
greater percentage than production increases because the demand for most agricultural
commodities is inelastic.20 However, almost all declines in price were less than 1.0% of prices at
the current standards, and most were less than 0.5%. Often, the change in price from the current
standard to 15 ppm-hr was minimal, at less than 0.01%, with the larger changes occurring only at
the 11 ppm-hr and 7 ppm-hr levels.
5.0.2 Crop A creage
Crop acreage was projected to decline with the introduction of the ozone standards
because additional productivity per acre reduces the demand for crop acreage. In aggregate,
farmers will be able to meet the demand for agricultural commodities using less land under
scenarios with lower ozone concentrations. Consistent with these expectations, the total cropped
area is slightly smaller for each model year in the alternative standard cases. However, land
allocation also depends on relative returns across various uses and is influenced by forest harvest
timing. Changes in land allocation between the agricultural and forestry sectors are discussed
later in this section.
Table 5-3 provides projections of acreage in each of the major U.S. crops, as well as
composites of all remaining crops and total cropland. The absolute change relative to the current
standard is presented for each alternative standard. Larger changes occurred in soybean and
sorghum acreage, whereas only minor changes occurred in all other crops, leading to an overall
slight decrease in crop acreage across all crops. This shift occurred largely because of differential
crop sensitivity to ozone concentrations. Note that the sum of the crop-specific changes will not
necessarily equal the total changes shown in Table 5-3 because some double-cropping is
reflected in the model (e.g., soybeans and winter barley).
20 Demand elasticities are measures of the responsiveness of the quantity demanded to a change in price.
Commodities with inelastic demands are those where consumers change the quantity of a good they purchase by
a smaller percentage than the change in market price. Many food products fall into this category because they are
relatively low-priced necessities.
5-4
-------
Table 5-3. Major Crop Acreage, Million Acres
Crop
Corn
Soybeans
Hay
Hard red winter wheat
Cotton
Hard red spring wheat
Policy
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010 2020 2030
92.9 87.1 79.7
Change with Respect to Current Standard
0.00 0.00 -0.01
-0.01 0.03 -0.04
-0.01 0.00 -0.02
73.3 71.9 71.7
Change with Respect to Current Standard
0.01 0.00 -0.02
0.02 -0.01 -0.19
0.06 -0.04 -0.09
44.0 42.0 41.0
Change with Respect to Current Standard
-0.01 -0.01 -0.01
0.01 -0.10 0.00
-0.01 -0.02 -0.03
20.6 19.5 15.8
Change with Respect to Current Standard
0.04 -0.02 -0.01
0.06 -0.07 -0.07
0.02 -0.01 -0.02
14.6 15.4 15.9
Change with Respect to Current Standard
-0.01 -0.01 0.00
-0.01 -0.03 -0.02
-0.01 -0.02 -0.01
13.7 12.5 12.2
Change with Respect to Current Standard
0.00 0.00 -0.01
-0.01 -0.02 0.01
0.00 0.00 0.01
2040
70.8
0.00
-0.01
-0.01
69.9
-0.01
-0.14
-0.10
39.2
0.00
-0.01
0.00
13.1
-0.01
0.02
0.01
15.7
0.00
0.01
0.01
11.4
0.00
0.00
0.00
(continued)
5-5
-------
Table 5-3. Major Crop Acreage, Million Acres (continued)
Crop
Sorghum
Switchgrass
All others3
Total
Policy
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010 2020 2030
10.7 10.7 11.3
Change with Respect to Current Standard
0.00 0.57 0.33
-0.01 0.57 0.37
0.00 0.55 0.35
0.0 13.4 11.2
Change with Respect to Current Standard
0.00 0.00 0.00
0.00 0.00 0.00
0.00 0.00 -0.01
41.7 40.7 43.3
Change with Respect to Current Standard
-0.01 0.01 -0.02
0.00 0.00 -0.04
-0.03 0.00 -0.05
311.5 313.8 302.5
Change with Respect to Current Standard
0.01 -0.03 -0.07
0.04 -0.19 -0.31
0.03 -0.12 -0.19
2040
11.6
-0.87
-0.88
-0.88
10.3
0.00
0.01
0.00
44.0
-0.02
-0.01
-0.02
285.1
-0.03
-0.14
-0.11
a Canola, durum wheat, fresh grapefruit, fresh orange, fresh tomato, grazing wheat, hybrid poplar, oats, potato,
processed grapefruit, processed orange, processed tomato, rice, rye, silage, soft red winter wheat, soft white
wheat, spring barley, sugar beet, sugarcane, sweet sorghum, winter barley.
Forestry Sector
As with agricultural crops, ozone diminishes growth in most tree species, and our
analysis began by estimating how much of this diminished growth would be reversed under the
more stringent ozone standards. Impacts are significantly higher in the forestry sector, especially
in hardwood species and species more prevalent in the southern regions. Impacts are more
significant for southern regions because of the higher baseline ozone concentrations in the South
Central and Southeast regions. Higher initial concentrations resulted in higher reductions to meet
5-6
-------
the alternative standards and, thus, higher impacts to tree growth. This relationship also
contributes to the larger changes in the forestry sector as a whole.
5.1.1 Production and Prices
Reducing ozone concentrations led to increased forest growth, which was reflected in
increased production in FASOMGHG. Some of the most substantial ozone standard impacts
occurred in saw log and pulp log harvest quantities and prices. Compared with the current
standard, alternative standard cases had consistently higher production except for softwood pulp
logs, where production increased only marginally and at times fell below the baseline estimates,
especially in 2040. The most significant impacts occurred in hardwood saw logs, where harvests
were projected to be more than 1% higher than under the current standard level by 2040. There
are some cases where production of pulp logs and saw logs moved in opposite directions, with
two primary explanations. The first is that as saw log production expands, the price for saw logs
drops, allowing processers to substitute saw logs for instances in which pulp logs are
traditionally used. The second is that even when the primary log size being harvested is pulp
logs, saw logs will generally also be present because of natural variation in tree growth rates (and
vice versa for harvest of saw logs). With higher growth rates, there would tend to be more saw
logs in stands harvested primarily for pulp logs over time.
The largest changes in production occurred at the 7 ppm-hr level. Changes from the
current standard to 15 ppm-hr were fairly small, whereas changes from 15 ppm-hr to 11 ppm-hr
and from 11 ppm-hr to 7 ppm-hr are generally of the same magnitude and range from 0.3% to
2.5% of the levels under current standards, depending on the product and year. Table 5-4
presents these changes by major product.
The impact of policy intervention on timber market prices was more substantial than the
change in production in terms of percentage changes compared with the current standard.
Although increases in production of forest products did not exceed 2.5% compared with the
current standard, changes in price were as large as 8.7%. As with agricultural products, many
forest products have relatively inelastic demand so prices tend to change by a larger percentage
than quantities. Table 5-5 lists absolute changes with respect to the current standard, whereas
Table 5-6 lists the percentage change in forest product prices for each year, alternative standard,
and forest product.
5-7
-------
Table 5-4. Forest Products Production, Million Cubic Feet
Product
Hardwood saw logs
Hardwood pulp logs
Softwood saw logs
Softwood pulp logs
Table 5-5. Forest
Product
Hardwood saw logs
Policy
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010 2020
3,697 3,472
Change with Respect to Current
1 2
27 17
61 10
2,592 2,277
Change with Respect to Current
1 1
29 17
65 35
4,666 5,186
Change with Respect to Current
0 0
0 28
13 27
75 ppb 3,469 3,923
Change with Respect to Current Standard
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Product Prices, U.S.
Policy
75 ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
1 3
2 12
6 16
Dollars per Cubic Foot
2010 2020
0.69 0.65
Change with Respect to Current
0.00 0.00
-0.01 -0.01
-0.01 -0.02
2030
3,776
Standard
10
37
35
2,601
Standard
-12
-29
-25
5,614
Standard
1
9
11
4,324
0
5
6
2030
0.39
Standard
0.00
-0.01
-0.02
2040
4,843
1
-4
-13
2,529
14
20
31
6,696
8
44
48
4,326
-12
-25
-31
2040
0.19
0.00
-0.01
-0.01
(continued)
5-8
-------
Table 5-5. Forest Product Prices, U.S. Dollars per Cubic Foot (continued)
Product
Hardwood pulp logs
Softwood saw logs
Softwood pulp logs
Policy
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010 2020 2030
0.24 0.44 0.22
Change with Respect to Current Standard
0.00 0.00 0.00
0.00 0.00 -0.01
0.00 -0.01 -0.02
2.31 1.91 1.60
Change with Respect to Current Standard
0.00 -0.01 -0.01
-0.01 -0.02 -0.02
-0.01 -0.03 -0.03
1.42 1.12 1.34
Change with Respect to Current Standard
0.00 0.00 0.00
-0.01 0.00 0.00
-0.01 0.00 -0.01
2040
0.12
0.00
0.00
-0.01
1.31
-0.01
-0.02
-0.03
0.94
0.00
0.00
-0.02
Table 5-6. Forest Product Prices and Percentage Change, U.S. Dollars per Cubic Foot
Product
Hardwood saw logs
Hardwood pulp logs
Policy
2010 2020
75 ppb 0.69 0.65
Percentage Change with Respect to
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
-0.28 0.13
-0.79 0.13
-1.59 -2.60
75 ppb 0.24 0.44
Percentage Change with Respect to
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
0.00 -0.15
-0.87 -1.95
-2.10 -3.52
2030
0.39
Current Standard
-0.16
-2.52
-8.72
0.22
Current Standard
-0.08
-2.06
-4.92
2040
0.19
0.94
-1.51
-7.12
0.12
-0.08
-2.64
-6.23
(continued)
5-9
-------
Table 5-6. Forest Product Prices and Percentage Change, U.S. Dollars per Cubic Foot
(continued)
Product Policy
Softwood saw logs
75ppb
2010
2.
Percentage
15 ppm-hr
11
7
Softwood pulp logs
ppm-hr
ppm-hr
75ppb
Percentage Change
15
11
7
ppm-hr
ppm-hr
ppm-hr
31
Change
-0.09
-o.
-o.
1.
with
-o.
26
46
42
Respect
14
-0.43
-1.
03
2020
1.
.91
with Respect to
-0.33
-1.
-1.
1.
.24
.54
.12
2030
1.
Current
-o.
-1.
-1.
1.
60
Standard
44
32
91
34
2040
1.
.31
-0.69
-1.
.40
-2.28
0.
.94
to Current Standard
0.
0.
-o.
.12
.13
.42
0.
-o.
-o.
15
19
82
0.
-o.
-2.
.18
.51
.17
5.1.2 Forest Acres Harvested
Harvested acres are projected to decline as a result of higher productivity in the policy
cases. The most significant reductions occurred in species found in the southern regions where
the largest ozone reductions would occur: natural pine and upland hardwoods. The difference
between the hardwood harvested acres in the current standard case and in the alternative
standards widens from 2010 to 2040, increasing to a difference of more than 4%. The impact to
total acres of softwood harvested was smaller, with differences remaining at less than 1% of the
current standard levels. Impacts to harvesting are larger for more stringent ozone standards, with
the largest shifts occurring between the 11 ppm-hr and 7 ppm-hr standards. Table 5-7 presents
the model results for forest acres harvested.
Table 5-7. Forest Acres Harvested, Thousand Acres
Product Policy 2010
2020 2030
Total hardwood 75 ppb 14,421 10,177 10,187
Change with Respect to Current Standard
15 ppm-hr -12
11 ppm-hr 101
7 ppm-hr 274
4 -20
-30 -167
-130 -257
2040
10,410
53
-136
-424
(continued)
5-10
-------
Table 5-7. Forest Acres Harvested, Thousand Acres (continued)
Product
Total softwood
Policy
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010
17,335
8
6
23
2020
15,205
Change with Respect to
13
60
134
2030
14,740
Current Standard
-10
-18
-81
2040
17,826
-27
1
62
5.1.3 Forest Inventory
Under FASOMGHG definitions, existing inventory includes only trees that have been
standing since the initial model year of 2000. All trees planted since then, including both
reforestation and afforestation, are included in new inventory. The model projected significant
increases in existing inventory for both hardwood and softwood species, although the increase
was greater in hardwood species. As with the crops, this difference is largely explained by
differential sensitivity to ozone between species. Hardwood species show a much higher
sensitivity to ozone levels and are thus modeled to respond more dramatically to reductions in
ozone concentration. The gap between the current standard and alternative standards widened
over time as inventory continued to accumulate. Because there is greater existing inventory in
each of the alternative standards, there is less demand for reforestation and afforestation to meet
future demand for forest products and therefore less new inventory than under the current
standard.
Some relatively large differences between ozone standards occurred in the forest
inventory projections. For example, existing hardwood inventory was projected to be 4.0%
higher under the 11 ppm-hr case than the 7 ppm-hr case by 2040. New hardwood inventory is
similarly sensitive, with the model projecting a 2.6% increase for this same comparison. For new
and existing inventory of both hardwoods and softwoods, the largest impacts occurred at the
11 ppm-hr standard. This type of nonlinear response can occur because of differences in the
relative impacts on alternative forest and agricultural products that lead to land reallocation.
Table 5-8 presents the model results for forest inventory.
5-11
-------
Table 5-8. Existing and New Forest Inventory, Million Cubic Feet
Product Policy 201
Existing hardwood 75 ppb 302
Percentage Change with
15 ppm-hr
1 1 ppm-hr 1
7 ppm-hr
Existing softwood 75 ppb 190
Percentage Change with
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
New hardwood 75 ppb 1
Percentage Change with
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
New softwood 75 ppb 9
Percentage Change with
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
0 2020
,813 345,013
Respect to Current Standard
12 -6
,745 6,337
698 2,357
,790 173,039
Respect to Current Standard
46 62
360 1,134
182 561
,968 10,008
Respect to Current Standard
0 -2
5 59
1 15
,135 64,727
Respect to Current Standard
-2 -17
12 -131
0 -98
2030
400,552
18
15,223
5,530
160,470
113
2,355
867
20,162
-6
208
76
109,869
-59
-1,596
-560
2040
459,892
79
29,155
10,454
161,991
223
3,965
1,858
33,159
0
1,317
446
118,954
-51
-2,209
-1,016
Cross-Sectoral Policy Impacts
One of the advantages of a model such as FASOMGHG for analysis of impacts on major
land-using activities is the ability to account for shifts in land use. Differentiated impacts on
productivity across products will lead to changes in market prices and in the relative profitability
of alternative land uses. In response, landowners will change their allocation of land across
different productive activities, which will contribute to market impacts. In addition, these
changes in land use have implications for GHG emissions and other environmental impacts. In
this section, we discuss changes in land use, net GHG emissions, and producer and consumer
welfare across the agricultural and forest sectors.
5-12
-------
5.2.1 Land Use
FASOMGHG projected changes in eight land use categories: existing forest,
reforestation, afforestation, cropland, pasture, cropland pasture,21 and lands enrolled in the
Conservation Research Program (CRP).22 The largest impacts were projected in afforestation.
The general projected pattern within these categories was a decline in total forest- and cropland,
coupled with small increases in both traditional pasture and cropland pasture. The areas of total
forest- and cropland declined as productivity and inventory increased because of decreased
ozone concentrations, implying that less land would be required to meet market demands. There
was almost no change in acreage retained in CRP.
The incremental impact of more stringent ozone standards was apparent in most of these
cases, especially reforestation, afforestation, and cropland pasture. Each of the standard levels,
the current and all alternatives, followed the same pattern of continuous decline in afforestation
over the projection years, though these declines were more pronounced at more stringent
standard levels. Generally, the additional impact of moving from the 11 ppm-hr standard to
7 ppm-hr was higher than shifts between other standard levels. Table 5-9 presents the model
results by major land use type.
Table 5-9. Land Use by Major Category, Thousand Acres
Product
Existing forest
Reforested
Policy
2010
75ppb 341,843
Change with Respect to
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
Change with Respect to
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
-5
-45
-58
70,879
Current
-4
73
250
2020
328,746
Standard
-4
-66
-189
112,945
Standard
12
-7
-203
2030
314,445
45
73
88
139,607
63
-144
-536
2040
307,835
-2
39
58
158,639
39
-283
-927
(continued)
21 Cropland pasture is managed land suitable for crop production (i.e., relatively high productivity) that is being used
as pasture.
22 Rangeland estimates are also included, but rangeland is held fixed in FASOMGHG by assumption because it
cannot be allocated to any other use.
5-13
-------
Table 5-9. Land Use by Major Category, Thousand Acres (continued)
Product
Afforested
Cropland
Pasture
Cropland pasture
Rangeland
CRP
Policy 2010 2020
75 ppb 12,656 9,748
Change with Respect to Current Standard
15 ppm-hr -5 -4
llppm-hr -11 -10
7 ppm-hr -14 -104
75 ppb 311,714 313,784
Change with Respect to Current Standard
15 ppm-hr 14 -27
llppm-hr 25 -117
7 ppm-hr 44 -195
75 ppb 84,429 85,113
Change with Respect to Current Standard
15 ppm-hr 0 0
llppm-hr 34 0
7 ppm-hr 34 -27
75 ppb 47,585 47,992
Change with Respect to Current Standard
15 ppm-hr -12 28
llppm-hr -4 192
7 ppm-hr -11 420
75 ppb 302,210 301,104
Change with Respect to Current Standard
15 ppm-hr 0 0
llppm-hr 0 0
7 ppm-hr 0 0
75 ppb 36,534 34,480
Change with Respect to Current Standard
15 ppm-hr 3 3
llppm-hr -9 -9
7 ppm-hr -9 -9
2030
4,566
-19
-55
-78
304,833
-62
-157
-274
86,810
4
21
-12
58,179
11
72
207
300,049
0
0
0
34,480
3
-9
-9
2040
4,461
-19
-55
-78
293,396
-17
-115
-114
86,075
0
32
-4
66,114
16
54
69
299,039
0
0
0
34,480
3
-9
-9
5-14
-------
5.2.2 Welfare
Welfare impacts resulting from the implementation of alternative standard levels
followed the same pattern between the agriculture and forestry sectors, although it was more
pronounced in forestry. Consumer surplus typically increased in both cases as higher
productivity under reduced ozone conditions tended to increase total production and reduce
market prices. Because demand for most forestry and agricultural commodities is inelastic, there
are more instances in which producer surplus declines. In some year/ozone concentration
combinations, the effect of falling prices on producer profits more than outweighs the effects of
higher production levels.
Percentage changes in agricultural sector consumer and producer surplus between the
current standard and the alternative standards were relatively small in many cases, with the
largest percentage change being a 0.4% decline in producer surplus in the 2035 model period.
However, the agricultural sector is a very large market, and even small percentage changes in
welfare can result in annualized values of tens or even hundreds of millions of dollars.
Table 5-10 provides consumer and producer surplus for the agricultural sectors under the current
standard, along with the change in surplus for each alternative standard. There is considerable
variability in the magnitude of consumer and producer impacts from year to year, which is not
surprising given the dynamic nature of the model and numerous adjustments taking place over
time in response to changes in net returns associated with alternative land uses.
Table 5-10. Consumer and Producer Surplus in Agriculture, Million 2010 U.S. Dollars
Product
Consumer
surplus
Policy
75ppb
2010
1,918,082
2015
1,940,673
2020 2025
1,968,142 1,995,
Change with Respect to
Producer
surplus
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15
19
-31
725,364
-2
24
46
831,565
1
13
36
815,072 863,
Change with Respect to
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
612
1,474
269
-1,255
-2,197
-1,873
346
2030
2,023,022
2035
2,050,791
2040
2,076,018
Current Standard
6
51
104
165
-7
42
90
878,986
10
20
26
836,692
3
13
46
863,308
Current Standard
980 -961
1,013
1,780
230
423
90
232
264
41
-3,413
-1,052
697
2,189
2,991
5-15
-------
The impacts of the scenarios with more stringent ozone standards were larger in the
forestry sector, with bigger increases in consumer surplus and greater declines in producer
surplus. Table 5-11 presents the model results of the welfare analysis in the forestry sector.
Table 5-11. Consumer and Producer Surplus in Forestry, Million 2010 U.S. Dollars
Product
Consumer
surplus
Producer
surplus
Policy
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010
721,339
7
44
86
93,322
-11
-41
-136
2015
793,234
Change with
31
48
187
121,476
Change with
-7
20
-48
2020
809,271
Respect to
118
360
694
153,997
Respect to
-141
-503
-892
2025
826,375
2030
875,
620
2035
894,705
2040
934,882
Current Standard
105
202
224
146,275
145,
2
688
734
913
36
56
91
146,115
597
712
779
133,132
Current Standard
-161
-178
-37
15
-880
-786
-46
55
156
-839
-858
-766
Because of the complex dynamics of the agriculture and forestry sectors and variability in
welfare impacts over time, it is often helpful to summarize the impacts in terms of annualized
values. Table 5-12 summarizes the annualized impacts of alternative ozone standards on
consumer and producer surplus in the agricultural and forestry sectors for 2010-2044.23 The
impacts of alternative standards on consumer surplus are positive for each of the tighter
standards for both agricultural and forestry sectors, with the benefits increasing with more
stringent requirements. For producer surplus, on the other hand, annualized impacts are negative
for the 15 ppm-hr and 11 ppm-hr scenarios, but a large positive value for the 7 ppm-hr case. For
the forestry sector, consumer surplus changes are positive for each scenario and increasing with
stringency, while changes in producer surplus are negative for all cases, becoming more negative
as stringency is increased. Overall, total surplus across both sectors decreases in the 15 ppm-hr
and 11 ppm-hr scenarios but increases substantially in the 7 ppm-hr case.
23 Each model period in FASOMGHG is representative of the 5-year period starting with that year, so results
reported for 2040 are representative of 2040-2044. Thus, we use values through 2044 in the annualization
calculations.
5-16
-------
Table 5-12. Annualized Changes in Consumer and Producer Surplus in Agriculture and
Forestry, 2010-2044, Million 2010 U.S. Dollars (4% Discount Rate)
Product Policy
Consumer surplus 75 ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Producer surplus 75 ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Total surplus 75 ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Agriculture
NA
Change with Respect to
4.5
25.4
36.7
NA
Change with Respect to
-4.7
-4.6
194.4
NA
-0.2
20.8
231.1
Forestry
NA
Current Standard
88.1
236.9
344.0
NA
Current Standard
-112.2
-264.4
-318.4
NA
-24.2
-27.5
25.6
Total
NA
92.5
262.3
380.7
NA
-116.9
-269.0
-124.0
NA
-24.4
-6.7
256.7
5.2.3 Greenhouse Mitigation Potential
The capacity for both the agricultural and forest sectors to sequester carbon is enhanced
in each of the alternative standard cases, with increasing magnitude as policy stringency is
increased. Although FASOMGHG projects fewer acres of forestland and total cropland, the
accelerated storage of carbon in trees and forestland and cropland soils outweighs any decline
from reductions in covered area. Carbon storage in both sectors is consistently higher in the
alternative standard cases, with the gap widening over time (see Figure 5-1 for change in forest
carbon stock). By 2040, the agricultural sector sequestered 0.1% more carbon under the
alternative standard cases and the forestry sector up to 2% more, resulting in gains of more than
1,600 million metric tons CCh equivalent (MMtCChe). Table 5-13 presents carbon sequestration
projections under the current standard and changes under each alternative standard.24 Note that
negative values in the row for the current standard indicate sequestration or carbon storage.
Negative values in the change rows indicate that the alternative standard stores more carbon than
the current standard (and vice versa for positive changes).
24 These are total stocks of net GHG emissions over time, not annual emissions. If the total stock of GHG is
becoming more negative over time, more net sequestration is taking place than emissions. If the total stock of
GHG is becoming less negative or positive over time, emissions are greater than the increase in sequestration.
5-17
-------
Notice that for the agricultural sector, the overall stock of net GHG would decrease over
time in the baseline because cropping activities involve fertilizer and chemical usage, fossil
fuels, running machinery, livestock emissions from enteric fermentation and manure
management, and so forth—all these GHG emissions are being released each year, while soil
carbon sequestration moves toward equilibrium within 25 years of a change in tillage. As soil
carbon reaches equilibrium, little additional sequestration is taking place each year but annual
emissions from other sources continue. Thus, over time, the annual emissions tend to outweigh
the increase in carbon stocked in agricultural soils, and net stock of GHG tends to become less
negative and eventually positive relative to the starting point.25
(70,000)
(75,000)
(80,000)
UP
8
(85,000)
(90,000)
(95,000)
— — Base U 75ppb n 15ppm " llppm
-7ppm
Figure 5-1. Carbon Storage in Forestry Sector, MMtCChe
25 This change is consistent with the fact that U.S. agriculture is a net source of emissions on an annual basis. The
value of the total GHG stock associated with agriculture is starting at a negative value because of the
FASOMGHG convention of accounting for total carbon sequestration present in agricultural soils in the first year
of the model run. A large stock of carbon is sequestered, but it does not increase by much over time.
5-18
-------
Table 5-13. Carbon Storage, MMtCO2e
Product
Agriculture
Forestry
Policy
75 ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75 ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010
-18,748
Change with
0
-2
-3
-74,679
Change with
-1
-19
-50
2020
-15,363
Respect to Current
-1
-5
-4
-79,171
Respect to Current
0
-103
-305
2030
-12,002
Standard
-1
-6
-6
-84,863
Standard
-16
-312
-832
2040
-8,469
-4
-10
-9
-89,184
-13
-593
-1,602
Changes in forestry sector carbon sequestration are largely driven by changes in forest
management, which include the increases in tree yield in the lower ozone environments. The
increased sequestration in this category outweighs losses in sequestration in the other major
forestry categories: afforestation and forest soil. Table 5-13 presents the detailed changes in
forestry carbon sequestration.
Table 5-13. Forestry Carbon Sequestration, MMtCChe
Product
Afforestation, trees
Afforestation, soils
Policy
75 ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75 ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010
-696
Change with
0
0
0
-691
Change with
0
1
1
2020
-1,516
Respect to Current
0
0
22
-538
Respect to Current
0
1
6
2030
-800
Standard
1
6
6
-373
Standard
2
5
8
2040
-1,054
2
7
7
-362
2
5
8
(continued)
5-19
-------
Table 5-13. Forestry Carbon Sequestration, MMtCOie (continued)
Product
Forest management
Forest soils
Policy
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010 2020 2030
-41,337 -43,022 -47,266
Change with Respect to Current Standard
0 0 -12
-17 -103 -305
-44 -335 -825
-28,194 -27,566 -27,011
Change with Respect to Current Standard
00-5
4 7 -10
5 17 -9
2040
-48,770
-14
-589
-1,596
-26,774
1
-6
-5
Summary
Impacts to both sectors generally mirror one another, although they are more prominent
in the forestry sector. Not only are tree species more responsive to changes in ozone, but the
largest reductions to meet the alternative standards will occur in regions with large forestry
sectors: South Central, Southeast, and Rocky Mountains. Reductions in agricultural regions are
comparatively moderate. Productivity of both crops and forests is projected to increase at each of
the alternative standard levels. This increase in supply resulted in decreased prices for forest
products and agricultural commodities, which benefits consumer welfare while reducing
producer welfare. Unless there are significant changes to wood products markets in particular,
producers will be forced to sell at reduced prices to absorb the increased supply. Nonetheless,
gains to consumers become increasingly large with more stringent ozone standards and, unlike
the 15 ppm-hr and 11 ppm-hr scenarios, we observe agricultural producer surplus gains in the
7 ppm-hr scenario. Gains to agricultural producers in the most stringent case are associated with
a decline in forestry returns that results in a net shift in land use toward agriculture. Gains to
consumers and agricultural producers under the 7 ppm-hr scenario are large enough to more than
offset producer surplus losses in the forestry sector, yielding an estimated annualized net benefit
of $256.7 million.
Increased productivity is also projected to affect land use both within and between the
agricultural and forest sectors. Within sectors, acreage is projected to shift from crops and tree
species that are more sensitive to ozone to those that are less sensitive because productivity in the
5-20
-------
former will be more substantially affected by reductions in ozone concentrations. For ozone-
sensitive crops and species, producers are projected to require less land to produce at the same or
higher levels. Forest acreage in particular is projected to decline sharply, driven by declines in
both reforestation and afforestation.
Despite reductions in crop and forest area, carbon sequestration is expected to increase
over time, led almost entirely by increased forest sequestration. Although there is less
reforestation and afforestation and lower sequestration in new inventory, the change is a result of
existing inventories becoming so much larger as trees grow faster. Lower sequestration in new
inventory is outweighed by increased inventory in standing forests, represented in the model as a
change in forest management.
Increased stringency in the ozone standard generally produces larger impacts on all of the
model outputs. However, the additional impact of moving from the current standard to
15 ppm-hr or 11 ppm-hr to 7 ppm-hr was sometimes marginal compared with changes occurring
between 15 ppm-hr and 11 ppm-hr. In particular, the impacts to the forestry sector, most notably
in forest inventories and the forest sector welfare analysis, tended to increase at a decreasing rate
after meeting the 11 ppm-hr standard.
The model results are subject to several limitations: First, the ozone concentration
response functions applied to crops and trees were using "median" parameters in Lehrer et al.
(2007)—the RYLs and RYGs calculated are thus "median" ones; second, the use of crop proxy
mapping and the forest-type mapping due to incomplete data specified in Section 4 adds to the
uncertainty of these model results; third, the potential changes in tree species mixes within forest
types due to ground ozone-level changes were not considered; and last, the international trade
component in FASOMGHG that assumes USDA-based future projections under current
conditions may present another uncertainty for the model results, especially when soybeans and
wheat are among the major crop commodities for U.S. exports and have relatively large
responses to changed ozone environments.
5-21
-------
SECTION 6
COUNTY-LEVEL AGRICULTURAL WELFARE
In addition to information on the estimated net benefits at the national level, it is
important to consider the regional distribution of benefits. In this study, we focused on
approximating the distribution of changes in agricultural producer surplus for major crops at the
county level. The decision to focus on agricultural producer surplus from crop production was
based on data availability, heterogeneity of impacts across crops and regions, and available
resources for conducting analyses. Impacts on consumer surplus are expected to be positive in all
regions, whereas impacts on forest producer surplus are generally expected to be negative,
although regions with the largest increases in productivity may experience net gains.
Calculating County-Level Agricultural Welfare
As introduced earlier, the agricultural component of FASOMGHG consists of 63
production regions (subregions) and 10 market regions (regions) simulating the U.S. agricultural
sector. To gain an understanding of the scenario effects at the county level, we employ a
downscaling calculation procedure to further disaggregate the 63-subregion simulation results.
The data on county-level crop production from the 2007 USDA Census of Agriculture
(Ag Census) are used to generate the county-level cropping patterns that reflect the production
differences between counties. The allocation of production across counties within a
FASOMGHG subregion is held constant under alternative scenarios. Specifically, for a select
crop, the county-level production percentage shares of that crop in a FASOMGHG subregion
under new market equilibriums are assumed to equal the percentage shares of the 2007 Ag
Census county-level production with respect to their FASOMGHG subregion's 2007 Ag Census
production. In mathematical terms, the county-level agricultural welfare calculation involves
Formulas (6.1) through (6.3)—from sector to region to county, they are as follows:
Ag Select Crop WelfareSubregion =
^•select crops Cr°P Production (crop) * Price (crop)
Scrop & livestock commodities Commodity Production (commodity-) * Price (commodity)
Ag Producer WelfareSubregion (6'1}
where the select crops' portion in agricultural producer welfare at FASOMGHG subregion level
is extracted. Note that the subregion-level ratio ———^^—ro would change under
Crop 81 Livestock Production * Price
different ozone environments, because new equilibriums of production and price levels could
6-1
-------
result under alternative ozone scenarios. Figure 6-1 illustrates how the select crops' portion is
defined over agricultural commodities produced in FASOMGHG subregions.
Figure 6-1. Relationship between Select Crops Used for Analysis and Total Agricultural
Production
. „ . _ ... .„ Select Crops'Gross Revenue (County)
Aq Select Crop Welt are ..„,,„*„ = ; *
county ZcountiesinSubRegionSelect Crops'Gross Revenue (County)
Ag Select Crop WelfareSubregion (6.2)
where the county-level agricultural welfare is thus obtained. Note that the county-level ratio
Select Crops'Gross Revenue (County) , , i-rv • 1 1,1,1
—; ; :— may also vary under different scenarios, because both the
2, Select Crops Gross Revenue (Counties)
production levels and prices could change under new ozone environments. Figure 6-2 depicts
how a county's portion is defined over the counties within a FASOMGHG subregion.
Figure 6-2. County Share of Total FASOMGHG Subregion Production Is Used to
Calculate County Gross Revenue and Production
Select Crop Productioncounty USDA
SelectCropProductioncountv = -—
county ZcountiesinSubRegionSelectCropProductwncountyUSDA
SeleCtCrOpProduCtiOUpAsoM Subregion (6.3)
6-2
-------
where the county-level production estimates for select crops are derived based on both the 2007
Ag Census data and the FASOMGHG simulation results. The calculation of the county share of
the total value across all counties within a FASOMGHG subregion as depicted in Figure 6-2 also
applies here.
In the actual calculation procedure, Formula (6.3) would be carried out first, followed by
Formulas (6.2) and (6.1).
Notice that the agricultural producer welfare at FASOMGHG subregion level in
Formula (6.1)—from which the select crops' portion is extracted—is defined as the area above
the supply curve(s) of inputs and endowments involved in agricultural commodities production
and below the equilibrium price(s) of the commodities, as shown in Figure 6-3.
Supply:
• Cropland/Pastureland
• I rri gat ion Water
• Hired Labor
• Other Inputs
Figure 6-3. Area of Producer Surplus
In addition, a mapping of USD A crops to FASOMGHG crops is involved in
Formula (6.3), as presented in Table 6-1.
The following FASOMGHG crops are not included in the county-level agricultural
welfare calculation because they lack 2007 Ag Census county-level production data: silage,
potato, tomato (fresh and processed), orange (fresh and processed; 75, 90, and 85 Ib boxes),
grapefruit (fresh and processed; 67, 85, and 80 Ib boxes), sweet sorghum, hybrid poplar, willow,
switchgrass, and crop residues.
6-3
-------
Table 6-1. Mapping of USDA Crops to FASOMGHG Crops
FASOMGHG Crop USDA 2007 Census Crop
Canola
Corn
Durum wheat
Hard red spring wheat
Hard red winter wheat
Soft red winter wheat
Soft white wheat
Hay
Oats
Rice
Sorghum
Soybeans
Spring barley
Winter barley
Sugar beet
Sugarcane
Canola
Corn
Durum wheat
Spring wheat
Winter wheat
Winter wheat
Wheat excluding spring, winter, and durum wheat
Hay
Oats
Rice
Sorghum
Soybeans
Barley
Barley
Sugar beet
Sugarcane
Changes in County-Level Agricultural Welfare: Alternative Scenarios versus
Current Standard (75 ppb)
When comparing the W126 ozone values under alternative standards and the current
standard 75 ppb scenario (as presented in Section 4), one can notice the following:
1. Under 15 ppm-hr, slight further reductions of ground ozone levels occurred in the
southern Rocky Mountains region.
2. Under 11 ppm-hr, larger further reductions of ground ozone levels occurred in the
Rocky Mountains and Pacific Southwest (southern California) regions; noticeable
further reductions occurred in the southern Corn Belt and the northern South Central
regions.
3. Under the most stringent 7 ppm-hr standard, significant further ground ozone
reductions occurred in the mid and southern Rocky Mountains regions and the Pacific
Southwest region. Noticeable reductions also occurred in the southern Corn Belt and
Great Plains regions. The South Central and Southeast regions experienced slight
ozone reductions.
6-4
-------
Taking those three observations into consideration, one can expect that the production of
wheat crops, which have relatively larger RYGs under reduced ozone environments, would
expand in one of its major production regions—the Rocky Mountains region, which experiences
significant ground ozone reductions in its southern half The county-level welfare increases in the
Rocky Mountains region, shown in Figure 6-4, correspond to this wheat production expansion.
The effects of this Rocky Mountains wheat expansion has implications for other wheat-
producing regions—such as the Lake States region, where wheat production decreases.
Compared with the Rocky Mountains region, producing wheat in the Lake States region becomes
less efficient in terms of enhancing producer and consumer welfare at the national level.
The Lake States region would also see production changes for other crops—because the
wheat production contraction implies more room for other alternatives—in particular, the highly
profitable ones. Soybean production in the Lake States region thus expanded, and this expansion
induces regional shifts of soybean production at the national level—the Great Plains and the
Corn Belt regions experience soybean production decreases. Moreover, the ripple effects on the
Great Plains region include larger corn production as well, as part of its soybean production
shifts to the Lake States region.
The consequences of the regional production shifts, reflected in county-level welfare, are
the increases in part of the Lake States and Rocky Mountains regions, as well as the decreases in
some of the Corn Belt and Great Plains counties—for earlier periods in 2010 under the
15 ppm-hr scenario.
Under the 11 ppm-hr and 7 ppm-hr scenarios in which the southern Corn Belt and
northern South Central regions also see noticeable ground ozone reductions, the regional shifts
that were principally propelled by the Rocky Mountains wheat changes under 15 ppm-hr would
now have to accommodate new changes from corn and soybean production in the Corn Belt
region—soybean is another crop that has large RYGs under reduced-ozone environments. In
addition, as Rocky Mountains ozone reductions get even greater, cotton production and revenues
in the South would be also influenced—the increased cotton supply has led to a price decrease,
and the cotton revenues in the South Central region have thus decreased.
Integrating the major changes induced by Corn Belt soybean and Rocky Mountains
cotton and wheat production stated previously, one would then see welfare increases in the
majority of southern Corn Belt counties and welfare decreases along the Mississippi River region
in the South Central region.
6-5
-------
Legend
FASOM Region
Figure 6-4. Changes in Crop Producer Welfare under Alternative Standards with
Respect to 75 ppb, 2010, Thousand 2010 U.S. Dollars
6-6
-------
When comparing the 11 ppm-hr and 7 ppm-hr scenarios, one can notice that the 7 ppm-hr
scenario effects on county-level crop producer welfare are essentially an intensification of the
11 ppm-hr scenario effects.
Figure 6-5 shows that by 2020, the Rocky Mountains effects are largely contained in the
region—the ripple effects on other regions, including Lake States, Corn Belt, and Great Plains,
are quite limited. This is because, by 2020, as the Renewable Fuel Standards (RFS2) become
fully phased in, the production capacity of corn in the Corn Belt and Lake States regions
becomes much more utilized—as does the production capacity of soybeans in these regions. The
further ground-level ozone reduction under the 15 ppm-hr scenario thus did not lead to major
nationwide changes in 2020.
Nonetheless, under the 11 ppm-hr and 7 ppm-hr scenarios in which the Corn Belt and
South Central regions experience noticeable further ozone reductions, the regional distributional
effects of the ozone standards start to become visible. In both cases, soybean production
expanded in the Corn Belt and South Central regions, contributing to county-level welfare
increases in these regions.
The ripple effects of Corn Belt and South Central soybean production expansion also
impact the Great Plains region, where less soybean production occurred and more production of
other grain crops took place—in particular, barley and wheat. In turn, the Great Plains changes
induced decreases of barley and wheat production in the Rocky Mountains region, the southern
area of which incurred even greater cotton production. The greater cotton production is reflected
in county-level welfare; the northern Rocky Mountains counties see welfare decreases, whereas
the southern Rocky Mountains counties see welfare increases.
The 7 ppm-hr scenario effects are generally intensified relative to the 11 ppm-hr scenario
effects again for 2020, in terms of county-level welfare changes. However, under this case, the
further enhanced soybean yields in the Corn Belt region have led to reduced corn production in
that region, despite the strong effects of RFS2 on corn production. This change leads to welfare
decreases in corn-producing counties, as shown in Figure 6-5.
6-7
-------
_egend
| FASOM Region
FASOM Subregion
o
o
Figure 6-5. Changes in Crop Producer Welfare under Alternative Standards with
Respect to 75 ppb, 2020, Thousand 2010 U.S. Dollars
6-8
-------
Legend
| FASOM Region
FASOM Subregion
o
o
1%
>'<£
&>>
&%
^1
$$
%£
v-S
*,*,
•^-5-
>/<&
-------
As shown in Figure 6-6, by 2030, in addition to the RFS2 effects and the ozone standard-
induced RYGs, the implications of land use changes also occurred for crop production—
Section 5 detailed that cropland pasture would expand under alternative standard scenarios
compared with the 75 ppb scenario. Consider, as the yield increase effects accumulate over time
for the trees, that less land would be needed for the forestry industry, assuming that the market
situation remains generally unchanged. Given that crop production also benefits from reduced-
ozone environments and, thus, less land would be needed for cropping, these reduced demands
for land would result in more land available for grazing and would in turn induce greater demand
for feed crops as livestock herds increase.
The South Central region is among the areas experiencing large RYGs for tree growth,
and consequently, it sees more land becoming available for grazing use. The pasture land
increase also induced greater corn production in this region, and the ripple effects reached out to
the Lake States region, reducing corn production there.
Other things being equal, the Lake States corn production decrease would imply welfare
decreases in this region. However, the opposite occurred for the Lake States counties, because by
2030, the increases in overall agricultural welfare—due to livestock production expansion as
presented in Section 5—outweigh the decreases in crop production; hence, welfare increases still
occurred for the Lake States region.
When the Corn Belt and South Central soybean RYG effects, along with the Rocky
Mountains cotton RYG effects, come in under the 11 ppm-hr scenario, greater soybean
production would occur in the South Central region. Because the land use change effects
discussed previously would become further intensified under the 11 ppm-hr scenario, corn
production in the South Central region would also expand. These increases, however, lead to
reduction in production of one of the most valuable crops in this region: cotton. The cotton price
decreases due to overall increased cotton supply further decreased the cotton revenues in this
region, and this cotton effect outweighs the corn and soybean effects, leading to welfare
decreases along the Mississippi River region.
Under the 7 ppm-hr scenario, soybean production in the southern Corn Belt region
increases further, contributing to the welfare increases in the region's southern area and welfare
decreases in the region's northern area.
