Heliyon,
Год журнала:
2024,
Номер
10(13), С. e33082 - e33082
Опубликована: Июнь 19, 2024
Monitoring
of
groundwater
resources
in
coastal
areas
is
vital
for
human
needs,
agriculture,
ecosystems,
securing
water
supply,
biodiversity,
and
environmental
sustainability.
Although
the
utilization
quality
index
(WQI)
models
has
proven
effective
monitoring
resources,
it
faced
substantial
criticism
due
to
its
inconsistent
outcomes,
prompting
need
more
reliable
assessment
methods.
Therefore,
this
study
addresses
concern
by
employing
data-driven
root
mean
squared
(RMS)
evaluate
Bhola
district
near
Bay
Bengal,
Bangladesh.
To
enhance
reliability
RMS-WQI
model,
research
incorporated
extreme
gradient
boosting
(XGBoost)
machine
learning
(ML)
algorithm.
For
GWQ,
utilized
eleven
crucial
indicators,
including
turbidity
(TURB),
electric
conductivity
(EC),
pH,
total
dissolved
solids
(TDS),
nitrate
(NO3-),
ammonium
(NH4+),
sodium
(Na),
potassium
(K),
magnesium
(Mg),
calcium
(Ca),
iron
(Fe).
In
terms
GW
concentration
K,
Ca
Mg
exceeded
guideline
limit
collected
samples.
The
computed
scores
ranged
from
54.3
72.1,
with
an
average
65.2,
categorizing
all
sampling
sites'
GWQ
as
"fair."
model
reliability,
XGBoost
demonstrated
exceptional
sensitivity
(R2
=
0.97)
predicting
accurately.
Furthermore,
exhibited
minimal
uncertainty
(<1%)
WQI
scores.
These
findings
implied
efficacy
accurately
assessing
areas,
that
would
ultimately
assist
regional
managers
strategic
planners
sustainable
management
resources.
Groundwater for Sustainable Development,
Год журнала:
2023,
Номер
23, С. 101049 - 101049
Опубликована: Ноя. 1, 2023
Groundwater
plays
a
pivotal
role
as
global
source
of
drinking
water.
To
meet
sustainable
development
goals,
it
is
crucial
to
consistently
monitor
and
manage
groundwater
quality.
Despite
its
significance,
there
are
currently
no
specific
tools
available
for
assessing
trace/heavy
metal
contamination
in
groundwater.
Addressing
this
gap,
our
research
introduces
an
innovative
approach:
the
Quality
Index
(GWQI)
model,
developed
tested
Savar
sub-district
Bangladesh.
The
GWQI
model
integrates
ten
water
quality
indicators,
including
six
heavy
metals,
collected
from
38
sampling
sites
study
area.
enhance
precision
assessment,
employed
established
machine
learning
(ML)
techniques,
evaluating
model's
performance
based
on
factors
such
uncertainty,
sensitivity,
reliability.
A
major
advancement
incorporation
metals
into
framework
index
model.
best
authors
knowledge,
marks
first
initiative
develop
encompassing
heavy/trace
elements.
Findings
assessment
revealed
that
area
ranged
'good'
'fair,'
indicating
most
indicators
met
standard
limits
set
by
Bangladesh
government
World
Health
Organization.
In
predicting
scores,
artificial
neural
networks
(ANN)
outperformed
other
ML
models.
Performance
metrics,
root
mean
square
error
(RMSE),
(MSE),
absolute
(MAE)
training
(RMSE
=
0.361;
MSE
0.131;
MAE
0.262),
testing
0.001;
0.00;
0.001),
prediction
evaluation
statistics
(PBIAS
0.000),
demonstrated
superior
effectiveness
ANN.
Moreover,
exhibited
high
sensitivity
(R2
1.0)
low
uncertainty
(less
than
2%)
rating
These
results
affirm
reliability
novel
monitoring
management,
especially
regarding
metals.
Journal of Contaminant Hydrology,
Год журнала:
2024,
Номер
261, С. 104307 - 104307
Опубликована: Янв. 21, 2024
The
Rooppur
Nuclear
Power
Plant
(RNPP)
at
Ishwardi,
Bangladesh
is
planning
to
go
into
operation
within
2024
and
therefore,
adjacent
areas
of
RNPP
gaining
adequate
attention
from
the
scientific
community
for
environmental
monitoring
purposes
especially
water
resources
management.
However,
there
a
substantial
lack
literature
as
well
datasets
earlier
years
since
very
little
was
done
beginning
RNPP's
construction
phase.
Therefore,
this
study
conducted
assess
potential
toxic
elements
(PTEs)
contamination
in
groundwater
its
associated
health
risk
residents
part
during
year
2014–2015.
For
achieving
aim
study,
samples
were
collected
seasonally
(dry
wet
season)
nine
sampling
sites
afterwards
analyzed
quality
indicators
such
temperature
(Temp.),
pH,
electrical
conductivity
(EC),
total
dissolved
solid
(TDS),
hardness
(TH)
PTEs
including
Iron
(Fe),
Manganese
(Mn),
Copper
(Cu),
Lead
(Pb),
Chromium
(Cr),
Cadmium
(Cd)
Arsenic
(As).
This
adopted
newly
developed
Root
Mean
Square
index
(RMS-WQI)
model
scenario
whereas
human
assessment
utilized
quantify
toxicity
PTEs.
In
most
sites,
concentration
found
higher
season
than
dry
Fe,
Mn,
Cd
As
exceeded
guideline
limit
drinking
water.
RMS
score
mostly
classified
terms
"Fair"
condition.
non-carcinogenic
risks
(expressed
Hazard
Index-HI)
revealed
that
around
44%
89%
adults
67%
100%
children
threshold
set
by
USEPA
(HI
>
1)
possessed
through
oral
pathway
season,
respectively.
Furthermore,
calculated
cumulative
HI
throughout
period.
carcinogenic
(CR)
PTEs,
magnitude
decreased
following
pattern
Cr
Cd.
Although
current
based
on
old
dataset,
findings
might
serve
baseline
reduce
future
hazardous
impact
power
plant.
