Water,
Год журнала:
2024,
Номер
16(17), С. 2519 - 2519
Опубликована: Сен. 5, 2024
Groundwater
represents
a
critical
resource
for
sustaining
the
livelihoods
of
both
urban
and
rural
populations,
facilitating
economic
social
development,
preserving
ecological
equilibrium.
This
study
leverages
groundwater
quality
monitoring
data
from
northern
Baiquan
spring
basin
(NBSB)
to
elucidate
hydrochemical
characteristics
decipher
temporal
variability
in
water
quality.
Findings
suggest
that
within
NBSB
is
predominantly
weakly
alkaline
characterized
as
hard-fresh,
with
HCO3−
Ca2+
predominant
ions,
which
collectively
demarcate
type
HCO3-Ca.
The
principal
constituents
are
influenced
by
weathering
carbonates
silicates
alongside
dissolution
gypsum
halite.
Moreover,
agricultural
operations
similar
human
activities
have
exerted
an
impact
on
attributes
NBSB’s
groundwater.
Generally,
fluctuations
anion
concentrations
over
time
more
pronounced
than
those
cations,
exemplified
significant
upward
trend
major
ion
at
BQ03
site
later
stages.
While
general
deemed
satisfactory,
most
sites
experienced
escalation
indices
time,
notably
BQ03,
warrants
serious
attention.
findings
this
research
contribute
efficient
management
sustainable
utilization
resources
NBSB.
Water,
Год журнала:
2024,
Номер
16(13), С. 1853 - 1853
Опубликована: Июнь 28, 2024
This
study
employed
hydrochemical
data,
traditional
hydrogeochemical
methods,
inverse
modeling,
and
unsupervised
machine
learning
techniques
to
explore
the
traits
origins
of
groundwater
in
Changbai
Mountain
region.
(1)
Findings
reveal
that
predominant
types
include
HCO3−Ca·Mg,
HCO3−Ca·Na·Mg,
HCO3−Mg·Na,
HCO3−Na·Mg.
The
average
metasilicic
acid
content
was
found
be
at
49.13
mg/L.
(2)
Rock
weathering
mechanisms,
particularly
silicate
mineral
weathering,
primarily
shape
chemistry,
followed
by
carbonate
dissolution.
(3)
Water-rock
interactions
involve
volcanic
dissolution
cation
exchange
adsorption.
Inverse
alongside
analysis
widespread
lithology,
underscores
complexity
reactions,
influenced
not
only
water-rock
but
also
evaporation
precipitation.
(4)
Unsupervised
learning,
integrating
SOM,
PCA,
K-means
techniques,
elucidates
types.
SOM
component
maps
a
close
combination
various
components.
Principal
(PCA)
identifies
first
principal
(PC1),
explaining
48.15%
variance.
second
(PC2)
third
(PC3)
components,
explain
13.2%
10.8%
variance,
respectively.
K
clustering
categorized
samples
into
three
main
clusters:
one
less
basaltic
geological
processes,
another
showing
strong
igneous
rock
characteristics,
affected
other
processes
or
anthropogenic
factors.