6-10
-------
By 2040 (see Figure 6-7), the effects of land use changes start to appear in the Great
Plains region, where more land is used for grazing. This also raises the demand for feed crops—
corn production expands in this region and the adjacent Lake States region. The South Central
region correspondingly reduced its corn production—the land use effects have already been
applied in the South Central region in 2030. Notice that the corn production increases in the
Great Plains and Lake States regions led to decreases in production of other crops, especially
wheat. These wheat production reductions led to net welfare decreases in counties in Great
Plains and Lake States regions.
Under the more stringent 11 ppm-hr and 7 ppm-hr scenarios, the effects of land use
changes on the Great Plains region would further increase, resulting in more corn production in
the Great Plains region, meanwhile reducing the soybean and wheat production in this region.
The ripple effects of these Great Plains changes result in less corn production in the southern
regions and the Lake States regions for this time and in a shift of more soybean production to the
Corn Belt region. Hence, under the more stringent 11 ppm-hr and 7 ppm-hr scenarios, there
would be considerable welfare increases in the southern Corn Belt counties and welfare
decreases in the Mississippi River region. The effects of heightened soybean RYGs were thus
intensified in the southern Corn Belt region and outweighed by the land use effects in the South
Central region.
6-11
-------
_egend
] FASOM Region
_ FASOM Subregion
?
v%
i5> 'J»
" *5i«
>:••€
vt-%
^S
II
?:s
^••^
5
o
*>•«
Figure 6-7. Changes in Crop Producer Welfare under Alternative Standards with
Respect to 75 ppb, 2040, Thousand 2010 U.S. Dollars
6-12
-------
Summary
Table 6-2 lists the driving factors causing crop producer welfare increases and decreases
across the counties discussed previously, by scenario and period modeled.
Table 6-2. Driving Factors Behind County-Level Welfare Changes
ScenarioVPeriod 2010
15ppm-hrvs. • Cotton and
75 OOY> wheat RYGs in
vv RM
1 1 ppm-hr vs. 7 ppb • Cotton and
wheat RYGs in
RM
• Soybean and
cotton RYGs
across southern
regions
7 ppm-hr vs. 75 ppb • Intensified
cotton and wheat
RYGs in RM
• Intensified
soybean and
cotton RYGs in
the South
2020
• Cotton and
wheat RYGs in
RM
• RFS2 policy
effects
• Cotton and
wheat RYGs in
RM
• Soybean and
cotton RYGs
across southern
regions
• RFS2 policy
effects
• Intensified
cotton and wheat
RYGs in RM
• Intensified
soybean and
cotton RYGs in
the South
• RFS2 policy
effects
2030
• Cotton and
wheat RYGs in
RM
• Land use effects
inSC
• Cotton and
wheat RYGs in
RM
• Soybean and
cotton RYGs
across southern
regions
• Land use effects
inSC
• Intensified
cotton and wheat
RYGs in RM
• Intensified
soybean and
cotton RYGs in
the South
• Land use effects
inSC
2040
• Cotton and
wheat RYGs in
RM
• Land use effects
inGP
• Cotton and
wheat RYGs in
RM
• Soybean and
cotton RYGs
across southern
regions
• Land use effects
inGP
• Intensified
cotton and wheat
RYGs in RM
• Intensified
soybean and
cotton RYGs in
the South
• Land use effects
inGP
To summarize, the crop producers' welfare in the southern Corn Belt region and the
southern Rocky Mountains region would generally experience increases in most policy cases and
across periods, whereas the counties along the Mississippi River region would experience
welfare decreases as ozone standards get more stringent. The counties in the Great Plains and the
Lake States regions, however, would experience alternate increases and decreases across periods.
6-13
-------
SECTION 7
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7-5
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APPENDIX A
DATA INPUTS: FOCUSING ON COMPARISON OF ALTERNATIVE OZONE
STANDARDS WITH CURRENT CONDITIONS
Our primary focus in the analyses conducted for this report involved comparisons
between impacts on the forestry and agricultural sectors under three ozone standards that are
more stringent than the current standard. Those inputs and results are presented in Sections 4-6
of this report. In the appendixes, we supplement the information with additional tables, figures,
and maps that compare all four scenarios where ozone standards are achieved and current
conditions. Because current ozone concentrations are well above the current standards in many
U.S. regions, meeting any of the four ozone standards examined would result in a large reduction
in ambient ozone concentrations. The reductions in concentrations associated with moving from
current conditions to any of the ozone standards are considerably larger than the incremental
reductions from moving between the standards examined.
In Appendix A, we present the data inputs used for assessing the impacts of achieving
each of the four ozone standards considered relative to current conditions. This appendix
includes a map of the change in ozone concentrations relative to current conditions, along with
maps, tables, and figures showing the relative yield losses (RYLs) and relative yield gains
(RYGs) by major crop for each FASOMGHG subregion. We also include maps breaking out
county-level changes in yields, These maps primarily serve to highlight the counties that grow a
given crop and are affected by changes in ozone concentrations.
A.I Ambient Ozone Concentration
Figure A-l presents the changes in W126 ozone values for alternative ozone standards
with respect to current conditions. The regional patterns of ozone reductions are virtually the
same across the 75 ppb, 15 ppm-hr, 11 ppm-hr, and 7 ppm-hr scenarios, although the magnitude
of regional reductions increases as the standard is tightened. For a closer examination of changes
arising from the alternative ozone standards (15 ppm-hr, 11 ppm-hr, and 7 ppm-hr) with respect
to the current 75 ppb standard, see Section 4.
A-l
-------
Legend
| [ C.SSOM Region
~7] FASOM SuDregion
Reduction from Current Conditions
Figure A-l. Ozone Reduction with Respect to Current Conditions under Alternative
Scenarios
Table A-l summarizes the information in Figure A-l. It presents the grouping of
FASOMGHG regions into three qualitative categories based on the relative level ozone
reductions under the current standard versus current conditions. The table also lists the major
crops grown in each FASOMGHG agricultural region. The underscored crops are the species
that are more susceptible to ground-level ozone effects than other crops, as their RYGs will
demonstrate.
Table A-l also suggests that the agricultural production in the Pacific Southwest, Rocky
Mountains, South Central, and Southeast regions are likely to experience the largest changes
under ozone control because they would have significant ozone reductions, and the most
"sensitive" crop species are present in these regions. Sizable effects would also be expected in
the Corn Belt region, given the level of ozone reductions and the exceptionally large crop
production in that region.
A-2
-------
Table A-l. General Ozone Reduction Levels with Respect to Current Conditions by
FASOMGHG Regions
Ozone Reduction
High
High
High
High
Medium
Medium
Medium
Low
Low
Low
FASOMGHG Region
PSW
SE
sc
RM
CB
sw
NE
GP
LS
PNWE
Major Crops"
Cotton. Rice, Wheat
Cotton. Corn, Soybeans. Wheat
Cotton. Corn, Rice, Soybeans. Wheat
Barley, Cotton. Wheat
Corn, Soybeans
Cotton. Sorghum, Wheat
Corn, Soybeans. Wheat
Corn, Sorghum, Soybeans. Wheat
Corn, Soybeans. Wheat
Barley, Wheat
a Underscored crops are the species that are more susceptible to ground-level ozone effects than other crops.
Note: CB = Corn Belt; GP = Great Plains; LS = Lake States; NE = Northeast; PNWE = Pacific Northwest—East
side; PSW = Pacific Southwest; RM = Rocky Mountains; SC = South Central; SE = Southeast; SW = Southwest.
A.2 Summarized Relative Yield Losses and Gains for Crops and Forest Types
This subsection presents data on RYLs and RYGs for crops and trees. See Section 3 for a
discussion of these measures and the equations used to calculate them.
Figures A-2 through A-7 show the subregion-level RYL estimates for major crops under
the current ambient ozone concentrations. These RYLs were calculated using the cropland ozone
surfaces. Table 3-3 in Section 3 presented the mapping of proxy crops to FASOMGHG crops,
and Table 2-7 in Section 2 provided definitions of FASOMGHG regions. Based on the
relationships between ozone concentrations and yield reductions applied (see Section 3.2), there
are currently substantial yield reductions due to ground-level ozone.
A-3
-------
Legend
] FASOM Rt«»A '] FASOM Suomgwn
Corn Relative Y,eW Los* W126 Current Conditrani
| 12- IS
,' n
Figure A-2. Map of Corn RYLs under Current Conditions
Legend
] FASOM Regan FASOM Sut-c a«i
Soybeans Relative Ywld Loss W126 Currenl Condili
S 11% -
2 MU.
OMS
73%
Figure A-3. Map of Soybean RYLs under Current Conditions
A-4
-------
Figure A-4. Map of Wheat RYLs under Current Conditions
Figure A-5. Map of Sorghum RYLs under Current Conditions
A-5
-------
Legend
3 FASOM Region | FASOM Subregion
Cotton Relative Yield Loss W126 Current Conditions
| 5.7B% - 13.86%
| 3.66% - 5.77%
[ 2.97%-3 85%
1,19% -2.96%
0.61%-1,16%
Missing Cotton
| More than 26
| 16-26
| 12-15
7-11
Less than 7
Figure A-6. Map of Cotton RYLs under Current Conditions
Legend
] FASOM Region FASOM Subregion
Potatoes Relative Yield Loss W126 Current Conditions
| 10 M% - M 1«% HI More lh
| 7,B4% • 10,37% ^B 16 - M
|S.S5%-7B3S Hi ^-15
I 3.65% - 5.54% jj^H 7 - 11
i 67% - 3.64% Leu thi
MtEsmg Potatoes
Figure A-7. Map of Potato RYLs under Current Conditions
As noted earlier, to implement the examination of scenarios with alternative ozone
impacts, we use the RYL differences to calculate RYGs. Among the major crops, winter wheat
and soybeans are more sensitive to ambient ozone concentration levels than corn and sorghum—
as indicated in Figures A-2 through A-7—which implies that they would benefit more from
A-6
-------
ozone control in terms of RYGs. Correspondingly, FASOMGHG crops mapped to winter wheat
or soybeans as proxy crops would have larger RYGs than other crops in general.
For RYLs under alternative ozone levels, one can expect that, in general, the yield losses
would become much smaller compared with the current ozone standard, because of the
substantial ozone reductions associated with meeting that standard (as presented in Figure A-l).
This is shown to be the case in the primary analyses presented in the main body of this report.
The magnitude of RYGs essentially depends on two factors: (1) the sensitivity of the
(proxy) crop to its ambient ozone concentration level, and (2) the difference between the ozone
levels being compared. In this appendix, we focus on comparing current conditions and the
"alternative" ozone levels defined by the standards considered. For FASOMGHG subregions
such as Minnesota and North Dakota, the RYG estimates are virtually zero because the room for
air quality improvement in these subregions is limited.
Figures A-8 through A-13 show the RYGs for major crops under alternative scenarios
relative to current conditions. RYGs for California are generally much larger than in other
regions, which largely reflects the significant room for improvement in ozone concentrations in
the Pacific Southwest region. Some major crops, including corn and sorghum, are estimated to
incur less positive effects from the improved ozone environments because of their relatively
moderate sensitivities to ambient ozone concentration levels. Note that in many cases, subregions
that show no change in yield for a given crop have no production of that crop in that subregion in
FASOMGHG. For instance, soybeans are relatively sensitive to ozone concentrations and there
are large reductions in ozone in California, but there are no impacts on soybean yields in that
region because no soybeans are produced in California in FASOMGHG.
A-7
-------
Figure A-8. Map of Corn RYGs under Alternative Scenarios
Figure A-9. Map of Soybean RYGs under Alternative Scenarios
A-8
-------
„<• ,<- f f f ,<•.<* f f f f f
-------
Figure A-12. Map of Cotton RYGs under Alternative Scenarios
f f f -F f f f -T f f
jr f f f f f f ,f f j-\f
Figure A-13. Map of Potato RYGs under Alternative Scenarios
The region-specific RYLs for softwood and hardwood forest types from which the RYGs
were derived are presented in Figures A-14 and A-15, respectively. Black cherry in the South is
A-10
-------
the most sensitive of the tree species examined. FASOMGHG assumes that there is no
substantial forest products production in the Great Plains or Southwest regions, so forests in
those regions are not modeled.
Pacific Northwest West
Legend
^ FASOM Region W126 Current Conditions
Softwood Relative Yield Loss ^H More than 26
| 5.08%-6.79%
| 2.18%-5.07%
0.7%-1.14%
0.69%
Missing Softwood
16-26
12-15
7-11
Less than 7
Figure A-14. Map of Softwood RYLs under Current Conditions
Note: RYL displayed in the Pacific Northwest—West region is for softwoods excluding Douglas fir. Douglas fir is
estimated to have no RYL associated with current ozone conditions.
A-ll
-------
Legend
^ FASOM Region W126 Current Conditions
Hardwood Relative Yield Loss I I More than 26
| 7.69%-9.18%
5,44% - 7,68%
3.99% - 5.43%
1.36% -3 98%
0.83%-1.35%
Missing Hardwood
16-26
12-15
7-11
Less 111 an 7
Figure A-15. Map of Hardwood RYLs under Current Conditions
Note: RYL displayed in the Southeast and South Central regions is an average of upland and bottomland hardwood
RYL (15.5% and 1.08%, respectively, in South Central and 17.13% and 1.23%, respectively, in Southeast). These
forest types are aggregated within FASOMGHG in other regions, consistent with the level of detail available from
U.S. Forest Service data.
Figures A-16 and A-17 present the derived RYG estimates for FASOMGHG forest types
relative to current conditions. The upland hardwood forests in the South Central and Southeast
regions have the largest RYGs among the various forest types. In addition, softwood and
hardwood forests in the Pacific Southwest region would incur relatively larger yield increases
than other forest types.
As for the agricultural sector, the differences between RYGs under alternative scenarios
are generally small. See the results presented in Sections 5 and 6 of the main body of this report
for more information comparing impacts across scenarios with reductions in ozone
concentrations.
A-12
-------
Figure A-16. Map of Softwood RYGs under Alternative Scenarios
Figure A-17. Map of Hardwood RYGs under Alternative Scenarios
A-13
-------
A.3 Detailed Data on RYLs and RYGs for Crops and Forest Types
Tables A-2 through A-13 display the RYLs and RYGs for major crops. The bar charts in
Figures A-18 through A-29 accompany the tables and present additional visuals for the RYLs
and RYGs.
A-14
-------
Table A-2. Corn RYLs under Alternative Scenarios (% change)
FASOMGHG Subregion
Alabama
Arizona
Arkansas
CaliforniaN
CaliforniaS
Colorado
Connecticut
Delaware
Florida
Georgia
Idaho
IllinoisN
IllinoisS
IndianaN
IndianaS
lowaW
lowaCent
lowaNE
lowaS
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Current
Conditions
0.31
0.41
0.25
0.74
0.88
0.45
0.17
0.56
0.14
0.16
0.30
0.05
0.15
0.12
0.23
0.02
0.02
0.03
0.03
0.17
0.32
0.07
0.01
0.68
0.10
0.10
0.02
0.14
0.17
0.05
0.06
75ppb
0.00
0.03
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
15 ppm-hr
0.00
0.01
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
11 ppm-hr
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
7 ppm-hr
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
(continued)
A-15
-------
Table A-2. Corn RYLs under Alternative Scenarios (% change) (continued)
FASOMGHG Subregion
Nevada
NewHampshire
NewJersey
NewMexico
NewYork
NorthCarolina
NorthDakota
OhioNW
OhioS
OhioNE
Oklahoma
Oregon
Pennsylvania
Rhodelsland
SouthCarolina
SouthDakota
Tennessee
TxHiPlains
TxRolingPl
TxCntBlack
TxEast
TxEdplat
TxCoastBe
TxSouth
TxTranspec
Utah
Vermont
Virginia
Washington
WestVirginia
Wisconsin
Wyoming
Current
Conditions
0.42
0.02
0.48
0.26
0.07
0.25
0.01
0.20
0.34
0.23
0.20
0.04
0.26
0.17
0.18
0.02
0.39
0.22
0.18
0.10
0.11
0.07
0.04
0.01
0.18
0.67
0.02
0.30
0.01
0.16
0.04
0.39
75ppb
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.01
15 ppm-hr
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.01
11 ppm-hr
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
7 ppm-hr
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
A-16
-------
Wyoming
WestVirginia
Virginia
Utah
TxSouth
TxEdplat
TxCntBlack '_
TxHiPlains '_
SouthDakota
Rhodelsland
Oregon
OhioNE
OhioNW
NorthCarolina
NewMexico
NewHampshire "
Nebraska
Missouri
Minnesota ~
Massachusetts ~_
Maine
Kentucky
lowaS
lowaCent
IndianaS
IllinoisS
Idaho
Florida
Connecticut
CaliforniaS '_
Arkansas
Alabama
^r
E
: — —
•
^
-
t
:
-
r
—
— E
—
—
-
—
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Figure A-18. Corn RYLs under Current Conditions (% change)
A-17
-------
Table A-3. Corn RYGs under Alternative Scenarios (% change)
FASOMGHG Subregion
Alabama
Arizona
Arkansas
CaliforniaN
CaliforniaS
Colorado
Connecticut
Delaware
Florida
Georgia
Idaho
IllinoisN
IllinoisS
IndianaN
IndianaS
lowaW
lowaCent
lowaNE
lowaS
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
NewHampshire
NewJersey
NewMexico
NewYork
NorthCarolina
75ppb
0.31
0.38
0.25
0.74
0.89
0.44
0.17
0.56
0.14
0.16
0.29
0.05
0.14
0.11
0.22
0.02
0.02
0.03
0.03
0.16
0.31
0.07
0.01
0.69
0.10
0.10
0.02
0.14
0.16
0.05
0.06
0.41
0.02
0.48
0.26
0.07
0.25
15 ppm-hr
0.31
0.40
0.25
0.74
0.89
0.44
0.17
0.56
0.14
0.16
0.29
0.05
0.14
0.11
0.22
0.02
0.02
0.03
0.03
0.16
0.31
0.07
0.01
0.69
0.10
0.10
0.02
0.14
0.16
0.05
0.06
0.42
0.02
0.48
0.26
0.07
0.25
11 ppm-hr
0.31
0.41
0.25
0.74
0.89
0.45
0.17
0.56
0.14
0.16
0.30
0.05
0.15
0.11
0.23
0.02
0.02
0.03
0.03
0.17
0.32
0.07
0.01
0.69
0.10
0.10
0.02
0.14
0.17
0.05
0.06
0.42
0.02
0.48
0.26
0.07
0.25
7 ppm-hr
0.31
0.41
0.25
0.74
0.89
0.45
0.17
0.56
0.14
0.16
0.30
0.05
0.15
0.12
0.23
0.02
0.02
0.03
0.03
0.17
0.32
0.07
0.01
0.69
0.10
0.10
0.02
0.14
0.17
0.05
0.06
0.42
0.02
0.48
0.26
0.07
0.25
(continued)
A-18
-------
Table A-3. Corn RYGs under Alternative Scenarios (% change) (continued)
FASOMGHG Subregion
NorthDakota
OhioNW
OhioS
OhioNE
Oklahoma
Oregon
Pennsylvania
Rhodelsland
SouthCarolina
SouthDakota
Tennessee
TxHiPlains
TxRolingPl
TxCntBlack
TxEast
TxEdplat
TxCoastBe
TxSouth
TxTranspec
Utah
Vermont
Virginia
Washington
WestVirginia
Wisconsin
Wyoming
75ppb
0.01
0.19
0.33
0.22
0.20
0.04
0.26
0.17
0.18
0.02
0.39
0.22
0.18
0.10
0.11
0.07
0.04
0.01
0.18
0.66
0.02
0.30
0.01
0.16
0.04
0.38
15 ppm-hr
0.01
0.19
0.33
0.22
0.20
0.04
0.26
0.17
0.18
0.02
0.39
0.22
0.18
0.10
0.11
0.07
0.04
0.01
0.18
0.67
0.02
0.30
0.01
0.16
0.04
0.38
11 ppm-hr
0.01
0.19
0.34
0.22
0.20
0.04
0.26
0.17
0.18
0.02
0.39
0.22
0.18
0.10
0.11
0.07
0.04
0.01
0.18
0.67
0.02
0.30
0.01
0.16
0.04
0.38
7 ppm-hr
0.01
0.20
0.34
0.23
0.20
0.04
0.26
0.17
0.18
0.02
0.40
0.22
0.18
0.10
0.11
0.07
0.04
0.01
0.18
0.67
0.02
0.30
0.01
0.16
0.04
0.39
A-19
-------
WestVirginia
Utah
TxSouth
TxEdplat
TxCntBlack
TxHiPlains "
SouthDakota
Rhodelsland
Oregon
OhioNE
OhioNW
NewMexico
NewHampshire
Nebraska
Missouri
Minnesota ~
Massachusetts
Maine
Kentucky
lowaS
lowaCent
IndianaS
Illin^iicC
Irl^ki^i
Florida
Connecticut
CaliforniaS
Arkansas
Alabama
0.
—
^^^^
=
^^^^
^=
i
=
I
=
^•^»
1
=
^^^^
:
s-i
^^^^™
DO 0.
•
-
—
20 0.
40 0.
50 0.
80 1.
• llppm
• 15ppm
• 75ppb
DO
Figure A-19. Corn RYGs under Alternative Scenarios (% change)
A-20
-------
Table A-4. Soybean RYLs under Alternative Scenarios (% change)
FASOMGHG Subregion
Alabama
Arkansas
Colorado
Delaware
Florida
Georgia
IllinoisN
IllinoisS
IndianaN
IndianaS
lowaW
lowaCent
lowaNE
lowaS
Kansas
Kentucky
Louisiana
Maine
Maryland
Michigan
Minnesota
Mississippi
Missouri
Nebraska
NewJersey
NewYork
NorthCarolina
NorthDakota
OhioNW
OhioS
OhioNE
Oklahoma
Pennsylvania
SouthCarolina
SouthDakota
Tennessee
Current
Conditions
6.15
5.68
6.73
7.65
4.84
4.57
2.69
4.28
3.91
5.10
1.72
1.70
1.93
2.03
3.41
5.95
3.45
0.69
8.30
3.55
1.49
4.40
4.37
2.10
7.76
2.96
5.35
1.14
4.70
6.20
5.00
4.52
5.38
4.63
1.51
6.60
75ppb
0.88
0.60
1.39
0.04
0.75
0.53
0.79
0.94
0.94
1.13
0.36
0.18
0.10
0.28
0.43
1.05
0.19
0.03
0.09
0.57
0.19
0.29
0.91
0.46
0.03
0.01
0.37
0.30
1.02
0.99
0.80
0.31
0.06
0.32
0.37
0.95
15 ppm-hr
0.88
0.60
1.05
0.04
0.75
0.53
0.79
0.94
0.94
1.13
0.36
0.18
0.10
0.28
0.42
1.05
0.19
0.03
0.09
0.57
0.19
0.29
0.91
0.44
0.03
0.01
0.37
0.30
1.02
0.99
0.80
0.31
0.06
0.32
0.37
0.95
11 ppm-hr
0.75
0.51
0.65
0.04
0.68
0.48
0.56
0.58
0.62
0.69
0.35
0.18
0.10
0.24
0.38
0.64
0.19
0.03
0.08
0.54
0.19
0.28
0.59
0.41
0.03
0.01
0.34
0.30
0.68
0.61
0.52
0.30
0.06
0.29
0.37
0.60
7 ppm-hr
0.42
0.44
0.45
0.04
0.40
0.28
0.34
0.29
0.34
0.34
0.32
0.17
0.10
0.20
0.35
0.32
0.19
0.03
0.06
0.51
0.18
0.27
0.33
0.36
0.03
0.01
0.20
0.25
0.39
0.31
0.29
0.30
0.05
0.18
0.31
0.32
(continued)
A-21
-------
Table A-4. Soybean RYLs under Alternative Scenarios (% change) (continued)
FASOMGHG Subregion
TxHiPlains
TxRolingPl
TxCntBlack
TxEast
TxEdplat
TxCoastBe
TxSouth
TxTranspec
Vermont
Virginia
WestVirginia
Wisconsin
Current
Conditions
4.72
4.06
3.94
3.33
3.28
2.57
0.69
4.61
1.81
6.04
4.98
2.39
75ppb
0.60
0.43
0.35
0.19
0.40
0.38
0.20
0.92
0.03
0.44
0.58
0.27
15 ppm-hr
0.47
0.41
0.35
0.19
0.37
0.38
0.20
0.79
0.03
0.44
0.58
0.27
11 ppm-hr
0.33
0.39
0.35
0.19
0.35
0.38
0.20
0.63
0.03
0.40
0.38
0.26
7 ppm-hr
0.30
0.38
0.35
0.19
0.34
0.38
0.20
0.59
0.03
0.23
0.18
0.25
A-22
-------
WestVirginia
Vermont
TxSouth
TxEdplat
TxCntBlack '_
TxHiPlains '_
SouthDakota
Pennsylvania
OhioNE
OhioNW
NorthCarolina
NewJersey ~
Missouri
Minnesota ~
Maryland
Louisiana
Kansas
lowaNE
lowaW
IndianaN
INinoisN
Florida
Colorado
Alabama
^«
^^^™
^^^
^^^
^—
^™
^
0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00
Figure A-20. Soybean RYLs under Current Conditions (% change)
A-23
-------
Table A-5. Soybean RYGs under Alternative Scenarios (% change)
FASOMGHG Subregion
Alabama
Arkansas
Colorado
Delaware
Florida
Georgia
IllinoisN
IllinoisS
IndianaN
IndianaS
lowaW
lowaCent
lowaNE
lowaS
Kansas
Kentucky
Louisiana
Maine
Maryland
Michigan
Minnesota
Mississippi
Missouri
Nebraska
NewJersey
NewYork
NorthCarolina
NorthDakota
OhioNW
OhioS
OhioNE
Oklahoma
Pennsylvania
75ppb
5.61
5.38
5.72
8.23
4.29
4.24
1.96
3.49
3.09
4.17
1.39
1.55
1.86
1.78
3.09
5.21
3.38
0.66
8.95
3.09
1.32
4.30
3.62
1.68
8.37
3.04
5.26
0.85
3.86
5.55
4.42
4.41
5.62
15 ppm-hr
5.61
5.38
6.09
8.23
4.29
4.24
1.96
3.49
3.09
4.17
1.39
1.55
1.86
1.78
3.10
5.21
3.38
0.66
8.95
3.09
1.32
4.30
3.62
1.70
8.37
3.04
5.26
0.85
3.86
5.55
4.42
4.41
5.62
11 ppm-hr
5.75
5.48
6.52
8.23
4.36
4.29
2.20
3.86
3.42
4.64
1.39
1.55
1.86
1.83
3.14
5.64
3.38
0.66
8.96
3.12
1.32
4.31
3.96
1.73
8.37
3.04
5.30
0.85
4.21
5.96
4.71
4.42
5.63
7 ppm-hr
6.11
5.56
6.73
8.24
4.66
4.49
2.42
4.16
3.71
5.01
1.43
1.56
1.86
1.87
3.17
5.99
3.38
0.66
8.98
3.15
1.34
4.31
4.22
1.78
8.37
3.04
5.45
0.90
4.52
6.28
4.96
4.42
5.64
(continued)
A-24
-------
Table A-5. Soybean RYGs under Alternative Scenarios (% change) (continued)
FASOMGHG Subregion
SouthCarolina
SouthDakota
Tennessee
TxHiPlains
TxRolingPl
TxCntBlack
TxEast
TxEdplat
TxCoastBe
TxSouth
TxTranspec
Vermont
Virginia
WestVirginia
Wisconsin
75ppb
4.51
1.15
6.05
4.33
3.77
3.73
3.25
2.98
2.25
0.49
3.86
1.82
5.96
4.63
2.18
15 ppm-hr
4.51
1.15
6.05
4.46
3.80
3.73
3.25
3.00
2.25
0.49
4.00
1.82
5.96
4.63
2.18
11 ppm-hr
4.54
1.16
6.43
4.61
3.83
3.73
3.25
3.03
2.25
0.49
4.17
1.82
6.01
4.85
2.18
7 ppm-hr
4.67
1.21
6.73
4.64
3.83
3.73
3.25
3.04
2.25
0.49
4.21
1.82
6.18
5.06
2.19
A-25
-------
I7ppm
llppm
115ppm
I75ppb
0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.1
Figure A-21. Soybean RYGs under Alternative Scenarios (% change)
A-26
-------
Table A-6. Winter Wheat RYLs under Alternative Scenarios (% change)
FASOMGHG Subregion
Alabama
Arizona
Arkansas
CaliforniaN
CaliforniaS
Colorado
Delaware
Florida
Georgia
Idaho
IllinoisN
IllinoisS
IndianaN
IndianaS
lowaW
lowaCent
lowaS
Kansas
Kentucky
Louisiana
Maryland
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
NewJersey
NewMexico
NewYork
NorthCarolina
NorthDakota
Current
Conditions
3.95
3.59
3.57
10.95
15.05
5.31
6.43
3.51
2.68
2.08
0.97
2.65
2.01
3.20
0.46
0.56
0.84
2.44
4.41
1.49
7.65
1.50
0.26
2.65
2.83
0.66
2.64
2.93
6.55
3.26
1.21
3.86
0.28
75 ppb
0.14
0.30
0.06
0.02
0.16
0.29
0.00
0.19
0.05
0.09
0.11
0.18
0.15
0.22
0.05
0.02
0.04
0.07
0.17
0.01
0.00
0.05
0.01
0.02
0.16
0.05
0.18
0.06
0.00
0.12
0.00
0.04
0.04
15 ppm-hr
0.14
0.16
0.06
0.01
0.10
0.17
0.00
0.19
0.05
0.06
0.11
0.18
0.15
0.22
0.05
0.02
0.04
0.05
0.17
0.01
0.00
0.05
0.01
0.02
0.16
0.05
0.14
0.05
0.00
0.07
0.00
0.04
0.04
11 ppm-hr
0.11
0.06
0.05
0.01
0.06
0.07
0.00
0.16
0.05
0.04
0.06
0.08
0.07
0.09
0.05
0.02
0.02
0.04
0.07
0.01
0.00
0.05
0.01
0.02
0.07
0.05
0.09
0.04
0.00
0.03
0.00
0.03
0.04
7 ppm-hr
0.04
0.04
0.04
0.01
0.03
0.04
0.00
0.06
0.02
0.03
0.03
0.02
0.03
0.03
0.04
0.01
0.01
0.03
0.02
0.01
0.00
0.05
0.01
0.02
0.02
0.03
0.05
0.04
0.00
0.02
0.00
0.01
0.03
(continued)
A-27
-------
Table A-6. Winter Wheat RYLs under Alternative Scenarios (% change) (continued)
FASOMGHG Subregion
OhioNW
OhioS
OhioNE
Oklahoma
Oregon
Pennsylvania
SouthCarolina
SouthDakota
Tennessee
TxHiPlains
TxRolingPl
TxCntBlack
TxEast
TxEdplat
TxCoastBe
TxSouth
TxTranspec
Utah
Vermont
Virginia
Washington
WestVirginia
Wisconsin
Wyoming
Current
Conditions
2.40
4.16
2.91
2.59
0.41
3.69
2.90
0.60
5.03
2.93
1.95
2.25
1.55
1.51
0.64
0.15
2.81
7.26
0.53
4.56
0.36
2.97
0.95
4.60
75 ppb
0.15
0.15
0.11
0.05
0.05
0.00
0.02
0.07
0.16
0.08
0.04
0.04
0.01
0.03
0.02
0.01
0.13
0.30
0.00
0.04
0.04
0.04
0.03
0.32
15 ppm-hr
0.15
0.15
0.11
0.04
0.05
0.00
0.02
0.07
0.16
0.05
0.04
0.04
0.01
0.03
0.02
0.01
0.09
0.16
0.00
0.04
0.03
0.04
0.03
0.24
11 ppm-hr
0.08
0.07
0.05
0.03
0.04
0.00
0.02
0.06
0.07
0.02
0.03
0.04
0.01
0.03
0.02
0.01
0.05
0.05
0.00
0.03
0.03
0.02
0.03
0.16
7 ppm-hr
0.03
0.02
0.02
0.03
0.04
0.00
0.01
0.04
0.02
0.02
0.03
0.04
0.01
0.03
0.02
0.01
0.04
0.04
0.00
0.01
0.03
0.01
0.03
0.09
A-28
-------
Wyoming
WestVirginia
Virginia
Utah
TxSouth
TxEdplat
TxCntBlack \
TxHiPlains ;
SouthDakota
Pennsylvania
Oklahoma
OhioS ;
NorthDakota
NewYork
NewJersey
Nebraska
Missouri
Minnesota ~
Maryland
Kentucky
lowaS '_
lowaW
IndianaN
INinoisN
Georgia
Delaware
CaliforniaS
Arkansas
Alabama
El
i
=
—
M^
: — 1
—
g
^—
r
.
;
i=h
0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16
00
Figure A-22. Winter Wheat RYLs under Current Conditions (% change)
A-29
-------
Table A-7. Winter Wheat RYGs under Alternative Scenarios (% change)
FASOMGHG Subregion
Alabama
Arizona
Arkansas
CaliforniaN
CaliforniaS
Colorado
Delaware
Florida
Georgia
Idaho
IllinoisN
IllinoisS
IndianaN
IndianaS
lowaW
lowaCent
lowaS
Kansas
Kentucky
Louisiana
Maryland
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
NewJersey
NewMexico
NewYork
NorthCarolina
NorthDakota
75ppb
3.97
3.41
3.64
12.28
17.53
5.30
6.88
3.44
2.69
2.04
0.88
2.53
1.89
3.08
0.41
0.54
0.81
2.43
4.44
1.50
8.29
1.47
0.25
2.70
2.75
0.62
2.53
2.96
7.01
3.25
1.23
3.98
0.24
15 ppm-hr
3.97
3.55
3.64
12.28
17.59
5.43
6.88
3.44
2.69
2.06
0.88
2.53
1.89
3.08
0.41
0.54
0.81
2.45
4.44
1.50
8.29
1.47
0.25
2.70
2.75
0.62
2.57
2.96
7.01
3.30
1.23
3.98
0.24
11 ppm-hr
4.01
3.66
3.65
12.29
17.65
5.54
6.88
3.47
2.70
2.09
0.92
2.64
1.97
3.22
0.42
0.54
0.83
2.47
4.55
1.50
8.29
1.47
0.25
2.70
2.84
0.62
2.63
2.97
7.01
3.35
1.23
3.99
0.24
7 ppm-hr
4.08
3.68
3.66
12.29
17.68
5.57
6.88
3.57
2.73
2.10
0.96
2.70
2.02
3.28
0.43
0.55
0.84
2.47
4.60
1.50
8.29
1.47
0.25
2.70
2.88
0.63
2.66
2.98
7.01
3.35
1.23
4.01
0.25
(continued)
A-30
-------
Table A-7. Winter Wheat RYGs under Alternative Scenarios (% change) (continued)
FASOMGHG Subregion
OhioNW
OhioS
OhioNE
Oklahoma
Oregon
Pennsylvania
SouthCarolina
SouthDakota
Tennessee
TxHiPlains
TxRolingPl
TxCntBlack
TxEast
TxEdplat
TxCoastBe
TxSouth
TxTranspec
Utah
Vermont
Virginia
Washington
WestVirginia
Wisconsin
Wyoming
75ppb
2.30
4.19
2.88
2.61
0.36
3.83
2.96
0.54
5.12
2.94
1.95
2.25
1.56
1.50
0.62
0.14
2.75
7.51
0.54
4.74
0.32
3.01
0.93
4.49
15 ppm-hr
2.30
4.19
2.88
2.61
0.36
3.83
2.96
0.54
5.12
2.97
1.95
2.25
1.56
1.50
0.62
0.14
2.80
7.65
0.54
4.74
0.32
3.01
0.93
4.57
11 ppm-hr
2.38
4.28
2.95
2.62
0.37
3.83
2.96
0.54
5.22
2.99
1.96
2.25
1.56
1.51
0.62
0.14
2.84
7.77
0.54
4.74
0.33
3.03
0.93
4.66
7 ppm-hr
2.43
4.32
2.98
2.62
0.37
3.83
2.97
0.56
5.27
3.00
1.96
2.25
1.56
1.51
0.62
0.14
2.85
7.79
0.54
4.76
0.33
3.05
0.93
4.73
A-31
-------
Wyoming
WestVirginia
Virginia
Utah
TxSouth
TxEdplat
TxCntBlack '_
TxHiPlains "
SouthDakota
Pennsylvania
Oklahoma
OhioS ~_
NorthDakota
NewYork
NewJersey
Nebraska
Missouri
Minnesota
Maryland
Kentucky
lowaS
lowaW
IndianaN
INinoisN
Georgia
Delaware
CaliforniaS
Arkansas
Alabama
0.
F-
^=
•
—
•
=
—
=
—
^~
^^=
^
DO 5.
DO 10
00 15.00 20
• 7ppm
• llppm
• 15ppm
• 75ppb
00
Figure A-23. Winter Wheat RYGs under Alternative Scenarios (% change)
A-32
-------
Table A-8. Sorghum RYLs under Alternative Scenarios (% change)
FASOMGHG Subregion
Alabama
Arizona
Arkansas
CaliforniaN
CaliforniaS
Colorado
Delaware
Florida
Georgia
IllinoisN
IllinoisS
IndianaN
IndianaS
lowaW
lowaCent
lowaNE
lowaS
Kansas
Kentucky
Louisiana
Maryland
Michigan
Mississippi
Missouri
Nebraska
NewJersey
NewMexico
NewYork
NorthCarolina
OhioNW
Oklahoma
Pennsylvania
SouthCarolina
Current
Conditions
0.40
0.47
0.50
0.67
1.03
0.57
0.74
0.45
0.36
0.17
0.37
0.16
0.37
0.11
0.10
0.10
0.08
0.29
0.51
0.23
0.93
0.35
0.39
0.41
0.22
0.65
0.40
0.13
0.48
0.39
0.39
0.50
0.42
75ppb
0.03
0.08
0.02
0.00
0.03
0.04
0.00
0.04
0.01
0.02
0.04
0.03
0.04
0.01
0.00
0.00
0.00
0.02
0.04
0.00
0.00
0.02
0.01
0.04
0.02
0.00
0.03
0.00
0.01
0.06
0.01
0.00
0.01
15 ppm-hr
0.03
0.04
0.02
0.00
0.02
0.02
0.00
0.04
0.01
0.02
0.04
0.03
0.04
0.01
0.00
0.00
0.00
0.01
0.04
0.00
0.00
0.02
0.01
0.04
0.02
0.00
0.02
0.00
0.01
0.06
0.01
0.00
0.01
11 ppm-hr
0.02
0.02
0.01
0.00
0.01
0.01
0.00
0.04
0.01
0.01
0.02
0.02
0.02
0.01
0.00
0.00
0.00
0.01
0.02
0.00
0.00
0.02
0.01
0.02
0.01
0.00
0.01
0.00
0.01
0.03
0.01
0.00
0.01
7 ppm-hr
0.01
0.01
0.01
0.00
0.01
0.01
0.00
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.02
0.01
0.01
0.01
0.00
0.01
0.00
0.00
0.01
0.01
0.00
0.00
(continued)
A-33
-------
Table A-8. Sorghum RYLs under Alternative Scenarios (% change) (continued)
FASOMGHG Subregion
SouthDakota
Tennessee
TxHiPlains
TxRolingPl
TxCntBlack
TxEast
TxEdplat
TxCoastBe
TxSouth
TxTranspec
Virginia
Wisconsin
Current
Conditions
0.12
0.62
0.37
0.27
0.25
0.26
0.21
0.09
0.03
0.32
0.34
0.13
75ppb
0.02
0.03
0.02
0.01
0.01
0.01
0.01
0.00
0.00
0.02
0.01
0.01
15 ppm-hr
0.02
0.03
0.01
0.01
0.01
0.01
0.01
0.00
0.00
0.02
0.01
0.01
11 ppm-hr
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.00
0.00
0.01
0.01
0.01
7 ppm-hr
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.00
0.00
0.01
0.00
0.01
A-34
-------
Wisconsin ~
TxTranspec '_
TxCoastBe '_
TxEast
TxRolingPI ~
Tennessee ~_
SouthCarolina
Oklahoma ~_
NorthCarolina
NewMexico
Nebraska
Mississippi ~
Maryland
Kentucky
lowaS \
lowaCent
IndianaS
IllinoisS
"
Georgia
Delaware
CaliforniaS ~
Arkansas
Alabama
_
=
^«
^«
^^^^
^^
^^^"
E
—
0.00 0.20 0.40 0.60 0.80 1.00 1.20
Figure A-24. Sorghum RYLs under Current Conditions (% change)
A-35
-------
Table A-9. Sorghum RYGs under Alternative Scenarios (% change)
FASOMGHG Subregion
Alabama
Arizona
Arkansas
CaliforniaN
CaliforniaS
Colorado
Delaware
Florida
Georgia
IllinoisN
IllinoisS
IndianaN
IndianaS
lowaW
lowaCent
lowaNE
lowaS
Kansas
Kentucky
Louisiana
Maryland
Michigan
Mississippi
Missouri
Nebraska
NewJersey
NewMexico
NewYork
NorthCarolina
OhioNW
Oklahoma
Pennsylvania
SouthCarolina
75ppb
0.37
0.39
0.48
0.68
1.01
0.54
0.74
0.41
0.35
0.15
0.33
0.13
0.33
0.10
0.09
0.10
0.08
0.27
0.47
0.23
0.94
0.34
0.39
0.37
0.20
0.66
0.37
0.13
0.47
0.33
0.38
0.50
0.42
15 ppm-hr
0.37
0.43
0.48
0.68
1.02
0.55
0.74
0.41
0.35
0.15
0.33
0.13
0.33
0.10
0.09
0.10
0.08
0.28
0.47
0.23
0.94
0.34
0.39
0.37
0.21
0.66
0.38
0.13
0.47
0.33
0.38
0.50
0.42
11 ppm-hr
0.38
0.45
0.49
0.68
1.02
0.56
0.74
0.42
0.35
0.15
0.35
0.14
0.36
0.10
0.09
0.10
0.08
0.28
0.49
0.23
0.94
0.34
0.39
0.39
0.21
0.66
0.39
0.13
0.47
0.36
0.38
0.50
0.42
7 ppm-hr
0.39
0.46
0.49
0.68
1.03
0.57
0.74
0.44
0.35
0.16
0.36
0.15
0.37
0.10
0.09
0.10
0.08
0.28
0.50
0.23
0.94
0.34
0.39
0.40
0.21
0.66
0.40
0.13
0.48
0.38
0.38
0.50
0.42
(continued)
A-36
-------
Table A-9. Sorghum RYGs under Alternative Scenarios (% change) (continued)
FASOMGHG Subregion
SouthDakota
Tennessee
TxHiPlains
TxRolingPl
TxCntBlack
TxEast
TxEdplat
TxCoastBe
TxSouth
TxTranspec
Virginia
Wisconsin
75ppb
0.10
0.59
0.35
0.26
0.24
0.25
0.20
0.08
0.02
0.30
0.33
0.12
15 ppm-hr
0.10
0.59
0.36
0.26
0.24
0.25
0.20
0.08
0.02
0.30
0.33
0.12
11 ppm-hr
0.10
0.61
0.36
0.26
0.24
0.25
0.20
0.08
0.02
0.31
0.34
0.12
7 ppm-hr
0.11
0.62
0.37
0.26
0.24
0.25
0.20
0.08
0.02
0.31
0.34
0.12
A-37
-------
Wisconsin
Virginia
TxTranspec
TxSouth
TxCoastBe
TxEdplat
TxEast
TxCntBlack
TxRolingPI
TxHiPlains
Tennessee
SouthDakota
SouthCarolina
Pennsylvania
Oklahoma
OhioNW
NorthCarolina
NewYork
NewMexico
NewJersey
Nebraska
Missouri
Mississippi
Michigan
Maryland
Louisiana
Kentucky
Kansas
lowaS
lowaNE
lowaCent
lowaW
IndianaS
IndianaN
IllinoisS
HlinoisN
Georgia
Florida
Delaware
Colorado
CaliforniaS
CaliforniaN
Arkansas
Arizona
Alabama
^=
•
1
1
=
^=
^^^^
^=
^"
• 7ppm
• llppm
• 15ppm
• 75ppb
0.00 0.20 0.40 0.60 0.80 1.00 1.20
Figure A-25. Sorghum RYGs under Alternative Scenarios (% change)
A-38
-------
Table A-10. Cotton RYLs under Alternative Scenarios (% change)
FASOMGHG Subregion
Alabama
Arizona
Arkansas
CaliforniaN
CaliforniaS
Florida
Georgia
IllinoisS
Kansas
Kentucky
Louisiana
Mississippi
Missouri
Nevada
NewMexico
NorthCarolina
Oklahoma
SouthCarolina
Tennessee
TxHiPlains
TxRolingPl
TxCntBlack
TxEast
TxEdplat
TxCoastBe
TxSouth
TxTranspec
Virginia
Current
Conditions
4.51
4.65
4.91
13.86
10.50
3.85
3.42
3.43
3.37
4.73
2.31
3.45
5.49
5.70
3.72
4.43
3.62
3.68
5.77
3.47
2.91
2.48
2.96
2.70
1.18
0.61
3.76
4.65
75ppb
0.51
1.07
0.42
0.20
0.59
0.50
0.25
0.59
0.20
0.67
0.07
0.14
0.66
0.39
0.64
0.17
0.27
0.19
0.58
0.32
0.23
0.21
0.25
0.23
0.11
0.12
0.67
0.28
15 ppm-hr
0.51
0.70
0.42
0.16
0.47
0.50
0.25
0.59
0.18
0.67
0.07
0.14
0.66
0.32
0.45
0.17
0.25
0.19
0.58
0.25
0.21
0.21
0.25
0.21
0.11
0.12
0.57
0.28
11 ppm-hr
0.44
0.32
0.32
0.12
0.33
0.45
0.22
0.34
0.16
0.38
0.07
0.14
0.41
0.25
0.25
0.15
0.24
0.17
0.34
0.17
0.19
0.21
0.25
0.18
0.11
0.12
0.46
0.25
7 ppm-hr
0.23
0.24
0.24
0.08
0.26
0.24
0.12
0.15
0.15
0.17
0.07
0.14
0.23
0.22
0.20
0.08
0.23
0.09
0.18
0.15
0.18
0.21
0.25
0.18
0.11
0.12
0.43
0.13
A-39
-------
TxSouth ^H
TxCoastBe ^^m
TxEast
TxCntBlack
TxRolingPI
TxHiPlains
Mississippi
Louisiana
Kansas
IllinoisS
Georgia
Florida I
0.00 2.
