This document introduces a GIS suitability model created by Geenex, a German company implementing solar farms in the southeastern US, to identify suitable parcels of land for constructing mid-sized solar farms in North and South Carolina. The model considers parcel size, distance to power stations and lines, and elevation variance, using a pass-fail approach. When demonstrated in Lancaster County, SC, the model identified 13 suitable parcels. The model could be improved by adding ranking criteria as some factors are difficult to model.

1. 1
Implementing
Solar
Power:
Suitability
Modeling
in
Land
Acquisition
for
Mid
Size
Solar
Farms
Abstract
Rising
fossil
fuel
costs
and
their
negative
effects
on
the
environment
have
led
to
promotion
and
implementation
of
solar
energy
in
the
United
States.
German-­‐owned
company
Geenex
works
to
implement
solar
energy
into
existing
power
grids
in
the
southeastern
United
States.
This
paper
introduces
a
GIS
suitability
model
tailored
to
locating
areas
for
mid-­‐sized
solar
farms
constructed
by
Geenex
in
North
and
South
Carolina.
The
suitability
model
aids
the
Geenex
land
acquisition
team
by
graphically
displaying
suitable
parcels
to
consider
for
leasing
and
construction.
The
model
takes
the
following
factors
into
account:
parcel
size,
parcel
distance
from
a
power
station,
parcel
distance
from
power
lines,
and
parcel
elevation
variance.
The
model
uses
a
pass-­‐fail
approach,
where
only
properties
that
are
acceptable
in
each
factor
are
included
in
the
final
results.
The
suitability
model
was
demonstrated
in
Northern
Lancaster
County,
South
Carolina,
resulting
in
13
passing
parcels
suitable
for
building
solar
farms.
Although
this
model
can
be
used
in
most
areas,
it
does
not
rank
the
resulting
suitable
parcels,
as
some
factors
in
parcel
selection
are
difficult
to
model
in
a
GIS.
With
increased
research
and
experience
acquiring
parcels
in
the
southeast,
this
model
can
be
improved,
possibly
ranking
the
parcels
using
an
ordinal
combination
or
an
inverse
distance
weighted
model.
Introduction
Oil,
coal
and
gas
prices
have
been
steadily
increasing
over
the
past
few
decades
as
current
supplies
have
diminished.
Although
more
known
fossil
fuel
reserves
exist
and
may
be
found
with
continued
searches
and
exploration,
digging
deeper
into
the
ocean
floor
and
searching
in
high
risk
and
dangerous
areas
such
as
the
Arctic
Circle
will
drive
prices
for
fossil
fuels
even
higher.
In
addition
to
experiencing
rising
costs,
fossil
fuels
are
harmful
to
the
environment.
The
burning
of
fossil
fuels
releases
carbon
dioxide,
methane
and
other
gasses
into
the
atmosphere
causing
a
greenhouse
effect
that
traps
heat
and
causes
temperature
increases.
Global
temperatures
have
increased
more
than
1
degree
C
over
the
past
century,
and
are
estimated
to
increase
at
least
an
additional
2.5
degrees
C
by
2100
as
a
result
of
greenhouse
gas
emissions
(Parmesan
2004).
These
temperature
increases
have
caused
sea
levels
to
rise,
species
habitats
to
change
and
decline,
and
weather
pattern
to
shift,
leading
to
extreme
droughts
in
some
areas.
Alternatives
to
fossil
fuels
include:
hydropower,
nuclear
energy,
wind
turbines,
geothermal
energy,
biofuels,
and
solar
power.
Solar
electricity,
or
photovoltaic
(PV)
cells
convert
sunlight
into
electricity.
Solar
electric
power
use
in
the
United
States
has
increased
from
334.2
megawatts
in
1997
to
6,220.3
megawatts
in
2013.
Despite
this
increase,
solar
power
accounts
for
only
.2%
of
the
electricity
generated
in
the
US
(Department
of
Energy,
Institute
for
Energy
Research).
In
addition
to
research
and
development
on
how
to
improve
photovoltaic
efficiency
and
reduce
costs,
linking
more
photovoltaic
cells
to
existing
electric
options
will
help
solar
power
grow
in
the
US.
