Scientific Data,
Journal Year:
2024,
Volume and Issue:
11(1)
Published: April 24, 2024
Abstract
Near-surface
atmospheric
moisture
is
a
key
environmental
and
hydro-climatic
variable
that
has
significant
implications
for
the
natural
human
systems.
However,
high-resolution
data
are
severely
lacking
fine-scale
studies.
Here,
we
develop
first
1
km
high
spatial
resolution
dataset
of
monthly
index
collection
in
China
(HiMIC-Monthly)
over
long
period
2003~2020.
HiMIC-Monthly
generated
by
light
gradient
boosting
machine
algorithm
(LightGBM)
based
on
observations
at
2,419
weather
stations
multiple
covariates,
including
land
surface
temperature,
vapor
pressure,
cover,
impervious
proportion,
population
density,
topography.
This
includes
six
commonly
used
indices,
enabling
assessment
conditions
from
different
perspectives.
Results
show
good
performance,
with
R
2
values
all
indices
exceeding
0.96
root
mean
square
error
absolute
within
reasonable
range.
The
exhibits
consistency
situ
various
temporal
regimes,
demonstrating
broad
applicability
strong
reliability.
Earth system science data,
Journal Year:
2024,
Volume and Issue:
16(5), P. 2407 - 2424
Published: May 22, 2024
Abstract.
Climate
change
has
precipitated
recurrent
extreme
events
and
emerged
as
an
imposing
global
challenge,
exerting
profound
far-reaching
impacts
on
both
the
environment
human
existence.
The
Universal
Thermal
Index
(UTCI),
serving
important
approach
to
comfort
assessment,
plays
a
pivotal
role
in
gauging
how
humans
adapt
meteorological
conditions
copes
with
thermal
cold
stress.
However,
existing
UTCI
datasets
still
grapple
limitations
terms
of
data
availability,
hindering
their
effective
application
across
diverse
domains.
We
have
produced
GloUTCI-M,
monthly
dataset
boasting
coverage
extensive
time
series
spanning
March
2000
October
2022,
high
spatial
resolution
1
km.
This
is
product
comprehensive
leveraging
multiple
sources
advanced
machine
learning
models.
Our
findings
underscored
superior
predictive
capabilities
CatBoost
forecasting
(mean
absolute
error,
MAE
=
0.747
°C;
root
mean
square
RMSE
0.943
coefficient
determination,
R2=0.994)
when
compared
models
such
XGBoost
LightGBM.
Utilizing
geographical
boundaries
stress
areas
at
scale
were
effectively
delineated.
Spanning
2001–2021,
annual
was
recorded
17.24
°C,
pronounced
upward
trend.
Countries
like
Russia
Brazil
key
contributors
increasing,
while
countries
China
India
exerted
more
inhibitory
influence
this
Furthermore,
contrast
datasets,
GloUTCI-M
excelled
portraying
distribution
finer
resolutions,
augmenting
accuracy.
can
enhance
our
capacity
evaluate
experienced
by
humans,
offering
substantial
prospects
wide
array
applications.
publicly
available
https://doi.org/10.5281/zenodo.8310513
(Yang
et
al.,
2023).
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(16), P. 3056 - 3056
Published: Aug. 20, 2024
In
the
post-pandemic
era,
outdoor
jogging
has
become
an
increasingly
popular
form
of
exercise
due
to
growing
emphasis
on
health.
It
is
essential
comprehensively
analyze
factors
influencing
spatial
distribution
activities
and
propose
planning
strategies
with
practical
guidance.
Using
multi-source
geospatial
big
data
multiple
models,
this
study
constructs
a
comprehensive
analytical
framework
examine
association
between
environmental
variables
frequency
in
Guangzhou.
Firstly,
trajectory
were
collected
from
fitness
app,
potential
selected
based
perspectives
built
environment,
street
perception,
natural
environment.