L
-
=
00 4.
DO 6.
DO 8.
DO 10
00 12
00 14
00 16
00
Figure A-26. Cotton RYLs under Current Conditions (% change)
A-40
-------
Table A-ll. Cotton RYGs under Alternative Scenarios (% change)
FASOMGHG Subregion
Alabama
Arizona
Arkansas
CaliforniaN
CaliforniaS
Florida
Georgia
IllinoisS
Kansas
Kentucky
Louisiana
Mississippi
Missouri
Nevada
NewMexico
NorthCarolina
Oklahoma
SouthCarolina
Tennessee
TxHiPlains
TxRolingPl
TxCntBlack
TxEast
TxEdplat
TxCoastBe
TxSouth
TxTranspec
Virginia
75ppb
4.19
3.76
4.73
15.87
11.06
3.49
3.29
2.94
3.28
4.27
2.30
3.42
5.11
5.64
3.20
4.46
3.48
3.63
5.50
3.26
2.76
2.32
2.80
2.54
1.08
0.49
3.21
4.59
15 ppm-hr
4.19
4.15
4.73
15.91
11.20
3.49
3.29
2.94
3.30
4.27
2.30
3.42
5.11
5.70
3.39
4.46
3.49
3.63
5.50
3.34
2.78
2.32
2.80
2.56
1.08
0.50
3.31
4.59
11 ppm-hr
4.27
4.54
4.83
15.96
11.35
3.54
3.31
3.20
3.33
4.57
2.30
3.42
5.38
5.78
3.61
4.48
3.51
3.65
5.76
3.42
2.81
2.32
2.80
2.59
1.08
0.50
3.42
4.62
7 ppm-hr
4.48
4.63
4.92
16.00
11.44
3.76
3.41
3.39
3.33
4.79
2.30
3.43
5.57
5.81
3.65
4.56
3.51
3.73
5.93
3.44
2.81
2.32
2.80
2.59
1.08
0.50
3.45
4.74
A-41
-------
Virginia
TxTranspec
TxSouth
TxCoastBe
TxEdplat
TxEast
TxCntBlack
TxRolingPI
TxHiPlains
Tennessee
SouthCarolina
Oklahoma
NorthCarolina
NewMexico
Nevada
Missouri
Mississippi
Louisiana
Kentucky
Kansas
IllinoisS
Georgia
Florida
CaliforniaS
CaliforniaN
Arkansas
Arizona
Alabama
— 1—
I7ppm
llppm
I15ppm
I75ppb
0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00
Figure A-27. Cotton RYGs under Alternative Scenarios (% change)
A-42
-------
Table A-12. Potato RYLs under Alternative Scenarios (% change)
FASOMGHG Subregion
Alabama
Arizona
CaliforniaN
CaliforniaS
Colorado
Connecticut
Delaware
Florida
Idaho
IllinoisN
IndianaN
lowaNE
Kansas
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Missouri
Montana
Nebraska
Nevada
NewJersey
NewMexico
NewYork
NorthCarolina
NorthDakota
OhioNW
Oregon
Pennsylvania
Rhodelsland
SouthDakota
Current
Conditions
7.83
8.21
14.16
12.79
10.07
6.79
10.71
5.09
7.35
4.15
5.84
3.16
6.22
5.33
1.67
10.80
5.53
5.27
2.38
6.56
3.38
4.79
9.49
10.37
7.35
4.50
8.58
2.09
6.99
3.25
6.95
6.70
2.79
75ppb
1.32
2.72
0.54
1.33
2.26
0.06
0.10
1.25
1.37
1.36
1.61
0.22
0.95
0.41
0.10
0.23
0.07
0.95
0.38
1.50
0.94
1.21
1.16
0.07
1.54
0.03
0.78
0.76
1.78
0.90
0.15
0.11
0.84
15 ppm-hr
1.32
1.98
0.46
1.14
1.70
0.06
0.10
1.25
1.14
1.36
1.61
0.22
0.85
0.41
0.10
0.23
0.07
0.95
0.38
1.50
0.93
1.09
1.01
0.07
1.17
0.03
0.78
0.76
1.78
0.86
0.15
0.11
0.83
11 ppm-hr
1.18
1.12
0.37
0.93
1.02
0.06
0.10
1.17
0.86
1.00
1.10
0.22
0.72
0.41
0.10
0.21
0.07
0.92
0.38
1.01
0.92
0.93
0.83
0.07
0.72
0.03
0.70
0.76
1.23
0.80
0.13
0.11
0.82
7 ppm-hr
0.71
0.88
0.30
0.78
0.75
0.06
0.09
0.82
0.75
0.65
0.64
0.22
0.64
0.41
0.10
0.16
0.07
0.88
0.34
0.61
0.74
0.78
0.75
0.07
0.61
0.03
0.41
0.64
0.74
0.78
0.10
0.11
0.69
(continued)
A-43
-------
Table A-12. Potato RYLs under Alternative Scenarios (% change) (continued)
FASOMGHG Subregion
Tennessee
TxHiPlains
TxRolingPl
TxEast
TxCoastBe
TxSouth
Utah
Virginia
Washington
WestVirginia
Wisconsin
Wyoming
Current
Conditions
9.48
6.81
6.08
5.54
3.81
2.05
11.30
7.37
2.38
6.19
3.64
8.47
75ppb
1.69
1.08
0.81
0.58
0.65
0.50
2.34
0.82
0.77
0.90
0.45
1.96
15 ppm-hr
1.69
0.86
0.77
0.58
0.65
0.50
1.77
0.82
0.74
0.90
0.45
1.70
11 ppm-hr
1.11
0.61
0.73
0.58
0.65
0.50
1.07
0.71
0.71
0.59
0.44
1.39
7 ppm-hr
0.60
0.55
0.71
0.58
0.65
0.50
0.87
0.42
0.70
0.31
0.44
1.09
A-44
-------
Wyoming
WestVirginia
Virginia
TxSouth
TxEast
TxHiPlains '_
SouthDakota
Pennsylvania
OhioNW
NorthCarolina
NewMexico
Nevada
Montana
Minnesota
Massachusetts
Maine
Kansas
IndianaN
Idaho
Delaware
Colorado
CaliforniaN
Alabama
— r~
—
^—
0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00
Figure A-28. Potato RYLs under Current Conditions (% change)
A-45
-------
Table A-13. Potato RYGs under Alternative Scenarios (% change)
FASOMGHG Subregion
Alabama
Arizona
CaliforniaN
CaliforniaS
Colorado
Connecticut
Delaware
Florida
Idaho
IllinoisN
IndianaN
lowaNE
Kansas
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Missouri
Montana
Nebraska
Nevada
NewJersey
NewMexico
NewYork
NorthCarolina
NorthDakota
OhioNW
Oregon
Pennsylvania
Rhodelsland
SouthDakota
75ppb
7.06
5.98
15.86
13.14
8.68
7.23
11.88
4.05
6.46
2.91
4.49
3.03
5.61
5.20
1.59
11.85
5.79
4.56
2.05
5.42
2.53
3.77
9.20
11.49
6.27
4.67
8.53
1.36
5.61
2.43
7.32
7.07
2.01
15 ppm-hr
7.06
6.78
15.96
13.35
9.30
7.23
11.88
4.05
6.71
2.91
4.49
3.03
5.72
5.20
1.59
11.85
5.79
4.56
2.05
5.42
2.53
3.90
9.38
11.49
6.67
4.67
8.53
1.36
5.61
2.48
7.32
7.07
2.01
11 ppm-hr
7.21
7.72
16.06
13.59
10.05
7.23
11.89
4.13
7.01
3.29
5.03
3.03
5.87
5.20
1.59
11.87
5.79
4.59
2.05
5.94
2.54
4.06
9.57
11.49
7.16
4.67
8.61
1.36
6.19
2.53
7.34
7.07
2.02
7 ppm-hr
7.73
7.98
16.14
13.76
10.35
7.23
11.89
4.50
7.13
3.65
5.51
3.03
5.94
5.20
1.59
11.93
5.79
4.63
2.08
6.37
2.73
4.22
9.66
11.49
7.28
4.67
8.93
1.48
6.72
2.56
7.36
7.07
2.16
(continued)
A-46
-------
Table A-13. Potato RYGs under Alternative Scenarios (% change) (continued)
FASOMGHG Subregion
Tennessee
TxHiPlains
TxRolingPl
TxEast
TxCoastBe
TxSouth
Utah
Virginia
Washington
WestVirginia
Wisconsin
Wyoming
75ppb
8.60
6.15
5.62
5.25
3.29
1.57
10.10
7.07
1.65
5.64
3.32
7.12
15 ppm-hr
8.60
6.37
5.66
5.25
3.29
1.57
10.74
7.07
1.67
5.64
3.32
7.40
11 ppm-hr
9.25
6.64
5.70
5.25
3.29
1.58
11.53
7.19
1.71
5.97
3.32
7.74
7 ppm-hr
9.80
6.71
5.72
5.25
3.29
1.58
11.75
7.50
1.72
6.27
3.33
8.07
A-47
-------
Wyoming
Wisconsin
WestVirginia
Washington
Virginia
utan
TxSouth
TxCoastBe
Tennessee
SouthDakota
Rhodelsland
Pennsylvania
Oregon
OhioNW
NorthDakota
NewYork
Nebraska
Montana
Missouri
Minnesota
Michigan
Massachusetts
Maine
lowaNE
IndianaN
=
F™
—
—
•
^=
^^
^^^
=
^^
F
F^^^
^
F
1
—
^^F-
=
^—
luano
Florida
Delaware
Connecticut
Colorado
Arizona
Alabama
0.
^^
r^
E
F^^
^m
DO 2.00 4.00 6.00 8.
DO 10
00 12
00 14
00 16
00 18
• 7ppm
• llppm
• 15ppm
• 75ppb
00
Figure A-29. Potato RYGs under Alternative Scenarios (% change)
A-48
-------
A.4 Derived County-Level Relative Yield Gains for Crops
This subsection presents the county-level RYGs and RYG changes for major crops under
alternative scenarios. The 75 ppb RYGs and RYG changes for alternative ozone standards versus
the 75 ppb scenario are selected for presentation in this subsection. The crop-specific, 2007
USDA Census of Agriculture-based county mappings are applied in this subsection. Figures A-
30 through A-35 present the RYGs for corn, soybeans, winter wheat, sorghum, cotton, and
potatoes under the 75 ppb scenario, respectively. Figures A-36 through A-41 display the RYG
changes for those crops under alternative ozone standards scenarios with respect to the 75 ppb
scenario.
] FASOM Region
1 FASOM Subregron
Corn Relative Yield Gain
Figure A-30. County-Level Corn RYGs under the 75 ppb Scenario
A-49
-------
Legend
I I FASOM Rejion
I I FASOM Subregion
Soybeans Relative Yield Gain
Figure A-31. County-Level Soybean RYGs under the 75 ppb Scenario
Legend
I I FASOM Region
] FASOM Subragion
Winter Wheat Relative Yield Gain
^^<<-^^w^^
Figure A-32. County-Level Winter Wheat RYGs under the 75 ppb Scenario
A-50
-------
Legend
~~l FASOM Raojon
I FASOM Subregion
Sorghum Relative Yield Gain
Figure A-33. County-Level Sorghum RYGs under the 75 ppb Scenario
Legend
I I FASOM Region
] FASOM Subregion
Cotton Relative Yield Cain
w^^
Figure A-34. County-Level Cotton RYGs under the 75 ppb Scenario
A-51
-------
Logend
I I FASOy Region
I FASOM Subreflion
Potatoes Relative Yield Gain
n<^<£<^^
Figure A-35. County-Level Potato RYGs under the 75 ppb Scenario
A-52
-------
Legend
~J FASOM Region
FASOM Subregion
Q0 °z-
V*
V*
V
\\
\\
\\
o
o
2.
Q.
o
3
o
\\
°- °0
s\
Figure A-36. County-Level Changes in Corn RYGs under Alternative Scenarios with
Respect to 75 ppb
A-53
-------
Legend
FASOM Region
FASOM Subregion
w
°* |
I
(D
I
E
O
2.
3
^
O
1
•t ^
\\
0T '%
'lik ub
O- '«
Figure A-37. County-Level Changes in Soybean RYGs under Alternative Scenarios with
Respect to 75 ppb
A-54
-------
Legend
J FASOM Region
_| FASOM Subregion
'. o. |
V* I
Figure A-38. County-Level Changes in Winter Wheat RYGs under Alternative Scenarios
with Respect to 75 ppb
A-55
-------
Legend
^ FASOM Region
^] FASOM Subregion
c °4- o
'* ° 2
!L
o.
O
O
c
o
-s—
\
\
"'* **
'
Figure A-39. County-Level Changes in Sorghum RYGs under Alternative Scenarios with
Respect to 75 ppb
A-56
-------
Legend
FASOM
FASOM
O
o, °
Region
Subregion
\\
o. '
-------
Legend
FASOM Region
FASOM Subregion
TJ
O
Ef
Figure A-41. County-Level Changes in Potato RYGs under Alternative Scenarios with
Respect to 75 ppb
A-58
-------
APPENDIX B
MODEL RESULTS: FOCUSING ON COMPARISON WITH CURRENT CONDITIONS
This appendix section focuses on presentation of model results for a comparison of the
current standard versus current conditions. Under current conditions, we assumed that ozone
concentrations continued at current levels, which exceed the current standard (75 ppb). As for the
results presented in Section 5, we present our findings for changes to production, prices, forest
inventory, land use, welfare, and greenhouse gas (GHG) mitigation potential. This section is
designed to be a standalone analysis providing an alternative comparison point to that provided
in Section 5.
B.I Agricultural Sector
As discussed in the main body of the report, ozone negatively affects growth in many
plants, leading to lower yields of agricultural crops. Thus, meeting the existing or more stringent
ozone standards would increase agricultural production by reducing ozone concentrations and
alleviating existing detrimental impacts. Consistent with expectations, the incremental impacts
when moving from current conditions to compliance with existing or more stringent standards
are found to be considerably larger than the incremental impacts of increasing stringency relative
to the current standard presented in Section 5. This is simply because the reductions in ozone
concentrations relative to current conditions are much bigger than those in moving between the
standards considered.
B.1.1 Production and Prices
Changes in U.S. agricultural production and prices were measured using Fisher indices
(see Tables B-l and B-2).1 Both primary and secondary commodity production levels are
projected to increase by 2040 as a result of increased productivity when the current ozone
standard is met. There is a greater difference in the production of primary commodities than
secondary commodities. The reason for secondary commodities experiencing smaller impacts is
that they use a number of other inputs in combination with primary commodities, so the
percentage impact on cost of production is smaller.
1 The Fisher price index is known as the "ideal" price index. It is calculated as the geometric mean of an index of
current prices and an index of past prices.
B-l
-------
Table B-l. Agricultural Production Fisher Indices (Current Conditions = 100)
Sector
Policy
2010
2020
2030
2040
Primary Commodities
Crops
Livestock
Farm products3
Processed
Meats
Mixed feeds
75ppb
75 ppb
75ppb
75 ppb
75 ppb
75 ppb
101.01
100.13
100.58
100.17
100.03
100.06
100.77
100.03
100.42
Secondary
100.10
100.01
100.01
100.97
100.36
100.43
Commodities
100.08
100.43
100.64
100.87
100.88
100.40
100.28
100.40
100.53
Farm products is the composite of crops and livestock.
prices
Consistent with expectations, increased production led to a general decline in market
because the equilibrium price adjusts to higher levels of supply.
Table B-2. Agricultural Price Fisher Indices (Current Conditions = 100)
Sector
Crops
Livestock
Farm products
Processed
Meats
Mixed feeds
Policy
75 ppb
75 ppb
75 ppb
75 ppb
75 ppb
75 ppb
2010
97.51
99.78
97.51
97.60
99.86
97.48
2020 2030
Primary Commodities
98.35 99.05
99.31 100.26
98.35 99.75
Secondary Commodities
98.34 98.69
100.00 99.75
98.98 99.11
2040
98.73
98.90
99.51
98.22
99.51
98.91
B.1.2 Crop A creage
Total crop acreage declines with the introduction of the ozone standards because higher
productivity per acre reduces the demand for cropland. In aggregate, farmers are able to meet the
demand for agricultural commodities using less land under the ozone standard scenarios.
Consistent with these expectations, the total cropped area is slightly smaller for each model year
in the ozone standard cases than under current conditions. However, land allocation over time
also depends on relative returns across various uses and is influenced by forest harvest timing.
B-2
-------
Discussion of the changes in land allocation between the agricultural and forestry sectors follows
later in this section.
Table B-3 provides baseline projections of acreage in each of the major U.S. crops, as
well as composites of all remaining crops and total cropland. The absolute change relative to
current conditions is presented for each policy scenario. Overall, there tended to be reallocation
of land from soybeans, winter wheat, and cotton to corn, hay, spring wheat, and other minor
crops. Increases in wheat for livestock grazing2 and spring barley dominated changes in other
minor crops. This shift occurred largely because of differential crop sensitivity to ozone
concentrations. Soybeans, winter wheat, and cotton are all relatively sensitive to ozone
concentrations, indicating that lowering ozone concentrations would significantly increase yield
for these crops and decrease demand for cropland. Corn, however, is not very sensitive to ozone
concentrations. Corn acreage increases are driven by increased demand for feed as livestock and
meat demand increase over time, as discussed in Section B. 1.1. The sum of the crop-specific
changes will not necessarily equal the total changes shown in Table B-3 because some double-
cropping is reflected in the model (e.g., soybeans and winter barley).
'• FASOMGHG includes categories of selected small grains (e.g., wheat, oats) that are planted to provide grazing for
livestock rather than being harvested. These crops would generally be planted as winter cover crops that also
provide grazing for livestock.
B-3
-------
Table B-3. Major Crop Acreage, Million Acres
Crop
Corn
Soybeans
Hay
Hard red winter wheat
Cotton
Policy
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010 2020 2030
92.7 87.2 79.0
Change with Respect to Current Conditions
0.14 -0.02 0.63
0.14 -0.02 0.63
0.13 0.01 0.60
0.14 -0.02 0.61
73.1 72.0 72.2
Change with Respect to Current Conditions
0.20 -0.12 -0.53
0.21 -0.12 -0.55
0.23 -0.13 -0.72
0.26 -0.16 -0.62
44.2 42.6 41.4
Change with Respect to Current Conditions
-0.22 -0.52 -0.44
-0.23 -0.53 -0.44
-0.21 -0.62 -0.43
-0.23 -0.54 -0.46
20.7 19.4 15.8
Change with Respect to Current Conditions
-0.06 0.07 0.07
-0.02 0.05 0.06
0.00 0.00 0.00
-0.04 0.36 3.68
15.1 15.9 16.3
Change with Respect to Current Conditions
-0.48 -0.44 -0.38
-0.48 -0.45 -0.38
-0.49 -0.47 -0.40
-0.48 -0.46 -0.39
2040
70.8
0.00
0.00
-0.01
-0.01
69.9
0.00
-0.01
-0.14
-0.10
39.2
0.00
0.00
-0.01
0.00
13.5
-0.02
-0.03
0.00
4.60
15.7
0.00
0.00
0.01
0.01
(continued)
B-4
-------
Table B-3. Major Crop Acreage, Million Acres (continued)
Crop
Hard red spring wheat
Sorghum
Switchgrass
All others3
Total
Policy
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010 2020 2030
13.7 12.5 12.2
Change with Respect to Current Conditions
0.01 0.02 -0.01
0.01 0.02 -0.02
0.00 0.00 0.00
0.01 0.02 0.00
10.6 11.3 11.6
Change with Respect to Current Conditions
0.14 0.03 0.07
0.14 0.03 0.06
0.13 0.03 0.11
0.14 0.01 0.09
0.0 13.4 11.2
Change with Respect to Current Conditions
0.00 0.00 0.00
0.00 0.00 0.01
0.00 0.00 0.00
0.00 0.00 0.00
42.0 40.9 43.8
Change with Respect to Current Conditions
-0.25 -0.22 -0.45
-0.25 -0.21 -0.46
-0.25 -0.21 -0.48
-0.28 -0.22 -0.50
311.9 315.5 304.7
Change with Respect to Current Conditions
-0.33 -1.69 -2.17
-0.31 -1.71 -2.24
-0.28 -1.88 -2.48
-0.30 -1.80 -2.36
2040
11.4
0.00
0.00
0.00
0.00
10.7
0.00
0.00
-0.01
-0.01
10.3
-0.01
-0.01
0.00
-0.01
44.0
0.00
-0.02
-0.01
-0.02
286.4
-1.28
-1.31
-1.42
-1.39
Canola, durum wheat, fresh grapefruit, fresh orange, fresh tomato, hybrid poplar, oats, potato, processed
grapefruit, processed orange, processed tomato, rice, rye, silage, soft red winter wheat, soft white wheat, spring
barley, sugar beet, sugarcane, sweet sorghum, wheat for livestock grazing, winter barley.
B-5
-------
B.2 Forestry Sector
As with agricultural crops, ozone diminishes growth in most tree species, and our
analysis began by estimating how much of this diminished growth would be reversed under the
proposed ozone standards. Impacts are significantly higher in the forestry sector, especially in
hardwood species and species more prevalent in the southern regions. Impacts are more
significant for southern regions because of the higher baseline ozone concentrations in the South
Central and Southeast regions than for other regions with major forestry sectors. Higher initial
concentrations resulted in greater reductions to meet the proposed standards and, thus, higher
impacts to tree growth. This relationship also contributes to the larger changes in the forestry
sector as a whole.
B.2.1 Production and trices
Reducing ozone concentrations leads to increased forest growth, which is reflected in
increased production and lower prices in FASOMGHG. Some of the most substantial ozone
standard impacts occurred in saw and pulp log harvest quantities and prices. Compared with
current conditions, the current ozone standard had consistently higher production except for
softwood pulp logs, where production increased only marginally and at times fell below the
baseline estimates, especially in 2040. The most significant impacts occurred in hardwood saw
logs, where harvests were projected to be more than 20% higher than baseline harvest by 2040
(see Figure B-l). Additional policy stringency resulted in marginal additional impacts for each of
the forest products (see Sections 4, 5, and 6 for additional information on the impacts associated
with moving between ozone concentration standards). Figure B-l depicts the differences in
hardwood saw log production under current conditions, compared with each of the policy cases.
Table B-4 presents these changes in numerical form for all the log products. Table B-5 presents
the prices of forest products per cubic foot.
B-6
-------
1,500
3.000
2.500
J.DOW
— — Hardwood SawtimberCurrent Conditions
• Hardwood Sawtimber 75ppb
—•— Hardwood Sawtimber 15ppm
^^^— Hardwood Sawt i mber 11 ppm
^^— Hardwood Sawtimber 7ppm
Figure B-l. Hardwood Saw Log Production, Million Cubic Feet
Table B-4. Forest Products Production, Million Cubic Feet
Product
Hardwood saw logs
Hardwood pulp logs
Policy
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010
3,401
Change with
296
297
324
358
2,258
Change with
335
336
363
400
2020
3,405
Respect to Current
67
68
83
77
2,175
Respect to Current
102
103
119
137
2030
3,541
Conditions
235
245
272
270
2,585
Conditions
17
5
-13
-9
2040
3,929
915
916
910
901
2,306
223
237
244
254
(continued)
B-7
-------
Table B-4. Forest Products Production, Million Cubic Feet (continued)
Product
Softwood saw logs
Softwood pulp logs
Policy
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010
4,521
Change with
145
145
145
158
3,398
Change with
70
72
72
77
2020
5,049
Respect to Current
137
137
137
165
3,872
Respect to Current
51
54
64
68
2030
5,458
Conditions
156
157
156
167
4,241
Conditions
-1,134
-1,135
-1,129
-1,129
2040
6,223
473
481
473
521
4,567
-1,896
-1,908
-1,921
-1,927
The impact of reduced ozone concentrations on timber market prices was even more
substantial compared with current conditions, although these differences are also linked to
significant differences in projected baseline prices and the impacts of increased harvest
quantities.
Table B-5. Forest Product Prices, U.S. Dollars per Cubic Foot
Product
Hardwood saw logs
Policy
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010 2020
1.07 1.19
Change with Respect to Current
-0.38 -0.55
-0.38 -0.55
-0.38 -0.56
-0.39 -0.57
2030
1.11
Conditions
-0.72
-0.72
-0.73
-0.74
2040
1.05
-0.86
-0.86
-0.86
-0.87
(continued)
B-8
-------
Table B-5. Forest Product Prices, U.S. Dollars per Cubic Foot (continued)
Product
Hardwood pulp logs
Softwood saw logs
Softwood pulp logs
Policy
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010 2020
0.49 0.92
Change with Respect to Current
-0.25 -0.66
-0.25 -0.66
-0.25 -0.66
-0.25 -0.66
2.61 2.25
Change with Respect to Current
-0.30 -0.35
-0.31 -0.35
-0.31 -0.37
-0.32 -0.38
1.68 1.58
Change with Respect to Current
-0.26 -0.46
-0.27 -0.46
-0.27 -0.46
-0.28 -0.47
2030
0.86
Conditions
-0.42
-0.41
-0.41
-0.43
2.08
Conditions
-0.48
-0.49
-0.50
-0.51
1.82
Conditions
-0.48
-0.48
-0.48
-0.49
2040
0.80
-0.46
-0.46
-0.46
-0.48
1.88
-0.57
-0.58
-0.59
-0.60
1.59
-0.64
-0.64
-0.65
-0.66
B. 2.2 Forest Acres Harvested
Harvested acres are projected to decline as a result of higher productivity in the policy
cases (i.e., higher growth rates lead to increased quantities of timber per acre such that demand
for forestry products can be made by harvesting fewer acres). The most significant reductions
occurred in species found in the southern regions where the largest ozone reductions would
occur: bottomland hardwoods, oak-pine, and planted pine. The difference between the harvested
acres of hardwoods under current conditions and the policy scenarios widens significantly from
2010 to 2040, increasing for a difference of approximately 5% to more than 30%. Impacts to
total harvested acres of softwoods are not as large in terms of percentage change, with
differences ranging from 2% to 10% by year. Table B-6 presents the number of forest acres
harvested, by thousand acres.
B-9
-------
Table B-6. Forest Acres Harvested, Thousand Acres
Policy
Current
Total hardwood conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
Total softwood conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010 2020
13,777 11,685
Change with Respect to Current
644 -1,508
631 -1,503
745 -1,537
918 -1,638
16,971 16,119
Change with Respect to Current
363 -914
371 -902
369 -854
387 -781
2030
13,486
Conditions
-3,300
-3,320
-3,467
-3,557
15,278
Conditions
-538
-548
-556
-620
2040
14,601
-4,191
-4,138
-4,326
-4,615
18,273
-447
-474
-446
-385
B. 2.3 Forest Inventory
The impacts observed for forest inventory were similar to those presented in Section 5,
but the magnitude of the difference is much larger because the change in ozone concentrations
relative to current conditions is substantially larger. The model projected significant increases in
existing inventory for both hardwood and softwood species, although the increase was greater in
hardwood species. As with the crops, this difference is largely explained by differential
sensitivity to ozone between species. Hardwood species show a much higher sensitivity to ozone
levels and are thus modeled to respond more dramatically to reductions in ozone concentration.
The gap between current conditions and the policy cases widened over time as inventory
continued to accumulate, and by 2040, hardwood inventory in each policy case was nearly twice
the inventory under current conditions. Accompanying the presence of increased existing
inventory in each of the policy cases is that new inventory in the reduced ozone environment was
lower than for current conditions beginning in 2025, with the gap increasing over time.
Continued growth in existing inventory reduces the incentive for expansion of new inventory.
The largest incremental standard differences occurred in the forest inventory projections.
In general, there were incremental differences between alternative ozone standards, with the
greatest impact occurring at the 11 ppm-hr standard. Figures B-2 and B-3 depict FASOMGHG
B-10
-------
projections for existing and new inventory under current conditions compared with each of the
policy cases.
700,000
600,000
500,000
400,000
300,000
200,000
100,000
2010 2015 2020 2025 2030 2035 2040
Current Conditions —»—75ppb -A — ISppm ,". llppm I 7ppm
Figure B-2. Existing Forest Inventory, Million Cubic Feet
1 Rn nnn
1 fin nnn
1 /in nnn
1/0,000
i nn nnn
an nnn
fin nnn
A n nnn
40,000
?n nnn
— —
_ j - " "5n
4tf "" ""'
wg^^
—f^*1^
^2_
^s^
_^JT
•**^^
2010 2015 2020 2025 2030 2035 2040
Current Conditions • 75ppb A ISppm
•llppm i' 7ppm
Figure B-3. New Forest Inventory, Million Cubic Feet
B.3 Cross-Sectoral Policy Impacts
B. 3.1 Land Use
The general pattern observed for allocation of land use over time within these categories
was a decline in forestland coupled with relatively stable cropland and small increases in both
B-ll
-------
traditional pasture and cropland pasture. Forestland declines as productivity and inventory
increase because of decreased ozone concentrations, implying that less forestland would be
required to meet market demands. Cropland increased to accommodate increases in feed demand
induced by livestock product expansion. Acreage retained in CRP also increased compared with
current conditions, due to reduced pressure to convert previously agricultural lands back to
agriculture because of higher crop yields. Table B-7 presents the land use by major category per
thousand acres.
The incremental impact of more stringent ozone policies is apparent in most of these
cases, especially reforestation, afforestation, and both types of pasture. The largest impacts were
projected in afforestation. Under current conditions, afforestation was projected to decline from
2010 to 2030, followed by substantial growth to 2040. The policy cases not only decline at a
faster rate but also continue to decline after 2030. This divergence causes large differences in
later projections, with policy cases projecting less than 30% of the projected afforestation under
current conditions.
Table B-7. Land Use by Major Category, Thousand Acres
Existing forest
Reforested
Afforested
Policy
Current conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010 2020 2030
343,180 331,162 318,911
Change with Respect to Current Conditions
-1,337 -2,420 -4,467
-1,342 -2,424 -4,422
-1,383 -2,485 -4,394
-1,395 -2,609 -4,379
70,445 115,416 147,303
Change with Respect to Current Conditions
435 -2,472 -7,693
431 -2,460 -7,630
509 -2,479 -7,838
685 -2,674 -8,229
13,429 11,692 8,884
Change with Respect to Current Conditions
-773 -1,947 -4,319
-777 -1,951 -4,338
-784 -1,957 -4,374
-787 -2,051 -4,397
2040
317,690
-9,854
-9,857
-9,815
-9,796
176,004
-17,363
-17,324
-17,646
-18,290
14,708
-10,247
-10,266
-10,301
-10,325
(continued)
B-12
-------
Table B-7. Land Use by Major Category, Thousand Acres (continued)
Cropland
Pasture
Cropland pasture
Rangeland
CRP
Policy
Current conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010 2020 2030
312,035 315,471 306,401
Change with Respect to Current Conditions
-321 -1,687 -1,568
-308 -1,714 -1,630
-296 -1,804 -1,725
-277 -1,881 -1,843
84,036 84,617 85,487
Change with Respect to Current Conditions
394 497 1,323
394 497 1,327
428 497 1,345
428 470 1,311
46,722 45,332 54,413
Change with Respect to Current Conditions
871 2,665 3,767
859 2,693 3,778
867 2,857 3,839
860 3,085 3,974
302,210 301,104 300,049
Change with Respect to Current Conditions
000
000
000
000
36,141 33,535 33,535
Change with Respect to Current Conditions
393 945 945
396 948 948
384 935 935
384 935 935
2040
293,902
-506
-523
-621
-621
81,939
4,136
4,136
4,168
4,132
60,840
5,280
5,296
5,333
5,349
299,039
0
0
0
0
33,535
945
948
935
935
B.3.2 Welfare
Welfare impacts resulting from the implementation of policy cases followed the same
pattern between the agriculture and forestry sectors, although the magnitude of the impacts was
larger in forestry. Consumer surplus increased substantially in both the agricultural and forestry
B-13
-------
sectors in both cases, as higher productivity under reduced ozone conditions tends to increase
total production and reduce market prices. Because demand for most forestry and agricultural
commodities is inelastic, producer surplus tends to decline, with higher productivity as the effect
of falling prices on profits more than outweighs the effects of higher production levels.
Differences in agriculture between current conditions and the policy cases were small
percentage changes relative to the surplus values, representing changes of between 0.1% and
0.3% from 2010 to 2040. In later years, more stringent policy had a more pronounced impact on
declines in producer surplus, although the level of policy intervention has little impact in other
areas. Table B-8 provides consumer and producer surplus for the agricultural sectors under
current conditions, along with the change to surplus for each policy case. Table B-9 provides the
consumer and producer surplus in the forestry sector.
Table B-8. Consumer and Producer Surplus in Agriculture Sector, Million 2010 U.S.
Dollars
Consumer
surplus
Producer
surplus
Policy
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010
1,916,213
1,854
1,869
1,873
1,823
735,135
-9,771
-9,159
-8,297
-9,501
2015
2020
1,939,184 1,967,111 1
Change with
1,491
1,489
1,514
1,537
Respect to
1,031
1,032
1,043
1,066
836,572 821,723
Change with
-5,007
-6,262
-7,204
-6,880
Respect to
-6,650
-5,671
-5,637
-4,871
2025
,993,473
Current
1,867
1,873
1,918
1,971
859,440
Current
3,725
2,764
3,955
4,148
2030
2,022,038
Conditions
990
984
1,032
1,081
874,133
Conditions
4,853
4,943
5,085
5,117
2035
2,049,773
1,008
1,018
1,028
1,034
835,532
1,160
1,201
-2,252
108
2040
2,074,636
1,379
1,382
1,391
1,425
865,536
-2,227
-1,530
-38
763
B-14
-------
Table B-9. Consumer and Producer Surplus in Forestry Sector, Million 2010 U.S. Dollars
Policy
Current
Consumer surplus conditions
2010
717,612
2015
787
2020
,431 804,351
Change with
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
Producer surplus conditions
3,728
3,734
3,772
3,813
97,659
5
5
5
5
128
,803
,834
,852
,990
Respect
4,920
5,037
5,279
5,613
,377 157,483
Change with
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
-4,337
-4,347
-4,378
-4,473
-6
-6
-6
-6
,900
,907
,880
,948
Respect
-3,486
-3,628
-3,990
-4,378
2025
820
,803
to Current
5
5
5
5
149
,572
,677
,774
,795
,474
to Current
-3
-3
-3
-3
,199
,361
,377
,236
2030
866
,378
2035
888
,448
2040
925,814
Conditions
9
9
9
9
154
,242
,244
,930
,976
,962
6
6
6
6
149
,257
,292
,313
,348
,226
9,068
9,666
9,780
9,848
140,291
Conditions
-9
-9
-9
-9
,050
,034
,929
,835
-3
-3
-3
-2
,111
,157
,056
,955
-7,158
-7,997
-8,016
-7,924
The policy impact was more substantial in the forestry sector, especially in declines in
producer surplus. Consumer surplus was higher in each ozone standard case than under current
conditions by between 0.5% and 1.1%, whereas producer surplus was up to 6.4% less than under
current conditions. Incremental differences in the ozone standard scenarios were also more
apparent in forestry producer surplus: Additional declines occurred for each policy level until the
most stringent 7 ppm-hr case, where the policy impact declined or remained the same.
B. 3.3 Greenhouse Mitigation Potential
The capacity for both the agricultural and forest sectors to sequester carbon is enhanced
by each of the policy cases, with increasing magnitude as policy stringency is increased.
Although FASOMGHG projects fewer acres of forestland and total cropland, the accelerated
storage of carbon in trees and forestland and cropland soils outweighs any decline from
reductions in area. Carbon storage in both sectors is consistently higher in the policy cases, with
the gap widening over time. By 2040, the agricultural sector sequestered 8% more carbon under
the policy cases and the forestry sector between 15 and 17%, resulting in gains of nearly 14,000
million metric tons CCh equivalent (MMtCChe). Table B-10 presents the carbon sequestration
projections for current conditions and each policy case. Negative values under current conditions
B-15
-------
indicate sequestration or carbon storage. Negative values in the change rows indicate that the
alternative stores more carbon than current conditions. Figure B-4 depicts the forestry values.
Table B-10. Carbon Storage, MMtCO2e
Agriculture
Forestry
Policy
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010
-18,690
Change with
-58
-58
-60
-61
-73,577
Change with
-1,102
-1,103
-1,121
-1,152
2020
-15,246
Respect to Current
-118
-119
-123
-122
-75,239
Respect to Current
-3,932
-3,932
-4,035
-4,237
2030
-11,815
Conditions
-187
-188
-193
-193
-76,781
Conditions
-8,082
-8,099
-8,394
-8,915
2040
-7,863
-606
-610
-616
-615
-77,343
-11,840
-11,853
-12,433
-13,442
(70,000)
175,000)
(80,000)
(85,000)
(90,000)
(95,000)
— — Current Conditions —•—75ppb —*—15ppm llppm 7ppm
Figure B-4. Carbon Storage in Forestry Sector, MMtCChe
B-16
-------
Changes in forestry sector carbon sequestration are largely driven by changes in forest
management, which includes the increases in tree yield in the lower ozone environments. The
increased sequestration in this category outweighs losses in sequestration in the other major
forestry categories: afforestation and forest soil. Table B-l 1 presents the detailed changes in
forestry carbon sequestration.