Originally
started
in
Germany,

2. 2
Geenex
is
a
vertically
integrated
company
that
aims
to
implement
solar
power
use
in
the
United
States.
One
of
the
main
components
Geenex
works
on
is
land
acquisition
to
build
solar
farms.
In
the
United
States,
Geenex
is
implementing
solar
farms
in
North
Carolina,
South
Carolina
and
Florida
as
of
November
2014
(Geenex).
The
solar
farms
being
built
by
Geenex
are
much
larger
than
the
solar
panels
implemented
on
rooftops
for
singular
residential
power.
Currently,
in
South
and
North
Carolina,
solar
farms
require
at
least
20.23
hectares
of
land.
These
mid-­‐sized
commercial
solar
farms
need
to
be
hooked
up
to
mid-­‐level
power
lines,
or
three-­‐phase
power
systems.
Three-­‐phase
power
systems
have
three
electrical
power
lines
running
through
them
and
originate
from
distribution
substations.
In
contrast,
the
solar
farms
in
Florida
deliver
more
power
and
require
32
hectares
of
land.
These
larger
projects
need
to
be
hooked
to
much
higher
voltage
transmission
lines
beginning
in
larger
transmission
stations.
Geenex
finds
suitable
parcels
for
building
solar
farms,
leases
the
land
from
the
current
property
owner,
and
constructs
a
solar
farm
that
will
link
to
the
electrical
grid
in
the
selected
area.
This
paper
provides
a
GIS
suitability
model
appropriate
for
aiding
Geenex,
and
similar
solar
power
businesses,
in
finding
suitable
land
for
building
mid-­‐sized
solar
farms.
Literature
Review
More
energy
strikes
the
earth’s
surface
in
one
hour
than
is
used
in
a
year
worldwide
(Lewis
2007).
However,
current
photovoltaic
cells
are
not
able
to
capture
this
incoming
energy,
with
theoretical
maximum
efficiency
capped
at
70%
(Lewis
2007).
The
amount
of
solar
power
that
can
be
generated
is
dependent
upon
the
incident
solar
radiation
received
at
a
location,
which
varies
between
latitudes
and
seasons
(Pasqualetti
1984).
In
addition
to
having
low
efficiency,
photovoltaic
cells
are
expensive
and
are
not
currently
competitive
with
fossil
fuel
prices.
An
additional
drawback
to
implementing
solar
power
is
that
it
requires
a
very
large
surface
area.
In
the
past
20
years,
however,
advancements
in
solar
technology
have
led
to
the
implementation
in
single
unit
homes
of
rooftop
photovoltaic
cells,
reducing
energy
costs
substantially
(Ellison
2006).
Currently,
solar
power
is
used
mainly
at
the
household
level
in
the
form
of
stand-­‐alone
modules
in
individual
homes
in
remote
areas,
where
it
is
hard
to
link
to
the
main
power
grid
(Bose
2008).
To
become
a
competitive
energy
source,
solar
power
must
be
accessible
to
a
much
larger
market.
Extending
solar
power
to
a
larger
market
involves
linking
solar
power
to
a
main
electric
grid
(Bose
2008).
Although
solar
use
is
currently
low,
20%
of
new
buildings
are
expected
to
be
fitted
with
solar
panels
in
the
coming
years
(Ellison
2006).
Solar
power
growth
rate
in
the
US
is
at
only
25%;
however,
the
global
solar
power
growth
rate
is
at
40%,
with
60%
in
Germany
(Ellison
2006).
Implementation
of
solar
power
can
be
especially
beneficial
in
the
daytime
during
the
summer,
when
solar
rays
are
at
their
strongest
and
energy
use
is
at
its
maximum
due
to
energy
requirements
for
cooling
(Bose
2008).
Background
The
first
step
in
implementing
a
solar
farm
is
to
identify
electrical
companies
that
are
willing
to
work
with
solar
power.
Geenex
has
found
that
in
the
southeast
US,
larger
electrical
companies
are
more
willing
to
incorporate

3. 3
solar
power
than
smaller
electrical
cooperative
companies.
