Urban Water Journal,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 14
Published: Dec. 25, 2024
This
study
assessed
land-use
impacts
on
surface-water
quality
and
explored
relationships
between
water
indexes
with
parameters.
Twenty-seven
samples,
collected
from
canals
located
in
agricultural,
industrial,
residential
areas,
were
analyzed
for
22
Water
index
(WQI),
heavy
metal
pollution
(HPI),
(MQI)
results
showed
poor
to
very
across
all
land
uses.
Agriculture
had
the
highest
WQI
(39),
followed
by
(12)
industrial
areas
(7).
Industrial
exhibited
HPI
MQI,
indicating
higher
areas.
Stepwise
multiple
regression
analysis
revealed
a
significant
correlation
electrical
conductivity
chemical
oxygen
demand
(COD),
explaining
71%
of
variance.
Discriminant
differentiated
three
uses
100%
accuracy
using
turbidity,
COD,
biochemical
demand,
Mg,
and,
Na.
Tailored
management
strategies
should
be
developed
each
land-used
type
improve
urban
Groundwater for Sustainable Development,
Journal Year:
2023,
Volume and Issue:
23, P. 101049 - 101049
Published: Nov. 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.
Ecological Informatics,
Journal Year:
2024,
Volume and Issue:
80, P. 102514 - 102514
Published: Feb. 13, 2024
This
study
assessed
water
quality
(WQ)
in
Tongi
Canal,
an
ecologically
critical
and
economically
important
urban
canal
Bangladesh.
The
researchers
employed
the
Root
Mean
Square
Water
Quality
Index
(RMS-WQI)
model,
utilizing
seven
WQ
indicators,
including
temperature,
dissolve
oxygen,
electrical
conductivity,
lead,
cadmium,
iron
to
calculate
index
(WQI)
score.
results
showed
that
most
of
sampling
locations
poor
WQ,
with
many
indicators
violating
Bangladesh's
environmental
conservation
regulations.
eight
machine
learning
algorithms,
where
Gaussian
process
regression
(GPR)
model
demonstrated
superior
performance
(training
RMSE
=
1.77,
testing
0.0006)
predicting
WQI
scores.
To
validate
GPR
model's
performance,
several
measures,
coefficient
determination
(R2),
Nash-Sutcliffe
efficiency
(NSE),
factor
(MEF),
Z
statistics,
Taylor
diagram
analysis,
were
employed.
exhibited
higher
sensitivity
(R2
1.0)
(NSE
1.0,
MEF
0.0)
WQ.
analysis
uncertainty
(standard
7.08
±
0.9025;
expanded
1.846)
indicates
RMS-WQI
holds
potential
for
assessing
inland
waterbodies.
These
findings
indicate
could
be
effective
approach
waters
across
study's
did
not
meet
recommended
guidelines,
indicating
Canal
is
unsafe
unsuitable
various
purposes.
implications
extend
beyond
contribute
management
initiatives
Environmental Health Insights,
Journal Year:
2024,
Volume and Issue:
18
Published: Jan. 1, 2024
Underground
water
quality
can
be
affected
by
natural
or
human-made
influences.
This
study
investigates
how
the
management
and
characteristics
of
hand-dug
wells
impact
in
3
suburbs
Kumasi,
Ghana,
using
a
combination
qualitative
quantitative
research
methods.
Descriptive
analysis,
including
frequency
percentages,
depicted
demographic
profiles
respondents.
Box
plot
diagrams
illustrated
distribution
physicochemical
parameters
(Total
Dissolved
Solid
[TDS],
Electrical
Conductivity
[EC],
Turbidity,
Oxygen
[DO],
Temperature).
Factor
analysis
evaluated
dominant
factors
among
these
parameters.
Cluster
(hierarchical
clustering)
utilized
sampling
points
as
variables
to
establish
spatial
variations
Cramer’s
V
correlation
test
explored
relationships
between
individual
perceptions
management.
One-way
ANOVA
verified
significant
mean
differences
Logistic
regression
models
assessed
influence
selected
well
features
(e.g.,
cover
apron)
on
TDS,
pH,
Temperature,
DO.
The
findings
revealed
that
proximity
human
settlements
affects
quality,
increasing
turbidity
is
associated
with
unmaintained
covers,
significantly
impacting
(
P
<
.05).
Over
80%
were
located
within
10
30
m
pollution
sources,
65.63%
situated
lower
ground
87.5%
being
unmaintained.
Other
contamination
sources
included
plastic
bucket/rope
usage
(87.50%),
defective
linings
(75%),
apron
fissures
(59.37%).
Presence
E.
coli,
Total
coliform,
Faecal
coliform
rendered
unpotable.
attributed
90.85%
time-based
organic
particle
decomposition
factors.
However,
found
establishing
association
factor
associations
difficult.
It
encouraged
promote
construction
maintenance
standards
ensure
are
properly
built
protected
from
sources.