Table B-ll. Forestry Carbon Sequestration, MMtCO2e
Afforestation, trees
Afforestation, soils
Forest management
Forest soils
Policy
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
Current
conditions
75ppb
15 ppm-hr
1 1 ppm-hr
7 ppm-hr
2010 2020 2030
-702 -1,579 -1,188
Change with Respect to Current Conditions
5 64 388
5 65 389
5 64 394
5 87 394
-732 -744 -851
Change with Respect to Current Conditions
41 206 478
41 206 480
41 207 482
41 212 485
-40,174 -38,847 -38,412
Change with Respect to Current Conditions
-1,164 -4,175 -8,854
-1,164 -4,174 -8,866
-1,181 -4,278 -9,159
-1,208 -4,509 -9,680
-28,270 -27,788 -27,485
Change with Respect to Current Conditions
77 222 475
77 222 470
81 229 465
82 239 466
2040
-2,028
974
976
981
981
-1,157
795
797
800
803
-35,366
-13,404
-13,418
-13,993
-15,000
-27,505
732
732
726
726
B-17
-------
B.4 Summary
Impacts to both sectors generally follow similar patterns, although they are generally
larger in the forestry sector. Not only are tree species more responsive to changes in ozone, but
some of the largest reductions to meet the proposed standards will occur in regions with large
forestry sectors: South Central, Southeast, and Rocky Mountains. Reductions in agricultural
regions are comparatively moderate. Productivity of both crops and forests is projected to
increase at each of the ozone standard levels examined. This increase in supply resulted in
decreased prices for forest products and agricultural commodities, which benefits consumer
welfare while reducing producer welfare. Unless there are significant changes to wood products
markets in particular, producers will be forced to sell at reduced prices in order for the increased
supply to be absorbed by the market.
Increased productivity is also projected to affect land use both within and between the
agricultural and forest sectors. Within sectors, acreage is generally projected to shift from crops
and tree species that are more sensitive to ozone to those that are less sensitive, because
productivity in the former will be more substantially affected by reductions in ozone
concentrations. For ozone-sensitive crops and species, producers are projected to require less
land to produce at the same or higher levels. Forest acreage in particular is projected to decline
sharply, driven by declines in both reforestation and afforestation.
Despite reductions in crop and forest area, carbon sequestration is expected to increase
over time, led almost entirely by increased forest sequestration. By 2040, the agricultural sector
sequestered 8% more carbon under the policy cases and the forestry sector between 15% and
17% more carbon, resulting in gains of nearly 14,000 MMtCChe.
B-18
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APPENDIX C
COUNTY-LEVEL AGRICULTURAL WELFARE ANALYSIS: FOCUSING ON
COMPARISON WITH CURRENT CONDITIONS
This appendix provides additional information on analyses of changes in county-level
agricultural welfare under the ozone standard scenarios relative to current conditions,
supplementing the information presented in Section 6. This section uses the comparison of
welfare when meeting the current standard versus current conditions as the representative
analysis. For incremental effects of the 15 ppm-hr, 11 ppm-hr, and 7 ppm-hr scenarios, see
Section 6.
Figure C-l presents the changes in county-level agricultural welfare for ozone
concentrations consistent with the current standard versus current conditions across the modeling
periods.
In 2010, the select crop producers' welfare in the Corn Belt region decreased under the
75 ppb scenario—this occurred because the prices of major agricultural commodities decline as
yields improve. Soybean production in the Corn Belt region expanded at the expense of corn
production. Soybeans have a much larger relative yield gain (RYG) than corn. The revenues lost
because of corn production contraction outweighed the revenues gained from expanding soybean
production (especially given lower prices for both commodities); therefore, the welfare in the
Corn Belt region decreased.
The welfare in the Mississippi River region (located in the South Central region) also
decreased because of the corn production contraction and soybean production expansion,
accompanied by falling prices for major agricultural products.
Although soybeans are a crop species that would benefit from reduced ozone
environments, soybean production in the Great Plains region decreased—this occurred because
soybeans have larger RYGs in the Corn Belt region than in the Great Plains region; hence,
soybean production shifted to the Corn Belt region. Correspondingly, corn and wheat production
in the Great Plains region increased. Nevertheless, the revenues lost because of soybean
production contraction are greater than the revenues gained from corn and wheat production
expansions; hence, we see welfare decreases in the Great Plains region. In the Rocky Mountains
region, the production of various kinds of wheat increased. However, in many counties in the
Rocky Mountains region, the effects of price decreases are greater than the effects of production
C-l
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increases on generating revenues. Hence, welfare decreases occurred in many of the counties in
the Rocky Mountains region.
Figure C-l. Changes in Crop Producer Welfare under 75 ppb with Respect to Current
Conditions, 2010-2040
In the Pacific Southwest region, wheat and cotton production increased because these two
crops have large RYGs under the 75 ppb scenario. Their production increases were large enough
relative to price declines (in part because this region is not a dominant producer of wheat or
cotton so has less influence on national prices) that they led to welfare increases in Pacific
Southwest region.
Similar patterns of welfare increases and decreases are shown for 2020, as presented in
Figure C-l.
C-2
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By 2030, in the South Central region, welfare increases instead of decreases start to
emerge for the majority of this region. This increase occurs because soybean production
expansion has gotten larger than it had been in earlier periods. Corn production increases also
accompany soybean production expansions as more land is freed from the forestry industry and
becomes pasture land for livestock production use, inducing greater demand for feed crop
production in the South Central region.
In addition, for the Corn Belt region, welfare decreases still occur in most of this region;
however, this time, the underlying reason is because some of the soybean production expansion
activities shifted to the South Central and Northeast regions, whereas corn production expanded
in part of the Corn Belt region. Overall, lower prices for the primary crops produced in the Corn
Belt are still leading to reductions in total producer surplus for the majority of this region.
Similar patterns of welfare increases and decreases are shown for 2040. The changes in
magnitudes are attributable to the price changes and production changes under new market
equilibriums.
C-3
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APPENDIX 6C: DISCUSSION OF SUPPLY CURVE SHIFTS
Economists often use consumer and producer surplus as metrics for measuring changes in
general welfare. Surplus occurs when the market price differs from what consumers are willing
to pay or what producers are willing to accept as payment. As the market price falls below what
some consumers are willing to pay this generates a consumer surplus for those consumers as
they are able to purchase a good or service for less than the maximum amount they are willing to
pay. Similarly, if the market price is greater than the minimum level that producers are willing to
accept as payment, then ^producer surplus is generated.
The nature of the impacts on, and the tradeoffs between, consumer and producer surplus
is related to the responsiveness of the quantities supplied and demanded to changes in price. The
degree of price responsiveness is calculated as the percentage change in that variable divided by
the percentage change in price, referred to as the elasticity of the variable. One of the most
commonly used elasticities is the own-price elasticity, calculated as the percentage change in
quantity demanded for a good or service divided by the percentage change in the price of that
good or service. If the own-price elasticity >1, consumers adjust the amount they are willing to
purchase by a greater percentage than the change in price and demand for the product is referred
to as elastic. When the own-price elasticity <1, on the other hand, demand is inelastic. For many
commodities that are relatively low-cost necessities, such as many agricultural goods, consumer
demand tends to be inelastic. This is depicted graphically in the steep slope of the demand curve
in Figure 6C-1.
At the alternative standard levels considered in this report, crop and forest yields
incorporated into FASOMGHG are higher under each of the more stringent alternative standards.
The yield increases led to increased quantities of agricultural and forest products, which impact
market price and therefore consumer and producer surplus. The precise nature of the tradeoffs
depends on a number of factors, including elasticities, but Figure 6C-1 provides an illustrative
example that is representative of FASOMGHG commodity projections in many markets.
The initial supply curve, quantity, and price are represented by So, Qo, and Po,
respectively. Consumer surplus is the area of the graph where the price consumers are willing to
pay as a function of quantity (D) is greater than the initial market price (Po), denoted by area A in
6C-1
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Figure 6C-1. Producer surplus is the area where producers are willing to accept (So) prices lower
than the market price, denoted by area B plus area C. As the supply of agricultural and forest
products increases due to increases in yields at more stringent alternative standards, the supply
curve shifts to the right (Si), representing more available product at any given price. The market
is now operating at a new equilibrium with a greater quantity produced and sold (Qi) and lower
prices (Pi). Consumer surplus now expands to include areas B, D, and E while producer surplus
loses area B and gains F and G. While consumers always benefit from such an outward shift in
the supply curve, the net change to producer surplus depends on the magnitude of the lost area B
in relation to gained areas F and G. If B is greater than F and G, producer surplus declines and
producers are worse off. Conversely, if B is less than what is gained in F and G, producers
benefit.
6C-2
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Figure 6C-1. Change in Consumer and Producer Surplus with Outward Supply Shift
P0 -
s>
6C-3
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1 APPENDIX 6D
2 i-Tree MODEL, METHODOLOGY, and RESULTS
3 6D.1 i-Tree Model Components
4 i-Tree version 4.0 offers several urban forest assessment applications including i-Tree
5 Eco, previously known as UFORE. The Urban Forest Effects (UFORE) model was developed to
6 aid in assessing urban forest structure, functions, and values (Nowak and Crane 2000). This
7 model contains protocols to measure and monitor urban forests as well as estimate ecosystem
8 functions and values.
9 The basic premise behind the UFORE model is that urban forest structure affects forest
10 functions and values. By having an accurate assessment of urban forest structure, better estimates
11 of functions and values can be produced. The model uses a sampling procedure to estimate
12 various measured structural attributes about the forest (e.g., species composition, number of
13 trees, diameter distribution) within a known sampling error. The model uses the measured
14 structural information to estimate other structural attributes (e.g., leaf area, tree and leaf biomass)
15 and incorporates local environmental data to estimate several functional attributes (e.g., air
16 pollution removal, carbon sequestration, building energy effects). Economic data from the
17 literature are used to estimate the value of some of the functions. The model includes the
18 following modules descriptions of which are excerpted from Nowak, 2008.
19 6D.2 Urban Forest Structure
20 Urban forest structure is the spatial arrangement and characteristics of vegetation in relation
21 to other objects (e.g., buildings) within urban areas (e.g., Nowak 1994a). This module quantifies
22 urban forest structure (e.g., species composition, tree density, tree health, leaf area, leaf and tree
23 biomass), value, diversity, and potential risk to pests.
24 6D.2.1 Sampling
25 Urban Forest Effect model assessments have used two basic types of sampling to
26 quantify urban forest structure: randomized grid and stratified random sampling. With the
27 randomized grid sampling, the study area is divided into equal-area grid cells based on the
28 desired number of plots and then one plot is randomly located within each grid cell. The study
6D-1
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1 area can then be subdivided into smaller units of analysis (i.e., strata) after the plots are
2 distributed (post-stratification). Plot distribution among the strata will be proportional to the
3 strata area. This random sampling approach allows for relatively easy assessment of changes
4 through future measurements (urban forest monitoring), but likely at the cost of increased
5 variance (uncertainty) of the population estimates. With stratified random sampling, the study
6 area is stratified before distributing the plots and plots are randomly distributed within each
7 stratum (e.g., land use). This process allows the user to distribute the plots among the strata to
8 potentially decrease the overall variance of the population estimate. For example, because tree
9 effects are often the primary focus of sampling, the user can distribute more plots into strata that
10 have more trees. The disadvantage of this approach is that it makes long-term change
11 assessments more difficult as a result of the potential for strata to change through time. There is
12 no significant difference in cost or time to establish plots regardless of sampling methods for a
13 fixed number of plots. However, there are likely differences in estimate precision. Pre-
14 stratification, if done properly, can reduce overall variance because it can focus more plots in
15 areas of higher variability. Any plot size can be used in UFORE, but the typical plot size used is
16 0.04 ha (0.1 ac). The number and size of plots will affect total cost of the data collection as well
17 as the variance of the estimates (Nowak et al. 2008).
18 6D.2.2 Data Collection Variables
19 There are four general types of data collected on a UFORE plot: 1) general plot
20 information used to identify the plot and its general characteristics; 2) shrub information used to
21 estimate shrub leaf area/biomass, pollution removal, and volatile organic compound (VOC)
22 emissions by shrubs; 3) tree information used to estimate forest structural attributes, pollution
23 removal, VOC emissions, carbon storage and sequestration, energy conservation effects, and
24 potential pest impacts of trees; and 4) ground cover data used to estimate the amount and
25 distribution of various ground cover types in the study area. Typically, shrubs are defined as
26 woody material with a diameter at breast height (dbh; height at 1.37 m [4.5 ft]) less than 2.54 cm
27 (1 in), whereas trees have a dbh greater than or equal to 2.54 cm (1 in). Trees and shrubs can also
28 be differentiated by species (i.e., certain species are always a tree or always a shrub) or with a
29 different dbh minimum threshold. For example, in densely forested areas, increasing the
30 minimum dbh to 12.7 cm (5 in) can substantially reduce the field work by decreasing the number
6D-2
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1 of trees measured, but less information on trees will be attained. Woody plants that are not 30.5
2 cm (12 in) in height are considered herbaceous cover (e.g., seedlings). Shrub masses within each
3 plot are divided into groups of same species and size, and for each group, appropriate data are
4 collected. Tree variables are collected on every measured tree. Field data are collected during the
5 in-leaf season to help assess crown parameters and health. More detailed information on plot
6 data collection methods and equipment can be found in the i-Tree User's Manual (i-Tree 2008).
7 6D.2.3 Leaf Area and Leaf Biomass
8 Leaf area and leaf biomass of individual open-grown trees (crown light exposure [CLE]
9 of 4 to 5) are calculated using regression equations for deciduous urban species (Nowak 1996). If
10 shading coefficients (percent light intensity intercepted by foliated tree crowns) used in the
11 regression did not exist for an individual species, genus or hardwood averages are used. For
12 deciduous trees that are too large to be used directly in the regression equation, average leaf area
13 index (LAI: m2 leaf area per m2 projected ground area of canopy) is calculated by the regression
14 equation for the maximum tree size based on the appropriate height-width ratio and shading
15 coefficient class of the tree. This LAI is applied to the ground area (m2) projected by the tree's
16 crown to calculate leaf area (m2). For deciduous trees with height-to-width ratios that are too
17 large or too small to be used directly in the regression equations, tree height or width is scaled
18 downward to allow the crown to the reach maximum (2) or minimum (0.5) height-to-width ratio.
19 Leaf area is calculated using the regression equation with the maximum or minimum ratio; leaf
20 area is then scaled back proportionally to reach the original crown volume. For conifer trees
21 (excluding pines), average LAI per height to-width ratio class for deciduous trees with a shading
22 coefficient of 0.91 is applied to the tree's ground area to calculate leaf area. The 0.91 shading
23 coefficient class is believed to be the best class to represent conifers because conifer forests
24 typically have approximately 1.5 times more LAI than deciduous forests (Barbour et al. 1980)
25 and 1.5 times the average shading coefficient for deciduous trees (0.83; see Nowak 1996) is
26 equivalent to LAI of the 0.91 shading coefficient. Because pines have lower LAI than other
27 conifers and LAI that are comparable to hardwoods (e.g., Jarvis and Leverenz 1983; Leverenz
28 and Hinckley 1990), the average shading coefficient (0.83) is used to estimate pine leaf area.
29 Leaf biomass is calculated by converting leaf area estimates using species-specific
30 measurements of grams of leaf dry weight/m2 of leaf area. Shrub leaf biomass is calculated as the
6D-3
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1 product of the crown volume occupied by leaves (m3) and measured leaf biomass factors (g/m3)
2 for individual species (e.g., Winer et al. 1983; Nowak 1991). Shrub leaf area is calculated by
3 converting leaf biomass to leaf area based on measured species conversion ratios (m2/g). As a
4 result of limitations in estimating shrub leaf area by the crown-volume approach, shrub leaf area
5 is not allowed to exceed a LAI of 18. If there are no leaf-biomass to-area or leaf-biomass-to-
6 crown-volume conversion factors for an individual species, genus or hardwood/conifer averages
7 are used. For trees in more forest stand conditions (higher plant competition), LAI for more
8 closed canopy positions (CLE 0-1) is calculated using a forest leaf area formula based on the
9 Beer-Lambert Law:
10 LAI = In(IIo)-k
11 where: I =light intensity beneath canopy; lo = light intensity above canopy; and k=light
12 extinction coefficient (Smith et al. 1991). The light extinction coefficients are 0.52 for conifers
13 and 0.65 for hardwoods (Jarvis and Leverenz 1983). To estimate the tree leaf area (LA):
14 LA = [ln(l - xs)-k] x r2
15 where xs is average shading coefficient of the species and r is the crown radius. For CLE 2-3:
16 LA is calculated as the average of leaf area from the open-grown (CLE 4-5) and closed canopy
17 equations (CLE 0-1). Estimates of LA and leaf biomass are adjusted downward based on crown
18 leaf dieback (tree condition). Trees are assigned to one of seven condition classes: excellent (less
19 than 1% dieback); good (1% to 10% dieback); fair (11% to 25% dieback); poor (26% to 50%
20 dieback); critical (51% to 75% dieback); dying (76% to 99% dieback); and dead (100% dieback).
21 Condition ratings range between 1 indicating no dieback and 0 indicating 100% dieback (dead
22 tree). Each class between excellent and dead is given a rating between 1 and 0 based on the
23 midvalue of the class (e.g., fair = 11% to 25% dieback is given a rating of 0.82 or 82% healthy
24 crown). Tree leaf area is multiplied by the tree condition factor to produce the final LA estimate.
25 6D.2.4 Carbon Storage and Annual Sequestration
26 This module calculates total stored carbon and gross and net carbon sequestered annually
27 by the urban forest. Biomass for each measured tree is calculated using allometric equations
28 from the literature (see Nowak 1994c; Nowak et al. 2002b). Equations that predict aboveground
6D-4
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1 biomass are converted to whole tree biomass based on a root-to-shoot ratio of 0.26 (Cairns et al.
2 1997). Equations that compute fresh weight biomass are multiplied by species- or genus-specific
3 conversion factors to yield dry weight biomass. These conversion factors, derived from average
4 moisture contents of species given in the literature, averaged 0.48 for conifers and 0.56 for
5 hardwoods (see Nowak et al. 2002b). Open-grown, maintained trees tend to have less
6 aboveground biomass than predicted by forest-derived biomass equations for trees of the same
7 dbh (Nowak 1994c). To adjust for this difference, biomass results for urban trees are multiplied
8 by a factor of 0.8 (Nowak 1994c). No adjustment is made for trees found in more natural stand
9 conditions (e.g., on vacant lands or in forest preserves). Because deciduous trees drop their
10 leaves annually, only carbon stored in wood biomass is calculated for these trees. Total tree dry
11 weight biomass is converted to total stored carbon by multiplying by 0.5 (Forest Products
12 Laboratory 1952; Chow and Rolfe 1989). The multiple equations used for individual species
13 were combined to produce one predictive equation for a wide range of diameters for individual
14 species. The process of combining the individual formulas (with limited diameter ranges) into
15 one more general species formula produced results that were typically within 2% of the original
16 estimates for total carbon storage of the urban forest (i.e., the estimates using the multiple
17 equations). Formulas were combined to prevent disjointed sequestration estimates that can occur
18 when calculations switch between individual biomass equations. If no allometric equation could
19 be found for an individual species, the average of results from equations of the same genus is
20 used. If no genus equations are found, the average of results from all broadleaf or conifer
21 equations is used
22 6D.2.5 Urban Tree Growth and Carbon Sequestration
23 To determine a base growth rate based on length of growing season, urban street tree
24 (Fleming 1988; Frelich 1992; Nowak 1994c), park tree (deVries 1987), and forest growth
25 estimates (Smith and Shifley 1984) were standardized to growth rates for 153 frost-free days
26 based on: standardized growth = measured growth x (153/number of frost-free days of
27 measurement). Average standardized growth rates for street (open-grown) trees were 0.83
28 cm/year (0.33 in/year). Growth rates of trees of the same species or genera were then compared
29 to determine the average difference between standardized street tree growth and standardized
30 park and forest growth rates. Park growth averaged 1.78 times less than street trees, and forest
6D-5
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1 growth averaged 2.29 times less than street tree growth. Crown light exposure measurements of
2 0 to 1 were used to represent forest growth conditions; 2 to 3 for park conditions; and 4 to 5 for
3 open-grown conditions. Thus, the standardized growth equations are:
4 Standardized growth (SG) _ 0.83 cm/year (0.33 in/year) x number of frost free days/153
5 and for: CLE 0-1: Base growth=SG/2.26; CLE 2-3: base growth=SG /1.78; and CLE 4-5: base
6 growth= SG. Base growth rates are adjusted based on tree condition. For trees in fair to excellent
7 condition, base growth rates are multiplied by 1 (no adjustment), poor trees' growth rates are
8 multiplied by 0.76, critical trees by 0.42, dying trees by 0.15, and dead trees by 0. Adjustment
9 factors are based on percent crown dieback and the assumption that less than 25% crown dieback
10 had a limited effect on dbh growth rates. The difference in estimates of carbon storage between
11 year x and year x + 1 is the gross amount of carbon sequestered annually.
12 6D.2.5 Air Pollution Removal
13 This module quantifies the hourly amount of pollution removed by the urban forest, its
14 value, and associated percent improvement in air quality throughout a year. Pollution removal
15 and percent air quality improvement are calculated based on field, pollution concentration, and
16 meteorologic data. This module is used to estimate dry deposition of air pollution (i.e., pollution
17 removal during nonprecipitation periods) to trees and shrubs (Nowak et al. 1998, 2000). This
18 module calculates the hourly dry deposition of ozone (Os), sulfur dioxide (SCh), nitrogen dioxide
19 (NCh), carbon monoxide (CO), and particulate matter less than nominal diameter of 10 um
20 (PMio) to tree and shrub canopies throughout the year based on tree-cover data, hourly NCDC
21 weather data, and U.S. Environmental Protection Agency pollution concentration monitoring
22 data. The pollutant flux (F; in g/m2/s) is calculated as the product of the deposition velocity (Vd;
23 in m/s) and the pollutant concentration (C; in g/m3):
24 F = Vd x C
25 Deposition velocity is calculated as the inverse of the sum of the aerodynamic (Ra), quasilaminar
26 boundary layer (Rb), and canopy (Re) resistances (Baldocchi et al. 1987):
27 Vd = (Ra + Rb + Rc)-l
6D-6
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1 Hourly meteorologic data from the closest weather station (usually airport weather
2 stations) are used in estimating Ra and Rb. In-leaf, hourly tree canopy resistances for Os, SCh,
3 and NCh are calculated based on a modified hybrid of big leaf and multilayer canopy deposition
4 models (Baldocchi et al. 1987; Baldocchi 1988). Because CO and removal of particulate matter
5 by vegetation are not directly related to transpiration, Re for CO is set to a constant for in-leaf
6 season (50,000 sec/m [15,240 sec/ft]) and leaf-off season (1,000,000 sec/m [304,800 sec/ft])
7 based on data from Bidwell and Fraser (1972). For particles, the median deposition velocity from
8 the literature (Lovett 1994) is 0.0128 m/s (0.042 ft/s) for the in-leaf season. Base particle Vd is
9 set to 0.064 m/s (0.021 ft/s) based on a LAI of 6 and a 50% resuspension rate of particles back to
10 the atmosphere (Zinke 1967). The base Vd is adjusted according to actual LAI and in-leaf versus
11 leaf-off season parameters. Bounds of total tree removal of Os, NO2, SO2, and PMio are
12 estimated using the typical range of published in-leaf dry deposition velocities (Lovett 1994).
13 Percent air quality improvement is estimated by incorporating local or regional boundary layer
14 height data (height of the pollutant mixing layer). More detailed methods on this module can be
15 found in Nowak et al. (2006a). The monetary value of pollution removal by trees is estimated
16 using the median externality values for the United States for each pollutant. These values, in
17 dollars per metric ton (mt) are: NO2, $6,752 mt-1; PMio, $4,508 mt-1; SO2, $1,653 mt-1; and
18 CO, $959 mt-1 (Murray et al. 1994). Values in dollars per short ton, commonly used in the U.S.,
19 are approximated 90% of those per metric ton. Recently, these values were adjusted to 2007
20 values based on the producer's price index (Capital District Planning Commission 2008) and are
21 now: NO2, $9,906 mt-1; PMio, $6,614 mt-1; SO2, $2,425 mt-1; and CO, $1,407 mt-1.
22 Externality values for Os are set to equal the value for NO2.
23 6.D.3 i-Tree Forecast Prototype Model Methods and Results
24 The i-Tree Forecast Prototype Model was built to simulate future forest structure (e.g.,
25 number of trees and sizes) and various ecosystem services based on annual projections of the
26 current forest structure data. There are 3 main components of the model:
27 1) Tree growth - simulates tree growth to annually project tree diameter, crown size and
28 leaf area for each tree
29 2) Tree mortality - annually removes trees from the projections based on user defined
30 mortality rates
6D-7
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1 3) Tree establishment - annually adds new trees to the projection. These inputs can be used
2 to illustrate the effect of the new trees or determine how many new trees need to be added
3 annually to sustain a certain level of tree cover or benefits.
4 6D.3.1. Tree Growth
5 Annual tree diameter growth is estimated for the region based on: 1) the length of
6 growing season, 2) species average growth rates, 3) tree competition, 4) tree condition, and 5)
7 current tree height relative to maximum tree height.
8 To determine a base growth rate based on length of growing season, urban street tree,
9 park tree, and forest growth estimates were standardized to growth rates for 153 frost free days
10 based on: Standardized growth = measured growth x (1537 number of frost free days of
11 measurement). Growth rates of trees of the same species or genera were also compared to
12 determine the average difference between standardized street tree growth and standardized park
13 and forest growth rates. Park growth averaged 1.78 times less than street trees, and forest growth
14 averaged 2.29 times less than street tree growth.
15 For this study, average standardized growth rates for open-grown (street) trees was input
16 as 0.26 in/yr for slow growing species, 0.39 in/yr for moderate growing species and 0.52 in/yr for
17 fast growing species. Crown light exposure (CLE) measurements of 0-1 were used to represent
18 forest growth conditions; 2-3 for park conditions; and 4-5 for open-grown conditions. Thus, for:
19 CLE 0-1: Base growth = Standardized growth (SG) 7 2.26; CLE 2-3: Base growth = SG 7 1.78;
20 and CLE 4-5: Base growth = SG. However, as the percent canopy cover increased or decreased,
21 the CLE correction factors were adjusted proportionally to the amount of available greenspace
22 (i.e., as tree cover dropped and available greenspace increased - the CLE adjustment factor
23 dropped; as tree cover increased and available greenspace dropped - the CLE adjustment factor
24 increased).
25 Base growth rates are also adjusted based on tree condition. For trees in fair to excellent
26 condition, base growth rates are multiplied by 1 (no adjustment), Trees in poor condition by
27 0.76, critical trees by 0.42, dying trees by 0.15, and dead trees by 0. Adjustment factors are based
28 on percent crown dieback and the assumption that less than 25 percent crown dieback had a
29 limited effect on dbh growth rates.
6D-8
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1 As trees approach their estimated maximum height, growth rates are reduced. Thus the
2 species growth rates as described above were adjusted based on the ratio between the current
3 height of the tree and the average height at maturity for the species. When a tree's height is over
4 80% of its average height at maturity, the amount of annual dbh growth is proportionally reduced
5 from full growth at 80% of height to /^ growth rate at height at maturity. The growth rate is
6 maintained at /^ growth until the tree is 125% past maximum height, when the growth rate is
7 then reduced to 0 in/yr.
8 Tree height, crown width, crown height and leaf area were then estimated based on tree
9 diameter each year. Height, crown height and crown width are calculated using species, genus,
10 order and family specific equations that were derived from measurements from urban tree data
11 (publication in preparation). If there was no equation for a particular species, then genus
12 equation was used, followed by the family and order equations if necessary. If no order equation
13 could be used, one average equation for all trees was used to estimate these parameters. Leaf area
14 was calculated from the crown height, tree height and crown width estimates based on standard i-
15 Tree methods.
16 Total canopy cover was calculated by summing the crown area of each tree in the
17 population. This estimate of crown area was adjusted to attain the actual tree cover of the study
18 area based on photo-interpretation. As trees often have overlapping crown, the sum of the crown
19 areas will often over estimate total tree cover as determined by aerial estimates. Thus the crown
20 overlap can be determined by comparing the two estimates:
21 % crown overlap = (sum of crown area - actual tree cover area) / sum of crown area
22
23 When future projections predicted an increase in percent canopy cover, the percent crown
24 overlap was held constant However, when 100% canopy cover was attained all new canopy
25 added was considered as overlapping canopy. When there was a projected decrease in percent
26 canopy cover, the percent crown overlap decreased in proportion to the increase in the amount of
27 available greenspace (i.e., as tree cover dropped and available greenspace increased - the crown
28 overlap decreased).
6D-9
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1 6D.3.2 Tree Mortality Rate
2 Canopy dieback is the first determinant for tree mortality with trees 50 - 75% dieback
3 having a mortality rate of 13.1% annual mortality rate; trees with 76-99% dieback having a 50%
4 annual mortality rate, and trees with 100% dieback having a 100% annual mortality rate. Trees
5 with less than 50% dieback have a user defined mortality rate that is adjusted based on the tree
6 size class and the current tree dbh.
7 Trees are placed into species size classes where small trees have an average height at
8 maturity of less than or equal to 40 ft (maximum dbh class = 20+ inches), medium trees have
9 mature tree height of 41- 60 ft (maximum dbh = 30 inches), and large trees have a mature height
10 of greater than 60 ft (maximum dbh = 40 inches). Each size class has a unique set of 7 DBH
11 ranges to which base mortality rates area assigned based on measured tree mortality by dbh class.
12 The same distribution of mortality by dbh class was used for all tree size classes, but the range of
13 the dbh classes differed by size class. The actual mortality rate for each dbh class was adjusted so
14 that the overall average mortality rate for the base population equaled the mortality rates
15 assigned by user. That is, the relative curve of mortality stayed the same among dbh classes, but
16 the actual values would change based on the user defined overall average rate.
17 6D.3.3. Tree Establishment
18 Based on the desired canopy cover level and the number of years desired to reach that
19 canopy level, the program calculates the number of trees needed to be established annually to
20 reach that goal given the model growth and mortality rate. In adding new trees to the model each
21 year, the species composition of new trees was assumed to be proportional to the current species
22 composition. Crown light exposure of newly established trees was also assumed to be
23 proportional to the current growth structure of the canopy. Newly established trees were input
24 with a starting dbh of 1 inch.
25 6D.4 Ozone Effects Analysis Methods
26 For this Risk and Exposure Assessment the U.S. Forest Service volunteered their
27 expertise to develop the methodology and run the i-Tree model to project the impact of ozone on
28 carbon sequestration and air pollution removal in selected urban areas. EPA provided CMAQ
6D-10
-------
1 model generated W126 results for current ambient ozone concentrations and for a model
2 adjusted scenario that just meets the current standard. These methods are described in Chapter 4
3 Air Quality Considerations. For the effects of Os, we used the concentration-response functions
4 for the 11 tree species analyzed in Chapter 5 to reduce the growth of the trees over a 25 period
5 and compared base model estimates (full-growth) with Os effected results (reduced growth).
6 Tree growth was only reduced in analyzed cities for the 11 species that had W126 equations.
7 We used a new forecast model (Nowak, 2012) components of which are described above
8 in the sections on tree growth, mortality, and establishment. This model simulated tree growth,
9 tree influx and mortality annually to estimate annual changes in number of trees, tree cover and
10 stored carbon. For these scenarios, we adjusted the annual mortality (3 or 4%) and influx rate
11 (between 1 and 6 trees / ha / yr) to keep canopy cover as close to current values as possible after
12 25 years. These base assumptions were consistent in both runs (full and reduced growth). Species
13 composition of new trees added annually was proportional to the current species population.
14 Carbon estimates: total carbon storage at the end of the 25 year period was contrasted
15 between the model runs to estimate the impact of reduced growth due to Cb. Differences in
16 number of trees and tree sizes at the end of 25 years will affect the carbon estimate.
17 Pollution removal: pollution removal was based calculating the average tons of air
18 pollutants removed. The forecast model was then used to project differences in estimated tree
19 cover (m2) between the model runs for each of the 25 years. These annual tons of pollutants
20 removed were summed to estimate the total impact over 25 years.
21 All model runs use the same assumptions, so difference in the estimates are due to
22 reduced growth. However, the magnitude of the impact over 25 years will be affected by the
23 assumptions. As you change the mortality and influx rates, the magnitude of the differences
24 between the model runs will differ. We tried to use reasonable estimates based on limited data on
25 mortality and influx rates. We used Nowak (2012, in press) to help estimate an influx rate and
26 the attached paper on tree mortality to estimate a mortality rate, but we reduced the rate to 3-4%
27 as forest stands are around 1% and the mortality in this paper was around 6%. We have limited
28 mortality data for urban trees, but based on the data and our experience, we believe 3-4% to be
29 reasonable, but it likely varies somewhere between 1% and 5%.
6D-11
-------
1 6D.5 i-Tree Results for O3 Welfare REA
2 Table 6D-1 Top Ten Most Common Tree Species (Species with available C-R functions
3 highlighted in red)
Rank
1
2
3
4
5
6
7
8
9
10
Percent of Top 10
species with C-R
function
Percent of Total Tree
Species with C-R
function
Baltimore
American beech
Black locust
American elm
Tree of heaven
White ash
Black cherry
White mulberry
Northern red oak
Red maple
White oak
8.5%
11.2%
Syracuse
European
buckthorn
Sugar maple
Black cherry
Boxelder
Norway maple
Northern white
cedar
Norway spruce
Staghorn sumac
Eastern
cottonwood
Eastern
hophornbeam
18.5%
20.2%
Chicago
European
buckthorn
Green ash
Boxelder
Black cherry
Hardwood
American elm
Sugar maple
White ash
Amur
honeysuckle
Silver maple
7.7%
10.5%
Atlanta
Sweetgum
Loblolly pine
Flowering
dogwood
Tulip tree
Water oak
Boxelder
Black cherry
White oak
Red maple
Southern red
oak
6.6%
8.9%
Tennessee
Chinese privet
Virginia pine
Eastern red
cedar
Hackberry
Flowering
dogwood
Amur
honeysuckle
Winged elm
Red maple
Black tupelo
American
beech
9.3%
17.4%
5 Summary: Data from 5 urban areas were simulated to estimate the effect of Os (based on
6 the W126 index) on tree ecosystem services of carbon storage and air pollution removal. There
7 were 6 runs: Standard run (i-Tree no adjustments), recent conditions (ozone adjusted), existing
8 standard (75 ppb), adjusted to 15 ppm-hrs, adjusted to 11 ppm-hrs, adjusted to 7 ppm-hs. Runs
9 for 75 ppb (existing standard) and adjusted to 15 ppm-hrs had the same results. The prototype i-
10 Tree Forecast model was used to estimate growth and ecosystem services by trees over a 25-year
11 period. Tree data from the urban areas were loaded in the Forecast model as a base case scenario
12 and simulated for 25 years. The tree growth was then adjusted downward based on the reduced
6D-12
-------
1 growth factors for 11 species using the W126 protocol and equations (only W126 species had
2 reduced growth). The differences between the scenarios are then contrasted (Standard = base
3 case; Os adjusted = W126 reduced growth) for the 25-year period. The model assumed an annual
4 influx of between 1-6 trees/ha/yr and a 3-4% annual mortality rate. These values are updated
5 based on new adjusted RYL values.
6 Table 6D-2 Os-Adjusted Canopy Cover for Recent Conditions, Existing and Alternative
7 W126 Standard Levels by Region
Region
Atlanta
Baltimore
Chicago
Syracuse
Tennessee
Area (ha)
34,139
20,917
993,036
6,501
630,614
Percent Canopy
Cover
Recent
52.1
28.5
21
26.9
37.7
After
25yrs
51.9
29.2
20.9
27.8
37.6
Os-Adjusted Percent Canopy Cover (after 25 years)
Recent
Conditions
45.2
26.5
19.1
24.3
34.9
Existing
Standard &
15 ppm-hrs
49.6
29.1
19.2
27.6
37.1
11 ppm-hrs
22.212
18.769
7.311
7.812
16.441
7 ppm-hrs
5.836
0.372
4.582
0.104
4.267
10 Table 6D-3
11
Relative Year Loss Index, Regeneration Rate, and Annual Tree Mortality
by Region
Region
Atlanta
Baltimore
Chicago
Syracuse
Tennessee
Relative Yield Loss Index
Recent Conditions
22.212
18.769
7.311
7.812
16.441
Existing Standard & 15 ppm-hrs
5.836
0.372
4.582
0.104
4.267
Regeneration Rate
(trees per ha)
2
2
1
6
1
Annual Percent
Mortality
4%
3%
3%
3%
4%
12
6D-13
-------
1 Table 6D-4 Pollution Removal over 25 years for Recent Conditions, Existing and
2 Alternative W126 Standards by Region (in metric tons)
Pollutant
and
Region
Standard
Run
Recent
Conditions
Existing
Standard
&15
ppm-hrs
11 ppm-
hrs
7 ppm-
hrs
Relative to Recent Conditions
Existing
Standard
&15
ppm-hrs
11 ppm-
hrs
7 ppm-
hrs
CO
Atlanta
Baltimore
Chicago
Syracuse
Tennessee
1,482
186
8,620
55
12,854
1,315
171
7,916
49
11,731
1,429
186
8,001
55
12,626
1,432
186
8,050
55
12,757
1,448
186
8,143
55
12,916
113
15
85
6
895
117
15
134
6
1,026
133
15
227
6
1,185
NO2
Atlanta
Baltimore
Chicago
Syracuse
Tennessee
6,852
1,968
104,247
50
54,381
6,081
1,809
95,738
45
49,632
6,605
1,963
96,766
50
53,419
6,621
1,963
97,364
50
53,973
6,693
1,963
98,489
50
54,645
524
155
1,028
6
3,788
540
155
1,626
6
4,341
613
155
2,751
6
5,013
03
Atlanta
Baltimore
Chicago
Syracuse
Tennessee
25,495
6,262
243,701
1,544
393,205
22,625
5,755
223,811
1,367
358,861
24,574
6,247
226,214
1,541
386,247
24,634
6,247
227,612
1,541
390,251
24,905
6,247
230,242
1,541
395,107
1,949
492
2,403
173
27,387
2,009
492
3,801
173
31,391
2,279
492
6,431
173
36,246
PMio
Atlanta
Baltimore
Chicago
Syracuse
Tennessee
20,009
4,242
171,106
840
175,883
17,756
3,899
157,140
743
160,521
19,286
4,232
158,827
838
172,771
19,333
4,232
159,809
838
174,562
19,545
4,232
161,655
838
176,734
1,530
333
1,687
94
12,250
1,577
333
2,669
94
14,041
1,789
333
4,515
94
16,213
S02
Atlanta
3,380
2,999
3,257
3,265
3,301
258
266
302
6D-14
-------
Pollutant
and
Region
Baltimore
Chicago
Syracuse
Tennessee
Standard
Run
852
29,675
71
59,371
Recent
Conditions
783
27,253
63
54,185
Existing
Standard
&15
ppm-hrs
850
27,546
71
58,320
11 ppm-
hrs
850
27,716
71
58,925
7 ppm-
hrs
850
28,036
71
59,658
Relative to Recent Conditions
Existing
Standard
&15
ppm-hrs
67
293
8
4,135
11 ppm-
hrs
67
463
8
4,740
7 ppm-
hrs
67
783
8
5,473
Total
Atlanta
Baltimore
Chicago
Syracuse
Tennessee
57,218
13,510
557,348
2,561
695,693
50,776
12,416
511,859
2,267
634,929
55,151
13,478
517,354
2,555
683,384
55,285
13,478
520,551
2,555
690,468
55,892
13,478
526,566
2,555
699,059
4,374
1,061
5,495
287
48,455
4,509
1,061
8,692
287
55,539
5,116
1,061
14,707
287
64,130
3 Table 6D-5 Carbon Storage after 25 years for Recent Conditions, Existing and
4 Alternative W126 Standards by Region (metric tons)
Region
Atlanta
Baltimore
Chicago
Syracuse
Tennessee
Current Carbon
Storage
1,331,096
598,533
17,480,805
181,382
17,020,383
Carbon Storage using
Standard Growth
Rates (normal i-Tree
run unadjusted)
1,426,626
577,824
19,560,361
169,356
20,568,155
Carbon Storage using Os Response-Adjusted
Growth Rates
Recent
Conditions
1,214,656
492,553
16,949,766
141,145
17,999,081
Existing
Standard
&15
ppm-hrs
1,314,673
570,680
17,052,562
167,869
19,668,486
11 ppm-
hrs
1,321,110
570,680
17,103,025
167,869
19,891,847
7 ppm-hrs
1,345,896
570,680
17,214,633
167,869
20,157,865
6D-15
-------
1 Table 6D-6
2
Change in Carbon Storage using Os Response-Adjusted Growth Rates after
25 years (relative to Unadjusted Rates)
Region
Atlanta
Baltimore
Chicago
Syracuse
Tennessee
Change in Carbon Storage (metric tons)
Recent
Conditions
-211,971
-85,271
-2,610,596
-28,210
-2,569,074
Existing
Standard
&15
ppm-hrs
-111,954
-7,144
-2,507,799
-1,486
-899,670
11 ppm-
hrs
-105,517
-7,144
-2,457,336
-1,486
-676,308
7 ppm-
hrs
-80,730
-7,144
-2,345,728
-1,486
-410,291
Change in Valuation (at $78.5 per metric ton)
Recent
Conditions
-$16,639,715
-$6,693,780
-$204,931,749
-$2,214,523
-$201,672,305
Existing
Standard &
15 ppm-hrs
-$8,788,364
-$560,813
-$196,862,261
-$116,683
-$70,624,078
11 ppm-hrs
-$8,283,055
-$560,813
-$192,900,876
-$116,683
-$53,090,178
7 ppm-hrs
-$6,337,302
-$560,813
-$184,139,650
-$116,683
-$32,207,827
5 Table 6D-7
6
Change in Carbon Storage using Os-Response-Adjusted Growth Rates after
25 years (relative to Recent Conditions)
Region
Atlanta
Baltimore
Chicago
Syracuse
Tennessee
Change in Carbon Storage (metric tons)
Existing
Standard &
15 ppm-hrs
100,017
78,127
102,796
26,724
1,669,404
11 ppm-hrs
106,454
78,127
153,260
26,724
1,892,766
7 ppm-hrs
131,241
78,127
264,868
26,724
2,158,783
Change in Valuation (at $78.5 per metric ton)
Existing
Standard &
15 ppm-hrs
$7,851,350
$6,132,967
$8,069,488
$2,097,840
$131,048,227
11 ppm-hrs
$8,356,660
$6,132,967
$12,030,872
$2,097,840
$148,582,126
7 ppm-hrs
$10,302,412
$6,132,967
$20,792,099
$2,097,840
$169,464,478
6D-16
-------
1 6D.6 References
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3 deciduous oak forest canopy. Atmospheric Environment 22:869-884.
