Environmental Science & Technology,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 21, 2025
Microplastic
(MP)
pollution
has
become
a
significant
global
concern
in
soil
systems.
The
spatial
risk
of
MPs
soils,
the
cascading
effects
climate,
human
activities,
and
air
quality,
ecosystem
gradients
from
natural
habitats,
agricultural
lands,
urban
soils
remain
largely
unknown.
We
compile
comprehensive
data
set
more
than
3000
site-year
field
observations
across
agricultural,
natural,
ecosystems
China.
By
using
interpretable
machine
learning
models
statistical
approaches,
our
findings
reveal
that
approximately
4.32%
China
face
potential
ecological
risks
MPs,
with
being
most
vulnerable
(e.g.,
14.7%
are
at
risk).
Climate
factors
temperature
precipitation),
activities
plastic
film
use),
quality
concentrations
atmospheric
particulate
matter)
have
been
identified
as
primary
drivers
MP
risk.
climate
(p
<
0.001)
significantly
impact
soils.
This
work
highlights
urgent
need
for
coordinated
management
to
mitigate
posed
by
especially
lands.
Animals,
Год журнала:
2024,
Номер
14(2), С. 350 - 350
Опубликована: Янв. 22, 2024
Plastic
pollution
is
a
global
diffuse
threat,
especially
considering
its
fragmentation
into
microplastics
(MPs)
and
nanoplastics
(NPs).
Since
the
contamination
of
aquatic
environment
already
well
studied,
most
studies
have
now
focused
on
soil.
Moreover,
number
exposure
routes
toxic
effects
MNPs
in
humans
continuously
increasing.
Although
can
cause
inflammation,
cytotoxicity,
genotoxicity
immune
toxicity
livestock
animals,
which
accumulate
ingested/inhaled
plastic
particles
transfer
them
to
through
food
chain,
research
this
topic
still
lacking.
In
farm
animals
as
missing
link
between
soil/plant
human
health
effects,
paper
aims
describe
their
importance
carriers
vectors
MNP
contamination.
As
early
stages,
there
no
standard
method
quantify
amount
characteristics
different
matrices.
Therefore,
creation
common
database
where
researchers
report
data
quantification
methods
could
be
helpful
for
both
standardization
future
training
an
AI
tool
predicting
abundant/dangerous
polymer(s),
thus
supporting
policy
decisions
reduce
perfectly
fitting
with
One
Health
principles.