Therefore,
the
solar
farms,
the
electrical
lines
they
link
up
to,
and
the
substation
or
transmission
station
the
solar
farms
connect
to,
all
need
to
be
in
the
territory
of
a
major
electrical
company
willing
to
work
with
Geenex.
For
the
projects
in
the
southeast
US,
electrical
companies
working
with
Geenex
include
Duke
Power,
South
Carolina
Electric
and
Gas
(SCE
&G),
and
Florida
Power
and
Light.
The
second
step
is
to
choose
counties
for
solar
farm
locations.
The
counties
should
have
the
majority
of
land
serviced
by
Duke,
SCE&G
or
Florida
Power
and
Light;
counties
should
also
be
relatively
flat.
County
selection
is
done
by
the
head
of
land
acquisition
for
that
solar
power
company.
Once
a
county
is
selected,
the
next
step
is
to
find
the
location
of
electrical
substations.
To
do
this,
parcels
are
searched
by
owner
name
to
locate
parcels
owned
by
the
major
electric
company
in
that
area;
this
will
show
all
of
the
substations
and
transmission
stations
available
to
link
to
solar
farms.
This
can
either
be
done
by
putting
the
parcel
data
in
Arcmap
and
selecting
parcels
by
owner
name,
or
through
the
county’s
GIS
website
(if
parcel
data
does
not
contain
this
information).
Once
all
parcels
owned
by
the
electric
company
are
selected,
they
are
viewed
(either
through
imagery
base
map
in
Arcmap
or
county
website)
to
see
which
parcels
contain
substations
(for
North
and
South
Carolina)
and
transmission
stations
(Florida).
Once
all
substations
or
transmission
stations
are
located,
a
final
station(s)
is
chosen
by
the
head
of
land
acquisition
for
further
analysis.
The
land
acquisition
team
often
prefers
substations
that
are
in
more
rural
areas
of
the
county,
where
there
are
larger
parcels
and
more
open
land.
Rural
areas
are
also
desirable
since
most
people
consider
large-­‐scale
solar
farms
to
be
unattractive,
and
do
not
want
a
solar
farm
in
their
back
yard,
so
building
in
more
remote
areas
would
reduce
complaints
from
residents.
Land
is
also
likely
to
be
less
expensive
in
more
rural
areas
than
in
more
densely
populated
areas.
Once
a
final
station
is
selected,
GIS
technology
is
used
to
select
a
final
list
of
parcels.
Before
narrowing
down
the
search
for
parcels,
the
power
lines
extending
from
the
substation
or
transmission
station
must
be
traced
by
creating
a
new
line
feature
class.
The
power
lines
drawn
will
extend
3.22
kilometers
from
the
substation,
as
that
is
the
maximum
distance
a
solar
farm
can
be
from
the
power
distribution
center
(substation
or
transmission
station).
Figure
1
describes
the
conceptual
model
used
to
select
parcels
for
mid
size
solar
farm.
Methods
The
next
steps
in
the
selection
process
uses
the
conceptual
model
from
figure
1
to
select
the
most
suitable
parcels.
This
model
focuses
on
selecting
parcels
for
a
mid-­‐sized
solar
farm
in
North
or
South
Carolina.
The
first
step
is
to
start
with
all
of
the
parcels
near
the
selected
substation.
This
can
either
be
done
using
all
county
parcels
or
extracting
a
selection
from
the
county
GIS
website.
Suitable
parcels
Parcels
over
20.23
hetacres
Relativley
dlat
parcels
Parcels
within
3.22
km
of
power
station
All
parcels
near
selcted
power
station
Parcels
within
182.88m
of
power
line

4. 4
From
the
available
parcels,
all
parcels
over
20.23
hectares
are
selected.
From
this
selection,
only
parcels
within
3.22
km
of
the
substation
are
selected.
The
threshold
of
3.22
km
is
chosen
to
keep
costs
within
an
acceptable
range.
Next,
the
power
line
feature
class
is
added,
and
parcels
within
182.88
meters
of
the
drawn
distribution
lines
are
selected
from
the
previously
selected
parcels.
The
solar
farm
must
be
connected
to
the
three-­‐phase
power;
however,
if
an
ideal
parcel
is
very
close
(within
182.88
meters
of
distribution
lines),
an
easement
can
be
used
to
link
the
two
at
minimal
cost.