4
5 Baldocchi, D.D.; Hicks, B.B. and Camara, P. (1987). A canopy stomatal resistance model for
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9 Bidwell, R.G.S., and D.E. Fraser. (1972). Carbon monoxide uptake and metabolism by leaves.
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13 Chow, P., and Rolfe, G.L. (1989). Carbon and hydrogen contents of short-rotation biomass of
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18 Rutgers University, New Brunswick, NJ. 143 pp.
19 Forest Products Laboratory. (1952). Chemical Analyses of Wood. Tech. Note 235. U.S.
20 Department of Agriculture, Forest Service, Forest Products Laboratory, Madison, WI. 4 pp.
21 Frelich, L.E. (1992). Predicting Dimensional Relationships for Twin Cities Shade Trees.
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26 Berlin, Germany.
6D-17
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1 Leverenz, J.W., and Hinckley, T.M. 1990. Shoot structure, leaf area index and productivity of
2 evergreen conifer stands. Tree Physiology 6:135-149.
3 Lovett, G.M. (1994). Atmospheric deposition of nutrients and pollutants in North America: An
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22 Ecosystem: Results of the Chicago Urban Forest Climate Project. USDA Forest Service General
23 Technical Report NE-186.
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27 Department of Agriculture, Forest Service, Northeastern Forest Experiment Station, Radnor, PA.
6D-18
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14 North Central Forest Experiment Station, St. Paul, MN. 10 pp.
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20 W.E., and H.W. Lull (Eds.). Forest Hydrology. Pergamon Press, Oxford, UK.
6D-19
-------
APPENDIX 6E:
Class I Areas and Weighted RBL at Current Standard and Alternative W126 Standard Levels
Class I Area
AcadiaNP(ME)
Aqua Tibia Wilderness (CA)
Alpine Lakes Wilderness (WA)
Anaconda Pintler Wilderness (MT)
Ansel Adams Wilderness (CA)
Arches NP (UT)
Badlands/Sage Creek Wilderness, area 1 (ND)
Badlands/Sage Creek Wilderness, area 2 (ND)
Bandelier Wilderness (NM)
Big Bend NP (TX)
Black Canyon of the Gunnison Wilderness
(CO)
Bob Marshall Wilderness (MT)
Bosque del Apache (NM)
Boundary Waters Canoe Area Wilderness
(MN)
Bradwell Bay Wilderness (FL)
Bridger Wilderness (WY)
Brigantine Wilderness (NJ)
Bryce Canyon NP (UT)
Cabinet Mountains Wilderness (MT)
Caney Creek Wilderness (AR)
Canyonlands NP (UT)
Cape Romain Wilderness (SC)
Capitol Reef NP (UT)
Caribou Wilderness (CA)
Number
of Grid
Cells
4
4
24
12
19
6
7
4
6
32
4
46
7
53
4
28
2
7
10
2
22
3
22
4
W126
f)(\n(i
(ZUUO —
2008)
5.97
30.94
3.67
9.38
31.22
16.69
9.94
9.80
12.19
10.30
15.27
5.40
12.17
4.48
8.92
12.19
18.09
18.13
4.86
7.48
17.05
11.00
17.83
17.43
Percent
of Basal
Area
Assessed
9.97
0.72
29.02
11.36
1.80
0.00
2.98
1.92
22.53
0.00
8.38
24.27
0.00
27.13
1.97
8.67
4.16
18.02
28.86
26.41
0.50
59.09
1.32
5.02
Weighted Biomass Loss
Recent
Conditions
0.14%
0.37%
0.04%
<0.01%
0.42%
75ppb
<0.01%
0.02%
0.01%
<0.01%
0.01%
6.47%
4.69%
1.02%
15 ppm-hrs
<0.01%
0.01%
0.01%
<0.01%
0.01%
11 ppm-hrs
<0.01%
0.01%
0.01%
<0.01%
0.01%
7 ppm-hrs
<0.01%
0.01%
0.01%
<0.01%
0.01%
No Data
2.21%
1.36%
0.21%
2.13%
1.27%
0.16%
2.03%
1.17%
0.09%
1.43%
0.82%
0.08%
No Data
0.73%
0.02%
0.13%
<0.01%
0.10%
<0.01%
0.06%
<0.01%
0.05%
<0.01%
No Data
0.45%
0.01%
0.34%
0.08%
1.27%
0.06%
0.14%
0.23%
0.02%
0.61%
0.33%
0.06%
<0.01%
0.06%
<0.01%
0.26%
0.01%
0.03%
0.05%
0.02%
0.12%
0.02%
0.06%
<0.01%
0.06%
<0.01%
0.21%
0.01%
0.03%
0.04%
0.02%
0.10%
0.02%
0.06%
<0.01%
0.06%
<0.01%
0.14%
0.01%
0.03%
0.03%
0.02%
0.06%
0.02%
0.06%
<0.01%
0.05%
<0.01%
0.12%
0.01%
0.03%
0.02%
0.02%
0.06%
0.02%
6E-1
-------
Class I Area
Carlsbad Caverns NP (NM)
Chassahowitzka Wilderness (FL)
Chircahua National Monument (AZ)
Chiricahua Wilderness (AZ)
Cohutta Wilderness (TN-GA)
Crater Lake NP (OR)
Craters of the Moon Wilderness (ID)
Cucamonga Wilderness (CA)
Desolation Wilderness (CA)
Diamond Peak Wilderness (OR)
Dolly Sods Wilderness (WV)
Domeland Wilderness (CA)
Eagle Cap Wilderness (OR)
Eagles Nest Wilderness (CO)
Emigrant Wilderness (CA)
Everglades NP (FL)
Fitzpatrick Wilderness (WY)
Flat Top Wilderness (CO)
Galiuro Wilderness (AZ)
Gates of the Mountains Wilderness (MT)
Gearhart Mountain Wilderness (OR)
Gila Wilderness (NM)
Glacier NP (MT)
Glacier Peak Wilderness (WA)
Goat Rocks Wilderness (WA)
Grand Canyon NP (AZ)
Grand TetonNP(WY)
Great Gulf Wilderness (NH)
Great Sand Dunes Wilderness (CO)
Great Smoky Mountains NP (NC-TN)
Guadalupe Mountains NP (TX)
Number
of Grid
Cells
7
3
7
9
5
10
8
2
5
4
2
10
19
12
10
42
15
16
7
4
4
29
40
31
10
73
17
2
5
26
7
W126
d{\{\/:
(ZUUo —
2008)
13.33
9.97
14.66
13.84
13.75
6.97
12.43
43.55
17.60
5.68
9.66
40.82
6.40
19.88
26.01
6.27
11.87
15.85
17.01
7.23
8.77
13.26
3.34
2.97
4.03
17.90
11.67
3.99
14.24
14.65
12.69
Percent
of Basal
Area
Assessed
0.10
5.07
3.70
9.15
40.00
18.94
14.39
0.13
0.88
22.26
36.08
0.13
28.76
16.33
3.37
0.07
5.65
27.42
3.98
81.56
34.53
25.88
26.38
26.11
28.41
10.90
9.04
8.30
38.30
30.18
1.08
Weighted Biomass Loss
Recent
Conditions
0.03%
0.04%
<0.01%
<0.01%
1.52%
0.34%
0.03%
0.05%
0.05%
0.01%
1.37%
0.05%
0.09%
1.64%
0.47%
0.01%
0.01%
2.37%
0.25%
0.68%
1.08%
1.06%
0.05%
0.02%
0.01%
2.03%
0.04%
<0.01%
1.42%
1.24%
0.09%
75 ppb 15 ppm-hrs
0.01%
0.01%
<0.01%
<0.01%
0.17%
0.07%
0.01%
0.01%
<0.01%
<0.01%
0.24%
<0.01%
0.03%
0.53%
0.02%
<0.01%
<0.01%
0.68%
0.09%
0.19%
0.21%
0.26%
0.01%
0.01%
<0.01%
0.56%
0.01%
<0.01%
0.24%
0.13%
0.02%
0.01%
0.01%
<0.01%
<0.01%
0.17%
0.07%
0.01%
<0.01%
<0.01%
<0.01%
0.24%
<0.01%
0.03%
0.38%
0.02%
<0.01%
<0.01%
0.53%
0.07%
0.17%
0.19%
0.20%
0.01%
0.01%
<0.01%
0.44%
0.01%
<0.01%
0.18%
0.13%
0.02%
11 ppm-hrs
0.01%
0.01%
<0.01%
<0.01%
0.11%
0.07%
<0.01%
<0.01%
<0.01%
<0.01%
0.19%
<0.01%
0.02%
0.18%
0.02%
<0.01%
<0.01%
0.33%
0.04%
0.16%
0.18%
0.13%
0.01%
0.01%
<0.01%
0.28%
0.01%
<0.01%
0.11%
0.08%
0.01%
7 ppm-hrs
0.01%
0.01%
<0.01%
<0.01%
0.05%
0.07%
<0.01%
<0.01%
<0.01%
<0.01%
0.13%
<0.01%
0.02%
0.12%
0.01%
<0.01%
<0.01%
0.26%
0.03%
0.13%
0.17%
0.11%
0.01%
0.01%
<0.01%
0.24%
<0.01%
<0.01%
0.09%
0.04%
0.01%
6E-2
-------
Class I Area
Hells Canyon Wilderness (ID-OR)
Hercules-Glades Wilderness (MO)
Hoover Wilderness (CA)
Isle Royale NP (MI)
James River Face Wilderness (VA)
Jarbridge Wilderness (NV)
John Muir Wilderness (CA)
Joshua Tree Wilderness (CA)
Joyce Kilmer-Slickrock Wilderness (NC-TN)
Kaiser Wilderness (CA)
Kalmiopsis Wilderness (OR)
Kings Canyon NP (CA)
La Garita Wilderness (CO)
Lassen Volcanic NP (CA)
Lava Beds/Black Lava Flow Wilderness (CA)
Lava Beds/Schonchin Wilderness (CA)
Linville Gorge Wilderness (NC)
Lostwood Wilderness (ND)
Lye Brook Wilderness (VT)
Mammoth Cave NP (KY)
Marble Mountain Wilderness (CA)
Maroon Bells-Snowmass Wilderness (CO)
Mazatzal Wilderness (AZ)
Medicine Lake Wilderness (MT)
Mesa Verde NP (CO)
Mingo Wilderness (MO)
Mission Mountains Wilderness (MT)
Mokelumne Wilderness (CA)
Moosehorn Wilderness (ME)
Mount Adams Wilderness (WA)
Mount Baldy Wilderness (AZ)
Number
of Grid
Cells
17
4
9
4
2
11
42
38
3
4
13
31
11
11
4
2
3
2
4
6
14
13
18
3
4
4
8
8
3
6
3
W126
d{\{\/:
(ZUUo —
2008)
6.63
11.09
27.17
5.45
8.22
16.30
36.36
29.24
14.55
33.59
6.02
41.68
15.76
17.77
10.09
10.04
10.85
4.42
6.03
14.48
6.67
15.39
22.99
6.20
16.40
14.82
5.53
20.63
2.65
3.78
17.80
Percent
of Basal
Area
Assessed
57.18
0.23
3.35
15.50
28.30
22.47
0.64
0.00
28.26
5.55
53.38
2.37
18.20
11.14
32.89
29.16
43.58
2.26
35.25
19.75
45.64
31.38
6.49
0.00
0.01
4.37
36.13
2.45
16.07
24.95
54.30
Weighted Biomass Loss
Recent
Conditions
0.14%
<0.01%
0.56%
0.32%
0.53%
2.09%
0.32%
75 ppb 15 ppm-hrs
0.04%
<0.01%
0.02%
0.04%
0.02%
0.32%
0.01%
0.04%
<0.01%
0.02%
0.04%
0.02%
0.26%
0.01%
11 ppm-hrs
0.03%
<0.01%
0.02%
0.04%
0.02%
0.18%
0.01%
7 ppm-hrs
0.03%
<0.01%
0.02%
0.04%
0.01%
0.16%
0.01%
No Data
1.34%
1.02%
<0.01%
0.81%
1.17%
0.67%
1.65%
2.22%
1.33%
0.09%
0.17%
0.81%
0.09%
2.42%
1.54%
0.09%
0.02%
<0.01%
0.02%
0.22%
0.05%
0.21%
0.28%
0.05%
0.05%
<0.01%
0.07%
0.01%
0.60%
0.54%
0.09%
0.02%
<0.01%
0.02%
0.16%
0.04%
0.02%
0.26%
0.05%
0.05%
<0.01%
0.07%
0.01%
0.45%
0.36%
0.05%
0.02%
<0.01%
0.01%
0.09%
0.04%
0.18%
0.24%
0.04%
0.05%
<0.01%
0.04%
0.01%
0.28%
0.16%
0.02%
0.02%
<0.01%
0.01%
0.08%
0.04%
0.17%
0.23%
0.03%
0.05%
<0.01%
0.02%
0.01%
0.22%
0.12%
No Data
<0.01%
0.06%
0.04%
0.21%
0.10%
0.02%
2.72%
<0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.82%
<0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.58%
<0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.31%
<0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.24%
6E-3
-------
Class I Area
Mount Hood Wilderness (OR)
Mount Jefferson Wilderness (OR)
Mount Ranier NP (WA)
Mount Washington Wilderness (OR)
Mount Zirkel Wilderness (CO)
Mountain Lakes Wilderness (OR)
North Absaroka Wilderness (WY)
North Cascades NP (WA)
Okefenokee Wilderness (GA)
Olympic NP (WA)
Otter Creek Wilderness (WV)
Pasayten Wilderness (WA)
Pecos Wilderness (NM)
Petrified Forest NP (AZ)
Pine Mountain Wilderness (AZ)
Pinnacles Wilderness (CA)
Point Reyes NS/Phillip Burton Wilderness 1
(CA)
Point Reyes NS/Phillip Burton Wilderness 2
(CA)
Point Reyes NS/Phillip Burton Wilderness 3
(CA)
Presidential Range-Dry River Wilderness (NH)
Rainbow Lakes Wilderness (WI)
Rawah Wilderness (CO)
Red Rock Lakes Wilderness (MT)
Redwood NP (CA)
Rocky Mountain NP (CO)
Saguaro Wilderness (AZ)
Saint Marks Wilderness (FL)
Salt Creek Wilderness (NM)
Number
of Grid
Cells
5
10
14
6
13
4
21
27
19
45
3
22
15
16
2
4
3
4
5
5
1
9
4
12
17
13
5
4
W126
d{\{\/:
(ZUUo —
2008)
4.00
4.32
4.34
4.72
15.38
8.15
9.25
2.31
9.83
1.77
8.67
2.42
12.75
16.71
21.52
14.03
1.42
1.44
1.57
4.12
4.48
17.59
10.50
6.63
18.55
13.60
9.71
12.21
Percent
of Basal
Area
Assessed
38.90
41.44
26.96
43.29
14.43
33.29
15.05
18.78
3.47
29.16
42.78
16.24
44.38
0.00
9.91
0.00
22.86
20.81
19.15
12.94
56.42
7.05
41.54
37.56
14.73
3.45
5.80
0.00
Weighted Biomass Loss
Recent
Conditions
0.01%
0.07%
0.01%
0.27%
1.19%
0.67%
<0.01%
<0.01%
0.02%
<0.01%
2.36%
0.01%
1.29%
75 ppb 15 ppm-hrs
<0.01%
0.03%
<0.01%
0.11%
0.35%
0.12%
<0.01%
<0.01%
<0.01%
<0.01%
0.49%
<0.01%
0.26%
<0.01%
0.03%
<0.01%
0.11%
0.28%
0.11%
<0.01%
<0.01%
<0.01%
<0.01%
0.49%
<0.01%
0.20%
11 ppm-hrs
<0.01%
0.03%
<0.01%
0.11%
0.22%
0.11%
<0.01%
<0.01%
<0.01%
<0.01%
0.37%
<0.01%
0.12%
7 ppm-hrs
<0.01%
0.03%
<0.01%
0.10%
0.17%
0.10%
<0.01%
<0.01%
<0.01%
<0.01%
0.24%
<0.01%
0.10%
No Data
1.83%
0.76%
0.51%
0.23%
0.16%
No Data
<0.01%
<0.01%
<0.01%
0.02%
0.47%
0.51%
0.02%
0.13%
0.60%
<0.01%
0.06%
<0.01%
<0.01%
<0.01%
<0.01%
0.05%
0.15%
<0.01%
0.02%
0.18%
<0.01%
<0.01%
<0.01%
<0.01%
<0.01%
<0.01%
0.05%
0.11%
<0.01%
0.02%
0.13%
<0.01%
<0.01%
<0.01%
<0.01%
<0.01%
<0.01%
0.05%
0.08%
<0.01%
0.02%
0.06%
<0.01%
<0.01%
<0.01%
<0.01%
<0.01%
<0.01%
0.05%
0.06%
<0.01%
0.02%
0.05%
<0.01%
<0.01%
No Data
6E-4
-------
Class I Area
San Gabriel Wilderness (CA)
San Gorgonio Wilderness (CA)
San Jacinto Wilderness (CA)
San Pedro Parks Wilderness (NM)
San Rafael Wilderness (CA)
Sawtooth Wilderness (ID)
Scapegoat Wilderness (MT)
Selway-Bitterroot Wilderness (MT-ID)
Seney Wilderness (MI)
SequioaNP(CA)
Shenandoah NP (VA)
Shining Rock Wilderness (NC)
Sierra Ancha Wilderness (AZ)
Sipsey Wilderness (AL)
South Warner Wilderness (CA)
Strawberry Mountain Wilderness (OR)
Superstition Wilderness (AZ)
Swanquarter Wilderness (NC)
Sycamore Canyon Wilderness (AZ)
Teton Wilderness (WY)
Theodore Roosevelt NP (9)
Thousand Lakes Wilderness (CA)
Three Sisters Wilderness (OR)
UL Bend Wilderness (MT)
Upper Buffalo Wilderness (AR)
Ventana Wilderness (CA)
Voyageurs NP (MN)
Washakie Wilderness (WY)
Weminuche Wilderness (CO)
West Elk Wilderness (CO)
Wheeler Peak Wilderness (NM)
Number
of Grid
Cells
5
7
7
4
15
12
16
62
4
31
22
4
4
4
6
6
12
1
6
27
9
2
19
5
3
15
10
41
29
13
4
W126
d{\{\/:
(ZUUo —
2008)
31.01
49.21
42.32
12.38
12.52
12.54
6.07
7.45
6.76
47.41
11.00
12.45
21.79
13.99
11.45
7.47
21.73
11.16
20.58
10.95
6.08
15.03
5.09
6.95
7.95
7.13
4.54
10.55
16.10
14.82
13.42
Percent
of Basal
Area
Assessed
0.02
0.35
1.20
44.76
0.00
33.72
26.84
30.59
18.25
2.91
25.88
23.40
27.05
30.20
11.76
53.42
0.00
24.90
37.58
2.11
0.50
13.31
34.51
42.50
11.36
0.16
40.95
9.58
23.51
37.30
29.72
Weighted Biomass Loss
Recent
Conditions
0.01%
0.09%
0.21%
1.63%
75 ppb 15 ppm-hrs
<0.01%
0.01%
0.01%
0.35%
<0.01%
0.01%
0.01%
0.26%
11 ppm-hrs
<0.01%
<0.01%
0.01%
0.16%
7 ppm-hrs
<0.01%
<0.01%
<0.01%
0.13%
No Data
0.18%
0.01%
0.14%
0.36%
1.25%
0.98%
0.97%
2.86%
1.29%
0.54%
0.67%
0.03%
<0.01%
0.03%
0.04%
0.03%
0.05%
0.12%
0.88%
0.20%
0.07%
0.17%
0.03%
<0.01%
0.03%
0.04%
0.03%
0.05%
0.12%
0.60%
0.20%
0.07%
0.16%
0.02%
<0.01%
0.02%
0.04%
0.02%
0.05%
0.11%
0.28%
0.18%
0.06%
0.15%
0.02%
<0.01%
0.02%
0.04%
0.02%
0.03%
0.07%
0.21%
0.12%
0.06%
0.14%
No Data
0.18%
3.67%
0.01%
0.24%
0.58%
0.10%
1.01%
0.07%
<0.01%
0.67%
<0.01%
1.43%
2.58%
0.72%
0.02%
1.44%
<0.01%
0.08%
0.05%
0.04%
0.31%
0.01%
<0.01%
0.10%
<0.01%
0.29%
0.40%
0.14%
0.02%
1.01%
<0.01%
0.08%
0.04%
0.04%
0.31%
0.01%
<0.01%
0.10%
<0.01%
0.21%
0.31%
0.10%
0.02%
0.50%
<0.01%
0.08%
0.04%
0.04%
0.31%
0.01%
<0.01%
0.10%
<0.01%
0.12%
0.20%
0.06%
0.01%
0.37%
<0.01%
0.06%
0.04%
0.04%
0.25%
0.01%
<0.01%
0.09%
<0.01%
0.10%
0.17%
0.05%
6E-5
-------
Class I Area
White Mountain Wilderness (NM)
Wichita Mountains (OK)
Wind Cave NP (SD)
Wolf Island Wilderness (GA)
Yellowstone NP (WY)
Yolla Bolly-Middle Eel Wilderness (CA)
Yosemite NP (CA)
Zion NP (UT)
All Areas Combined
Number
of Grid
Cells
5
4
5
1
84
13
35
13
1952
W126
d{\{\/:
(ZUUo —
2008)
11.46
11.89
12.47
7.46
10.00
10.65
29.42
18.70
13.59
Percent
of Basal
Area
Assessed
24.98
0.17
93.24
14.17
4.14
46.51
5.73
1.99
16.85
Weighted Biomass Loss
Recent
Conditions
0.28%
0.12%
4.38%
0.03%
0.01%
0.53%
0.90%
0.12%
0.51%
75ppb
0.07%
15 ppm-hrs
0.01%
2.66%
0.01%
<0.01%
0.03%
0.03%
0.02%
0.11%
0.05%
0.01%
2.65%
0.01%
<0.01%
0.02%
0.03%
0.02%
0.09%
11 ppm-hrs
0.03%
0.01%
2.64%
0.01%
<0.01%
0.02%
0.02%
0.01%
0.06%
7 ppm-hrs
0.03%
0.01%
2.02%
<0.01%
<0.01%
0.02%
0.02%
0.01%
0.05%
6E-6
-------
APPENDIX 6F: RELATIVE BIOMASS LOSS AND CROP YIELD LOSS ESTIMATES
This appendix provides an analysis of the median of the composite exposure-response (E-R) functions for tree seedlings and crops.
Tables 6F-1 and 6F-2 below provide estimates of the relative loss for trees and crops respectively at various W126 index values using the
composite E-R functions for each species. The median of the composite functions is calculated for all 12 tree species as well as for the 11
species excluding cottonwood. The median of the composite functions for all 12 tree species and all 10 crop species is consistent with the
green line shown in Figures 6-5 and 6-6. Tables 6F-3 and 6F-4 below provide estimates of the number of species for trees and crops
respectively that would be below various benchmarks (e.g., 2 percent biomass loss for trees) at various W126 index values.
Table 6F-1 Relative Biomass Loss for Individual Tree Seedlings and Median at Various W126 Index Values
W126
21
19
17
15
13
11
9
7
Tree Species with E-R Functions
Douglas
Fir
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Loblolly
0.5%
0.5%
0.4%
0.4%
0.3%
0.3%
0.2%
0.2%
Virginia
Pine
1.2%
1.1%
1.0%
0.9%
0.8%
0.6%
0.5%
0.4%
Red
maple
2.4%
2.1%
1.8%
1.5%
1.2%
1.0%
0.7%
0.5%
Sugar
maple
4.1%
2.3%
1.2%
0.6%
0.3%
0.1%
0.0%
0.0%
Red
Alder
6.8%
6.0%
5.3%
4.5%
3.8%
3.1%
2.4%
1.8%
Ponderosa
Pine
8.6%
7.6%
6.7%
5.8%
4.9%
4.1%
3.2%
2.4%
Aspen
12.4%
11.1%
9.8%
8.4%
7.1%
5.9%
4.6%
3.4%
Tulip
Poplar
14.3%
11.8%
9.4%
7.4%
5.5%
3.9%
2.6%
1.5%
Eastern
White
Pine
14.9%
12.7%
10.7%
8.8%
7.0%
5.4%
3.9%
2.6%
Black
Cherry
41.9%
38.8%
35.6%
32.2%
28.6%
24.8%
20.9%
16.7%
Cottonwood
97.5%
95.4%
92.0%
86.8%
79.1%
68.8%
55.7%
40.6%
Median
(All 12
species)
7.7%
6.8%
6.0%
5.2%
4.4%
3.5%
2.5%
1.7%
Median (11
species, no
cottonwood)
6.8%
6.0%
5.3%
4.5%
3.8%
3.1%
2.4%
1.5%
6F-1
-------
Table 6F-2 Relative Crop Yield Loss for Individual Crops at Various W126 Index Values
W126
21
19
17
15
13
11
9
7
Crop Species with E-R Functions
Barley
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Lettuce
0.9%
0.6%
0.3%
0.2%
0.1%
0.0%
0.0%
0.0%
Field Corn
1.0%
0.8%
0.6%
0.4%
0.2%
0.2%
0.1%
0.0%
Grain Sorghum
1.1%
0.9%
0.8%
0.6%
0.5%
0.3%
0.2%
0.1%
Peanut
5.4%
4.5%
3.7%
2.9%
2.2%
1.6%
1.1%
0.7%
Potato
13.5%
12.0%
10.5%
9.1%
7.7%
6.3%
4.9%
3.6%
Cotton
10.0%
8.7%
7.4%
6.2%
5.0%
3.9%
2.9%
2.0%
Soybean
10.0%
8.8%
7.6%
6.4%
5.3%
4.3%
3.3%
2.3%
Winter Wheat
10.4%
8.3%
6.4%
4.8%
3.5%
2.3%
1.5%
0.8%
Kidney Bean
18.4%
15.0%
11.9%
9.2%
6.8%
4.7%
3.0%
1.8%
Median (10 species)
7.7%
6.4%
5.1%
3.9%
2.8%
2.0%
1.3%
0.8%
Table 6F-3 Number of Tree Species Below Relative Biomass Loss Benchmarks at Various W126 Index Values
W126
21
19
17
15
13
11
9
7
Number of Tree Species (out of 11 species) with Relative Biomass Loss Less than or Equal to Specific Benchmarks
1%
2
2
o
J
4
4
5
5
5
2%
3
3
5
5
5
5
5
7
5%
5
5
5
6
7
8
10
10
10%
7
7
9
10
10
10
10
10
15%
10
10
10
10
10
10
10
10
20%
10
10
10
10
10
10
10
11
6F-2
-------
Table 6F-4 Number of Crop Species Below Yield Benchmarks at Various W126 Index Values
W126
21
19
17
15
13
11
9
7
Number of Crop Species (out of 10 species) with Relative Yield Loss Less than or Equal to Specific Benchmarks
1%
2
4
4
4
4
4
4
6
2%
4
4
4
4
4
5
6
7
5%
4
5
5
6
6
9
10
10
10%
7
8
8
10
10
10
10
10
15%
9
10
10
10
10
10
10
10
20%
10
10
10
10
10
10
10
10
6F-3
-------
APPENDIX 7A: ADDITIONAL INFORMATION FOR SCREENING-LEVEL
ASSESSMENT OF VISIBLE FOLIAR INJURY IN NATIONAL PARKS
This appendix presents the Os exposure and soil moisture data used in the assessment of
visible foliar injury risk in national parks (Section 7.3) and the park-by-park results of that
assessment.
In Figure 7A-1, we provide a plot of the relationship between the percentage of biosites with
any visible foliar injury and Os exposure; we used this figure to define the base scenario (i.e., 17.7%
of biosites at 10.46 ppm-hrs). In Figures 7A-2 through 7A-5, we provide a plot of the relationship
between the percentage of biosites with any visible foliar injury and Os exposure by soil moisture
categorization; we used this figure to define 4 scenarios for any injury (i.e., 5%, 10%, 15%, and
20%). Figure 7A-6 provides the map of parks with and without an Os monitor located within park
boundaries. In Figure 7A-7, we identify the parks with and without currently identified sensitive
species.
Biosite Index > 0
in
o
o
(N
o
Jj ¥ -
.
2
a.
in
p -
o
8 -|
o
10
20
W126 (ppm-hrs)
30
40
Figure 7A-1 Defining Base Scenario (all FHM biosites, any injury)
7A-1
-------
Biosite Index > 0
"tn
M—
O
o
Q.
O
LT>
C\(
d>
o
C\(
o
LT>
o
o
ci
LO
p
d>
o
o
I
10
I
20
I
30
I
40
W126(ppm-hrs)
Figure 7A-2 Defining 5% Scenario (5% of FHM biosites showing injury)
Biosite Index > 0
in
-------
Biosite Index > 0
in
o
I
§ -I
o
10
30
40
20
W126(ppm-hrs)
Figure 7A-4 Defining 15% Scenario (15% of FHM biosites showing injury)
Biosite Index > 0
in
(N -
o
o
••e
o
CL
in
P -
o
o
P -
o
10
20
30
40
W126(ppm-hrs)
Figure 7A-5 Defining 20% Scenario (20% of FHM biosites showing injury)
7A-3
-------
HAFO
CIRO
GOSPFOBU
v J
r SHEN
NERI GRSP
BOWAPETE
: COLM
CAREARCH BLCA
LAME GRCA
MOJA. WUPA CACHCHCU
ELMApE™
5APU
GICL
WHSA
1—1_
Not monitored (172 parks, 80%)
Monitored (42 parks, 20%)
use
(Parks identified by park code; not all park labels shown due to overlap. National Parks are prioritized in mapping.)
Figure 7A-6 214 National Parks included in the Screening-Level Assessment
7A-4
-------
NEPE GRKO
JODA / WHO
HflFOCRMO CRT!
CIRO
GOSPFOBU
ROMO
GR6A
ARCHCOLM
CARECANY BLCA FIFO
-ZIONBRCA
~GICA MEVE
GRCA
WUPA CAC;HCHCU
TUZ1 ""VMAPETR""0
TONT SAPU
CAGR 'GICL
SAGU WHSA
"""""" GAWtf
Species present (203 parks, 95%)
Species not present (11 parks, 5%)
(Parks identified by park code; not all park labels shown due to overlap. National Parks are prioritized in mapping. Data
source: NFS, 2003, 2006b)
Figure 7A-7 Presence of Os-Sensitive Species in 214 Parks
Figure 7A-8 provides the 3-month timeframe for the W126 index value for parks with Os
monitors from 2006 through 2010. In Figures 7A-9 through 7A-11, we provide pie charts illustrating
the fraction of monitors at parks in locations with different soil moisture categorizations for the 7-
month average, 5-month average, and 3-month average, respectively. Soil moisture estimates are
based on the Palmer Z index, where estimates above 0 are wetter and estimates below 0 are drier. The
soil moisture categories are based on NOAA's classifications for Palmer Z data (NOAA, 2012c).
7A-5
-------
40%
0%
JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND
3 months used in W126 estimate
Figure 7A-8 Timeframe of W126 Estimates for 57 Monitors Located in Parks
2006
2007
2008
15% 10%
Otol
35%
-1.25to 0
40%
2009
2010
2006-2010
Figure 7A-9 7-month Palmer Z (March-September) at 57 Monitors Located in Parks
7A-6
-------
2006
2007
2008
2009
2010
2006-2010
14%
17%
Otol
28%
-1.25 toO
41%
Figure 7A-10 5-Month Palmer Z (April-August) at 57 Monitors Located in Parks
2006
2007
2008
2009
2010
2006-2010
Figure 7A-11 3-Month Palmer Z (Monitor-specific) at 57 Monitors Located in Parks
7A-7
-------
In Table 7A-1, we provide the 7-month, 5-month, and 3-month soil moisture average for each
park with an Os monitor. In Table 7A-2, we provide the W126 estimates and the timeframe
corresponding to those W126 estimates for each park with an Os monitor. In Figures 7A-12 through
7A-16, we provide larger scale maps of the foliar injury results for the 214 parks that are provided in
Chapter 7. In Table 7A-3, we provide the Os estimates at 214 parks using the interpolated surfaces
just meeting the existing 75 ppb standard and potential alternative W126 standards. Table 7A-4
provides the W126 estimates in each park from the interpolated surfaces for the 3-year average of
2006-2008 and after adjusting to just meet the existing and potential alternative standard levels.
7A-8
-------
Table 7A-1 Average Soil Moisture Data (Palmer Z) by Averaging Time for 57 O3 Monitors
Located in Parks*
Monitor
Site ID
230090102
230090103
311651001
460710001
460711001
480430101
370110002
490370101
250010002
350153001
160310001
80771001
450790021
450210002
160230101
210131002
60270101
560111013
490471002
300298001
300351001
40058001
Park Name
Acadia National Park
Acadia National Park
Agate Fossil Beds
National Monument
Badlands National
Park
Badlands National
Park
Big Bend National
Park
Blue Ridge Parkway
Canyonlands National
Park
Cape Cod National
Seashore
Carlsbad Caverns
National Park
City of Rocks
National Reserve
Colorado National
Monument
Congaree National
Park
Cowpens National
Battlefield
Craters of the Moon
National Monument
Cumberland Gap
National Historical
Park
Death Valley
National Park
Devil's Tower
National Monument
Dinosaur National
Monument
Glacier National Park
Glacier National Park
Grand Canyon
National Park
7-Month (Mar-Sept)
2006 2007 2008 2009 2010
1 69 1 08 1 98 2 58 0 84
1 69 1 08 1 98 2 58 0 84
-1.40 -1.56 -0.17 1.94 1.95
-0.57 -1.41 1.27 2.06 2.15
-0.57 -1.41 1.27 2.06 2.15
-0.67 1.78 0.11 -0.25 0.96
-0.27 -1.95 -1.13 1.14 -0.94
-0.91 -0.21 -0.42 -1.39 0.77
1.36 0.13 1.06 1.47 0.80
-0.07 1.12 -0.56 -0.90 1.23
-0.08 -1.87 -0.68 1.24 -0.12
-054 -025 -026 -0 17 -0 11
-0 60 -1 35 -0 04 -0 20 -1 05
-0 84 -2 18 -1 58 -0 05 -1 24
0.87 -2.15 -1.50 1.63 -0.25
0.73 -1.66 -0.50 1.55 0.09
-1 30 -171 -1 15 -1 85 -052
-1.77 -0.78 1.41 1.45 0.63
-0.57 -1.57 0.50 -0.69 -0.46
-0.16 -1.14 0.15 -0.14 1.01
-1.07 -0.88 0.43 -0.50 1.43
-0.54 -1.40 0.03 -1.48 0.39
5-Month (Apr-Aug)
2006 2007 2008 2009 2010
2 98 1 28 0 50 3 83 -0 19
2 98 1 28 0 50 3 83 -0 19
-2.37-1.57-0.31 2.76 2.96
-1.86-1.48 1.88 2.17 2.90
-1.86-1.48 1.88 2.17 2.90
-0.65 2.04 -0.07 0.04 1.30
-0.38 -1.92 -1.37 0.64 -1.09
-1.87 -0.27 0.05 -1.27 0.97
2.83 0.20 0.23 2.06 -0.98
-0.47 0.95 -0.41 -0.69 1.43
-0.93-2.19-0.66 1.82 0.67
-124-051 -023 030 024
-0 38 -1 15 0 09 0 09 -0 95
-0 57 -2 07 -1 55 -0 39 -1 14
0.30 -2.51 -1.67 2.33 0.28
0.39 -1.43-0.17 1.82 0.67
-1 59-1 95-097-201 -032
-2.66 -0.71 2.02 1.52 1.60
-1.52 -1.98 0.38 -0.49 -0.05
-0.05 -1.20 0.09 -0.13 1.65
-1.68-0.96 0.57 -0.11 2.12
-0.95 -1.41 0.60 -1.42 0.74
3-Month (monitor specific)
2006 2007 2008 2009 2010
526 1 83 -049 081 021
526 1 83 -049 081 021
.. -1.83 -0.49 4.06 -
- 1.31 1.38 2.41
-2.46 -1.93
-2.67 2.76 -1.75 -0.63 0.71
-0.20 -1.80 -1.76 1.28 -0.73
-2.57 -0.59 0.53 -0.90 0.76
5.37 -0.27 0.49 0.83 -1.06
- 1.82 -1.69 0.40 0.68
0.40
.. -0.63 -0.01 -1.15 0.14
-1 81 -104 -009 -024 -149
-054 -141 -1 95 -025 -1 17
- -2.88 -1.62 0.27 -0.95
- -1.37 -0.15 2.77 1.37
-?08 -2 11 -078 -1 55 -1 31
- 0.95 1.63 1.52
- -1.92 0.92 -0.49 -0.39
-0.54 -0.48 -0.01 -0.87 1.96
0.85 2.15
-1.74 -2.63 0.27 -2.20 0.41
7A-9
-------
Monitor
Site ID
320330101
370870036
470090102
471550101
471550102
180890022
60650008
60651004
60719002
60893003
80830101
60711001
530530012
530090016
530091004
482731001
40170119
60690003
40190021
360910004
Park Name
Great Basin National
Park
Great Smoky
Mountains National
Park
Great Smoky
Mountains National
Park
Great Smoky
Mountains National
Park
Great Smoky
Mountains National
Park
Indiana Dunes
National Lakeshore
Joshua Tree National
Park
Joshua Tree National
Park
Joshua Tree National
Park
Lassen Volcanic
National Park
Mesa Verde National
Park
Mojave National
Preserve
Mount Rainier
Wilderness
Olympic National
Park
Olympic National
Park
Padre Island National
Seashore
Petrified Forest
National Park
Pinnacles National
Monument
Saguaro National
Park
Saratoga National
Historical Park
7-Month (Mar-Sept)
2006 2007 2008 2009 2010
0.28 -2.14 -1.62 0.27 -0.35
-0.27 -1.95 -1.13 1.14 -0.94
0.65 -1.80 -0.61 1.76 -0.67
0.65 -1.80 -0.61 1.76 -0.67
-0.27 -1.95 -1.13 1.14 -0.94
0.98 0.22 0.88 0.45 0.37
-1.30 -1.71 -1.15 -1.85 -0.52
-1 30 -171 -1 15 -1 85 -052
-1 30 -171 -1 15 -1 85 -052
0.41 -1.07 -1.98 0.14 0.59
-0.54 -0.25 -0.26 -0.17 -0.11
-1.30 -1.71 -1.15 -1.85 -0.52
-1.25 -0.64 -0.05 -0.78 1.62
-0 89 0 07 0 05 0 02 1 55
-0.77 0.12 0.22 -0.26 1.32
-1.50 3.14 -1.31 -1.69 0.88
-0.54 -1.40 0.03 -1.48 0.39
1.87 -1.25 -1.57 -0.20 1.06
-0.31 -0.84 0.17 -1.62 0.29
1.09 0.44 1.13 1.42 -0.90
5-Month (Apr-Aug)
2006 2007 2008 2009 2010
-0.08 -2.43 -1.64 0.87 -0.23
-0.38-1.92-1.37 0.64 -1.09
0.60 -1.48 -0.47 1.64 -0.68
0.60 -1.48 -0.47 1.64 -0.68
-0.38-1.92-1.37 0.64 -1.09
1.00 0.91 0.06 0.50 0.81
-1.59 -1.95 -0.97 -2.01 -0.32
-1 59-1 95-097-201 -032
-1 59-1 95-097-201 -032
0.10 -0.82-1.81 0.42 1.22
-1.24-0.51-0.23 0.30 0.24
-1.59 -1.95 -0.97 -2.01 -0.32
-1.00 -1.02 0.25 -0.81 1.75
-065 -041 025 -0 12 1 37
-0.53 -0.27 0.44 -0.40 1.30
-1.91 3.60 -1.29-2.59 0.18
-0.95 -1.41 0.60 -1.42 0.74
1.66 -1.05-1.37-0.14 1.68
-0.70 -0.84 0.63 -1.52 0.66
1.89 0.59 0.39 2.37 -1.29
3-Month (monitor specific)
2006 2007 2008 2009 2010
-0.49 -3.05 -1.58 1.29 0.50
-0.20 -1.80 -1.76 1.28 -0.73
1.06 -1.69 -0.86 0.75 -0.80
1.06 -1.03 -0.78 0.75 -0.80
-0.50 -1.71 -2.06 1.14 -0.51
1.59 -0.94 2.47 0.16 1.41
-1.84 -2.52 -0.78 -2.34 0.28
- -2.11 -1.27 -1.55 -0.60
-?08 -2 11 -076 -1 55 -060
-1.41 -0.80 -1.86 0.64 -0.03
-1.53 -0.48 0.16 0.10 0.14
- -2.11 -0.78 -1.50 -0.60
-1.15 -0.37 -0.07 -1.05 2.84
1.34
- -0.15 0.36 -
- 1.24 -2.34 -
-2.48 -2.63 0.27 -0.30 0.41
-0.16 -1.17 -1.38 -0.18 0.46
-0.43 -0.64 -0.99 -1.65 1.24
2.60 -0.43 -0.66 -0.64 -1.18
7 A-10
-------
Monitor
Site ID
311570005
61070006
61070009
511130003
380070002
380530002
40070010
271370034
460330132
560391011
60430003
60431002
60431003
60431004
60431005
Park Name
Scotts Bluff National
Monument
Sequoia-Kings
Canyon National Park
Sequoia-Kings
Canyon National Park
Shenandoah National
Park
Theodore Roosevelt
National Park
Theodore Roosevelt
National Park
Tonto National
Monument
Voyageurs National
Park
Wind Cave National
Park
Yellowstone National
Park
Yosemite National
Park
Yosemite National
Park
Yosemite National
Park
Yosemite National
Park
Yosemite National
Park
7-Month (Mar-Sept)
2006 2007 2008 2009 2010
-1.40 -1.56 -0.17 1.94 1.95
1.07 -1.74 -1.71 -0.51 0.49
1.07 -1.74 -1.71 -0.51 0.49
-0.24 -1.56 0.94 0.31 -1.19
-0.43 -0.32 -1.87 1.18 1.39
-0.68 -0.01 -1.55 0.68 2.76
-1.33 -1.49 0.88 -0.67 0.21
-1.28 -0.06 0.60 -0.28 -0.58
0.01 -0.46 1.73 1.13 1.00
-2.91 -2.68 -0.75 0.08 -0.57
1.07 -1.74 -1.71 -0.51 0.49
1.07 -1.74 -1.71 -0.51 0.49
1.07 -1.74 -1.71 -0.51 0.49
1.07 -1.74 -1.71 -0.51 0.49
1.07 -1.74 -1.71 -0.51 0.49
5-Month (Apr-Aug)
2006 2007 2008 2009 2010
-2.37-1.57-0.31 2.76 2.96
0.98 -1.81-1.52-0.25 1.15
0.98 -1.81-1.52-0.25 1.15
-0.16-1.52 1.22 0.91 -1.92
-1.21 -0.23 -2.04 0.92 1.14
-1.14 0.21 -1.69 0.88 2.49
-1.82-1.63 1.94 -0.45 0.13
-1.55 -1.38 0.88 -0.33 -0.85
-0.70-0.13 2.38 0.86 1.58
-3.04 -2.66 -0.52 0.82 0.02
0.98 -1.81-1.52-0.25 1.15
0.98 -1.81-1.52-0.25 1.15
0.98 -1.81-1.52-0.25 1.15
0.98 -1.81 -1.52-0.25 1.15
0.98 -1.81 -1.52-0.25 1.15
3-Month (monitor specific)
2006 2007 2008 2009 2010
3.42
-0.29 -1.80 -1.38 -0.60 -0.33
-0.88 -1.80 -1.43 -0.60 -0.33
1.13 -1.22 2.33 0.96 -1.31
-0.87 -1.26 -1.46 0.34 1.12
-1.84 -1.54 -1.19 0.56 2.12
-2.31 -1.83 0.97 0.67 -0.08
-1.23 -0.20 1.19 1.13 -2.25
-1.79 -0.05 2.09 1.24 0.36
-2.94 -2.64 0.06 -0.29 0.34
-0.29 -2.30 -1.38 -0.60 -0.33
- -2.07 -1.43 -
- -0.97
- -2.30 -1.38 -0.60 -0.33
1.19 -0.60 -0.33
*Nine Parks have more than 1 monitor.
