Abstract.
Understanding
the
spatiotemporal
characteristics
of
long-
and
short-term
exposure
to
ground
ozone
is
crucial
for
improving
environmental
management
health
studies.
However,
such
studies
have
been
constrained
by
availability
high-resolution
data.
To
address
this,
we
characterized
ground-level
variations
risks
across
multiple
spatial
(pixel,
county,
region,
national)
temporal
(daily,
monthly,
seasonal,
annual)
scales
using
daily
1-km
data
from
2000
2020,
derived
satellite
LST
via
a
machine-learning
method.
The
model
provided
reliable
estimates,
validated
through
rigorous
cross-validation
direct
comparison
with
external
measurements.
Our
long-term
estimates
revealed
seasonal
shifts
in
high-exposure
centers:
spring
eastern
China,
summer
North
China
Plain
(NCP),
autumn
Pearl
River
Delta
(PRD).
A
non-monotonous
trend
was
observed,
levels
rising
2001–2007
at
rate
0.47
μg/m3/year,
declining
after
2008
(-0.58
μg/m3/year),
increasing
significantly
2016–2020
(1.16
accompanied
regional
fluctuations.
Notably,
increased
0.63
μg/m3/year
NCP
during
second
phase,
6.38
PRD
third
phase.
Exposure
over
100
μg/m3
shifted
June
May,
exceeding
160
were
primarily
seen
NCP,
showing
an
expanding
trend.
day-to-day
analysis
highlights
influence
meteorological
factors
on
extreme
events.
These
findings
emphasize
need
stronger
mitigation
efforts.
Remote Sensing,
Год журнала:
2024,
Номер
16(7), С. 1298 - 1298
Опубликована: Апрель 7, 2024
Air
pollution
has
been
standing
as
one
of
the
most
pressing
global
challenges.
The
changing
patterns
air
pollutants
at
different
spatial
and
temporal
scales
have
substantially
studied
all
over
world,
which,
however,
were
intricately
disturbed
by
COVID-19
subsequent
containment
measures.
Understanding
fine-scale
stages
epidemic’s
course
is
necessary
for
better
identifying
region-specific
drivers
preparing
environmental
decision
making
during
future
epidemics.
Taking
China
an
example,
this
study
developed
a
multi-output
LightGBM
approach
to
estimate
monthly
concentrations
six
major
(i.e.,
PM2.5,
PM10,
NO2,
SO2,
O3,
CO)
in
revealed
distinct
spatiotemporal
each
pollutant
course.
5-year
period
2019–2023
was
selected
observe
changes
from
pre-COVID-19
era
lifting
performance
our
model,
assessed
cross-validation
R2,
demonstrated
high
accuracy
with
values
0.92
0.95
0.90
0.79
0.82
CO.
Notably,
there
improvement
particulate
matter,
particularly
although
PM10
exhibited
rebound
northern
regions.
SO2
CO
consistently
declined
across
country
(p
<
0.001
p
0.05,
respectively),
while
O3
southern
regions
experienced
notable
increase.
Concentrations
Beijing–Tianjin–Hebei
region
effectively
controlled
mitigated.
findings
provide
critical
insights
into
trends
quality
public
health
emergencies,
help
guide
development
targeted
interventions,
inform
policy
aimed
reducing
disease
burdens
associated
pollution.
Geomatics Natural Hazards and Risk,
Год журнала:
2024,
Номер
15(1)
Опубликована: Июль 30, 2024
Among
the
most
intense
geological
disasters,
landslides
frequently
occur
throughout
world.
These
phenomena
have
been
studied
using
space
geodetic
techniques,
including
Global
Navigation
Satellite
Systems
(GNSS)
and
Multi-Temporal
Interferometric
Synthetic
Aperture
Radars
(MT-InSAR).
Nevertheless,
complete
mapping
analysis
of
landslides'
surface
deformation
in
areas
can
be
complicated
due
to
a
large
diversity
kinematics,
such
as
periods
quiescence
acceleration
toe
crown.
One
these
is
Cuenca
Ecuador,
where
investigation
revealed
that
was
located
urban
areas,
with
more
noticeable
effects.
In
contrast,
its
crown
mainly
rural
green
land
area.
this
study,
we
show
potential
synergistic
use
COSMO-SkyMed
(CSK)
Sentinel-1A
(S1A)
synthetic
aperture
radar
(SAR)
data
for
comprehensively
monitoring
landslides.
To
aim,
used
Long-Short
Term
Memory
(LSTM)
Convolutional
Neural
Networks
(CNN)
two
different
Deep
Learning
Algorithms
(DLAs)
integrate
results
temporal
spatial
domain,
respectively.
A
cross-comparison
made
nine
GPS-derived
deformations
visual
effects
(i.e.
crack
width
pattern)
on
field.
This
validation
against
GPS
observation
reveals
RMSEs
final
MT-InSAR-derived
velocity
after
applying
synergic
double
band
SAR
dataset
decrease
at
than
73%
stations.
