Critical Reviews in Environmental Science and Technology,
Journal Year:
2023,
Volume and Issue:
53(20), P. 1817 - 1835
Published: March 23, 2023
With
the
continuous
release
into
environments,
emerging
contaminants
(ECs)
have
attracted
widespread
attention
for
potential
risks,
and
numerous
studies
been
conducted
on
their
identification,
environmental
behavior
bioeffects,
removal.
Owing
to
superiority
of
dealing
with
high-dimensional
unstructured
data,
a
new
data-driven
approach,
machine
learning
(ML),
has
gradually
applied
in
research
ECs.
This
review
described
fundamental
principle,
algorithms,
workflow
ML,
summarized
advances
ML
applications
typical
ECs
(per-
polyfluoroalkyl
substances,
nanoparticles,
antibiotic
resistance
genes,
endocrine-disrupting
chemicals,
microplastics,
antibiotics,
pharmaceutical
personal
care
products).
methods
showed
practicability,
reliability,
effectiveness
predicting
or
analyzing
occurrence,
distribution,
removal
ECs,
various
algorithms
derived
models
were
developed
optimized
obtain
better
performance.
Moreover,
size
homogeneity
data
set
strongly
influence
application
choosing
appropriate
different
characteristics
is
crucial
addressing
specific
problems
related
sets.
Future
efforts
should
focus
improving
quality
adopting
more
advanced
developing
quantitative
structure-activity
relationship,
promoting
applicability
domains
interpretability
models.
In
addition,
development
codeless
tools
will
benefit
accessibility
Journal of Sea Research,
Journal Year:
2023,
Volume and Issue:
194, P. 102410 - 102410
Published: June 30, 2023
Once
critically
thought
of
only
as
a
menace
in
the
marine
environment,
plastics
particulates,
especially
microplastics
(MPs)
are
gradually
gaining
access
into
human
body.
However,
among
diverse
sources
exposure
examined,
seafood
might
be
most
critical,
it
is
deemed
"necessary
evil".
Seafood
consumption
recent
years
has
experienced
geometric
increase
and
so
its
likelihood
to
stealthily
introduce
food-borne
humans.
This
because
organisms
have
become
repositories
MPs
their
domiciled
microbial
community,
which
often
not
beneficial.
We
ratiocinated
that
steady
will
multiple
risks
presented
plastic
composites,
leachates
exogenously
formed
adsorbents
(antibiotic
resistance
bacteria:
ARBs,
antibiotic
genes:
ARGs,
heavy
metals
noxious
aromatics)
pose.
critical
dearth
literature
affords
collaged
comprehension
whole
picture
regarding
this
issue,
impede
progress
risk
assessment
control
measures.
In
regard,
study
aimed
update
knowledge
on
known
trends
delve
deeper
suggest
unknowns
for
safety
security,
ultimately,
well-being.
Catalysts,
Journal Year:
2023,
Volume and Issue:
13(5), P. 846 - 846
Published: May 6, 2023
Microplastic
(MP)
pollution
has
emerged
as
a
significant
environmental
concern,
with
exposure
to
it
linked
numerous
negative
consequences
for
both
ecosystems
and
humans.
To
tackle
this
complex
issue,
innovative
technologies
that
are
capable
of
effectively
eliminating
MPs
from
the
environment
necessary.
In
review,
we
examined
variety
bare
composite
photocatalysts
employed
in
degradation
process.
An
in-depth
assessment
benefits
drawbacks
each
catalyst
was
presented.
Additionally,
explored
photocatalytic
mechanisms
factors
influencing
degradation.
The
review
concludes
by
addressing
current
challenges
outlining
future
research
priorities,
which
will
help
guide
efforts
mitigate
MP
contamination.
Critical Reviews in Environmental Science and Technology,
Journal Year:
2023,
Volume and Issue:
53(20), P. 1817 - 1835
Published: March 23, 2023
With
the
continuous
release
into
environments,
emerging
contaminants
(ECs)
have
attracted
widespread
attention
for
potential
risks,
and
numerous
studies
been
conducted
on
their
identification,
environmental
behavior
bioeffects,
removal.
Owing
to
superiority
of
dealing
with
high-dimensional
unstructured
data,
a
new
data-driven
approach,
machine
learning
(ML),
has
gradually
applied
in
research
ECs.
This
review
described
fundamental
principle,
algorithms,
workflow
ML,
summarized
advances
ML
applications
typical
ECs
(per-
polyfluoroalkyl
substances,
nanoparticles,
antibiotic
resistance
genes,
endocrine-disrupting
chemicals,
microplastics,
antibiotics,
pharmaceutical
personal
care
products).
methods
showed
practicability,
reliability,
effectiveness
predicting
or
analyzing
occurrence,
distribution,
removal
ECs,
various
algorithms
derived
models
were
developed
optimized
obtain
better
performance.
Moreover,
size
homogeneity
data
set
strongly
influence
application
choosing
appropriate
different
characteristics
is
crucial
addressing
specific
problems
related
sets.
Future
efforts
should
focus
improving
quality
adopting
more
advanced
developing
quantitative
structure-activity
relationship,
promoting
applicability
domains
interpretability
models.
In
addition,
development
codeless
tools
will
benefit
accessibility
