Sustainability,
Год журнала:
2024,
Номер
16(15), С. 6656 - 6656
Опубликована: Авг. 3, 2024
With
the
continuous
development
of
cities,
surface
urban
heat
island
intensity
(SUHII)
is
increasing,
leading
to
deterioration
thermal
environment,
increasing
energy
consumption,
and
endangering
health
residents.
Understanding
spatio-temporal
scale
difference
gradient
effect
spatial
patterns
on
impact
SUHII
crucial
for
improving
climate
resilience
cities
promoting
sustainable
development.
This
paper
investigated
characteristics
changes
at
different
time
periods
based
local
zones
(LCZs)
downscaled
land
temperature
(LST)
data.
Meanwhile,
landscape
pattern
indicators
multiscale
geographically
weighted
regression
(MGWR)
model
were
utilized
analyze
impacts
multiple
spatial–temporal
scales.
The
results
indicated
that
each
LCZ
type
exhibited
diverse
in
periods.
High
occurred
summer
daytime
autumn
nighttime.
Compact
high-rise
buildings
(LCZ1/2/4)
showed
markedly
higher
during
or
nighttime,
except
heavy
industry.
extent
influence
dominant
factors
exhibit
obvious
differences
effects.
At
regional
scale,
highly
regular
compacted
built-up
areas
tended
increase
SUHII,
while
single
continuously
distributed
had
a
greater
SUHII.
PLAND
(1/2/4/5/10)
trend
diminishing
from
suburban
areas.
In
areas,
1,
2,
LCZ4
was
major
factor
affecting
whereas,
2
10
influencing
can
provide
scientific
reference
mitigating
effects
constructing
an
ecologically
‘designed’
city.
Case Studies in Thermal Engineering,
Год журнала:
2024,
Номер
55, С. 104151 - 104151
Опубликована: Фев. 19, 2024
Urban
microclimate
faces
serious
challenges
due
to
increased
urbanization
and
frequent
heatwave
events.
Many
studies
focused
on
investigating
the
holistic
quantitative
relationships
between
urban
morphology
factors
heat
island
intensity
at
city
scale,
but
less
effort
has
been
devoted
exploring
a
block
scale.
Additionally,
there
is
lack
of
fast
prediction
methods
for
local
climate
zones
(LCZ)
planning
design.
To
address
these
challenges,
this
study
proposes
Long
Short-Term
Memory
Networks
(LSTM)
model
predict
effects
air
temperature
under
zones.
The
spatial
features
were
characterized
quantified
employing
post-interpretation
method.
Pearl
River
New
Town
(PRNT),
downtown
area
Guangzhou,
China,
was
considered
as
research
implementation.
results
showed
that
accuracy
best
when
using
historical
three-time
step
data,
with
R2
0.975.
LCZ
A
highest
accuracy,
an
0.990.
5
lowest
0.881.
Moreover,
effect
found
be
greater
than
land
cover
type.
In
regard,
sky
view
factor
(SVF)
impact,
followed
by
aspect
ratio
(AR)
pervious
surface
fraction
(PSF).
Nevertheless,
warming
in
built
type
stronger
cover.
During
period,
maximum
minimum
changes
recorded
4
A,
respectively,
values
9.7
°C
8.6
°C.
It
shown
low-rise
areas
are
more
resilient
high-rise
during
periods.
This
because
generally
exhibit
smaller
increase
temperature.
These
findings
provide
better
understanding
relationship
form,
method
rapidly
predicting
neighborhood
block.
provides
guidance
support,
great
significance
climate-friendly
planning.
Atmosphere,
Год журнала:
2025,
Номер
16(1), С. 40 - 40
Опубликована: Янв. 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.