Large-­‐scale
solar
farms
require
a
relatively
flat
area
and
cannot
be
located
on
a
steep
slope
or
hill.
Therefore,
parcels
with
high
elevation
variance
are
removed
from
the
currently
selected
parcels
that
are
over
20.23
hectares,
within
3.22
km
of
the
power
station
and
182.88
meters
of
distribution
lines.
The
final
remaining
parcels
are
then
considered
suitable
for
building
a
solar
farm.
This
conceptual
model
in
Figure
1
can
also
be
expressed
as
the
following
mathematical
models.
Figure
2:
Mathematical
Model
for
selecting
suitable
parcels
for
mid
size
solar
farm
For
each
factor
in
this
mathematical
model,
parcels
that
meet
the
criteria
are
given
a
value
1
while
the
rest
are
given
a
value
of
0.
For
example,
for
factor
1,
parcels
that
are
over
20.23
hectares
are
given
a
value
of
1;
the
rest
of
the
parcels
receive
a
value
of
0.
In
order
to
determine
parcel
elevation
variance
for
factor
4,
elevation
standard
deviation
is
used.
Standard
deviation
is
a
good
measure
of
how
much
the
elevation
varies
in
a
parcel.
Parcels
with
high
variation
in
elevation
are
not
suitable
for
building
a
solar
farm;
therefore
parcels
with
a
standard
deviation
greater
than
10
are
removed,
and
given
a
value
of
0.
When
all
4
factors
are
multiplied
together,
only
parcels
that
receive
a
1
for
each
factor
will
have
a
final
value
of
1.
All
parcels
with
a
value
of
1
are
the
parcels
suitable
for
solar
implementation.
Study
Area
Example
For
this
study,
Northern
Lancaster
County,
South
Carolina
was
chosen
to
implement
the
suitability
model
for
mid-­‐sized
solar
farms
described
above.
In
Lancaster
County,
the
major
electrical
provider
is
Duke
Energy.
The
selected
Duke
Energy
substation
is
in
Northern
Lancaster
County,
at
approximately
34.82
degrees
0
N
80.830
W.
This
area
of
Lancaster
County
is
rural
with
many
large
parcels.
Parcels
were
extracted
from
the
Lancaster
County
GIS
website;
the
parcel
selection
was
approximately
9.66
km
in
width
by
12.87
km
in
length.
Factor
1:
Parcel
Size
Factor
2:
Parcel
Distancefrom
Substation
Factor
3:
Parcel
Distance
from
Powerline
Factor
4:
Parcel
Elevation
Variance
Resulting
Parcels

5. 5
Figure
3:
Map
of
Parcels
near
selected
substation
in
Lancaster
County
with
power
lines
Figure
3
shows
the
study
region
in
Lancaster
County,
SC;
the
distribution
substation
is
in
the
center
of
the
study
area
parcels
with
power
lines
extending
north,
northeast
and
south
from
the
substation.
In
order
to
select
the
most
suitable
parcels
from
this
area,
the
model
below
in
Figure
4
was
implemented
in
model
builder
in
Arcmap.
Figure
4:
Suitability
model
for
locating
parcels
for
mid
size
solar
farm
The
data
for
this
model
uses
the
parcel
data
from
Lancaster
County
extracted
from
the
Lancaster
County
GIS
website
as
well
as
a
power
line
shapefile
traced
by
hand
(Lancaster
County
Assessors
Office).
To
determine
elevation
variance,
elevation
data
from
United
States
Geological
Survey’s
National
Elevation
Dataset
was
used;
this
data
set
contained
19m
resolution
data
(United
States
Geological
Survey).
The
Lancaster
County
parcel
data
contained
very
little
information
about
each
parcel,
similar
to
much
of
the
free
GIS
data
available
to
Geenex.
Therefore,
step
one
in
the
model
is
to
add
an
area
field
for
each
parcel
and
calculate
the
area
for
each
parcel
using
the
calculate
field
tool.
After
each
parcel
contained
an
area
field,
parcels
over
20.23
hectares
(50
acres)
were
selected
using
the
select
by

6. 6
attributes
tool.