For
example,
using
street-view
imagery,
objective
elements
such
as
greenery
subjective
safety
perception
extracted
human-centric
perspective.
Secondly,
included
three
models:
backward
stepwise
regression,
optimal
parameters-based
geographical
detector,
geographically
weighted
regression
(GWR)
model.
These
models
served,
screen
significant
variables,
identify
synergistic
effects
among
quantify
heterogeneity
effects,
respectively.
Finally,
area
was
clustered
results
GWR
model
urban
clear
positions
significance.
The
indicated
following:
(1)
Factors
related
environment
significantly
influence
distribution.
(2)
Public
sports
facilities,
level
greenery,
identified
key
activities,
representing
aspects
service
(3)
Specifically,
each
factor
displayed
variation.
instance,
facilities
positively
correlated
city
center.
(4)
Lastly,
divided
into
four
clusters,
different
local
associative
characteristics
activities.
zonal
recommendations
have
implications
for
planners
policymakers
aiming
create
jogging-friendly
environments.
Ecological Indicators,
Journal Year:
2023,
Volume and Issue:
158, P. 111424 - 111424
Published: Dec. 16, 2023
The
urban
thermal
environment
is
closely
related
to
the
well-being
of
many
city
dwellers.
Rich
achievements
have
been
obtained
for
canopy
layer
heat
island
(CLUHI)
studies.
Nevertheless,
monitoring
and
associated
factors
CLUHI
not
systematically
timely
reviewed.
Therefore,
this
paper
aimed
solve
issue
some
extent
by
reviewing
fruitful
research
progress
from
above-mentioned
two
aspects.
main
findings
were
as
follows.
(1)
Eight
methods
adopted
obtain
near-surface
temperature
data
research,
including
four
observation,
numerical
modeling,
remote
sensing
assimilation
methods.
(2)
Air
was
usually
used
rather
than
apparent
indices.
Obvious
differences
existed
between
them,
especially
under
humid
hot
or
cold
windy
conditions.
(3)
intensity
generally
defined
in
suburban
rural
stations
regions
population,
land
cover/land
use,
etc,
derived
using
regression
analysis
impervious
surface
percentage.
(4)
diurnal,
monthly,
seasonal
interannual
variation
has
analyzed
various
global
regions.
(5)
Six
types
analyzed,
meteorological
conditions,
air
pollution,
socioeconomic
factors,
underlying
condition,
inland
coastal
type
landform
combined
effects
factors.
(6)
Five
potential
directions
proposed,
improvement
acquisition,
sharing
considering
indices,
focus
on
rarely
studied
regions,
cities
scales,
improving
calculation
intensity,
attention
more
driving
force
This
review
can
provide
references
future
construction
climate-resilient,
livable,
low-carbon
cities.
Land,
Journal Year:
2025,
Volume and Issue:
14(3), P. 645 - 645
Published: March 18, 2025
Blue
and
green
spaces
are
well-known
for
their
benefits
in
improving
urban
thermal
environments.
However,
the
optimal
configuration
of
green,
blue,
grey
(GBGSs)
physical
mental
health
residents
remains
unclear.
Therefore,
we
employed
land
surface
temperature
(LST),
near-surface
air
(SAT),
Humidex
to
analyze
GBGS.
The
results
indicated
following:
(1)
spatial
distribution
Perceptual
Urban
Thermal
Environments
(PTEs)
is
consistent
with
that
Surface
(STEs).
most
perceptual
indicators
lower
than
daytime
LST
higher
SAT.
(2)
have
cooling
efficiency
spaces.
(3)
coverage
space
less
40%,
at
least
35%
space,
blue
covers
between
15%
25%,
which
balance
environment.
Moreover,
increasing
simplifying
recommended
where
below
30%.
In
areas
30–40%
enhancing
complexity
fragmentation
boundaries
more
effective.
Maintaining
30%
optimizing
aggregation
improves
over
40%.
This
study
provides
scientific
foundation
GBGSs
development
renovations.
