Microplastics, microfibers and associated microbiota biofilm analysis in seawater, a case study from the Vesuvian Coast, southern Italy
Journal of Hazardous Materials,
Journal Year:
2025,
Volume and Issue:
488, P. 137468 - 137468
Published: Feb. 4, 2025
The
growing
concerns
regarding
pollution
from
microplastics
(MPs)
and
microfibers
(MFs)
have
driven
the
scientific
community
to
develop
new
solutions
for
monitoring
ecosystems.
However,
many
of
proposed
technologies
still
include
protocols
treating
environmental
samples
that
may
alter
plastic
materials,
leading
inaccurate
results
both
in
observation
counting.
For
this
reason,
we
are
refining
a
protocol,
based
on
optical
microscopy
without
use
pretreatments,
applicable
different
matrices,
which
allows
not
only
counting
but
also
complete
morphological
characterization
MPs
MFs.
Previously,
protocol
has
successfully
been
tested
marine
sediments
Vesuvian
area
Gulf
Naples
(Italy)
with
good
results.
In
present
study,
MFs
seawater
collected
same
geographical
provide
comprehensive
overview
their
distribution
environments.
enabled
collection
information
colonies
microorganisms
microparticles.
Next
Generation
Sequencing
(NGS)
metagenomic
us
characterize
microbiota
composition
sampled
MPs,
so-called
Plastisphere.
analytical
approach
allowed
several
potentially
pathogenic
bacteria,
represent
potential
threat
environment
human
health.
fact,
they
exploit
ability
form
biofilms
plastics
proliferate
Language: Английский
Quantification of polystyrene microplastics in water, milk, and coffee using thermogravimetry coupled with Fourier transform infrared spectroscopy (TGA-FTIR)
Chemosphere,
Journal Year:
2024,
Volume and Issue:
368, P. 143777 - 143777
Published: Nov. 1, 2024
Rapid
quantification
of
plastic
contaminants,
particularly
microplastics
(MPs),
in
foods
is
a
challenge.
This
study
introduces
novel
method
using
Fourier
transform
infrared
spectroscopy
coupled
with
thermogravimetric
(TGA-FTIR)
and
chemometric
analysis
for
the
MPs
foods.
A
model
was
performed
polystyrene
(PS)
(1
μm)
added
to
various
foods,
namely,
water,
milk,
coffee
without
any
pretreatment.
Foods
were
spiked
PS
microbeads
at
different
concentrations,
heated
TGA,
FTIR
spectra
gases
evolved
from
TGA
collected
over
time.
The
spectral
data
used
construct
Gram-Schmidt
profile
identify
characteristic
peak.
spectrum
corresponding
peak
maxima
extracted
represent
specific
concentration.
dataset
selected
their
associated
concentrations
preprocessed
prior
calibration
cross-validation
PLS
regression
models,
each
food
matrix
studied.
results
showed
that
models
reliably
predicted
content
R
Language: Английский