Worldwide Examination of Magnetic Responses to Heavy Metal Pollution in Agricultural Soils
Agriculture,
Journal Year:
2024,
Volume and Issue:
14(5), P. 702 - 702
Published: April 29, 2024
Over
the
last
decade,
a
large
number
of
studies
have
been
conducted
on
heavy
metals
and
magnetic
susceptibility
(χlf)
measurement
in
soils.
Yet,
global
understanding
soil
contamination
responses
remains
elusive
due
to
limited
scope
or
sampling
sites
these
studies.
Hence,
we
attempted
explore
pollution
proxy
scale.
Through
meta-analysis
data
from
102
published
studies,
our
research
aimed
provide
worldwide
overview
metal
agriculture
We
mapped
geographic
distribution
nine
(Cr,
Cu,
Zn,
Pb,
Ni,
As,
Cd,
Mn,
Fe)
agricultural
soils
explored
their
sources
contributions.
Since
2011,
The
accumulation
has
escalated,
with
industrial
activities
(31.5%)
being
largest
contributor,
followed
by
inputs
(27.1%),
atmospheric
deposition
(22.66%),
natural
(18.74%).
study
reports
χlf
ranging
6.45
×
10−8
m3/kg
319.23
χfd
0.59%
12.85%,
majority
samples
below
6%,
indicating
influence
mainly
human
activities.
Pearson’s
correlation
redundancy
analysis
show
significant
positive
correlations
Cu
(r
=
0.51–0.53)
Mn
Fe
0.50–0.53),
while
As
were
shown
be
key
factors
variation
response.
average
load
index
2.03
suggests
moderate
pollution,
higher
areas
high
χlf.
Regression
confirms
is
considered
non-polluted
26×10−8
polluted
above
this
threshold,
all
showing
linear
(R
0.72),
that
relationship
between
geochemical
properties
continues
exist
This
provides
new
insights
for
large-scale
quality
assessment
Language: Английский
Evaluation of machine learning models for accurate prediction of heavy metals in coal mining region soils in Bangladesh
Ram Proshad,
No information about this author
Krishno Chandra,
No information about this author
Maksudul Islam
No information about this author
et al.
Environmental Geochemistry and Health,
Journal Year:
2025,
Volume and Issue:
47(5)
Published: April 23, 2025
Language: Английский
Interpretable machine learning models reveal the partnership of microplastics and perfluoroalkyl substances in sediments at a century scale
Journal of Hazardous Materials,
Journal Year:
2024,
Volume and Issue:
486, P. 137018 - 137018
Published: Dec. 26, 2024
Language: Английский
Chemical Fractions and Magnetic Simulation Based on Machine Learning for Trace Metals in a Sedimentary Column of Lake Taihu
Water,
Journal Year:
2024,
Volume and Issue:
16(18), P. 2604 - 2604
Published: Sept. 14, 2024
In
this
study,
the
chemical
fractions
(CFs)
of
trace
metal
(TMs)
and
multiple
magnetic
parameters
were
analysed
in
sedimentary
column
from
centre
Lake
Taihu.
The
column,
measuring
53
cm
length,
was
dated
using
210Pb
137Cs
to
be
124
years
old.
Surface
layers
found
contain
significantly
higher
concentrations
Cd,
Co,
Cu,
Pb,
Sb,
Ti,
Zn
than
middle
bottom
layers.
core
contained
a
substantial
amount
ferrimagnetic
minerals.
Most
TMs
present
residual
state,
except
for
Mn
Pb.
Cd
exhibited
most
significant
variation
with
depth.
pollution
load
index
(PLI)
indicated
moderate
levels
region,
whereas
risk
assessment
code
(RAC)
classified
as
being
heavily
polluted.
Multiple
linear
regression
(MLR)
random
forest
(RF),
support
vector
machine
(SVM),
XGBoost
(1.7.7.1)
learning
models
used
simulate
RAC
total
concentration
TMs,
physical
indicators
sediments
input
variables.
MLR
model
outperformed
RF,
SVM,
simulating
CFs
R2
up
0.668
0.87.
SHapley
Additive
exPlanations
(SHAP)
method
reveals
that
χarm/χ
is
dominant
factor
influencing
As
models.
For
Co
Cu
RF
models,
C%
N%
exhibit
greater
contributions.
Language: Английский