7 A-11
-------
Table 7A-2 Ozone Exposure in 57 Os Monitors Located in Parks*
Monitor site ID
230090102
230090103
311651001
460710001
460711001
480430101
370110002
490370101
250010002
350153001
160310001
80771001
450790021
450210002
160230101
210131002
60270101
560111013
490471002
300298001
300351001
40058001
320330101
370870036
470090102
471550101
471550102
Park Name
Acadia National Park
Acadia National Park
Agate Fossil Beds National
Monument
Badlands National Park
Badlands National Park
Big Bend National Park
Blue Ridge Parkway
Canyonlands National Park
Cape Cod National
Seashore
Carlsbad Caverns National
Park
City of Rocks National
Reserve
Colorado National
Monument
Congaree National Park
Cowpens National
Battlefield
Craters of the Moon
National Monument
Cumberland Gap National
Historical Park
Death Valley National Park
Devil's Tower National
Monument
Dinosaur National
Monument
Glacier National Park
Glacier National Park
Grand Canyon National
Park
Great Basin National Park
Great Smoky Mountains
National Park
Great Smoky Mountains
National Park
Great Smoky Mountains
National Park
Great Smoky Mountains
National Park
W126
2006 2007 2008 2009 2010
10.59 7.89 7.64 7.02 5.24
6.37 6.41 4.72 5.21 4.13
8.27 12.76 5.85
2.23 2.54 3.85
16.74 8.01
11.62 10.60 10.55 8.62 8.47
9.88 11.46 8.81 4.71 8.19
18.06 16.93 17.06 12.23 13.24
13.47 13.16 12.89 5.25 7.03
8.65 17.50 11.37 7.09
6.02
11.61 15.04 4.13 8.75
12.31 10.78 9.45 3.97 6.32
14.30 7.87 16.05 3.24 8.81
10.17 10.88 5.68 7.82
18.36 10.12 3.58 7.31
29.18 32.55 25.57 15.30 10.61
7.09 5.42 5.44
10.33 13.34 8.39 13.80
2.90 2.29 3.98 3.53 2.44
4.91 3.93
21.66 18.68 17.02 10.10 14.95
15.54 15.79 16.94 10.19 11.44
11.46 13.35 11.50 4.59 7.89
12.97 12.69 10.44 5.31 10.27
18.87 20.66 14.15 9.03 15.16
19.59 23.51 16.23 7.32 11.94
3-Month Timeframe for W126
2006 2007 2008 2009 2010
MJJ AMJ MJJ MAM MAM
MJJ AMJ MJJ MAM MAM
- JAS MJJ JJA -
JAS AMJ JJA
J J f\ J J f\ ~~ ~~ ~~
AMJ MAM MAM MAM MAM
AMJ AMJ AMJ AMJ AMJ
MJJ MJJ AMJ MAM AMJ
MJJ MJJ MJJ AMJ MJJ
- AMJ AMJ MJJ AMJ
JJA
- JJA MJJ JAS AMJ
MAM MAM MAM FMA MAM
MJJ AMJ JJA FMA MAM
JJA MJJ MAM JAS
- MJJ MJJ MJJ MJJ
MJJ MJJ MJJ JJA JAS
- JAS JAS JJA
- MJJ MJJ MJJ MJJ
JJA MAM MAM AMJ AMJ
MJJ MJJ
MJJ AMJ AMJ JJA AMJ
JJA MJJ MJJ AMJ AMJ
AMJ AMJ AMJ AMJ AMJ
AMJ MAM AMJ MAM MAM
AMJ AMJ MJJ MAM MAM
MJJ JJA MJJ MJJ ASO
7 A-12
-------
Monitor site ID
180890022
60650008
60651004
60719002
60893003
80830101
60711001
530530012
530090016
530091004
482731001
40170119
60690003
40190021
360910004
311570005
61070006
61070009
511130003
380070002
380530002
40070010
271370034
460330132
560391011
60430003
60431002
60431003
60431004
60431005
Park Name
Indiana Dunes National
Lakeshore
Joshua Tree National Park
Joshua Tree National Park
Joshua Tree National Park
Lassen Volcanic National
Park
Mesa Verde National Park
Mojave National Preserve
Mount Rainier Wilderness
Olympic National Park
Olympic National Park
Padre Island National
Seashore
Petrified Forest National
Park
Pinnacles National
Monument
Saguaro National Park
Saratoga National Historical
Park
Scotts Bluff National
Monument
Sequoia-Kings Canyon
National Park
Sequoia-Kings Canyon
National Park
Shenandoah National Park
Theodore Roosevelt
National Park
Theodore Roosevelt
National Park
Tonto National Monument
Voyageurs National Park
Wind Cave National Park
Yellowstone National Park
Yosemite National Park
Yosemite National Park
Yosemite National Park
Yosemite National Park
Yosemite National Park
W126
2006 2007 2008 2009 2010
8.79 12.21 3.66 2.42 3.91
24.36 19.97 27.43 19.66 23.39
26.37 30.05 18.81 20.47
55.48 52.46 50.99 39.93 43.92
18.97 15.10 18.98 7.64 9.63
23.44 17.57 13.41 15.05 11.94
28.50 38.92 19.91 19.39
3.19 3.30 1.18 2.20 1.86
0.52
0.28 0.93
8.19 3.66
19.16 16.60 19.40 9.04 12.71
17.14 14.85 19.78 11.41 9.79
19.57 17.06 20.13 11.01 15.31
6.68 10.38 9.26 5.40 5.98
6.20
50.09 53.38 57.24 29.13 26.93
66.07 62.88 56.91 55.51 53.79
16.43 14.40 12.07 7.63 10.84
7.71 5.54 5.55 3.95 4.19
9.45 6.29 6.31 4.22 5.17
26.39 23.24 25.40 13.67 16.90
5.33 5.19 3.86 4.94 7.66
20.52 12.20 5.92 5.75 5.61
12.98 9.96 8.84 7.63 11.54
33.78 29.68 42.51 25.70 27.34
12.60 10.03
11.61
6.95 15.52 6.58 9.43
27.83 5.18 14.28
3-Month Timeframe for W126
2006 2007 2008 2009 2010
JJA AMJ JAS MJJ JJA
AMJ AMJ MJJ AMJ AMJ
- MJJ AMJ JJA JJA
MJJ MJJ JJA JJA JJA
JAS JJA MJJ JJA JAS
MJJ MJJ AMJ JJA AMJ
- MJJ MJJ JAS JJA
MAM MAM JAS FMA MAM
JAS
- JAS JAS -
- AMJ AMJ -
AMJ AMJ AMJ AMJ AMJ
JAS AMJ MJJ JAS JAS
MJJ MJJ AMJ MAM AMJ
JJA MJJ AMJ MAM MJJ
JJA
JJA JJA JJA JAS JAS
JAS JJA MJJ JAS JAS
AMJ AMJ AMJ MAM JAS
JAS JJA AMJ AMJ AMJ
JJA JJA MJJ AMJ MAM
MJJ MJJ AMJ AMJ AMJ
AMJ AMJ MAM MAM MAM
JJA JJA JJA JJA JAS
AMJ AMJ MAM MAM AMJ
JJA MJJ JJA JAS JAS
- AMJ MJJ
- JAS
- MJJ JJA JAS JAS
- JAS JAS JAS
*Nine parks have more than 1 monitor
7 A-13
-------
CA'
ISC
Key:
All 5 years
4 years
3 years
2 years
1 year
No years
Figure 7A-12 Foliar Injury Results Map for Base Scenario for 214 parks
(Parks identified by park code; not all park labels shown due to overlap. National Parks are prioritized in mapping.)
7 A-14
-------
GLAC
FO
I
UN
EPE GRKO
BIHO
THRO
KNRI
t)RCA
CRLA
HAFO
ELL
CRMO GRTE
not.
LIBI
HWHIS
OETO
JE«1
BADL
LAVO
GOSPFOBU
AGFO
SCBL
W-
SARAMiM/i
-,HOFR
DEWAy
DINO
ROMO
YOSE
/
PINN
SEKI
DEVA
ARCHCOLM
CARECANY BLCA F>-FO
.ZIONBRCA
GLCA MEVI
HOCU
GRSA BEOL
FOLS
TAPR
ALPOGETT
^ECHOH'
r^HENF
GRSP^
.^..,.PETE
NS
WICR OZAR
MACA
*?o
*\
CAB1
MOJA .
JOTR /
^~~i
r&ru CAVO
WUPA tHLU r PERI"
TU2' PEF° ELMAPETRPEC° "M«
TONT SAPLJ CH|C HOSI
CAGR GICL 1
IPI SAGU WHSA
^St, ' f~H 1 R
^>HRftU!rcftA^iGtSiWtf
FODO
BISOCOGflBlRI GUCO
STRI
GRSM
KtMO_
J NAT(i
ARPS
"KEMO N1S'COSW
HOBE
ANDE
iFODA
LYJO
\BICY
3I5C
Key: All 5 years 4 years
2 years
1 year
No years
Figure 7A-13 Foliar Injury Results Map for 5% Scenario for 214 parks
(Parks identified by park code; not all park labels shown due to overlap. National Parks are prioritized in mapping.)
7 A- 15
-------
Key:
All 5 years
4 years
3 years
2 years
1 year
No years
Figure 7A-14 Foliar Injury Results Map for 10% Scenario for 214 parks
(Parks identified by park code; not all park labels shown due to overlap. National Parks are prioritized in mapping.)
7 A-16
-------
CAW
CHCUBANDFOUN <
ELMAPETR
SAPU
GICL
SAG%,R WHSA
Key:
All 5 years
4 years
3 years
2 years
1 year
No
Figure 7A-15 Foliar Injury Results Map for 15% Scenario for 214 parks
(Parks identified by park code; not all park labels shown due to overlap. National Parks are prioritized in mapping.)
7 A-17
-------
ARCHCOLM
BLCA
NERI -
APCOPETE
ZIONBRCA
GLCA MEVE
ISC
Key: All 5 years 4 years 2 years 1 year
No years
Figure 7A-16 Foliar Injury Results Map for 20% Scenario for 214 parks
(Parks identified by park code; not all park labels shown due to overlap. National Parks are prioritized in mapping.)
7 A-18
-------
Table 7A-3
2010)
Data for 214 Parks Based on Interpolated Ozone Exposure Surface (2006-
Park
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
info
Acadia National Park
ACAD
ME
Y
2
Northeast
Agate Fossil Beds
National Monument
AGFO
NE
Y
1
West North Central
Alibates Flint
Quarries National
Monument
ALFL
TX
Y
0
South
Allegheny Portage
Railroad National
Historic Site
ALPO
PA
Y
0
Northeast
Amistad National
Recreation Area
AMIS
TX
Y
0
South
Andersonville
National Historic Site
ANDE
GA
0
0
Southeast
Antietam National
Battlefield
ANTI
Criteria
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
2006
7.47
1.69
No
Yes
Yes
Yes
Yes
19.16
-1.40
Yes
Yes
No
No
No
19.52
-0.72
Yes
Yes
Yes
Yes
No
10.96
-0.27
Yes
Yes
Yes
Yes
No
12.83
-1.59
Yes
Yes
No
No
No
15.92
-1.55
Yes
Yes
No
No
No
15.36
-0.28
Yes
2007
6.55
1.08
No
Yes
Yes
Yes
Yes
14.76
-1.56
Yes
Yes
No
No
No
9.59
2.36
No
Yes
Yes
Yes
Yes
11.69
-0.02
Yes
Yes
Yes
Yes
No
7.15
4.10
No
Yes
Yes
Yes
Yes
13.34
-1.92
Yes
Yes
No
No
No
15.10
-0.53
Yes
2008
5.68
1.98
No
Yes
Yes
Yes
Yes
10.32
-0.17
No
Yes
Yes
Yes
No
11.64
-0.27
Yes
Yes
Yes
Yes
No
10.53
-0.01
Yes
Yes
Yes
Yes
No
8.26
-0.72
No
Yes
Yes
Yes
No
11.45
-0.47
Yes
Yes
Yes
Yes
No
11.85
1.24
Yes
2009
5.54
2.58
No
Yes
Yes
Yes
No
7.44
1.94
No
Yes
Yes
Yes
Yes
7.93
-0.58
No
Yes
Yes
No
No
6.01
-0.23
No
Yes
Yes
No
No
7.79
-0.68
No
Yes
Yes
No
No
4.60
0.76
No
Yes
No
No
No
5.96
0.05
No
2010
4.35
0.84
No
Yes
No
No
No
8.85
1.95
No
Yes
Yes
Yes
Yes
8.10
1.40
No
Yes
Yes
Yes
Yes
11.04
-0.64
Yes
Yes
Yes
Yes
No
6.46
0.59
No
Yes
Yes
No
No
8.01
-1.17
No
Yes
Yes
No
No
15.32
-1.09
Yes
7 A-19
-------
Park
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
info
MD
Y
0
Northeast
Apostle Islands
National Lakeshore
APIS
WI
Y
0
East North Central
Appomattox Court
House National
Historical Park
APCO
VA
Y
0
Southeast
Arches National Park
ARCH
UT
Y
0
Southwest
Arkansas Post
National Memorial
ARPO
AR
Y
0
South
Assateague Island
National Seashore
ASIS
MD
Y
0
Northeast
Aztec Ruins National
Monument
AZRU
NM
Y
0
Southwest
Badlands National
Park
Criteria
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
2006
Yes
Yes
Yes
No
4.27
-1.55
No
No
No
No
No
12.87
-0.64
Yes
Yes
Yes
Yes
No
19.08
-0.91
Yes
Yes
Yes
Yes
No
16.65
-0.76
Yes
Yes
Yes
Yes
No
16.15
0.20
Yes
Yes
Yes
Yes
No
11.41
0.10
Yes
Yes
Yes
Yes
No
14.98
-0.57
2007
Yes
Yes
Yes
No
5.47
-1.01
No
Yes
No
No
No
12.06
-1.19
Yes
Yes
Yes
Yes
No
16.53
-0.21
Yes
Yes
Yes
Yes
No
13.09
-1.16
Yes
Yes
Yes
Yes
No
15.63
-1.50
Yes
Yes
No
No
No
17.91
0.10
Yes
Yes
Yes
Yes
No
8.11
-1.41
2008
Yes
Yes
Yes
Yes
3.79
-0.09
No
Yes
No
No
No
9.02
-0.12
No
Yes
Yes
Yes
No
15.59
-0.42
Yes
Yes
Yes
Yes
No
7.42
0.53
No
Yes
Yes
No
No
16.12
0.27
Yes
Yes
Yes
Yes
No
12.02
0.02
Yes
Yes
Yes
Yes
No
4.30
1.27
2009
Yes
Yes
No
No
2.67
-1.77
No
No
No
No
No
5.15
0.36
No
Yes
No
No
No
9.71
-1.39
No
Yes
No
No
No
6.43
2.26
No
Yes
Yes
Yes
Yes
7.34
1.38
No
Yes
Yes
Yes
Yes
5.04
-0.50
No
Yes
No
No
No
3.85
2.06
2010
Yes
Yes
Yes
No
3.20
0.80
No
Yes
No
No
No
8.99
-0.74
No
Yes
Yes
Yes
No
15.20
0.77
Yes
Yes
Yes
Yes
No
9.98
-1.65
No
Yes
No
No
No
12.78
-1.70
Yes
Yes
No
No
No
10.86
0.75
Yes
Yes
Yes
Yes
No
4.69
2.15
7A-20
-------
Park
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
info
BADL
SD
Y
2
West North Central
Bandelier National
Monument
BAND
NM
Y
0
Southwest
Bent's Old Fort
National Historic Site
BEOL
CO
Y
0
Southwest
Big Bend National
Park
BIBE
TX
Y
1
South
Big Cypress National
Preserve
BICY
FL
Y
0
Southeast
Big Hole National
Battlefield
BIHO
MT
Y
0
West North Central
Big South Fork
National River and
Recreation Area
BISO
TN
Y
0
Central
Criteria
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
2006
Yes
Yes
Yes
Yes
No
16.50
-0.91
Yes
Yes
Yes
Yes
No
21.90
-0.88
Yes
Yes
Yes
Yes
No
11.95
-0.67
Yes
Yes
Yes
Yes
No
9.84
-0.31
No
Yes
Yes
Yes
No
5.91
-1.10
No
Yes
No
No
No
12.74
0.16
Yes
Yes
Yes
Yes
No
11.19
2007
No
Yes
No
No
No
15.38
0.04
Yes
Yes
Yes
Yes
No
14.35
0.27
Yes
Yes
Yes
Yes
No
9.83
1.78
No
Yes
Yes
Yes
Yes
7.73
-1.10
No
Yes
Yes
No
No
7.99
-1.77
No
Yes
No
No
No
16.75
-1.80
Yes
Yes
No
No
No
9.17
2008
No
Yes
No
No
No
12.69
-0.84
Yes
Yes
Yes
Yes
No
15.44
-0.80
Yes
Yes
Yes
Yes
No
9.92
0.11
No
Yes
Yes
Yes
No
6.29
1.48
No
Yes
Yes
Yes
Yes
6.91
-0.83
No
Yes
Yes
No
No
10.68
-0.57
Yes
Yes
Yes
Yes
No
6.70
2009
No
Yes
No
No
No
6.88
-0.95
No
Yes
Yes
No
No
9.34
-0.62
No
Yes
Yes
Yes
No
8.29
-0.25
No
Yes
Yes
Yes
No
3.65
-0.28
No
Yes
No
No
No
5.39
-0.06
No
Yes
No
No
No
5.91
1.54
No
Yes
Yes
Yes
Yes
6.31
2010
No
Yes
Yes
Yes
No
9.82
-0.54
No
Yes
Yes
Yes
No
11.35
0.26
Yes
Yes
Yes
Yes
No
8.01
0.96
No
Yes
Yes
No
No
5.64
0.55
No
Yes
No
No
No
5.53
0.61
No
Yes
No
No
No
8.12
-0.19
No
Yes
Yes
No
No
8.13
7A-21
-------
Park
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
info
Big Thicket National
Preserve
BETH
TX
Y
0
South
Bighorn Canyon
National Recreation
Area
BICA
MT
Y
0
West North Central
Biscayne National
Park
BISC
FL
Y
0
Southeast
Black Canyon of the
Gunnison National
Park
BLCA
CO
Y
0
Southwest
Blue Ridge Parkway
BLRI
NC
Y
1
Southeast
Bluestone National
Scenic River
BLUE
WV
Y
0
Central
Booker T.
Washington National
Monument
BOWA
VA
Criteria
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
2006
-0.51
Yes
Yes
Yes
Yes
No
11.44
-1.83
Yes
Yes
No
No
No
9.14
-0.41
No
Yes
Yes
Yes
No
19.02
-0.54
Yes
Yes
Yes
Yes
No
12.85
-0.08
Yes
Yes
Yes
Yes
No
11.27
0.34
Yes
Yes
Yes
Yes
No
13.28
-0.64
Yes
Yes
2007
1.70
No
Yes
Yes
Yes
Yes
9.08
-0.59
No
Yes
Yes
Yes
No
7.41
0.22
No
Yes
Yes
No
No
15.26
-0.25
Yes
Yes
Yes
Yes
No
14.30
-1.31
Yes
Yes
No
No
No
14.52
-0.54
Yes
Yes
Yes
Yes
No
14.74
-1.19
Yes
Yes
2008
0.18
No
Yes
Yes
No
No
9.44
0.55
No
Yes
Yes
Yes
No
5.68
0.59
No
Yes
No
No
No
14.70
-0.26
Yes
Yes
Yes
Yes
No
11.53
-0.33
Yes
Yes
Yes
Yes
No
9.78
0.35
No
Yes
Yes
Yes
No
11.57
-0.12
Yes
Yes
2009
0.09
No
Yes
Yes
No
No
6.59
0.62
No
Yes
Yes
No
No
3.82
0.13
No
Yes
No
No
No
8.67
-0.17
No
Yes
Yes
Yes
No
5.50
0.96
No
Yes
No
No
No
5.54
1.00
No
Yes
Yes
Yes
No
5.59
0.36
No
Yes
2010
-0.60
No
Yes
Yes
No
No
5.07
0.16
No
Yes
No
No
No
5.40
0.86
No
Yes
No
No
No
11.44
-0.11
Yes
Yes
Yes
Yes
No
9.71
-0.81
No
Yes
Yes
Yes
No
8.77
-0.50
No
Yes
Yes
Yes
No
9.99
-0.74
No
Yes
7A-22
-------
Park
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
info
Y
0
Southeast
Bryce Canyon
National Park
BRCA
UT
Y
0
Southwest
Buffalo National
River
BUFF
AR
Y
0
South
Cabrillo National
Monument
CABR
CA
0
0
West
Canaveral National
Seashore
CANA
FL
Y
0
Southeast
Canyon de Chelly
National Monument
CACH
AZ
Y
0
Southwest
Canyonlands National
Park
CANY
UT
Y
1
Southwest
Cape Cod National
Seashore
CACO
MA
Criteria
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
2006
Yes
Yes
No
21.01
0.03
Yes
Yes
Yes
Yes
No
14.06
-0.13
Yes
Yes
Yes
Yes
No
5.31
0.03
No
Yes
No
No
No
13.47
-1.90
Yes
Yes
No
No
No
17.97
-0.54
Yes
Yes
Yes
Yes
No
19.25
-0.91
Yes
Yes
Yes
Yes
No
12.19
1.36
Yes
Yes
2007
Yes
Yes
No
18.06
-1.53
Yes
Yes
No
No
No
12.08
-1.27
Yes
Yes
No
No
No
6.20
-2.53
No
Yes
No
No
No
9.29
-1.26
No
Yes
No
No
No
16.13
-1.40
Yes
Yes
No
No
No
17.03
-0.21
Yes
Yes
Yes
Yes
No
10.78
0.13
Yes
Yes
2008
Yes
Yes
No
17.01
-0.68
Yes
Yes
Yes
Yes
No
7.03
3.23
No
Yes
Yes
Yes
Yes
6.65
-1.19
No
Yes
Yes
No
No
7.15
-0.12
No
Yes
Yes
No
No
14.84
0.03
Yes
Yes
Yes
Yes
No
16.38
-0.42
Yes
Yes
Yes
Yes
No
11.83
1.06
Yes
Yes
2009
No
No
No
11.72
-1.30
Yes
Yes
No
No
No
5.81
1.23
No
Yes
Yes
Yes
Yes
5.67
-1.98
No
No
No
No
No
5.28
0.06
No
Yes
No
No
No
7.63
-1.48
No
Yes
No
No
No
10.82
-1.39
Yes
Yes
No
No
No
5.00
1.47
No
Yes
2010
Yes
Yes
No
14.89
0.14
Yes
Yes
Yes
Yes
No
8.39
-0.15
No
Yes
Yes
Yes
No
5.20
0.18
No
Yes
No
No
No
6.13
-0.90
No
Yes
Yes
No
No
10.91
0.39
Yes
Yes
Yes
Yes
No
13.06
0.77
Yes
Yes
Yes
Yes
No
7.26
0.80
No
Yes
7A-23
-------
Park
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ar ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
info
Y
1
Northeast
Cape Hatteras
National Seashore
CAHA
NC
Y
0
Southeast
Cape Lookout
National Seashore
CALO
NC
Y
0
Southeast
Capitol Reef National
Park
CARE
UT
Y
0
Southwest
Capulin Volcano
National Monument
CAVO
NM
Y
0
Southwest
Carlsbad Caverns
National Park
CAVE
NM
Y
1
Southwest
Casa Grande Ruins
National Monument
CAGR
NM
Y
0
Southwest
Catoctin Mountain
Park
CATO
MD
Criteria
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
2006
Yes
Yes
Yes
12.46
1.15
Yes
Yes
Yes
Yes
Yes
12.31
0.20
Yes
Yes
Yes
Yes
No
20.45
-0.44
Yes
Yes
Yes
Yes
No
19.33
-0.91
Yes
Yes
Yes
Yes
No
21.24
0.63
Yes
Yes
Yes
Yes
No
17.20
-0.01
Yes
Yes
Yes
Yes
No
14.08
-0.19
Yes
Yes
2007
Yes
Yes
No
12.19
-1.60
Yes
Yes
No
No
No
9.23
-1.31
No
Yes
No
No
No
17.98
-0.87
Yes
Yes
Yes
Yes
No
13.02
0.04
Yes
Yes
Yes
Yes
No
9.80
0.73
No
Yes
Yes
Yes
No
11.44
-1.38
Yes
Yes
No
No
No
17.33
-1.10
Yes
Yes
2008
Yes
Yes
Yes
13.06
-0.77
Yes
Yes
Yes
Yes
No
12.08
-0.91
Yes
Yes
Yes
Yes
No
16.78
-0.55
Yes
Yes
Yes
Yes
No
13.75
-0.84
Yes
Yes
Yes
Yes
No
10.03
0.19
No
Yes
Yes
Yes
No
18.53
0.30
Yes
Yes
Yes
Yes
No
13.22
0.53
Yes
Yes
2009
Yes
Yes
No
5.17
-0.02
No
Yes
No
No
No
4.86
-0.06
No
Yes
No
No
No
11.33
-1.35
Yes
Yes
No
No
No
8.35
-0.95
No
Yes
Yes
Yes
No
9.32
-0.88
No
Yes
Yes
Yes
No
9.19
-1.63
No
Yes
No
No
No
5.97
1.08
No
Yes
2010
Yes
No
No
10.27
-0.43
No
Yes
Yes
Yes
No
6.70
-0.68
No
Yes
Yes
No
No
14.67
0.46
Yes
Yes
Yes
Yes
No
11.10
-0.54
Yes
Yes
Yes
Yes
No
9.57
1.18
No
Yes
Yes
Yes
Yes
12.42
0.02
Yes
Yes
Yes
Yes
No
15.35
-0.94
Yes
Yes
7A-24
-------
Park
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
info
Y
0
Northeast
Cedar Breaks
National Monument
CEBR
UT
Y
0
Southwest
Chaco Culture
National Historical
Park
CHCU
NM
Y
0
Southwest
Chamizal National
Memorial
CHAM
TX
0
0
South
Chattahoochee River
National Recreation
Area
CHAT
GA
Y
0
Southeast
Chesapeake and Ohio
Canal National
Historical Park
CHOH
MD
Y
0
Northeast
Chickamauga and
Chattanooga National
Military Park
CHCH
GA
Y
0
Southeast
Criteria
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
2006
Yes
Yes
No
21.15
0.03
Yes
Yes
Yes
Yes
No
16.04
0.10
Yes
Yes
Yes
Yes
No
14.30
-0.67
Yes
Yes
Yes
Yes
No
26.64
-0.57
Yes
Yes
Yes
Yes
No
14.86
-0.23
Yes
Yes
Yes
Yes
No
18.80
-0.17
Yes
Yes
Yes
Yes
No
20.45
2007
Yes
Yes
No
17.82
-1.53
Yes
Yes
No
No
No
17.57
0.10
Yes
Yes
Yes
Yes
No
12.17
1.78
Yes
Yes
Yes
Yes
Yes
22.60
-2.10
Yes
Yes
No
No
No
14.70
-0.99
Yes
Yes
Yes
Yes
No
21.34
-1.96
Yes
Yes
No
No
No
10.37
2008
Yes
Yes
No
17.53
-0.68
Yes
Yes
Yes
Yes
No
13.41
0.02
Yes
Yes
Yes
Yes
No
10.63
0.11
Yes
Yes
Yes
Yes
No
13.04
-0.45
Yes
Yes
Yes
Yes
No
11.88
1.06
Yes
Yes
Yes
Yes
Yes
13.10
-0.51
Yes
Yes
Yes
Yes
No
8.28
2009
Yes
Yes
Yes
12.29
-1.30
Yes
Yes
No
No
No
6.16
-0.50
No
Yes
Yes
No
No
7.14
-0.25
No
Yes
Yes
No
No
9.61
1.13
No
Yes
Yes
Yes
Yes
6.15
0.42
No
Yes
Yes
No
No
6.87
1.24
No
Yes
Yes
Yes
Yes
9.49
2010
Yes
Yes
No
15.51
0.14
Yes
Yes
Yes
Yes
No
10.13
0.75
No
Yes
Yes
Yes
No
8.68
0.96
No
Yes
Yes
Yes
No
12.26
0.29
Yes
Yes
Yes
Yes
No
14.46
-1.14
Yes
Yes
Yes
Yes
No
11.09
-0.82
Yes
Yes
Yes
Yes
No
7.85
7A-25
-------
Park
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
info
Chickasaw National
Recreation Area
CHIC
OK
Y
0
South
Chiricahua National
Monument
Wilderness
CHIR
AZ
Y
0
Southwest
City of Rocks
National Reserve
CIRO
ID
Y
1
Northwest
Colonial National
Historical Park
COLO
VA
Y
0
Southeast
Colorado National
Monument
COLM
CO
Y
1
Southwest
Congaree National
Park
COSW
SC
Y
1
Southeast
Coronado National
CORO
AZ
Y
0
Criteria
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
2006
-1.38
Yes
Yes
No
No
No
16.29
-0.31
Yes
Yes
Yes
Yes
No
16.49
-0.08
Yes
Yes
Yes
Yes
No
15.27
0.99
Yes
Yes
Yes
Yes
No
19.38
-0.54
Yes
Yes
Yes
Yes
No
13.67
-0.60
Yes
Yes
Yes
Yes
No
13.40
-0.31
Yes
Yes
Yes
Yes
2007
2.02
No
Yes
Yes
Yes
Yes
12.64
-0.84
Yes
Yes
Yes
Yes
No
18.96
-1.87
Yes
Yes
No
No
No
15.17
-1.35
Yes
Yes
No
No
No
15.48
-0.25
Yes
Yes
Yes
Yes
No
12.11
-1.35
Yes
Yes
No
No
No
11.23
-0.84
Yes
Yes
Yes
Yes
2008
0.58
No
Yes
Yes
Yes
No
14.77
0.17
Yes
Yes
Yes
Yes
No
13.85
-0.68
Yes
Yes
Yes
Yes
No
16.46
0.22
Yes
Yes
Yes
Yes
No
15.14
-0.26
Yes
Yes
Yes
Yes
No
11.41
-0.04
Yes
Yes
Yes
Yes
No
14.44
0.17
Yes
Yes
Yes
Yes
2009
0.70
No
Yes
Yes
Yes
No
10.24
-1.62
No
Yes
No
No
No
10.48
1.24
Yes
Yes
Yes
Yes
Yes
6.01
1.02
No
Yes
Yes
Yes
Yes
6.40
-0.17
No
Yes
Yes
No
No
4.62
-0.20
No
Yes
No
No
No
8.63
-1.62
No
Yes
No
No
2010
-0.10
No
Yes
Yes
No
No
11.96
0.29
Yes
Yes
Yes
Yes
No
6.46
-0.12
No
Yes
Yes
No
No
13.43
-0.90
Yes
Yes
Yes
Yes
No
10.38
-0.11
No
Yes
Yes
Yes
No
7.18
-1.05
No
Yes
Yes
No
No
10.77
0.29
Yes
Yes
Yes
Yes
7A-26
-------
Park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
info
Southwest
Cowpens National
Battlefield
COWP
SC
Y
1
Southeast
Crater Lake National
Park
CRLA
OR
Y
1
Northwest
Craters of the Moon
National Monument
CRMO
ID
Y
0
Northwest
Cumberland Gap
National Historical
Park
CUGA
KY
Y
1
Central
Cumberland Island
National Seashore
CUIS
GA
Y
0
Southeast
Curecanti National
Recreation Area
CURE
CO
Y
0
Southwest
Cuyahoga Valley
National Park
CUVA
OH
Y
Criteria
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
2006
No
15.34
-0.84
Yes
Yes
Yes
Yes
No
8.31
-0.22
No
Yes
Yes
Yes
No
11.29
1.06
Yes
Yes
Yes
Yes
Yes
13.39
0.56
Yes
Yes
Yes
Yes
No
11.26
-1.76
Yes
Yes
No
No
No
19.63
-0.54
Yes
Yes
Yes
Yes
No
11.02
1.76
Yes
Yes
Yes
2007
No
9.62
-2.18
No
Yes
No
No
No
5.10
-1.42
No
No
No
No
No
13.23
-1.91
Yes
Yes
No
No
No
15.24
-1.66
Yes
Yes
No
No
No
9.74
-0.98
No
Yes
Yes
Yes
No
16.32
-0.25
Yes
Yes
Yes
Yes
No
13.64
-0.22
Yes
Yes
Yes
2008
No
16.82
-1.58
Yes
Yes
No
No
No
6.14
-1.00
No
Yes
Yes
No
No
11.64
-1.22
Yes
Yes
Yes
Yes
No
10.96
-0.46
Yes
Yes
Yes
Yes
No
7.12
-0.84
No
Yes
Yes
No
No
14.33
-0.26
Yes
Yes
Yes
Yes
No
9.86
1.23
No
Yes
Yes
2009
No
3.71
-0.05
No
Yes
No
No
No
4.28
-0.42
No
Yes
No
No
No
7.57
1.23
No
Yes
Yes
Yes
Yes
5.13
1.51
No
Yes
Yes
Yes
No
4.50
0.98
No
Yes
No
No
No
9.78
-0.17
No
Yes
Yes
Yes
No
6.14
0.51
No
Yes
Yes
2010
No
9.29
-1.24
No
Yes
Yes
Yes
No
4.51
0.70
No
Yes
No
No
No
7.72
0.06
No
Yes
Yes
No
No
8.06
-0.35
No
Yes
Yes
No
No
6.33
-1.43
No
Yes
No
No
No
10.76
-0.11
Yes
Yes
Yes
Yes
No
9.42
-0.64
No
Yes
Yes
7A-27
-------
Park
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Purlc NUTTIP
A CUJY 1'ltllllV
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
info
0
Central
Death Valley National
Park
DEVA
CA
Y
1
West
Delaware Water Gap
National Recreation
Area
DEWA
PA
Y
0
Northeast
Devil's Tower
National Monument
DETO
WY
Y
1
West North Central
Dinosaur National
Monument
DINO
CO
Y
1
Southwest
Effigy Mounds
National Monument
EFMO
IA
Y
0
East North Central
Eisenhower National
Historic Site
EISE
PA
Y
0
Northeast
El Malpais National
Monument
ELMA
NM
Criteria
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
2006
Yes
Yes
30.56
-0.67
Yes
Yes
Yes
Yes
No
12.57
0.54
Yes
Yes
Yes
Yes
No
14.08
-1.77
Yes
Yes
No
No
No
18.30
-0.55
Yes
Yes
Yes
Yes
No
6.85
0.44
No
Yes
Yes
No
No
11.82
-0.06
Yes
Yes
Yes
Yes
No
16.53
0.44
Yes
Yes
2007
Yes
No
30.77
-1.22
Yes
Yes
Yes
Yes
No
11.69
0.30
Yes
Yes
Yes
Yes
No
10.69
-0.78
Yes
Yes
Yes
Yes
No
11.79
-0.91
Yes
Yes
Yes
Yes
No
9.26
1.50
No
Yes
Yes
Yes
Yes
16.79
-0.80
Yes
Yes
Yes
Yes
No
14.25
-0.35
Yes
Yes
2008
Yes
Yes
28.53
-0.99
Yes
Yes
Yes
Yes
No
10.59
-0.03
Yes
Yes
Yes
Yes
No
8.72
1.41
No
Yes
Yes
Yes
Yes
11.89
0.12
Yes
Yes
Yes
Yes
No
5.25
2.41
No
Yes
Yes
Yes
No
13.00
1.07
Yes
Yes
Yes
Yes
Yes
12.29
0.13
Yes
Yes
2009
No
No
17.99
-1.37
Yes
Yes
No
No
No
4.57
0.62
No
Yes
No
No
No
5.38
1.45
No
Yes
Yes
Yes
No
7.64
-0.43
No
Yes
Yes
No
No
4.96
1.01
No
Yes
Yes
Yes
No
6.01
0.89
No
Yes
Yes
No
No
7.02
-0.75
No
Yes
2010
Yes
No
16.37
-0.06
Yes
Yes
Yes
Yes
No
10.91
-0.62
Yes
Yes
Yes
Yes
No
5.93
0.63
No
Yes
No
No
No
15.39
-0.29
Yes
Yes
Yes
Yes
No
5.10
1.76
No
Yes
Yes
Yes
No
14.02
-0.60
Yes
Yes
Yes
Yes
No
9.89
0.44
No
Yes
7A-28
-------
Park
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
info
Y
0
Southwest
El Morro National
Monument
ELMO
NM
Y
0
Southwest
Eleanor Roosevelt
National Historic Site
ELRO
NY
Y
0
Northeast
Everglades National
Park
EVER
FL
Y
0
Southeast
Fire Island National
Seashore
FIIS
NY
Y
0
Northeast
Florissant Fossil Beds
National Monument
FLFO
CO
Y
0
Southwest
Fort Bowie National
Historic Site
FOBO
AZ
Y
0
Southwest
Fort Davis National
Historic Site
FODA
TX
Criteria
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
2006
Yes
Yes
No
16.49
0.10
Yes
Yes
Yes
Yes
No
6.69
1.09
No
Yes
Yes
Yes
Yes
9.74
-0.38
No
Yes
Yes
Yes
No
14.41
0.50
Yes
Yes
Yes
Yes
No
23.55
-1.14
Yes
Yes
Yes
Yes
No
18.43
-0.31
Yes
Yes
Yes
Yes
No
17.77
-0.67
Yes
Yes
2007
Yes
Yes
No
14.40
0.10
Yes
Yes
Yes
Yes
No
10.69
0.44
Yes
Yes
Yes
Yes
No
8.01
-0.23
No
Yes
Yes
No
No
13.72
0.76
Yes
Yes
Yes
Yes
No
19.10
0.53
Yes
Yes
Yes
Yes
No
14.42
-0.84
Yes
Yes
Yes
Yes
No
9.85
1.78
No
Yes
2008
Yes
Yes
No
12.61
0.02
Yes
Yes
Yes
Yes
No
8.84
1.13
No
Yes
Yes
Yes
Yes
6.36
0.49
No
Yes
Yes
No
No
16.62
0.13
Yes
Yes
Yes
Yes
No
19.14
-0.32
Yes
Yes
Yes
Yes
No
17.48
0.17
Yes
Yes
Yes
Yes
No
9.88
0.11
No
Yes
2009
Yes
No
No
6.89
-0.50
No
Yes
Yes
No
No
5.65
1.42
No
Yes
Yes
Yes
No
3.72
-0.36
No
Yes
No
No
No
9.02
0.56
No
Yes
Yes
Yes
No
9.94
0.54
No
Yes
Yes
Yes
No
11.58
-1.62
Yes
Yes
No
No
No
8.34
-0.25
No
Yes
2010
Yes
Yes
No
10.00
0.75
No
Yes
Yes
Yes
No
8.10
-0.90
No
Yes
Yes
No
No
5.52
0.73
No
Yes
No
No
No
15.06
-0.90
Yes
Yes
Yes
Yes
No
13.30
0.78
Yes
Yes
Yes
Yes
No
13.33
0.29
Yes
Yes
Yes
Yes
No
7.55
0.96
No
Yes
7A-29
-------
Park
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ar ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
info
Y
0
South
Fort Donelson
National Battlefield
FODO
TN
Y
0
Central
Fort Laramie National
Historic Site
FOLA
WY
Y
0
West North Central
Fort Lamed National
Historic Site
FOLS
KS
Y
0
South
Fort Necessity
National Battlefield
FONE
PA
Y
0
Northeast
Fort Pulaski National
Monument
FOPU
GA
Y
0
Southeast
Fort Raleigh National
Historic Site
FORA
NC
Y
0
Southeast
Fort Union National
Monument
FOUN
NM
Criteria
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
2006
Yes
Yes
No
13.87
-0.42
Yes
Yes
Yes
Yes
No
19.12
-1.78
Yes
Yes
No
No
No
17.88
-0.55
Yes
Yes
Yes
Yes
No
13.24
0.27
Yes
Yes
Yes
Yes
No
8.24
-1.51
No
Yes
No
No
No
12.57
1.15
Yes
Yes
Yes
Yes
Yes
15.09
-0.93
Yes
Yes
2007
Yes
Yes
Yes
21.85
-2.13
Yes
Yes
No
No
No
15.54
-1.26
Yes
Yes
No
No
No
6.83
1.75
No
Yes
Yes
Yes
Yes
12.73
0.24
Yes
Yes
Yes
Yes
No
6.70
-1.13
No
Yes
Yes
No
No
12.38
-1.60
Yes
Yes
No
No
No
10.60
-0.08
Yes
Yes
2008
Yes
Yes
No
9.42
-0.15
No
Yes
Yes
Yes
No
11.83
-0.08
Yes
Yes
Yes
Yes
No
7.59
1.66
No
Yes
Yes
Yes
Yes
8.45
0.35
No
Yes
Yes
Yes
No
6.16
-0.74
No
Yes
Yes
No
No
13.19
-0.77
Yes
Yes
Yes
Yes
No
10.97
-0.91
Yes
Yes
2009
Yes
Yes
No
6.51
1.50
No
Yes
Yes
Yes
Yes
7.98
0.95
No
Yes
Yes
No
No
8.67
1.23
No
Yes
Yes
Yes
Yes
6.06
-0.14
No
Yes
Yes
No
No
4.29
0.43
No
Yes
No
No
No
5.11
-0.02
No
Yes
No
No
No
7.54
-0.17
No
Yes
2010
Yes
No
No
10.98
0.14
Yes
Yes
Yes
Yes
No
9.60
1.79
No
Yes
Yes
Yes
Yes
10.41
0.71
No
Yes
Yes
Yes
No
10.61
-0.88
Yes
Yes
Yes
Yes
No
5.58
-1.06
No
Yes
No
No
No
10.53
-0.43
Yes
Yes
Yes
Yes
No
8.57
0.69
No
Yes
7A-30
-------
Park
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Purlc NUTTIP
A CUJY 1'ltllllV
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Purlc NUTTIP
A CUJY 1'ltllllV
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
info
Y
0
Southwest
Fort Washington Park
FOWA
MD
Y
0
Northeast
Fossil Butte National
Monument
FOBU
WY
Y
0
West North Central
Fredericksburg and
Spotsylvania Co.
