The Science of The Total Environment,
Год журнала:
2024,
Номер
934, С. 173237 - 173237
Опубликована: Май 17, 2024
Per-
and
poly-fluoroalkyl
substances
(PFAS)
have
emerged
as
newly
regulated
micropollutants,
characterised
by
extreme
recalcitrance
environmental
toxicity.
Constructed
wetlands
(CWs),
a
nature-based
solution,
gained
widespread
application
in
sustainable
water
wastewater
treatment
offer
multiple
societal
benefits.
Despite
CWs
potential,
knowledge
gaps
persist
their
PFAS
removal
capacities,
associated
mechanisms,
modelling
of
fate.
This
study
carried
out
systematic
literature
review,
supplemented
unpublished
experimental
data,
demonstrating
the
promise
for
from
influents
varying
sources
characteristics.
Median
performances
64,
46,
0
%
were
observed
five
free
surface
(FWS),
four
horizontal
subsurface
flow
(HF),
18
vertical
(VF)
wetlands,
respectively.
adsorption
substrate
or
plant
root/rhizosphere
was
deemed
key
mechanism.
Nevertheless,
available
dataset
resulted
unsuitable
quantitative
analysis.
Data-driven
models,
including
regression
models
machine
learning-based
Artificial
Neural
Networks
(ANN),
employed
to
predict
removal.
These
showed
better
predictive
performance
compared
various
mechanistic
which
include
two
isotherms.
The
results
affirmed
that
artificial
intelligence
is
an
efficient
tool
emerging
contaminants
with
limited
chemical
properties.
In
summary,
this
consolidated
evidence
supporting
use
mitigating
new
legacy
contaminants.
Further
research,
especially
long-term
monitoring
full-scale
treating
real
wastewater,
crucial
obtain
additional
data
model
development
validation.