Next,
parcels
were
selected
from
within
the
existing
set
using
the
select-­‐by-­‐location
tool,
to
identify
parcels
that
were
within
3.22
km
(2
miles)
of
the
chosen
substation.
The
resulting
parcels
are
over
20.23
hectares
and
within
3.22
km
of
the
substation.
The
select-­‐by-­‐location
tool
was
used
again
to
locate
parcels
from
current
selection
that
were
within
182.88m
(600
ft)
of
the
distribution
lines.
Next,
the
zonal
statistics
were
used
to
determine
elevation
characteristics
(mean,
minimum,
maximum,
range,
standard
deviation,
etc.)
for
each
parcel.
This
procedure
manipulated
the
elevation
data,
in
raster
form,
to
use
with
the
vector
data
for
this
model.
The
statistical
elevation
data
was
then
linked
to
the
parcel
attribute
table
using
the
add/join
tool.
The
final
step
of
the
model
was
to
eliminate
the
parcels
with
the
highest
variance
in
elevation
using
the
standard
deviation
value
calculated
for
each
parcel.
From
the
selected
parcels
that
were
over
20.23
hectares,
within
3.22
km
of
the
substation,
and
182.88
m
of
the
power
lines,
a
final
select-­‐by-­‐attributes
function
was
used
to
select
the
parcels
with
a
standard
deviation
value
of
ten
or
less.
Parcels
with
a
standard
deviation
greater
than
10
were
considered
to
have
too
much
elevation
variance.
This
suitability
model
did
not
use
the
reclassify
and
times
function
described
in
the
mathematical
model
in
figure
2,
as
the
majority
of
the
data
used
in
this
model
was
in
vector
format.
The
raster
elevation
data
was
aggregated
with
the
vector
parcels
using
zonal
statistics
as
a
table.
The
final
resulting
parcels
were
added
to
the
original
map
to
show
which
parcels
were
suitable
to
build
a
mid
range
solar
farm
on
near
the
study
substation
shown
in
Figure
5.
Results
Figure
5:
Map
of
parcels
selected
by
the
suitability
model
The
results
of
the
suitability
model
described
above
show
13
parcels
that
fit
the
requirements
for
building
a
mid-­‐range
solar
farm.
The
following
parcels
were
then
linked
to
owner
name
and
address,
parcel
number
and
approximate
sale
price
data
from
the
Lancaster
County
GIS
website
(Lancaster
County

7. 7
Assessors
Office).
Each
selected
parcel
was
identified
by
comparing
the
output
map
above
(Figure
5)
with
the
online
map
from
Lancaster
County
GIS.
As
the
parcel
data
was
extracted
for
free
from
Lancaster
County,
the
shapefile
did
not
contain
much
information.
However,
other
county
data
shapefiles
used
for
land
acquisition
by
Geenex
contains
parcel
information
in
the
database
file,
and
this
extra
step
is
not
needed.
The
map
with
selected
parcels
as
well
as
a
file
linking
each
parcel
to
owner
and
price
details
was
then
sent
to
the
head
of
land
acquisition
for
negotiating
a
single
parcel
to
lease.
The
selected
parcel
directly
south
of
the
power
station
was
chosen
and
leased
in
October
2014,
and
construction
of
a
mid-­‐sized
solar
farm
will
commence
in
early
2015.
Conclusion
The
suitability
model
for
selecting
parcels
for
mid-­‐sized
solar
farms
can
be
used
for
most
regions
in
the
US.
The
selected
area
must
contain
a
substation,
with
the
extending
distribution
lines
traced
into
a
new
feature
class
as
well
as
parcel
shapefile
data.
The
selected
area
must
also
contain
elevation
data.
Although
the
USGS
has
elevation
data
for
almost
all
areas
of
the
United
States,
some
areas
have
data
with
large
resolutions.
Larger
elevation
data
resolutions,
such
as
200m,
can
still
be
applied
in
the
model,
but
may
not
yield
accurate
results,
as
the
larger
cell
size
will
not
adequately
capture
variance
in
elevation.
