Sustainability,
Год журнала:
2023,
Номер
16(1), С. 256 - 256
Опубликована: Дек. 27, 2023
As
a
crucial
aspect
of
the
climate
system,
changes
in
Africa’s
atmospheric
layer
thickness,
i.e.,
vertical
distance
spanning
specific
Earth’s
atmosphere,
could
impact
its
weather,
air
quality,
and
ecosystem.
This
study
did
not
only
examine
trends
but
also
applied
deep
autoencoder
artificial
neural
network
to
detect
years
with
significant
anomalies
thickness
atmosphere
over
given
homogeneous
region
(derived
rotated
principal
component
analysis)
fingerprint
global
warming
on
changes.
The
broader
implication
this
is
further
categorize
regions
Africa
that
have
experienced
their
system.
reveals
an
upward
trend
between
1000
850
hPa
across
substantial
parts
since
1950.
Notably,
spatial
breadth
rise
peaks
during
boreal
summer.
Correlation
analysis,
supported
by
network,
suggests
signals
increasing
extent
more
pronounced
(since
2000s)
south-central
(specifically
Congo
Basin).
Additionally,
Sahel
Sahara
Desert
sees
no
increase
austral
summer,
resulting
from
counteracting
effect
positive
North
Atlantic
Oscillation,
which
prompts
colder
conditions
northern
Africa.
impacts
temperature
moisture
distribution
layer,
our
contributes
historical
assessment
for
sustainable
MOJ Ecology & Environmental Sciences,
Год журнала:
2024,
Номер
9(1), С. 24 - 27
Опубликована: Фев. 14, 2024
Samples
of
total
suspended
particles
were
taken
at
points
located
in
the
vicinity
two
polluted
rivers
Puebla,
México,
an
affluent
Atoyac
River
(UPMP),
Nexapa
(ICATEP),
a
point
some
distance
from
(UTIM)
and
one
far
this
stream
(sCarlos).
1
L
water
samples
streams
(aAtoyac
Nexapa).
Sampling
extraction
organic
contaminants
was
performed
according
to
USEPA
method
TO13A
analyzed
by
gas
chromatography/mass
spectrometry.
In
addition,
DNA
extracted
sequenced.
previous
work,
group
semi-volatile
emerging
8
compounds
with
lower
volatility
selected.
Water
concentrations
studied
much
higher
for
aAtoyac
than
Nexapa.
The
results
obtained
allow
us
establish
that
present
are
aerosolized
therefore
can
affect
population
is
exposed
aerosols
heavily
decreasing
concentration
order
UPMP>ICATEP>UTIM>sCarlos
decrease
their
relative
body.
We
conclude
proximity
contaminated
bodies
implies
serious
risks
human
health.
It
worth
mentioning
represent
only
first
glance
problem.
A
deeper
evaluation
obviously
require
more
sampling
varying
distances
determine
time-space
variations
pollutant’s
bioaerosols
near
bodies.
CLEAN - Soil Air Water,
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 28, 2024
Abstract
This
study
focuses
on
the
hydro‐sedimentological
characterization
and
modeling
of
Dhauliganga
River
in
Uttarakhand,
India.
Field
data
collected
from
2018–2020,
including
stage,
velocity,
suspended
sediment
concentration
(SSC),
showed
notable
variations
influenced
by
melting
snow,
glaciers,
precipitation.
Challenges
accurately
rivers
with
a
topography
sparse
gauging
stations
were
addressed
using
artificial
neural
networks
(ANN).
The
calibrated
models
precisely
predicted
stage‐discharge
sediment‐discharge
relationships,
demonstrating
effectiveness
machine
learning,
particularly
ANN‐based
modeling,
such
challenging
terrains.
model's
performance
was
assessed
coefficient
determination
(
R
2
),
root
mean
square
error
(RMSE),
(MSE).
During
calibration
phase,
model
exhibited
values
0.96
for
discharge
0.63
SSC,
accompanied
low
RMSE
5.29
cu
m
s
–1
0.61
g
SSC.
Subsequently,
prediction
maintained
its
robustness,
achieving
0.97
along
5.67
0.68
also
found
strong
agreement
between
water
flow
estimates
derived
traditional
methods,
ANN,
actual
measurements.
load,
both
varied
annually,
potentially
modifying
aquatic
habitats
through
deposition,
altering
communities.
These
findings
offer
crucial
insights
into
dynamics
studied
river,
providing
valuable
applications
sustainable
water‐resource
management
terrains
addressing
environmental
concerns
related
to
sedimentation,
quality,
ecosystem.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Сен. 2, 2024
A
comprehensive
approach
is
essential
in
India's
ongoing
battle
against
air
pollution,
combining
technological
advancements,
regulatory
reinforcement,
and
widespread
societal
engagement.
Bridging
gaps
involves
deploying
sophisticated
pollution
control
technologies
addressing
the
rural–urban
disparity
through
innovative
solutions.
The
review
found
that
integrating
Artificial
Intelligence
Machine
Learning
(AI&ML)
quality
forecasting
demonstrates
promising
results
with
a
remarkable
model
efficiency.
In
this
study,
initially,
we
compute
PM2.5
concentration
over
India
using
surface
mass
of
5
key
aerosols
such
as
black
carbon
(BC),
dust
(DU),
organic
(OC),
sea
salt
(SS)
sulphates
(SU),
respectively.
study
identifies
several
regions
highly
vulnerable
to
due
specific
sources.
Indo-Gangetic
Plains
are
notably
impacted
by
high
concentrations
BC,
OC,
SU
resulting
from
anthropogenic
activities.
Western
experiences
higher
DU
its
proximity
Sahara
Desert.
Additionally,
certain
areas
northeast
show
significant
contributions
OC
biogenic
Moreover,
an
AI&ML
based
on
convolutional
autoencoder
architecture
underwent
rigorous
training,
testing,
validation
forecast
across
India.
reveal
exceptional
precision
prediction,
demonstrated
evaluation
metrics,
including
Structural
Similarity
Index
exceeding
0.60,
Peak
Signal-to-Noise
Ratio
ranging
28–30
dB
Mean
Square
Error
below
10
μg/m3.
However,
challenges
persist,
necessitating
robust
frameworks
consistent
enforcement
mechanisms,
evidenced
complexities
predicting
concentrations.
Implementing
tailored
regional
strategies,
technologies,
strengthening
frameworks,
promoting
sustainable
practices,
encouraging
international
collaboration
policy
measures
mitigate