Battlefields Memorial
National Military Park
FRSP
VA
Y
0
Southeast
Friendship Hill
National Historic Site
FRHI
PA
Y
0
Northeast
Gateway National
Recreation Area
GATE
NY
Y
0
Northeast
Gauley River National
Recreation Area
GARI
WV
Y
0
Central
Criteria
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
2006
Yes
Yes
No
21.90
-0.25
Yes
Yes
Yes
Yes
No
19.10
-2.29
Yes
Yes
No
No
No
13.71
-0.14
Yes
Yes
Yes
Yes
No
13.34
0.27
Yes
Yes
Yes
Yes
No
14.68
0.50
Yes
Yes
Yes
Yes
No
10.65
0.05
Yes
Yes
Yes
Yes
No
17.89
2007
Yes
Yes
No
20.65
-1.67
Yes
Yes
No
No
No
15.76
-2.32
Yes
Yes
No
No
No
13.91
-1.32
Yes
Yes
No
No
No
14.84
0.24
Yes
Yes
Yes
Yes
No
12.72
0.76
Yes
Yes
Yes
Yes
No
13.40
-0.15
Yes
Yes
Yes
Yes
No
18.11
2008
Yes
Yes
No
15.16
1.02
Yes
Yes
Yes
Yes
Yes
11.89
-1.39
Yes
Yes
No
No
No
9.63
0.58
No
Yes
Yes
Yes
No
8.34
0.35
No
Yes
Yes
Yes
No
9.77
0.13
No
Yes
Yes
Yes
No
9.14
0.33
No
Yes
Yes
Yes
No
13.42
2009
Yes
No
No
8.28
1.04
No
Yes
Yes
Yes
Yes
6.84
-0.77
No
Yes
Yes
No
No
4.91
0.23
No
Yes
No
No
No
6.07
-0.14
No
Yes
Yes
No
No
6.06
0.56
No
Yes
Yes
No
No
4.94
0.26
No
Yes
No
No
No
6.97
2010
Yes
Yes
No
18.03
-1.06
Yes
Yes
Yes
Yes
No
8.10
-1.22
No
Yes
Yes
No
No
10.49
-1.36
Yes
Yes
No
No
No
10.24
-0.88
No
Yes
Yes
Yes
No
15.33
-0.90
Yes
Yes
Yes
Yes
No
7.47
-1.00
No
Yes
Yes
No
No
14.87
7A-31
-------
Park
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
info
George Washington
Birthplace National
Monument
GEWA
VA
Y
0
Southeast
George Washington
Memorial Parkway
GWMP
VA
Y
0
Southeast
Gettysburg National
Military Park
GETT
PA
Y
0
Northeast
Gila Cliff Dwellings
National Monument
GICL
NM
Y
0
Southwest
Glacier National Park
GLAC
MT
Y
1
West North Central
Glen Canyon National
Recreation Area
GLCA
UT
Y
0
Southwest
Golden Gate National
Recreation Area
GOGA
CA
Y
0
Criteria
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
2006
0.99
Yes
Yes
Yes
Yes
No
18.03
-0.24
Yes
Yes
Yes
Yes
No
12.33
-0.06
Yes
Yes
Yes
Yes
No
15.37
0.44
Yes
Yes
Yes
Yes
No
3.66
-0.62
No
Yes
No
No
No
20.49
-0.47
Yes
Yes
Yes
Yes
No
1.67
1.15
No
No
No
No
2007
-1.35
Yes
Yes
No
No
No
18.23
-1.56
Yes
Yes
No
No
No
16.75
-0.80
Yes
Yes
Yes
Yes
No
11.66
-0.35
Yes
Yes
Yes
Yes
No
3.23
-1.01
No
Yes
No
No
No
17.62
-1.05
Yes
Yes
Yes
Yes
No
1.36
-1.17
No
No
No
No
2008
0.22
Yes
Yes
Yes
Yes
No
15.37
0.94
Yes
Yes
Yes
Yes
No
13.25
1.07
Yes
Yes
Yes
Yes
Yes
12.00
0.13
Yes
Yes
Yes
Yes
No
4.06
0.29
No
Yes
No
No
No
17.04
-0.36
Yes
Yes
Yes
Yes
No
2.25
-1.68
No
No
No
No
2009
1.02
No
Yes
Yes
Yes
Yes
6.82
0.31
No
Yes
Yes
No
No
6.10
0.89
No
Yes
Yes
No
No
7.41
-0.75
No
Yes
Yes
No
No
4.22
-0.32
No
Yes
No
No
No
10.73
-1.39
Yes
Yes
No
No
No
1.69
-0.20
No
No
No
No
2010
-0.90
Yes
Yes
Yes
Yes
No
18.65
-1.19
Yes
Yes
Yes
Yes
No
14.38
-0.60
Yes
Yes
Yes
Yes
No
11.50
0.44
Yes
Yes
Yes
Yes
No
3.22
1.22
No
Yes
No
No
No
13.34
0.43
Yes
Yes
Yes
Yes
No
1.68
1.17
No
No
No
No
7A-32
-------
Park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
info
West
Golden Spike
National Historic Site
GOSP
UT
Y
0
Southwest
Grand Canyon
National Park
GRCA
AZ
Y
1
Southwest
Grand Teton National
Park
GRTE
WY
Y
0
West North Central
Grant-Kohrs Ranch
National Historic Site
GRKO
MT
Y
0
West North Central
Great Basin National
Park
GRBA
NV
Y
1
West
Great Sand Dunes
National Park
GRSA
CO
Y
0
Southwest
Great Smoky
Mountains National
Park
GRSM
TN
Y
Criteria
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
2006
No
20.72
-0.33
Yes
Yes
Yes
Yes
No
21.51
-0.99
Yes
Yes
Yes
Yes
No
12.78
-0.80
Yes
Yes
Yes
Yes
No
5.29
-0.16
No
Yes
No
No
No
16.78
0.28
Yes
Yes
Yes
Yes
No
19.35
-0.98
Yes
Yes
Yes
Yes
No
16.53
0.19
Yes
Yes
Yes
2007
No
21.66
-2.36
Yes
Yes
No
No
No
17.08
-1.57
Yes
Yes
No
No
No
12.69
-1.51
Yes
Yes
No
No
No
7.55
-1.14
No
Yes
Yes
No
No
16.47
-2.14
Yes
Yes
No
No
No
17.15
0.17
Yes
Yes
Yes
Yes
No
15.41
-1.87
Yes
Yes
No
2008
No
15.13
-1.34
Yes
Yes
No
No
No
17.30
-0.54
Yes
Yes
Yes
Yes
No
11.48
-0.26
Yes
Yes
Yes
Yes
No
6.50
0.15
No
Yes
Yes
No
No
16.81
-1.62
Yes
Yes
No
No
No
14.87
-1.06
Yes
Yes
Yes
Yes
No
13.15
-0.87
Yes
Yes
Yes
2009
No
11.02
-0.07
Yes
Yes
Yes
Yes
No
10.68
-1.59
Yes
Yes
No
No
No
7.56
0.92
No
Yes
Yes
No
No
5.23
-0.14
No
Yes
No
No
No
10.51
0.27
Yes
Yes
Yes
Yes
No
9.61
-0.67
No
Yes
Yes
Yes
No
5.61
1.45
No
Yes
Yes
2010
No
8.43
-0.43
No
Yes
Yes
Yes
No
14.74
0.03
Yes
Yes
Yes
Yes
No
9.21
0.93
No
Yes
Yes
Yes
No
4.95
1.01
No
Yes
Yes
Yes
No
11.84
-0.35
Yes
Yes
Yes
Yes
No
12.16
-0.21
Yes
Yes
Yes
Yes
No
10.16
-0.80
No
Yes
Yes
7A-33
-------
Park
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
info
4
Central
Green Springs
National Historic
Landmark District
GRSP
VA
0
0
Southeast
Greenbelt Park
GREE
MD
0
0
Northeast
Guadalupe Mountains
National Park
GUMO
TX
Y
0
South
Guilford Courthouse
National Military Park
GUCO
NC
Y
0
Southeast
Gulf Islands National
Seashore
GUIS
FL
Y
0
Southeast
Hagerman Fossil Beds
National Monument
HAFO
ID
Y
0
Northwest
Harpers Ferry
National Historical
Park
HAFE
Criteria
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
2006
Yes
No
13.49
-0.05
Yes
Yes
Yes
Yes
No
23.81
-0.25
Yes
Yes
Yes
Yes
No
18.72
-0.67
Yes
Yes
Yes
Yes
No
17.70
0.76
Yes
Yes
Yes
Yes
No
18.13
-1.99
Yes
Yes
No
No
No
9.95
1.25
No
Yes
Yes
Yes
Yes
15.43
-0.21
Yes
2007
No
No
12.60
-1.08
Yes
Yes
Yes
Yes
No
19.77
-1.67
Yes
Yes
No
No
No
10.66
1.78
Yes
Yes
Yes
Yes
Yes
23.09
-1.85
Yes
Yes
No
No
No
16.89
-2.41
Yes
Yes
No
No
No
15.73
-1.67
Yes
Yes
No
No
No
15.28
-0.94
Yes
2008
Yes
No
11.04
0.22
Yes
Yes
Yes
Yes
No
16.65
1.02
Yes
Yes
Yes
Yes
Yes
9.99
0.11
No
Yes
Yes
Yes
No
16.91
0.71
Yes
Yes
Yes
Yes
No
11.16
-0.49
Yes
Yes
Yes
Yes
No
11.00
-0.94
Yes
Yes
Yes
Yes
No
12.08
1.02
Yes
2009
Yes
No
5.58
0.14
No
Yes
No
No
No
7.83
1.04
No
Yes
Yes
Yes
Yes
9.09
-0.25
No
Yes
Yes
Yes
No
8.05
-0.01
No
Yes
Yes
No
No
7.32
0.71
No
Yes
Yes
No
No
9.78
0.82
No
Yes
Yes
Yes
No
5.86
0.36
No
2010
Yes
No
10.34
-1.52
No
Yes
No
No
No
21.15
-1.06
Yes
Yes
Yes
Yes
No
8.79
0.96
No
Yes
Yes
Yes
No
14.46
-0.79
Yes
Yes
Yes
Yes
No
8.54
-0.71
No
Yes
Yes
Yes
No
7.84
0.38
No
Yes
Yes
No
No
14.76
-1.16
Yes
7A-34
-------
Park
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ar ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
info
WV
Y
0
Central
Home of F. D.
Roosevelt National
Historic Site
HOFR
NY
Y
0
Northeast
Hopewell Culture
National Historical
Park
HOCU
OH
Y
0
Central
Hopewell Furnace
National Historic Park
HOFU
PA
Y
0
Northeast
Horseshoe Bend
National Military Park
KOBE
AL
Y
0
Southeast
Hot Springs National
Park
HOSP
AR
Y
0
South
Hovenweep National
Monument
HOVE
CO
Y
0
Southwest
Criteria
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
2006
Yes
Yes
Yes
No
7.01
1.09
No
Yes
Yes
Yes
Yes
14.30
0.32
Yes
Yes
Yes
Yes
No
16.24
0.84
Yes
Yes
Yes
Yes
No
16.37
-1.07
Yes
Yes
Yes
Yes
No
11.89
-0.51
Yes
Yes
Yes
Yes
No
20.25
-0.72
Yes
Yes
Yes
Yes
No
9.05
2007
Yes
Yes
Yes
No
10.69
0.44
Yes
Yes
Yes
Yes
No
17.10
-1.26
Yes
Yes
No
No
No
16.31
0.12
Yes
Yes
Yes
Yes
No
15.38
-2.36
Yes
Yes
No
No
No
9.03
-0.61
No
Yes
Yes
Yes
No
16.77
-0.23
Yes
Yes
Yes
Yes
No
11.48
2008
Yes
Yes
Yes
Yes
8.77
1.13
No
Yes
Yes
Yes
Yes
11.01
0.83
Yes
Yes
Yes
Yes
No
16.49
-0.10
Yes
Yes
Yes
Yes
No
10.80
-0.11
Yes
Yes
Yes
Yes
No
5.67
1.96
No
Yes
Yes
Yes
Yes
15.04
-0.34
Yes
Yes
Yes
Yes
No
4.92
2009
Yes
No
No
No
5.70
1.42
No
Yes
Yes
Yes
Yes
8.23
0.81
No
Yes
Yes
Yes
No
5.73
1.02
No
Yes
Yes
Yes
Yes
5.05
1.37
No
Yes
Yes
Yes
No
4.81
2.38
No
Yes
Yes
Yes
No
9.91
-0.78
No
Yes
Yes
Yes
No
4.83
2010
Yes
Yes
Yes
No
8.14
-0.90
No
Yes
Yes
No
No
10.48
-0.11
Yes
Yes
Yes
Yes
No
15.07
-0.60
Yes
Yes
Yes
Yes
No
8.31
-0.82
No
Yes
Yes
Yes
No
9.53
-1.15
No
Yes
Yes
Yes
No
10.81
0.33
Yes
Yes
Yes
Yes
No
5.73
7A-35
-------
Park
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
info
Indiana Dunes
National Lakeshore
INDU
IN
Y
1
Central
Jean Lafitte National
Historical Park and
Preserve
JELA
LA
Y
0
South
Jewel Cave National
Monument
JECA
SD
Y
0
West North Central
JohnD. Rockefeller
Jr. Memorial Parkway
JODR
WY
0
0
West North Central
John Day Fossil Beds
National Monument
JODA
OR
Y
0
Northwest
John Muir National
Historic Site
JOMU
CA
Y
0
West
Joshua Tree National
Park
JOTR
CA
Y
3
Criteria
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
2006
0.98
No
Yes
Yes
Yes
No
17.22
-1.65
Yes
Yes
No
No
No
14.59
0.01
Yes
Yes
Yes
Yes
No
12.49
-0.80
Yes
Yes
Yes
Yes
No
6.16
0.48
No
Yes
Yes
No
No
6.72
1.87
No
Yes
Yes
Yes
Yes
32.12
-1.30
Yes
Yes
Yes
No
2007
0.22
Yes
Yes
Yes
Yes
No
13.43
-0.36
Yes
Yes
Yes
Yes
No
9.74
-0.46
No
Yes
Yes
Yes
No
12.13
-1.51
Yes
Yes
No
No
No
4.57
-2.24
No
No
No
No
No
2.99
-1.25
No
No
No
No
No
28.20
-1.71
Yes
Yes
Yes
No
2008
0.88
No
Yes
No
No
No
6.69
0.41
No
Yes
Yes
No
No
7.22
1.73
No
Yes
Yes
Yes
Yes
10.89
-0.26
Yes
Yes
Yes
Yes
No
4.60
-1.75
No
No
No
No
No
5.31
-1.57
No
No
No
No
No
30.70
-1.15
Yes
Yes
Yes
Yes
2009
0.45
No
Yes
No
No
No
7.20
-1.16
No
Yes
Yes
No
No
5.26
1.13
No
Yes
Yes
Yes
No
7.56
0.92
No
Yes
Yes
No
No
3.86
-0.96
No
Yes
No
No
No
3.95
-0.20
No
Yes
No
No
No
23.82
-1.85
Yes
Yes
No
No
2010
0.37
No
Yes
No
No
No
8.93
-0.05
No
Yes
Yes
Yes
No
5.73
1.00
No
Yes
No
No
No
9.58
0.93
No
Yes
Yes
Yes
No
4.04
0.31
No
Yes
No
No
No
3.63
1.06
No
Yes
No
No
No
25.07
-0.52
Yes
Yes
Yes
Yes
7A-36
-------
Park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
info
West
Kennesaw Mountain
National Battlefield
Park
KEMO
GA
Y
0
Southeast
Kings Mountain
National Military Park
KIMO
SC
Y
0
Southeast
Knife River Indian
Villages National
Historic Site
KNRI
ND
Y
0
West North Central
Lake Mead National
Recreation Area
LAME
NV
Y
0
West
Lake Meredith
National Recreation
Area
LAMR
TX
Y
0
South
Lake Roosevelt
National Recreation
Area
LARO
WA
Y
0
Northwest
Lassen Volcanic
National Park
LAVO
Criteria
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
2006
No
26.52
-0.57
Yes
Yes
Yes
Yes
No
15.10
-0.56
Yes
Yes
Yes
Yes
No
7.25
-0.68
No
Yes
Yes
No
No
23.51
-1.32
Yes
Yes
No
No
No
19.51
-0.72
Yes
Yes
Yes
Yes
No
5.47
0.08
No
Yes
No
No
No
21.31
0.41
Yes
2007
No
22.62
-2.10
Yes
Yes
No
No
No
15.78
-1.98
Yes
Yes
No
No
No
3.08
-0.01
No
Yes
No
No
No
17.81
-1.49
Yes
Yes
No
No
No
9.60
2.36
No
Yes
Yes
Yes
Yes
3.85
-1.20
No
Yes
No
No
No
15.29
-1.07
Yes
2008
No
14.10
-0.45
Yes
Yes
Yes
Yes
No
15.44
-1.06
Yes
Yes
Yes
Yes
No
4.87
-1.55
No
No
No
No
No
17.64
-1.31
Yes
Yes
No
No
No
11.66
-0.27
Yes
Yes
Yes
Yes
No
2.89
-0.78
No
No
No
No
No
18.03
-1.98
Yes
2009
No
9.05
1.13
No
Yes
Yes
Yes
Yes
4.66
-0.08
No
Yes
No
No
No
3.94
0.68
No
Yes
No
No
No
12.43
-1.79
Yes
Yes
No
No
No
7.93
-0.58
No
Yes
Yes
No
No
3.86
-0.67
No
Yes
No
No
No
10.42
0.14
No
2010
No
13.61
0.29
Yes
Yes
Yes
Yes
No
8.54
-1.26
No
Yes
No
No
No
4.68
2.76
No
Yes
Yes
No
No
15.30
-0.33
Yes
Yes
Yes
Yes
No
8.10
1.40
No
Yes
Yes
Yes
Yes
2.72
1.00
No
No
No
No
No
11.59
0.59
Yes
7A-37
-------
Park
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ar ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
info
CA
Y
1
West
Lava Beds National
Monument
LABE
CA
Y
0
West
Little Bighorn
Battlefield National
Monument
LIBI
IN
Y
0
Central
Little River Canyon
National Preserve
LIRI
MT
Y
0
West North Central
Lyndon B. Johnson
National Historical
Park
LYJO
TX
Y
0
South
Mammoth Cave
National Park
MACA
KY
Y
0
Central
Manassas National
Battlefield Park
MANA
VA
Y
0
Southeast
Criteria
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
2006
Yes
Yes
Yes
No
11.08
0.43
Yes
Yes
Yes
Yes
No
11.16
-1.41
Yes
Yes
No
No
No
17.64
-1.06
Yes
Yes
Yes
Yes
No
13.69
-1.59
Yes
Yes
No
No
No
12.09
1.00
Yes
Yes
Yes
Yes
Yes
13.38
-0.24
Yes
Yes
Yes
Yes
No
43.41
2007
Yes
Yes
Yes
No
8.09
-1.08
No
Yes
Yes
No
No
8.71
-0.03
No
Yes
Yes
Yes
No
18.72
-2.50
Yes
Yes
No
No
No
6.58
4.10
No
Yes
Yes
Yes
Yes
20.19
-1.39
Yes
Yes
No
No
No
14.37
-1.56
Yes
Yes
No
No
No
37.02
2008
Yes
No
No
No
9.78
-1.78
No
Yes
No
No
No
9.09
0.22
No
Yes
Yes
Yes
No
10.94
0.12
Yes
Yes
Yes
Yes
No
7.90
-0.72
No
Yes
Yes
No
No
11.44
-0.28
Yes
Yes
Yes
Yes
No
11.44
0.94
Yes
Yes
Yes
Yes
No
39.34
2009
Yes
Yes
Yes
No
5.59
-0.20
No
Yes
No
No
No
6.15
0.60
No
Yes
Yes
No
No
5.39
1.02
No
Yes
Yes
Yes
No
8.17
-0.68
No
Yes
Yes
No
No
6.23
0.98
No
Yes
Yes
No
No
5.09
0.31
No
Yes
No
No
No
30.02
2010
Yes
Yes
Yes
No
7.59
1.27
No
Yes
Yes
Yes
Yes
4.67
0.27
No
Yes
No
No
No
9.72
-0.80
No
Yes
Yes
Yes
No
7.27
0.59
No
Yes
Yes
No
No
10.03
-0.33
No
Yes
Yes
Yes
No
12.08
-1.19
Yes
Yes
Yes
Yes
No
29.02
7A-38
-------
Park
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
info
Manzanar National
Historic Site
MANZ
CA
0
0
West
Marsh-Billings-
Rockefeller National
Historical Park
MABI
VT
Y
0
Northeast
Mesa Verde National
Park
MEVE
CO
Y
1
Southwest
Minute Man National
Historical Park
MIMA
MA
Y
0
Northeast
Mississippi National
River And Recreation
Area
MISS
MN
Y
0
East North Central
Missouri National
Recreational River
MNRR
NE
Y
0
West North Central
Mojave National
Preserve
MOJA
CA
Y
Criteria
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
2006
-1.30
Yes
Yes
Yes
No
No
4.23
1.18
No
Yes
No
No
No
20.79
-0.54
Yes
Yes
Yes
Yes
No
8.26
1.06
No
Yes
Yes
Yes
Yes
7.48
-0.29
No
Yes
Yes
No
No
9.89
-0.06
No
Yes
Yes
Yes
No
28.70
-1.24
Yes
Yes
Yes
2007
-1.71
Yes
Yes
Yes
No
No
6.27
-0.26
No
Yes
Yes
No
No
17.13
-0.25
Yes
Yes
Yes
Yes
No
9.66
0.34
No
Yes
Yes
Yes
No
8.20
0.27
No
Yes
Yes
Yes
No
6.06
1.31
No
Yes
Yes
Yes
Yes
26.92
-1.47
Yes
Yes
Yes
2008
-1.15
Yes
Yes
Yes
Yes
No
6.21
0.42
No
Yes
Yes
No
No
13.92
-0.26
Yes
Yes
Yes
Yes
No
6.40
1.69
No
Yes
Yes
Yes
Yes
4.61
0.36
No
Yes
No
No
No
4.12
1.16
No
Yes
No
No
No
25.28
-1.33
Yes
Yes
Yes
2009
-1.85
Yes
Yes
Yes
No
No
4.06
0.34
No
Yes
No
No
No
12.06
-0.17
Yes
Yes
Yes
Yes
No
4.71
1.22
No
Yes
Yes
Yes
No
4.72
-0.34
No
Yes
No
No
No
4.25
1.13
No
Yes
No
No
No
19.31
-1.86
Yes
Yes
No
2010
-0.52
Yes
Yes
Yes
Yes
No
4.45
-1.25
No
No
No
No
No
11.62
-0.11
Yes
Yes
Yes
Yes
No
5.88
-0.17
No
Yes
No
No
No
4.55
1.15
No
Yes
Yes
No
No
5.26
2.63
No
Yes
Yes
Yes
No
19.85
-0.43
Yes
Yes
Yes
7A-39
-------
Park
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ar ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
info
1
West
Monocacy National
Battlefield
MONO
MD
Y
0
Northeast
Montezuma Castle
National Monument
MOCA
AZ
Y
0
Southwest
Morristown National
Historical Park
MORR
NJ
Y
0
Northeast
Mount Rainier
Wilderness
MORA
WA
Y
1
Northwest
Mount Rushmore
National Memorial
MORU
SD
Y
0
West North Central
Muir Woods National
Monument
MUWO
CA
Y
0
West
Natchez Trace
Parkway
NATR
AL
Y
Criteria
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
2006
Yes
No
17.17
-0.19
Yes
Yes
Yes
Yes
No
25.01
-1.45
Yes
Yes
Yes
No
No
19.03
0.15
Yes
Yes
Yes
Yes
No
5.43
-1.01
No
Yes
No
No
No
11.68
0.01
Yes
Yes
Yes
Yes
No
0.99
0.43
No
No
No
No
No
15.19
-1.25
Yes
Yes
Yes
2007
No
No
17.44
-1.10
Yes
Yes
Yes
Yes
No
19.96
-1.95
Yes
Yes
No
No
No
18.20
0.91
Yes
Yes
Yes
Yes
No
2.28
-0.26
No
No
No
No
No
7.59
-0.46
No
Yes
Yes
No
No
1.14
-1.08
No
No
No
No
No
12.22
-1.21
Yes
Yes
Yes
2008
No
No
14.47
0.53
Yes
Yes
Yes
Yes
No
23.10
-0.31
Yes
Yes
Yes
Yes
No
14.78
-0.31
Yes
Yes
Yes
Yes
No
3.28
0.08
No
Yes
No
No
No
5.99
1.73
No
Yes
Yes
Yes
Yes
1.80
-1.78
No
No
No
No
No
6.95
0.78
No
Yes
Yes
2009
No
No
6.47
1.08
No
Yes
Yes
Yes
Yes
10.27
-1.56
No
Yes
No
No
No
5.66
0.53
No
Yes
No
No
No
3.96
-0.52
No
Yes
No
No
No
4.54
1.13
No
Yes
Yes
No
No
1.27
-0.20
No
No
No
No
No
4.81
0.97
No
Yes
No
2010
Yes
No
16.84
-0.94
Yes
Yes
Yes
Yes
No
12.48
-0.31
Yes
Yes
Yes
Yes
No
14.80
-0.62
Yes
Yes
Yes
Yes
No
2.34
1.47
No
No
No
No
No
4.80
1.00
No
Yes
No
No
No
1.22
1.27
No
No
No
No
No
7.31
-0.87
No
Yes
Yes
7A-40
-------
Park info
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
0
Southeast
National Mall &
Memorial Parks
NACC
DC
0
0
Southeast
Natural Bridges
National Monument
NABR
UT
Y
0
Southwest
Navajo National
Monument
NAVA
AZ
Y
0
Southwest
New River Gorge
National River
NERI
WV
Y
0
Central
Nez Perce National
Historical Park
NEPE
ID
Y
0
Northwest
Ninety Six National
Historic Site
NISI
SC
Y
0
Southeast
North Cascades
National Park
NOCA
WA
Y
Criteria
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
2006
Yes
No
21.89
-0.25
Yes
Yes
Yes
Yes
No
20.14
-0.91
Yes
Yes
Yes
Yes
No
20.51
-0.54
Yes
Yes
Yes
Yes
No
10.90
0.05
Yes
Yes
Yes
Yes
No
4.00
-0.41
No
Yes
No
No
No
15.07
-1.05
Yes
Yes
Yes
Yes
No
2.99
-0.78
No
No
No
2007
Yes
No
20.39
-1.67
Yes
Yes
No
No
No
17.02
-0.21
Yes
Yes
Yes
Yes
No
17.19
-1.40
Yes
Yes
No
No
No
14.07
-0.15
Yes
Yes
Yes
Yes
No
7.25
-1.51
No
Yes
No
No
No
14.88
-1.67
Yes
Yes
No
No
No
1.86
-0.56
No
No
No
2008
No
No
16.18
1.02
Yes
Yes
Yes
Yes
Yes
16.82
-0.42
Yes
Yes
Yes
Yes
No
17.49
0.03
Yes
Yes
Yes
Yes
No
9.44
0.33
No
Yes
Yes
Yes
No
5.29
-0.14
No
Yes
No
No
No
14.71
-1.08
Yes
Yes
Yes
Yes
No
2.14
-0.23
No
No
No
2009
No
No
7.71
1.04
No
Yes
Yes
Yes
Yes
9.95
-1.39
No
Yes
No
No
No
10.00
-1.48
No
Yes
No
No
No
5.33
0.26
No
Yes
No
No
No
5.35
0.21
No
Yes
No
No
No
4.66
0.12
No
Yes
No
No
No
3.31
-0.40
No
Yes
No
2010
No
No
20.38
-1.06
Yes
Yes
Yes
Yes
No
12.50
0.77
Yes
Yes
Yes
Yes
No
13.08
0.39
Yes
Yes
Yes
Yes
No
8.20
-1.00
No
Yes
Yes
Yes
No
4.23
0.71
No
Yes
No
No
No
7.20
-1.33
No
Yes
No
No
No
2.31
1.41
No
No
No
7A-41
-------
Park
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
info
0
Northwest
Ocmulgee National
Monument
OCMU
GA
Y
0
Southeast
Olympic National
Park
OLYM
WA
Y
2
Northwest
Oregon Caves
National Monument
ORCA
OR
Y
0
Northwest
Organ Pipe Cactus
National Monument
ORPI
AZ
Y
0
Southwest
Ozark National Scenic
Riverways
OZAR
MO
Y
0
Central
Padre Island National
Seashore
PAIS
TX
Y
1
South
Palo Alto Battlefield
National Historic Site
PAAL
TX
0
Criteria
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
2006
No
No
18.94
-1.47
Yes
Yes
No
No
No
2.74
-0.90
No
No
No
No
No
8.91
0.13
No
Yes
Yes
Yes
No
12.40
-0.31
Yes
Yes
Yes
Yes
No
16.63
0.12
Yes
Yes
Yes
Yes
No
5.01
-0.98
No
Yes
No
No
No
4.27
-1.08
No
Yes
No
2007
No
No
16.03
-0.59
Yes
Yes
Yes
Yes
No
1.49
0.23
No
No
No
No
No
3.43
-0.39
No
Yes
No
No
No
8.76
-0.84
No
Yes
Yes
Yes
No
17.40
-1.01
Yes
Yes
Yes
Yes
No
4.37
4.66
No
Yes
No
No
No
3.84
1.18
No
Yes
No
2008
No
No
14.33
-1.32
Yes
Yes
No
No
No
1.73
0.03
No
No
No
No
No
5.44
-0.43
No
Yes
No
No
No
15.96
0.17
Yes
Yes
Yes
Yes
No
8.88
2.40
No
Yes
Yes
Yes
Yes
5.79
0.31
No
Yes
No
No
No
4.51
1.63
No
Yes
Yes
2009
No
No
8.10
1.00
No
Yes
Yes
No
No
2.06
-0.40
No
No
No
No
No
4.09
-0.05
No
Yes
No
No
No
7.16
-1.62
No
Yes
No
No
No
7.01
0.72
No
Yes
Yes
No
No
5.04
-1.53
No
No
No
No
No
3.82
-1.42
No
No
No
2010
No
No
8.97
-0.07
No
Yes
Yes
Yes
No
1.79
1.17
No
No
No
No
No
3.38
1.95
No
Yes
No
No
No
9.83
0.29
No
Yes
Yes
Yes
No
9.23
-0.17
No
Yes
Yes
Yes
No
5.19
3.02
No
Yes
Yes
Yes
No
3.47
2.29
No
Yes
No
7A-42
-------
Park
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
info
0
South
Pea Ridge National
Military Park
PERI
AR
Y
0
South
Pecos National
Historical Park
PECO
NM
Y
0
Southwest
Petersburg National
Battlefield
PETE
VA
Y
0
Southeast
Petrified Forest
National Park
PEFO
AZ
Y
1
Southwest
Petroglyph National
Monument
PETR
NM
Y
0
Southwest
Pictured Rocks
National Lakeshore
PIRO
MI
Y
0
East North Central
Pinnacles National
Monument
FINN
CA
Y
Criteria
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
2006
No
No
17.70
-0.42
Yes
Yes
Yes
Yes
No
17.84
-0.91
Yes
Yes
Yes
Yes
No
14.41
0.47
Yes
Yes
Yes
Yes
No
18.54
-0.54
Yes
Yes
Yes
Yes
No
16.66
1.31
Yes
Yes
Yes
Yes
Yes
6.41
-1.53
No
Yes
No
No
No
14.06
1.87
Yes
Yes
Yes
2007
No
No
8.18
-1.41
No
Yes
No
No
No
14.01
0.04
Yes
Yes
Yes
Yes
No
14.70
-1.22
Yes
Yes
Yes
Yes
No
16.00
-1.40
Yes
Yes
No
No
No
15.54
1.10
Yes
Yes
Yes
Yes
Yes
9.18
-1.15
No
Yes
Yes
Yes
No
12.79
-1.25
Yes
Yes
Yes
2008
No
No
5.47
3.29
No
Yes
Yes
Yes
No
13.48
-0.84
Yes
Yes
Yes
Yes
No
15.51
0.22
Yes
Yes
Yes
Yes
No
17.49
0.03
Yes
Yes
Yes
Yes
No
12.68
0.16
Yes
Yes
Yes
Yes
No
4.55
0.10
No
Yes
No
No
No
16.44
-1.57
Yes
Yes
No
2009
No
No
5.71
0.88
No
Yes
No
No
No
9.27
-0.95
No
Yes
Yes
Yes
No
5.62
0.58
No
Yes
No
No
No
8.56
-1.48
No
Yes
No
No
No
10.35
-0.33
No
Yes
Yes
Yes
No
3.23
-0.48
No
Yes
No
No
No
9.83
-0.20
No
Yes
Yes
2010
No
No
7.83
-0.15
No
Yes
Yes
No
No
10.48
-0.54
Yes
Yes
Yes
Yes
No
11.19
-1.21
Yes
Yes
Yes
Yes
No
12.05
0.39
Yes
Yes
Yes
Yes
No
10.84
0.47
Yes
Yes
Yes
Yes
No
4.24
0.11
No
Yes
No
No
No
8.87
1.06
No
Yes
Yes
7A-43
-------
Park
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
info
1
West
Pipe Spring National
Monument
PISP
AZ
Y
0
Southwest
Pipestone National
Monument
PIPE
MN
Y
0
East North Central
Piscataway Park
PISC
MD
Y
0
Northeast
Point Reyes National
Seashore
PORE
CA
Y
0
West
Poverty Point
National Monument
POPO
LA
0
0
South
Prince William Forest
Park
PRWI
VA
Y
0
Southeast
Rainbow Bridge
National Monument
RABR
UT
Y
Criteria
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
2006
Yes
Yes
21.00
-1.45
Yes
Yes
No
No
No
6.94
0.43
No
Yes
Yes
No
No
20.83
0.00
Yes
Yes
Yes
Yes
No
1.61
0.43
No
No
No
No
No
13.76
-0.99
Yes
Yes
Yes
Yes
No
15.40
0.37
Yes
Yes
Yes
Yes
No
20.61
-0.91
Yes
Yes
Yes
2007
Yes
No
16.32
-1.74
Yes
Yes
No
No
No
5.37
0.30
No
Yes
No
No
No
19.99
-1.82
Yes
Yes
No
No
No
1.36
-1.08
No
No
No
No
No
7.39
-0.23
No
Yes
Yes
No
No
15.91
-1.46
Yes
Yes
No
No
No
17.32
-0.21
Yes
Yes
Yes
2008
No
No
16.86
-1.11
Yes
Yes
Yes
Yes
No
4.36
0.22
No
Yes
No
No
No
14.57
0.82
Yes
Yes
Yes
Yes
No
2.29
-1.78
No
No
No
No
No
4.48
1.47
No
Yes
Yes
No
No
10.16
0.58
No
Yes
Yes
Yes
No
17.53
-0.42
Yes
Yes
Yes
2009
Yes
No
11.30
-1.71
Yes
Yes
No
No
No
4.43
-0.33
No
Yes
No
No
No
7.95
0.69
No
Yes
Yes
No
No
1.58
-0.20
No
No
No
No
No
4.68
0.38
No
Yes
No
No
No
5.71
0.67
No
Yes
No
No
No
10.27
-1.39
No
Yes
No
2010
Yes
Yes
16.17
-0.34
Yes
Yes
Yes
Yes
No
5.66
2.42
No
Yes
Yes
Yes
Yes
17.35
-1.11
Yes
Yes
Yes
Yes
No
1.71
1.27
No
No
No
No
No
8.22
-1.88
No
Yes
No
No
No
12.94
-1.05
Yes
Yes
Yes
Yes
No
13.43
0.77
Yes
Yes
Yes
7A-44
-------
Park
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
info
0
Southwest
Redwood National
Park
REDW
CA
Y
0
West
Richmond National
Battlefield Park
RICH
VA
Y
0
Southeast
Rock Creek Park
ROCR
DC
Y
0
Southeast
Rocky Mountain
National Park
ROMO
CO
Y
0
Southwest
Saguaro National Park
SAGU
AZ
Y
1
Southwest
Saint Croix National
Scenic Riverway
SACN
WI
Y
0
East North Central
Salinas Pueblo
Missions National
Monument
SAPU
NM
Criteria
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
2006
Yes
No
9.21
0.43
No
Yes
Yes
Yes
No
16.76
-0.05
Yes
Yes
Yes
Yes
No
21.83
-0.22
Yes
Yes
Yes
Yes
No
19.31
-0.97
Yes
Yes
Yes
Yes
No
16.16
-0.31
Yes
Yes
Yes
Yes
No
6.40
-0.66
No
Yes
Yes
No
No
16.46
0.03
Yes
Yes
2007
Yes
No
3.32
-1.08
No
Yes
No
No
No
17.16
-1.08
Yes
Yes
Yes
Yes
No
19.69
-1.38
Yes
Yes
No
No
No
17.74
0.27
Yes
Yes
Yes
Yes
No
12.93
-0.84
Yes
Yes
Yes
Yes
No
5.94
-0.03
No
Yes
Yes
No
No
12.77
0.62
Yes
Yes
2008
Yes
No
4.86
-1.78
No
No
No
No
No
17.47
0.22
Yes
Yes
Yes
Yes
No
16.29
0.78
Yes
Yes
Yes
Yes
No
18.51
-0.05
Yes
Yes
Yes
Yes
No
15.59
0.17
Yes
Yes
Yes
Yes
No
4.21
0.18
No
Yes
No
No
No
11.93
-0.20
Yes
Yes
2009
No
No
3.60
-0.20
No
Yes
No
No
No
6.33
0.14
No
Yes
Yes
No
No
7.74
1.06
No
Yes
Yes
Yes
Yes
10.54
0.77
Yes
Yes
Yes
Yes
No
9.60
-1.62
No
Yes
No
No
No
3.89
-1.04
No
Yes
No
No
No
8.88
-1.50
No
Yes
2010
Yes
No
4.23
1.27
No
Yes
No
No
No
13.63
-1.52
Yes
Yes
No
No
No
19.67
-1.00
Yes
Yes
Yes
Yes
No
15.48
0.60
Yes
Yes
Yes
Yes
No
12.26
0.29
Yes
Yes
Yes
Yes
No
4.02
0.87
No
Yes
No
No
No
9.93
-0.03
No
Yes
7A-45
-------
Park
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
info
Y
0
Southwest
Santa Monica
Mountains National
Recreation Area
SAMO
CA
Y
0
West
Saratoga National
Historical Park
SARA
NY
Y
1
Northeast
Scotts Bluff National
Monument
SCBL
NE
Y
1
West North Central
Sequoia-Kings
Canyon National Park
SEKI
CA
Y
2
West
Shenandoah National
Park
SHEN
VA
Y
1
Southeast
Shiloh National
Military Park
SHIL
TN
Y
0
Central
Sleeping Bear Dunes
National Lakeshore
SLBE
Criteria
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
2006
Yes
Yes
No
15.95
0.03
Yes
Yes
Yes
Yes
No
5.96
1.09
No
Yes
Yes
Yes
Yes
19.08
-1.40
Yes
Yes
No
No
No
44.59
-0.12
Yes
Yes
Yes
Yes
No
13.11
-0.23
Yes
Yes
Yes
Yes
No
13.82
-0.42
Yes
Yes
Yes
Yes
No
8.84
-0.12
No
2007
Yes
Yes
No
13.68
-2.53
Yes
Yes
No
No
No
9.14
0.44
No
Yes
Yes
Yes
No
15.43
-1.56
Yes
Yes
No
No
No
38.05
-1.72
Yes
Yes
Yes
No
No
11.74
-1.02
Yes
Yes
Yes
Yes
No
18.18
-2.13
Yes
Yes
No
No
No
11.09
-1.20
Yes
2008
Yes
Yes
No
16.41
-1.19
Yes
Yes
Yes
Yes
No
7.78
1.13
No
Yes
Yes
Yes
Yes
11.45
-0.17
Yes
Yes
Yes
Yes
No
43.95
-1.43
Yes
Yes
Yes
No
No
10.07
0.26
No
Yes
Yes
Yes
No
7.78
-0.15
No
Yes
Yes
No
No
5.58
0.55
No
2009
No
No
No
13.86
-1.98
Yes
Yes
No
No
No
5.24
1.42
No
Yes
Yes
Yes
No
8.08
1.94
No
Yes
Yes
Yes
Yes
31.74
-1.18
Yes
Yes
Yes
Yes
No
5.62
0.51
No
Yes
No
No
No
6.16
1.50
No
Yes
Yes
Yes
Yes
5.37
0.22
No
2010
Yes
Yes
No
10.36
0.18
No
Yes
Yes
Yes
No
6.07
-0.90
No
Yes
Yes
No
No
10.16
1.95
No
Yes
Yes
Yes
Yes
30.55
-0.02
Yes
Yes
Yes
Yes
No
9.46
-0.98
No
Yes
Yes
Yes
No
9.33
0.14
No
Yes
Yes
Yes
No
6.01
0.04
No
7A-46
-------
Park
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
info
MI
Y
0
East North Central
Stones River National
Battlefield
STRI
TN
Y
0
Central
Sunset Crater Volcano
National Monument
SUCR
AZ
Y
0
Southwest
Tallgrass Prairie
National Preserve
TAPR
KS
Y
0
South
Theodore Roosevelt
National Park
THRO
ND
Y
2
West North Central
Timpanogos Cave
National Monument
TICA
UT
Y
0
Southwest
Timucuan Ecological
And Historic Preserve
TIMU
FL
Y
0
Southeast
Tonto National
Monument
TONT
Criteria
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
2006
Yes
Yes
Yes
No
14.70
-0.42
Yes
Yes
Yes
Yes
No
22.26
-0.54
Yes
Yes
Yes
Yes
No
16.18
-0.71
Yes
Yes
Yes
Yes
No
8.34
-0.55
No
Yes
Yes
Yes
No
21.45
0.01
Yes
Yes
Yes
Yes
No
13.28
-2.28
Yes
Yes
No
No
No
24.01
-1.33
Yes
2007
Yes
Yes
Yes
No
20.43
-2.13
Yes
Yes
No
No
No
18.36
-1.40
Yes
Yes
No
No
No
6.58
0.52
No
Yes
Yes
No
No
5.68
-0.16
No
Yes
No
No
No
19.71
-2.12
Yes
Yes
No
No
No
11.63
-1.29
Yes
Yes
No
No
No
21.11
-1.49
Yes
2008
Yes
No
No
No
11.59
-0.15
Yes
Yes
Yes
Yes
No
21.05
0.03
Yes
Yes
Yes
Yes
No
6.22
0.93
No
Yes
Yes
No
No
5.99
-1.71
No
No
No
No
No
15.96
-0.69
Yes
Yes
Yes
Yes
No
7.84
-0.16
No
Yes
Yes
No
No
24.48
0.88
Yes
2009
Yes
No
No
No
6.33
1.50
No
Yes
Yes
Yes
Yes
10.72
-1.48
Yes
Yes
No
No
No
6.92
1.49
No
Yes
Yes
Yes
Yes
4.13
0.93
No
Yes
No
No
No
11.75
0.62
Yes
Yes
Yes
Yes
No
5.48
0.79
No
Yes
No
No
No
13.32
-0.67
Yes
2010
Yes
Yes
No
No
10.02
0.14
No
Yes
Yes
Yes
No
13.38
0.39
Yes
Yes
Yes
Yes
No
7.81
0.83
No
Yes
Yes
No
No
4.58
2.08
No
Yes
Yes
No
No
13.49
-0.35
Yes
Yes
Yes
Yes
No
6.67
-1.01
No
Yes
Yes
No
No
16.29
0.21
Yes
7A-47
-------
Park
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
info
AZ
Y
1
Southwest
Tumacacori National
Historical Park
TUMA
AZ
Y
0
Southwest
Tuzigoot National
Monument
TUZI
AZ
Y
0
Southwest
Valley Forge National
Historical Park
VAFO
PA
Y
0
Northeast
Vanderbilt Mansion
National Historic Site
VAMA
NY
Y
0
Northeast
Vicksburg National
Military Park
VICK
MS
Y
0
South
Voyageurs National
Park
VOYA
MN
Y
1
East North Central
Walnut Canyon
National Monument
WACA
Criteria
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
2006
Yes
No
No
No
12.86
-0.31
Yes
Yes
Yes
Yes
No
22.90
-1.45
Yes
Yes
No
No
No
17.35
0.84
Yes
Yes
Yes
Yes
No
7.32
1.09
No
Yes
Yes
Yes
Yes
14.37
-1.23
Yes
Yes
Yes
Yes
No
5.15
-1.39
No
No
No
No
No
22.79
-0.54
Yes
2007
Yes
No
No
No
10.25
-0.84
No
Yes
Yes
Yes
No
18.12
-1.95
Yes
Yes
No
No
No
16.86
0.12
Yes
Yes
Yes
Yes
No
10.68
0.44
Yes
Yes
Yes
Yes
No
8.55
-0.81
No
Yes
Yes
Yes
No
5.02
-0.23
No
Yes
No
No
No
18.83
-1.40
Yes
2008
Yes
Yes
Yes
No
14.17
0.17
Yes
Yes
Yes
Yes
No
20.55
-0.31
Yes
Yes
Yes
Yes
No
17.33
-0.10
Yes
Yes
Yes
Yes
No
8.69
1.13
No
Yes
Yes
Yes
Yes
5.50
1.12
No
Yes
Yes
Yes
No
3.52
0.41
No
Yes
No
No
No
21.78
0.03
Yes
2009
Yes
Yes
Yes
No
8.67
-1.62
No
Yes
No
No
No
9.75
-1.56
No
Yes
No
No
No
6.10
1.02
No
Yes
Yes
Yes
Yes
5.75
1.42
No
Yes
Yes
Yes
Yes
4.86
0.02
No
Yes
No
No
No
4.34
-0.26
No
Yes
No
No
No
11.04
-1.48
Yes
2010
Yes
Yes
Yes
No
11.39
0.29
Yes
Yes
Yes
Yes
No
12.22
-0.31
Yes
Yes
Yes
Yes
No
15.54
-0.60
Yes
Yes
Yes
Yes
No
8.19
-0.90
No
Yes
Yes
Yes
No
7.61
-1.48
No
Yes
No
No
No
6.44
0.13
No
Yes
Yes
No
No
13.39
0.39
Yes
7A-48
-------
Park
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
ame
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
info
AZ
Y
0
Southwest
Whiskeytown-Shasta-
Trinity National
Recreation Area
WHIS
CA
Y
0
West
White Sands National
Mounument
WHSA
NM
Y
0
Southwest
Wilson's Creek
National Battlefield
WICR
MO
Y
0
Central
Wind Cave National
Park
WICA
SD
Y
1
West North Central
Wright Brothers
National Memorial
WRBR
NC
0
0
Southeast
Wupatki National
Monument
WUPA
AZ
Y
0
Southwest
Yellowstone National
Park
Criteria
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
2006
Yes
Yes
Yes
No
16.27
0.41
Yes
Yes
Yes
Yes
No
15.30
1.31
Yes
Yes
Yes
Yes
Yes
15.89
-0.49
Yes
Yes
Yes
Yes
No
18.06
-0.28
Yes
Yes
Yes
Yes
No
12.57
1.15
Yes
Yes
Yes
Yes
Yes
21.67
-0.54
Yes
Yes
Yes
Yes
No
11.63
-1.43
2007
Yes
No
No
No
9.22
-1.07
No
Yes
Yes
Yes
No
9.45
0.72
No
Yes
Yes
Yes
No
10.47
0.24
Yes
Yes
Yes
Yes
No
10.88
-0.94
Yes
Yes
Yes
Yes
No
12.38
-1.60
Yes
Yes
No
No
No
17.82
-1.40
Yes
Yes
No
No
No
9.75
-1.50
2008
Yes
Yes
Yes
No
13.00
-1.98
Yes
Yes
No
No
No
9.64
0.56
No
Yes
Yes
Yes
No
7.11
2.81
No
Yes
Yes
Yes
Yes
5.85
1.50
No
Yes
Yes
Yes
Yes
13.19
-0.77
Yes
Yes
Yes
Yes
No
19.57
0.03
Yes
Yes
Yes
Yes
No
9.43
-0.24
2009
Yes
No
No
No
8.66
0.14
No
Yes
Yes
Yes
No
7.61
-0.60
No
Yes
Yes
No
No
5.97
1.00
No
Yes
Yes
No
No
5.36
1.59
No
Yes
Yes
Yes
No
5.11
-0.02
No
Yes
No
No
No
10.22
-1.48
No
Yes
No
No
No
7.11
0.57
2010
Yes
Yes
Yes
No
11.65
0.59
Yes
Yes
Yes
Yes
No
9.36
0.80
No
Yes
Yes
Yes
No
7.96
0.23
No
Yes
Yes
No
No
5.46
1.57
No
Yes
Yes
Yes
No
10.53
-0.43
Yes
Yes
Yes
Yes
No
13.14
0.39
Yes
Yes
Yes
Yes
No
9.25
0.19
7A-49
-------
Park
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
Park Name
Park Code
Primary State
Species in park?