The
suitability
model
will
also
work
in
areas
where
power
lines
do
not
extend
3.22
km
from
a
substation;
power
lines
may
end
or
split
into
smaller
one-­‐phase
power
lines.
This
model
will
also
work
for
areas
where
parcel
data
does
not
extend
3.22
km
from
the
substation,
for
example,
when
the
county
border
is
close
to
the
substation
and
parcel
data
is
not
available
for
the
neighboring
county.
Although
the
example
in
Lancaster
County
used
only
one
substation,
this
model
will
work
for
finding
suitable
parcels
for
multiple
substations,
as
long
as
all
substations
are
combined
in
one
shapefile.
In
addition
to
Lancaster
County,
this
model
has
also
been
used
to
locate
suitable
parcels
for
solar
farms
in
Anderson
County,
SC
using
multiple
substations.
Although
this
model
is
designed
for
locating
parcels
for
mid-­‐sized
solar
farms,
it
can
be
easily
altered
to
identify
suitable
parcels
for
larger
farms.
To
do
this,
a
transmission
station
is
used
instead
of
a
substation,
and
the
traced
feature
class
will
involve
extending
transmission
lines
instead
of
phase-­‐three
distribution
lines.
The
only
change
needed
is
when
selecting
parcels
by
size,
to
adjust
the
cutoff
point
to
be
32
hectares
of
land,
as
opposed
to
23.23
hectares.
Although
this
model
finds
all
parcels
suitable
for
solar
implementation,
it
does
not
rank
these
suitable
parcels.
Factors
that
are
difficult
to
model
in
a
GIS
are
used
after
suitable
parcels
are
selected
to
determine
the
optimal
parcel
for
leasing.
These
factors
include
what
type
of
landcover
the
parcel
has.
Wooded
parcels
are
undesirable,
as
trees
need
to
be
removed;
however
the
price
of
removal
depends
on
the
area
and
can
be
affordable
in
some
regions.
Selected
suitable
parcels
located
near
other
parcels
that
are
considered
to
be
unsightly
or
unfavorable
(power
or
chemical
plants,
landfills)
to
nearby
residents
are
considered
desirable,
as
few
people
are
likely
to
live
in
that
area
and
oppose
a
solar
farm
near
them.
Another
factor
that
cannot
be
modeled
in
a
GIS
is
the
likelihood
the
parcel
owner
is
willing
to
lease;
property
owners
may
live
elsewhere
and
be
interested
in
leasing
the
land.
However,
this
information
can
only
be
obtained
by
contacting
each
property
owner.
Although
information
on
property
value
can
be
found,
leasing
prices
for
each
parcel
are
determined

8. 8
through
negotiations
with
each
parcel
owner.
With
increased
research
and
experience
acquiring
parcels
in
the
southeast,
this
model
can
be
improved,
possibly
ranking
the
parcels
using
an
ordinal
combination
or
an
inverse
distance
weighted
model.
References
Bose,
Deb
Kumar.
“Prospects
of
Solar
Power
in
India
Under
Global
Warming”.
Economic
and
Political
Weekly
43(2008)
14-­‐17.
Department
of
Energy.
“Renewable
Energy:
An
Overview”,
Energy
Efficiency
and
Renewable
Energy
Clearninghouse
(2001).
Accessed
December
11,
2014.
DOE/GO-­‐102001-­‐1102.
Ellison,
Katherine.
Solar
power:
The
future
looks
bright,
Frontiers
in
Ecology
and
the
Environment,
4(8)
448.
Geenex.
“Geenex-­‐
A
solar
company”
Accessed
December
10,
2014.
http://www.geenexsolar.com/.
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for
Energy
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“Solar”.
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December
11,
2014.
http://instituteforenergyresearch.org/topics/encyclopedia/solar/
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Office.
Accessed
December
12,
2014.
http://qpublic5.qpublic.net/sc_search2.php?county=sc_lancaster.
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Nathan
S.
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Cost-­‐Effective
Solar
Energy
Use”.
Science
315(2007)
798-­‐801.
Parmesan,
Camille.
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impacts
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the
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Change
2004
1-­‐47.
Pasqualetti,
Martin
J.
and
Byron
A.
Miller.
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Dec
10,
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