ACS ES&T Water,
Год журнала:
2024,
Номер
4(11), С. 4758 - 4773
Опубликована: Окт. 31, 2024
Malaysia
is
facing
growing
threats
to
its
water
resources
due
emerging
contaminants.
This
requires
innovative
and
sustainable
solutions
for
monitoring
quality
remediation.
perspective
explores
the
potential
of
analytical
address
this
challenge.
We
highlight
critical
role
multimodal
spectroscopy
techniques,
lab-on-chip
technologies,
AI-powered
sensing
in
enhancing
detection
These
advancements
offer
greater
precision
efficiency
management.
Beyond
detection,
remediation
such
as
phytoremediation
nanotechnology
embracing
techniques
can
minimize
environmental
impact
while
fostering
a
harmonious
relationship
with
natural
ecosystems.
In
addition,
effective
policy
frameworks
are
essential
facilitating
adoption
these
advancements.
Regulatory
should
align
sustainability
goals
ensure
that
effectively
integrated
into
national
management
strategies.
The
collaboration
among
government
agencies,
research
institutions,
industry
stakeholders,
civil
society
necessary
create
an
enabling
environment
widespread
practices.
conclusion,
achieving
multifaceted
approach
focused
on
innovation,
sustainability,
collaboration.
By
harnessing
forces,
overcome
challenges
posed
by
contaminants
clean
future
generations.
Water Environment Research,
Год журнала:
2023,
Номер
95(6)
Опубликована: Май 19, 2023
Anaerobic
digestion
(AD)
of
sludge
is
a
key
approach
to
recover
useful
bioenergy
from
wastewater
treatment
and
its
stable
operation
important
plant
(WWTP).
Because
various
biochemical
processes
that
are
not
fully
understood,
AD
can
be
affected
by
many
parameters
thus
modeling
becomes
tool
for
monitoring
controlling
their
operation.
In
this
case
study,
robust
model
predicting
biogas
production
was
developed
using
ensembled
machine
learning
(ML)
based
on
the
data
full-scale
WWTP.
Eight
ML
models
were
examined
three
them
selected
as
metamodels
create
voting
model.
This
had
coefficient
determination
(R2
)
at
0.778
root
mean
square
error
(RMSE)
0.306,
outperformed
individual
models.
The
Shapley
additive
explanation
(SHAP)
analysis
revealed
returning
activated
temperature
influent
features,
although
they
in
different
ways.
results
study
have
demonstrated
feasibility
absence
high-quality
input
improving
prediction
through
assembling
PRACTITIONER
POINTS:
Machine
applied
anaerobic
digesters
plant.
A
created
exhibits
better
performance
predication.
high
quality
data,
indirect
features
identified
production.
Journal of Hazardous Materials,
Год журнала:
2025,
Номер
unknown, С. 138090 - 138090
Опубликована: Апрель 1, 2025
Wastewater
treatment
plants
(WWTPs)
are
significant
sources
of
per-
and
polyfluoroalkyl
substances
(PFAS)
pollution,
but
comprehensive
monitoring
management
impractical
cost-prohibitive.
To
strengthen
programs,
we
developed
machine
learning
(ML)
models
to
predict
both
total
PFAS
individual
in
wastewater
liquid
solid
matrices
based
on
a
statewide
database
compiled.
The
public
WWTP-PFAS-CA
(2020-2023)
comprises
200
WWTPs
across
California
with
concentrations
influent,
effluent,
biosolids
as
well
processes.
More
than
80
%
exhibit
an
increased
sum
the
39
(hereafter
PFAS)
over
half
these
facilities
facing
risk
surpassing
70
ng/L
threshold
for
levels
wastewater.
Additionally,
data-driven
ML
tool
(assessing
risk,
occurrences,
predicting
specific
concentrations)
WWTPs.
Our
achieved
∼80
accuracy
WWTP
biosolids.
Key
factors
influencing
distribution
include
size,
source,
county
population,
gross
domestic
product.
our
knowledge,
this
is
first
approach
model
at
scale.
RSC Advances,
Год журнала:
2025,
Номер
15(16), С. 12125 - 12151
Опубликована: Янв. 1, 2025
Greywater
constitutes
a
significant
portion
of
urban
wastewater
and
is
laden
with
numerous
emerging
contaminants
that
have
the
potential
to
adversely
impact
public
health
ecosystem.
ACS ES&T Engineering,
Год журнала:
2024,
Номер
4(9), С. 2252 - 2262
Опубликована: Авг. 27, 2024
Ensuring
the
safety
and
reliability
of
water
distribution
system
(WDS)
manifests
significant
importance
for
residential,
commercial,
industrial
needs
may
benefit
from
structure
deterioration
models
early
warning
pipe
breaks.
However,
challenges
exist
in
model
calibration
with
limited
monitoring
data,
unseen
underground
conditions,
or
high
computing
requirements.
Herein,
a
novel
deep
learning-based
DeeperGCN
framework
was
proposed
to
predict
failure
by
cooperating
graph
convolutional
network
(GCN)
processing.
The
achieved
much
deeper
architectures
designed
utilize
spatial
temporal
data
simultaneously.
Two
representation
methods
three
GCN
were
compared,
showing
best
predictions
"Pipe_as_Edge"
method
DeeperGEN
model.
To
identify
priority
maintenance
directly,
prediction
targets
assigned
as
binary
classification
question
determine
break
not
over
1-,
3-,
5-year
periods,
accuracies
96.91,
96.73,
97.23%,
respectively.
issue
imbalance
observed
addressed
through
varied
evaluation
metrics,
resulting
weighted
F1
scores
>0.96.
demonstrated
potential
applications
visualizing
97.09,
96.31,
97.81%
across
periods
2015,
example.
