A land use regression model to explain the spatial variation of nitrate concentration – A regional scale case study in the north-west of Ireland
Journal of Contaminant Hydrology,
Год журнала:
2025,
Номер
270, С. 104528 - 104528
Опубликована: Фев. 28, 2025
Regional-scale
groundwater
contamination
estimation
is
crucial
for
sustainable
water
management.
The
primary
obstacles
in
evaluating
include
limited
data
availability,
small
sample
sizes,
and
difficulties
linking
concentration
levels
to
land
use
patterns.
Linear
regression
identifies
the
relationship
between
measured
concentrations
both
natural
human-influenced
factors.
However,
difficulty
with
this
method
lies
choosing
a
group
of
regressors
that
meet
all
necessary
criteria
model
when
multiple
potential
exist.
This
study
introduces
buffer-based
land-use
linear
develop
catchment-scale
predicting
nitrate
groundwater.
successfully
captures
85
%
spatial
variability
across
area,
as
indicated
by
validation
results
from
32
training
sites.
model's
prediction
capability
ability
capture
were
found
be
good
development
(R2
=
0.89)
0.80)
steps.
performed
well
accuracy
assessment
error
processes
(RMSE
0.025
MAE
0.020).
In
future,
LUR
can
reparameterised
latest
available
time
series
datasets
climate
change
scenarios.
While
focused
on
sub-catchment
Bonet
River,
methodology
has
applied
border
area.
Future
studies
more
robust
accurate
predictor
variables
explain
influence
sources,
transport
attenuation
improve
technique
better
adaptation
applicability
other
areas.
Язык: Английский
Regression-based machine learning models for nitrate and chloride prediction in surface water in a small agricultural sand plain sub-watershed in southwestern Ontario, Canada
Frontiers in Environmental Science,
Год журнала:
2025,
Номер
13
Опубликована: Март 28, 2025
Machine
learning
(ML)
models
have
proven
to
be
an
efficient
technique
for
better
understanding
and
quantification
of
surface
water
quality,
especially
in
agricultural
watersheds
where
considerable
anthropogenic
activities
occur.
However,
there
is
a
lack
systematic
investigations
that
can
examine
the
application
different
ML
regression
settings
predict
quality
using
group
input
variables,
including
hydrological
(e.g.,
flow),
meteorological
precipitation),
field
crop
cover)
conditions.
In
this
study,
multiple
models,
support
vector
machine
(SVM)
trees
(RT),
were
employed
on
2-year
dataset
collected
from
sand
plain
sub-watershed
southwestern
Ontario,
Canada
(i.e.,
Lower
Whitemans
Creek)
nitrate
chloride
concentrations
at
nine
sampling
sites
within
sub-watershed.
The
prediction
capabilities
these
determined
evaluation
metrics
coefficient
determination
(R
2
)
root-mean
squared
error
(RMSE).
general,
Gaussian
Process
Regression
(GPR)
model
was
optimal
algorithm
0.99
0.98
respectively
training
testing).
According
results
feature
importance
analysis,
it
found
conditions
(specifically
location
site
(main
channel
or
tributary
site)
most
crucial
variables
accurate
predictions
output
variables.
This
study
underscores
implemented
effectively
quantify
properties
easily
measurable
parameters.
These
assist
decision
makers
advancing
successful
actions
steps
towards
protecting
available
resources.
Язык: Английский
Geo-Spatial Insights into Heavy Metal Contamination and Ecological Implications in River Sediments: Identifying Agrochemical Impacts Through Pollution Indices in Morocco’s Sidi Allal Tazi Region, Sebou Basin
Research Square (Research Square),
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 18, 2025
Abstract
Sediments
in
agricultural
ecosystems
serve
as
critical
indicators
of
environmental
pollution,
particularly
regions
subjected
to
intensive
practices.
This
research
evaluates
the
hazards
and
implications
heavy
metal
(HM)
contamination
river
sediments
from
Sidi
Allal
Tazi
area
within
Morocco’s
Sebou
basin.
Twenty
sediment
samples
were
extracted
strategically
designated
locations,
levels
analyzed
using
a
multi-index
integration
approach,
multi-statistical
analyses
(MSA),
Geographic
Information
Systems
(GIS).
The
results
revealed
considerable
spatial
variability
HM
concentrations,
with
Cd
As
displaying
highest
levels.
Statistical
analysis,
incorporating
Principal
Component
Analysis
(PCA),
identified
anthropogenic
activities
primary
contributors
contamination.
Hierarchical
Cluster
(HCA)
categorized
metals
based
on
common
pollution
pathways,
while
GIS
mapping
distribution
across
vulnerable
areas.
Pollution
like
Geo-accumulation
Index
(I
geo)
well
Load
(PLI).
that
75%
sites
under
“very
high
pollution”,
emphasizing
severity
Contamination
Factor
(CF)
classified
90%
100%
contamination”.
Risk
indices
indicated
significant
ecological
threats,
contributing
an
RI
exceeding
600
many
areas,
signifying
risk”.
These
findings
highlight
urgent
need
for
targeted
mitigation
strategies
sustainable
methodologies
provides
comprehensive
framework
assessing
managing
contamination,
offering
insights
policymakers
managers.
Язык: Английский
Optimizing irrigation and planting strategies to prevent non-point source pollution in the Hetao Irrigation District using SWAT-MODFLOW-RT3D model
The Science of The Total Environment,
Год журнала:
2024,
Номер
957, С. 177757 - 177757
Опубликована: Дек. 1, 2024
Язык: Английский
Revealing nitrate sources seasonal difference between groundwater and surface water in China's largest fresh water lake (Poyang Lake): Insights from sources proportion, dynamic evolution and driving forces
The Science of The Total Environment,
Год журнала:
2024,
Номер
958, С. 178134 - 178134
Опубликована: Дек. 17, 2024
Язык: Английский
Changes in Nutrient Surpluses and Contents in Soils of Cereals and Kiwifruit Fields
Agronomy,
Год журнала:
2024,
Номер
14(11), С. 2556 - 2556
Опубликована: Окт. 31, 2024
Knowledge
of
nutrient
surpluses
in
soils
is
critical
to
optimize
management
and
minimize
adverse
environmental
effects.
We
investigated
the
two
regions
over
25
years
(1992
2017)
south
Loess
Plateau,
China.
One
region
has
cereals
as
main
crop,
whereas
other
region,
cereal
crops
was
changed
kiwi
orchards.
The
inputs
nitrogen
(N),
phosphorus
(P),
potassium
(K)
increased
rapidly
(by
74%,
77%,
103%
from
1992
2017
region;
by
91%,
204%,
368%
kiwifruit
region),
while
outputs
were
relatively
stable,
which
resulted
increasing
(the
annual
averaged
N,
P,
K
178,
62,
12
kg
ha−1
y−1
for
486,
96,
153
region)
lower
use
efficiency
(NUE).
higher
N
surplus
orchard-dominated
caused
high
nitrate
accumulation
(3071
0–5
m
11–20
y
orchard)
deeper
soil
profiles.
Similarly,
P
available
K.
This
highlights
that
comprehensive
measures
should
be
taken
control
surpluses,
will
help
balance
losses
intensive
horticultural
crop
systems.
Язык: Английский