Sustainability,
Journal Year:
2024,
Volume and Issue:
16(16), P. 7157 - 7157
Published: Aug. 20, 2024
This
study
aims
to
investigate
the
characteristics
of
energy
consumption
and
outdoor
thermal
comfort
within
high-density
urban
fabric
Changsha.
Two
different
types
building
(residential
office),
as
well
three
forms
(point,
slab,
enclosed)
were
analyzed
under
local
climate
zone
scheme.
Utilizing
ENVI-met
5.6.1
EnergyPlus
23.2.0
software,
simulations
conducted
assess
144
architectural
models.
Then,
multiple
regression
spatial
applied
predict
area.
The
results
showed
following:
(1)
In
area
Changsha,
central
business
district
historic
old
town
adjacent
Xiangjiang
River
are
identified
areas
with
high
use
intensity.
(2)
Among
residential
categories,
point-types
LCZ-3
LCZ-6,
slab-type
LCZ-4,
exhibit
lowest
contrast,
enclosed
office
buildings,
LCZ-2
LCZ-5,
characterized
by
highest
(3)
Urban
form
parameters
such
floor
ratio
shape
coefficient
have
a
significant
impact
on
EUIwinter,
while
EUIsummer
is
highly
related
normalized
difference
vegetation
index
(BSC).
(4)
LCZ-4
stands
out
its
notably
lower
cooling
heating
intensity,
coupled
excellent
comfort,
making
it
particularly
well-suited
for
climatic
conditions
Atmosphere,
Journal Year:
2025,
Volume and Issue:
16(1), P. 40 - 40
Published: Jan. 2, 2025
In
order
to
assess
the
spatial
and
temporal
characteristics
of
urban
thermal
environment
in
Zhengzhou
City
supplement
climate
adaptation
design
work,
based
on
Landsat
8–9
OLI/TIRS
C2
L2
data
for
12
periods
from
2019–2023,
combined
with
lLocal
zone
(LCZ)
classification
subsurface
classification,
this
study,
we
used
statistical
mono-window
(SMW)
algorithm
invert
land
surface
temperature
(LST)
classify
heat
island
(UHI)
effect,
analyze
differences
distribution
environments
areas
aggregation
characteristics,
explore
influence
LCZ
landscape
pattern
temperature.
The
results
show
that
proportions
built
natural
types
Zhengzhou’s
main
metropolitan
area
are
79.23%
21.77%,
respectively.
most
common
landscapes
wide
mid-rise
(LCZ
5)
structures
large-ground-floor
8)
structures,
which
make
up
21.92%
20.04%
study
area’s
total
area,
varies
seasons,
pooling
during
summer
peaking
winter,
strong
or
extremely
islands
centered
suburbs
a
hot
cold
spots
aggregated
observable
features.
As
building
heights
increase,
UHI
1–6)
increases
then
reduces
spring,
summer,
autumn
decreases
winter
as
increase.
Water
bodies
G)
dense
woods
A)
have
lowest
effects
among
settings.
Building
size
is
no
longer
primary
element
affecting
LST
buildings
become
taller;
instead,
connectivity
clustering
take
center
stage.
Seasonal
variations,
variations
types,
responsible
area.
should
see
an
increase
vegetation
cover,
gaps
must
be
appropriately
increased.