# Monitors in park
Climate Region
info
YELL
WY
Y
1
West North Central
Yosemite National
Park
YOSE
CA
Y
5
West
Zion National Park
ZION
UT
Y
0
Southwest
Criteria
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
W126 (ppm-hrs)
PZ (7 -mo avg)
Exceeds Base scenario?
Exceeds 5% scenario?
Exceeds 10% scenario?
Exceeds 15% scenario?
Exceeds 20% scenario?
2006
Yes
Yes
No
No
No
29.22
0.71
Yes
Yes
Yes
Yes
No
21.68
-0.37
Yes
Yes
Yes
Yes
No
2007
No
Yes
No
No
No
26.24
-1.38
Yes
Yes
Yes
No
No
17.57
-1.40
Yes
Yes
No
No
No
2008
No
Yes
Yes
Yes
No
33.88
-1.47
Yes
Yes
Yes
No
No
17.66
-0.70
Yes
Yes
Yes
Yes
No
2009
No
Yes
Yes
No
No
21.64
-0.21
Yes
Yes
Yes
Yes
No
12.78
-1.43
Yes
Yes
No
No
No
2010
No
Yes
Yes
Yes
No
20.68
0.35
Yes
Yes
Yes
Yes
No
16.92
0.16
Yes
Yes
Yes
Yes
No
7A-50
-------
Table 7A-4 Os Estimates at 214 Parks for Recent Conditions and Just Meeting the
Existing and Alternative W126 Standard Levels
Park Name
Acadia National Park
Agate Fossil Beds National Monument
Alibates Flint Quarries National Monument
Allegheny Portage Railroad National Historic Site
Amistad National Recreation Area
Andersonville National Historic Site
Antietam National Battlefield
Apostle Islands National Lakeshore
Appomattox Court House National Historical Park
Arches National Park
Arkansas Post National Memorial
Assateague Island National Seashore
Aztec Ruins National Monument
Badlands National Park
Bandelier National Monument
Bandelier Wilderness
Bent's Old Fort National Historic Site
Big Bend National Park
Big Cypress National Preserve
Big Hole National Battlefield
Big South Fork National River and Recreation Area
Big Thicket National Preserve
Bighorn Canyon National Recreation Area
Biscayne National Park
Black Canyon of the Gunnison National Park
Blue Ridge Parkway
Bluestone National Scenic River
Booker T. Washington National Monument
Bryce Canyon National Park
Buffalo National River
Canaveral National Seashore
Canyon de Chelly National Monument
Canyonlands National Park
Cape Cod National Seashore
Cape Lookout National Seashore
Capitol Reef National Park
Capulin Volcano National Monument
Carlsbad Caverns National Park
2006-
2008
6.01
15.24
12.66
10.88
8.35
13.03
13.80
4.53
10.70
16.69
12.33
16.23
12.06
10.14
12.22
12.18
15.59
10.30
6.48
9.82
12.57
9.22
9.59
6.50
15.27
12.07
11.33
12.58
18.13
9.70
9.41
15.18
17.05
11.25
10.46
17.83
14.10
13.41
Just
meet
75ppb
0.30
5.65
2.70
0.41
1.66
2.27
1.71
0.81
1.71
4.51
1.78
1.07
4.17
4.77
3.23
3.15
3.91
1.64
2.51
2.41
3.37
2.17
2.14
3.20
3.59
2.06
2.05
1.74
4.72
2.30
2.84
5.00
4.68
0.37
2.10
4.88
3.34
4.54
15
ppm-
hrs
0.30
5.00
2.22
0.41
1.59
2.27
1.71
0.81
1.71
3.65
1.78
1.07
3.42
4.62
2.55
2.49
3.01
1.54
2.51
2.20
3.37
2.17
2.14
3.20
2.91
2.06
2.05
1.74
3.90
2.30
2.84
3.96
3.80
0.37
2.10
4.02
2.61
3.72
11
ppm-
hrs
0.30
4.21
1.62
0.36
1.51
2.10
1.32
0.81
1.59
2.52
1.78
1.02
2.37
4.44
1.67
1.64
1.90
1.41
2.42
1.94
2.32
2.17
2.14
3.10
2.02
1.87
1.55
1.61
2.79
2.09
2.71
2.57
2.63
0.37
1.99
2.85
1.70
2.67
7ppm-
hrs
0.30
3.36
1.46
0.32
1.49
1.38
0.82
0.79
1.07
2.19
1.78
0.80
1.95
3.58
1.44
1.41
1.56
1.38
1.98
1.78
1.36
2.17
1.79
2.63
1.76
1.23
0.98
1.08
2.43
1.88
2.08
2.11
2.27
0.36
1.45
2.48
1.47
2.42
7A-51
-------
Park Name
Casa Grande Ruins National Monument
Catoctin Mountain Park
Cedar Breaks National Monument
Chaco Culture National Historical Park
Channel Islands National Park
Chattahoochee River National Recreation Area
Chesapeake and Ohio Canal National Historical Park
Chickamauga and Chattanooga National Military Park
Chickasaw National Recreation Area
Chiricahua National Monument Wilderness
City of Rocks National Reserve
Colonial National Historical Park
Colorado National Monument
Congaree National Park
Cowpens National Battlefield
Crater Lake National Park
Craters of the Moon National Monument
Cumberland Gap National Historical Park
Cumberland Island National Seashore
Curecanti National Recreation Area
Cuyahoga Valley National Park
Death Valley National Park
Delaware Water Gap National Recreation Area
Devil's Tower National Monument
Dinosaur National Monument
Ebey's Landing National Historical Reserve
Effigy Mounds National Monument
Eisenhower National Historic Site
El Malpais National Monument
El Morro National Monument
Eleanor Roosevelt National Historic Site
Eleven Point National Wild and Scenic River
Everglades National Park
Fire Island National Seashore
Florissant Fossil Beds National Monument
Fort Bowie National Historic Site
Fort Davis National Historic Site
Fort Donelson National Battlefield
Fort Laramie National Historic Site
Fort Lamed National Historic Site
2006-
2008
15.01
14.49
18.46
13.66
5.58
20.74
13.40
17.29
12.24
14.85
17.60
15.34
16.06
11.64
13.25
6.97
13.87
11.76
9.22
15.41
11.13
29.76
11.75
11.94
16.24
1.54
6.68
13.47
13.47
13.61
8.57
14.09
6.27
14.98
19.52
16.76
12.08
14.76
16.41
10.16
Just
meet
75ppb
5.22
0.32
4.14
3.92
1.13
4.97
2.01
5.08
1.81
5.64
3.64
2.55
4.27
1.52
1.95
2.06
3.33
2.95
0.82
3.55
3.40
2.72
0.26
3.40
4.51
1.06
0.97
0.38
3.69
3.87
0.03
3.53
2.77
0.47
5.65
6.26
3.26
2.49
5.69
1.78
15
ppm-
hrs
3.73
0.32
3.45
3.16
1.06
4.97
2.01
5.08
1.81
4.72
2.91
2.55
3.46
1.52
1.95
2.00
2.82
2.95
0.82
2.81
3.40
2.53
0.26
3.40
3.78
1.06
0.97
0.38
2.93
3.08
0.03
3.53
2.77
0.47
4.01
5.13
2.80
2.49
4.91
1.75
11
ppm-
hrs
2.06
0.32
2.53
2.13
0.99
4.63
1.71
3.74
1.81
3.48
1.97
2.38
2.39
1.41
1.81
1.92
2.22
2.02
0.78
1.86
2.47
2.32
0.26
3.40
2.79
1.06
0.97
0.38
1.92
2.02
0.03
2.68
2.68
0.47
2.22
3.64
2.22
1.65
3.98
1.70
7ppm-
hrs
1.67
0.32
2.25
1.74
0.91
3.09
1.10
2.20
1.81
3.16
1.71
1.63
2.07
0.93
1.20
1.88
1.96
1.20
0.59
1.62
1.53
2.10
0.26
2.78
2.34
1.06
0.97
0.38
1.60
1.68
0.03
1.97
2.23
0.47
1.73
3.25
2.08
0.93
3.17
1.69
7A-52
-------
Park Name
Fort Necessity National Battlefield
Fort Pulaski National Monument
Fort Union National Monument
Fort Washington Park
Fossil Butte National Monument
Fredericksburg and Spotsylvania Co. Battlefields Memorial
National Military Park
Friendship Hill National Historic Site
Gateway National Recreation Area
Gauley River National Recreation Area
George Washington Memorial Parkway
Gettysburg National Military Park
Gila Cliff Dwellings National Monument
Glacier National Park
Glen Canyon National Recreation Area
Golden Gate National Recreation Area
Golden Spike National Historic Site
Grand Canyon National Park
Grand Teton National Park
Grant-Kohrs Ranch National Historic Site
Great Basin National Park
Great Sand Dunes National Park
Great Smoky Mountains National Park
Green Springs National Historic Landmark District
Greenbelt Park
Guadalupe Mountains National Park
Guilford Courthouse National Military Park
Gulf Islands National Seashore
Hagerman Fossil Beds National Monument
Harpers Ferry National Historical Park
Hohokam Pima National Monument
Home of F. D. Roosevelt National Historic Site
Hopewell Culture National Historical Park
Hopewell Furnace National Historic Park
Horseshoe Bend National Military Park
Hot Springs National Park
Hovenweep National Monument
Indiana Dunes National Lakeshore
Isle Royale National Park
Jean Lafitte National Historical Park and Preserve
Jewel Cave National Monument
2006-
2008
10.70
6.86
10.80
19.34
16.79
11.79
11.47
12.34
10.22
17.34
13.75
12.74
3.34
17.59
1.61
19.07
17.88
11.67
7.14
16.44
14.31
14.65
11.40
19.98
12.69
19.02
15.23
15.56
13.95
13.45
8.66
13.87
16.42
13.81
8.16
16.40
8.35
5.45
12.87
11.99
Just
meet
75ppb
1.46
2.06
3.35
5.78
4.48
2.22
1.98
0.45
2.54
4.18
0.39
3.92
0.68
5.05
0.59
4.22
4.96
2.68
2.46
3.58
3.20
3.35
1.96
5.84
4.10
1.39
5.01
3.28
1.91
4.88
0.03
3.43
0.30
3.50
1.59
5.23
3.87
0.91
3.36
6.33
15
ppm-
hrs
1.46
2.06
2.87
5.78
3.74
2.22
1.98
0.45
2.54
4.18
0.39
3.16
0.68
4.11
0.56
3.29
4.09
2.50
2.24
3.40
2.51
3.35
1.96
5.84
3.53
1.39
5.01
2.74
1.91
3.51
0.03
3.43
0.30
3.50
1.59
4.22
3.87
0.91
3.36
6.33
11
ppm-
hrs
1.08
1.98
2.26
5.37
2.72
2.05
1.44
0.45
1.69
3.86
0.39
2.16
0.68
2.84
0.53
2.10
2.92
2.27
1.97
3.16
1.65
2.43
1.82
5.44
2.79
1.26
4.66
2.07
1.57
1.91
0.03
2.50
0.30
3.24
1.59
2.85
3.05
0.91
3.34
6.33
7ppm-
hrs
0.72
1.59
1.92
3.43
2.27
1.35
0.93
0.45
0.87
2.43
0.39
1.88
0.61
2.43
0.49
1.78
2.56
1.95
1.72
3.00
1.43
1.48
1.20
3.54
2.61
0.73
3.09
1.84
1.03
1.52
0.03
1.58
0.30
2.09
1.59
2.37
2.17
0.89
3.28
5.05
7A-53
-------
Park Name
John D. Rockefeller Jr. Memorial Parkway
John Day Fossil Beds National Monument
John Muir National Historic Site
Joshua Tree National Park
Kennesaw Mountain National Battlefield Park
Kings Mountain National Military Park
Knife River Indian Villages National Historic Site
Lake Mead National Recreation Area
Lake Meredith National Recreation Area
Lake Roosevelt National Recreation Area
Lassen Volcanic National Park
Lava Beds National Monument
Little Bighorn Battlefield National Monument
Little River Canyon National Preserve
Lyndon B. Johnson National Historical Park
Mammoth Cave National Park
Manassas National Battlefield Park
Manzanar National Historic Site
Marsh-Billings-Rockefeller National Historical Park
Mesa Verde National Park
Minute Man National Historical Park
Mississippi National River And Recreation Area
Missouri National Recreational River
Mojave National Preserve
Monocacy National Battlefield
Montezuma Castle National Monument
Morristown National Historical Park
Mount Rainier Wilderness
Mount Rushmore National Memorial
Muir Woods National Monument
Natchez Trace Parkway
National Mall
Natural Bridges National Monument
Navajo National Monument
New River Gorge National River
Nez Perce National Historical Park
Ninety Six National Historic Site
North Cascades National Park
Ocmulgee National Monument
Olympic National Park
2006-
2008
11.17
5.58
4.55
29.01
21.03
14.46
4.62
19.19
12.67
4.32
17.83
9.96
9.69
15.52
8.13
14.48
12.99
42.52
5.38
16.40
7.92
6.56
6.10
26.41
16.06
21.85
17.40
4.34
11.52
1.25
11.40
19.47
17.09
17.27
10.84
6.08
14.17
2.39
16.13
1.77
Just
meet
75ppb
2.70
2.28
1.19
2.93
4.64
1.20
2.47
3.15
2.70
1.37
1.80
1.84
2.24
3.49
2.02
3.53
3.20
1.95
0.38
5.44
0.26
0.86
2.02
3.24
1.17
9.96
0.35
1.76
6.01
0.64
1.57
8.26
5.06
6.06
2.21
2.13
1.31
1.24
3.22
1.13
15
ppm-
hrs
2.55
2.21
1.07
2.58
4.64
1.20
2.47
2.74
2.22
1.37
1.70
1.75
2.24
3.49
2.00
3.53
3.20
1.78
0.38
4.28
0.26
0.86
1.96
2.86
1.17
7.20
0.35
1.76
6.01
0.62
1.57
8.26
4.12
4.75
2.21
2.00
1.31
1.24
3.22
1.13
11
ppm-
hrs
2.36
2.13
0.94
2.16
4.33
1.12
2.47
2.22
1.62
1.37
1.57
1.64
2.24
3.11
1.98
2.38
2.98
1.58
0.38
2.78
0.26
0.86
1.89
2.41
1.01
3.75
0.35
1.76
6.01
0.60
1.50
7.66
2.84
3.04
1.53
1.84
1.21
1.24
2.88
1.13
7ppm-
hrs
2.03
2.09
0.81
1.86
2.88
0.74
2.16
1.99
1.46
1.36
1.45
1.57
1.87
1.97
1.98
1.38
2.03
1.39
0.38
2.34
0.26
0.86
1.70
2.12
0.69
2.85
0.35
1.76
4.79
0.57
1.38
4.80
2.40
2.51
0.85
1.76
0.80
1.23
1.60
1.13
7A-54
-------
Park Name
Oregon Caves National Monument
Organ Pipe Cactus National Monument
Ozark National Scenic Riverways
Padre Island National Seashore
Palo Alto Battlefield National Historic Site
Pea Ridge National Military Park
Pecos National Historical Park
Petersburg National Battlefield
Petrified Forest National Park
Petroglyph National Monument
Pictured Rocks National Lakeshore
Pinnacles National Monument
Pipe Spring National Monument
Pipestone National Monument
Piscataway Park
Point Reyes National Seashore
Poverty Point National Monument
Prince William Forest Park
Rainbow Bridge National Monument
Redwood National Park
Richmond National Battlefield Park
Rock Creek Park
Rocky Mountain National Park
Saguaro National Park
Saint Croix National Scenic Riverway
Salinas Pueblo Missions National Monument
San Juan Island National Historical Park
Santa Monica Mountains National Recreation Area
Saratoga National Historical Park
Scotts Bluff National Monument
Sequoia-Kings Canyon National Park
Shenandoah National Park
Shiloh National Military Park
Sleeping Bear Dunes National Lakeshore
Stones River National Battlefield
Sunset Crater Volcano National Monument
Tallgrass Prairie National Preserve
Theodore Roosevelt National Park
Timpanogos Cave National Monument
Timucuan Ecological And Historic Preserve
2006-
2008
6.35
11.69
13.82
4.49
3.26
10.33
12.59
14.52
16.92
14.55
6.47
14.03
17.49
4.94
18.55
1.50
7.72
13.75
17.41
6.63
17.04
19.20
18.55
14.34
5.48
11.55
1.32
15.19
7.08
16.07
43.35
11.00
14.29
8.23
15.27
19.34
9.22
6.09
19.03
10.40
Just
meet
75ppb
1.44
4.10
3.51
1.04
0.88
1.63
3.35
2.03
5.43
3.36
1.07
0.86
4.01
1.57
5.66
0.42
0.80
3.15
5.48
1.25
2.83
5.99
7.00
6.68
0.79
3.28
0.89
3.22
0.05
5.42
1.98
1.77
2.59
1.46
3.89
7.42
1.70
3.45
5.90
0.30
15
ppm-
hrs
1.42
3.29
3.51
1.04
0.88
1.63
2.63
2.03
4.17
2.56
1.07
0.79
3.34
1.57
5.66
0.41
0.80
3.15
4.39
1.22
2.83
5.99
5.24
5.15
0.79
2.60
0.89
2.63
0.05
4.60
1.74
1.77
2.59
1.46
3.89
5.65
1.70
3.45
4.70
0.30
11
ppm-
hrs
1.40
2.28
2.59
1.04
0.88
1.46
1.70
1.86
2.55
1.56
1.07
0.72
2.44
1.57
5.26
0.40
0.80
2.92
2.94
1.18
2.55
5.55
3.06
3.25
0.79
1.71
0.89
2.02
0.05
3.61
1.48
1.62
2.00
1.46
2.69
3.40
1.70
3.45
3.01
0.29
7ppm-
hrs
1.37
1.98
1.86
1.04
0.88
1.28
1.46
1.18
2.07
1.30
1.06
0.65
2.17
1.44
3.36
0.39
0.80
1.88
2.48
1.15
1.39
3.54
2.43
2.75
0.78
1.48
0.89
1.49
0.05
2.87
1.24
1.09
1.44
1.46
1.58
2.77
1.70
2.86
2.47
0.21
7A-55
-------
Park Name
Tonto National Monument
Tumacacori National Historical Park
Tuzigoot National Monument
Valley Forge National Historical Park
Vanderbilt Mansion National Historic Site
Vicksburg National Military Park
Voyageurs National Park
Walnut Canyon National Monument
Whiskeytown-Shasta-Trinity National Recreation Area
White Sands National Monument
Wilson's Creek National Battlefield
Wind Cave National Park
Wupatki National Monument
Yellowstone National Park
Yosemite National Park
Zion National Park
2006-
2008
22.44
11.85
19.66
17.76
8.75
8.84
4.54
19.87
12.67
11.00
10.66
12.47
18.61
9.99
29.42
18.70
Just
meet
75ppb
8.54
5.02
9.02
0.29
0.04
0.96
0.96
7.58
1.03
3.83
2.90
8.13
7.14
2.64
1.67
3.79
15
ppm-
hrs
5.93
4.13
6.62
0.29
0.04
0.96
0.96
5.73
0.95
3.08
2.90
8.11
5.49
2.56
1.54
3.13
11
ppm-
hrs
2.91
2.97
3.59
0.29
0.04
0.96
0.96
3.39
0.87
2.11
2.18
8.08
3.37
2.45
1.39
2.26
7ppm-
hrs
2.25
2.64
2.76
0.29
0.04
0.96
0.90
2.74
0.78
1.85
1.61
6.44
2.78
2.10
1.25
1.99
7A-56
-------
Figures 7A-17 through 7-23 provide information regarding the geographic distribution of the 214
parks in this screening assessment. Figure 7A-17 provides a pie chart of the breakdown of all 214 parks
into the 9 NOAA climate regions. Figures 7A-18 through 7A-23 provide the geographic distribution of
the results of the five scenarios compared to the geographic breakdown of all 214 parks for all five years,
at least four years, at least three years, at least two years, at least one year, and no years, respectively.
Figure Error! No text of specified style in document.A-24 shows the distribution of parks across
different W126 index values in recent conditions and after adjustments to just meet the existing and
alternative standards. Figure 7A-25 illustrates the cumulative distribution of W126 estimates in 214 parks.
West
9%
West North
Central
9%
Central
8%
Figure 7A-17 Breakdown of 214 Parks by Climate Region
7A-57
-------
Scenario
Figure 7A-18 Parks Exceeding Benchmark Criteria for all 5 years (2006-2010) by Scenario
and Climate Region
7A-58
-------
200
150
Cfi
^
«
G-
=tt
50
u
• East North Central
• Northwest
• West North Central
West
• Central
• South
• Northeast
• Southeast
• Southwest
All 2 14 Parks
8
11
20
19
17
21
27
39
52
Base
0
1
0
10
2
0
16
14
41
5% of
biosites
7
6
20
15
17
21
27
39
52
10% of
biosites
1
3
6
9
15
16
25
16
36
15% of
biosites
1
0
o
6
4
9
9
24
12
36
20% of
biosites
0
0
0
0
0
0
0
0
0
Scenario
Figure 7A-19 Parks Exceeding Benchmark Criteria for at least 4 years (2006-2010) by
Scenario and Climate Region
7A-59
-------
200
100
50
0 -
• East North Central
• Northwest
• West North Central
West
• Central
• South
• Northeast
• Southeast
• Southwest
^M ,
All 214
Parks
8
11
20
19
17
21
27
39
52
Base
0
2
6
12
8
1
19
27
49
, ^™ ,
5% of
biosites
8
9
20
16
17
21
27
39
52
10% of
biosites
3
3
14
12
16
19
26
29
50
, ^™ ,
15% of
biosites
1
3
9
8
15
12
26
22
49
20% of
biosites
0
0
0
0
0
1
o
J
0
0
Scenario
Figure 7A-20 Parks Exceeding Benchmark Criteria for at least 3 years (2006-2010) by
Scenario and Climate Region
7A-60
-------
200
150
50
0 -
• East North Central
• Northwest
• West North Central
West
• Central
• South
• Northeast
• Southeast
• Southwest
• •
All 214
Parks
8
11
20
19
17
21
27
39
52
• •
Base
0
o
J
9
12
15
7
20
33
50
• •
5% of
biosites
8
9
20
16
17
21
27
39
52
• •
10% of
biosites
5
4
18
12
17
19
27
39
52
• •
15% of
biosites
2
3
14
12
17
17
26
32
52
— •
20% of
biosites
0
1
2
1
1
4
9
1
1
Scenario
Figure 7A-21 Parks Exceeding Benchmark Criteria for at least 2 years (2006-2010) by
Scenario and Climate Region
7A-61
-------
200
50
u -
• East North Central
• Northwest
• West North Central
West
• Central
• South
• Northeast
• Southeast
• Southwest
All 214
Parks
8
11
20
19
17
21
27
39
52
Base
1
o
6
14
13
17
19
23
35
52
5% sites,
any injury
8
10
20
16
17
21
27
39
52
10% sites,
any injury
7
6
19
16
17
21
27
39
52
15% sites,
any injury
5
5
16
15
17
20
26
36
52
20% sites,
any injury
2
o
6
8
3
9
15
18
11
o
6
Scenario
Figure 7A-22 Parks Exceeding Benchmark Criteria for at least 1 year (2006-2010) by
Scenario and Climate Region
7A-62
-------
200
50
0 -
• East North Central
• Northwest
• West North Central
West
• Central
• South
• Northeast
• Southeast
• Southwest
All 214
Parks
8
11
20
19
17
21
27
39
52
Base
7
8
6
6
0
2
4
4
0
5% of
biosites
0
1
0
3
0
0
0
0
0
10% of
biosites
1
5
1
3
0
0
0
0
0
15% of
biosites
3
6
4
4
0
1
1
3
0
20% of
biosites
6
8
12
16
8
6
9
28
49
Scenario
Figure 7A-23 Parks Exceeding Benchmark Criteria for no years (2006-2010) by Scenario
and Climate Region
7A-63
-------
100%
i>15 ppm-hrs
13-15 ppm-hrs
11-13 ppm-hrs
9-11 ppm-hrs
17-9 ppm-hrs
I <7 ppm-hrs
2006-2008 Just meeting 75 15 ppm-hrs 11 ppm-hrs 7 ppm-hrs
ppb
Adjustment Scenarios
Figure Error! No text of specified style in document.A-24 Percent of 214 Parks at Different
W126 Index Values After Adjustments to Just Meeting the Existing and
Alternative Standards (3-year average)
7A-64
-------
10 15 20 25 30
W126 Index Value (ppm-hrs)
35
40
Figure Error! No text of specified style in document.A-25 Cumulative Distribution of W126
in 214 Parks
7A-65
-------
In addition to the W126 benchmarks for foliar injury described in section 7.3, we
evaluated alternative benchmarks for all biosites (reflecting all categories of soil moisture) that
also consider the percentage of FHM biosites showing any foliar injury. These alternative
benchmarks are derived from the black line in Figure 7-11 in section 7.2. Table 7A-5 shows the
alternative W126 benchmarks from this evaluation. Table 7A-6 shows the percentage of all
FFDVI biosites with foliar injury at various alternative secondary standard levels.
Table 7A-5 Alternative W126 Benchmarks for All FHM Biosites in All Soil Moisture
Categories
Percentage of All FHM Biosites (Any Injury)
5% of biosites
10% of biosites
15% of biosites
Corresponding W126 Benchmark
3.4 ppm-hrs
4.7 ppm-hrs
6.5 ppm-hrs
Table 7A-6 Percentage of All FHM Biosites Showing Foliar Injury at Alternative
Secondary Standard Levels
Alternative Secondary Standard Level
7 ppm-hrs
9 ppm-hrs
1 1 ppm-hrs
13 ppm-hrs
15 ppm-hrs
Percentage of All FHM Biosites (Any Injury)
15.8%
17.1%
17.8%
18.0%
18.1%
7A-66
-------
APPENDIX 7B:
NATIONAL PARKS CASE STUDY LARGE SCALE MAPS
7B-1
-------
Figure 7B-1 GRSM Canopy Sensitive Species Cover
7B-2
-------
<20%
20%-40%
40%-60%
• 60%-8-%
Appalachian Trail
Figure 7B-2 GRSM Canopy Sensitive Species Trail Cover
7B-3
-------
No Data
0%
40%-6«
Figure 7B-3 GRSM Canopy Sensitive Species Overlooks (3km)
7B-4
-------
Figure 7B-4 GRSM Subcanopy Sensitive Species Cover
7B-5
-------
Tree Subcanopy
•^^— No Data
<20%
2096-40%
40%-60%
— 60%-80%
Appalachian Trail
Figure 7B-5 GRSM Subcanopy Sensitive Species Trail Cover
7B-6
-------
Tall Shrub
No Data
<20%
20%-40%
40%-60%
60%-80%
Figure 7B-6 GRSM Tall Shrub Sensitive Species Cover
7B-7
-------
<20%
20%-40%
40%-60%
• 60%-80%
' >80%
Appalachian Trail
Figure 7B-7 GRSM Tall Shrub Sensitive Species Trail Cover
7B-8
-------
Short Shrub
No Data
0%
<20%
20%-40%
40%-60%
^B 60%-80%
^^| >80%
Figure 7B-8 GRSM Short Shrub Sensitive Species Cover
7B-9
-------
Short Shrub
^^^No Data
^^—0%
<20%
20%-40%
— 40%-60%
^^^ 60%-BO%
: Appalachian Trail
Figure 7B-9 GRSM Short Shrub Sensitive Species Trail Cover
7B-10
-------
->~ -V
L
Herbaceous
No Data
0%
<20%
20%-40%
40%-60%
>80%
Figure 7B-10 GRSM Herbaceous Sensitive Species Cover
7B-11
-------
Herbaceous
"^^^™ No data
0%
<20%
20%-40%
— 40%-60%
• 80%-100%
Appalachian Trail
Figure 7B-11 GRSM herbaceous Sensitive Species Trail Cover
7B-12
-------
Canopy
No Data
<20%
20-40%
40-60%
^H >80%
Figure 7B-12 ROMO Canopy Sensitive Species Cover
7B-13
-------
Canopy
^^^— No data
<20%
20-40%
40-60%
^^— 60-80%
= CONST
Figure 7B-13 ROMO Canopy Sensitive Species Trail Cover
7B-14
-------
Sub-Canopy
No Data
<20%
20-40%
40-60%
.;
Figure 7B-14 ROMO Subcanopy Sensitive Species Cover
7B-15
-------
Sub Canopy
^^^— No Data
<20%
20%- 40%
40%-60%
— >80%
=^ CONST
Figure 7B-15 ROMO Subcanopy Sensitive Species Trail Cover
7B-16
-------
Tall Shrub
No Data
<20%
20-40%
I >80%
Figure 7B-16 ROMO Tall Shrub Sensitive Species Cover
7B-17
-------
Tall Shrub
•^^— No Data
<20%
20-40%
40-60%
^^— 60-80%
^^— >80%
= CONST
U J
Figure 7B-17 ROMO Tall Shrub Sensitive Species Trail Cover
7B-18
-------
Short Shrub
No Data
0%
<20%
Figure 7B-18 ROMO Short Shrub Sensitive Species Cover
7B-19
-------
Short Shrub
^^^— No Data
<20%
20-40%
40-60%
60-80%
= CONST
Figure 7B-19 ROMO Short Shrub Sensitive Species Trail Cover
7B-20
-------
Dwarf Shrub
No Data
^| 0%
<20%
H 20-40%
I 40-60%
Figure 7B-20 ROMO Dwarf Shrub Sensitive Species Cover
7B-21
-------
Figure 7B-21 ROMO Dwarf Shrub Sensitive Species Trail Cover
7B-22
-------
Herbaceous
No Data
<20%
Figure 7B-22 ROMO Herbaceous Sensitive Species Cover
7B-23
-------
Herbaceous
^^^— No data
<20%
20-40%
40-60%
^^— 60-80%
>80%
^^= CONST
Figure 7B-23 ROMO Herbaceous Sensitive Species Trail Cover
7B-24
-------
Emergent
No Data
0%
<20%
Figure 7B-24 ROMO Emergent Sensitive Species Cover
7B-25
-------
Emergent Tree
^^^— No Data
<20%
= CONST
Figure 7B-25 ROMO Emergent Sensitive Species Trail Cover
7B-26
-------
Canopy
No Data
0%
<20%
20-40%
^^| 60-80%
Figure 7B-26 SEKI Canopy Sensitive Species Cover
7B-27
-------
Canopy
^-^ No Data
<20%
20%-40%
- 40%-60%
— 60%-80%
>80%
^=^= John Muir Trail
Figure 7B-27 SEKI Canopy Sensitive Species Cover
7B-28
-------
Tall Shrub
No Data
<20%
20-40%
^H 40-60%
Figure 7B-28 SEKI Tall Shrub Sensitive Species Cover
7B-29
-------
Tall Shrub
•^^— No Data
20%-40%
40%-60%
^— 69%-80%
^=^= John Muir Trail
Figure 7B-29 SEKI Tall Shrub Sensitive Species Trail Cover
7B-30
-------
Herbaceous
No Data
<20%
20-40%
\r ,.:'.- j--.-^a- .•r^>,(-Sr\'
Figure 7B-30 SEKI Herbaceous Sensitive Species Cover
7B-31
-------
Herbaceous
^^— No Data
<20%
20%-40%
40%-60%
^^— 60%-80%
^^— >80%
^=^= John Muir Trail
Figure 7B-31 SEKI Herbaceous Sensitive Species Trail Cover
7B-32
-------
United States Office of Air Quality Planning and Standards Publication No. EPA-452/R-14-005b
Environmental Protection Health and Environmental Impacts Division August 2014
Agency Research Triangle Park, NC
------- |