Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Май 25, 2025
Nitrate
(NO3-)
is
a
crucial
component
of
atmospheric
pollutants,
and
understanding
its
sources
formation
mechanisms
holds
significant
importance
for
air
pollution
control.
In
this
study,
stable
isotope
techniques
Bayesian
Mixing
Models
(Mix
SIAR)
were
applied
to
analyze
the
primary
processes
NO3-
in
PM2.5
PM10
Beijing
2022.
The
results
indicate
that
contribution
vehicle
exhaust,
coal
combustion,
biomass
burning,
soil
emissions
33.9%,
20.5%,
29.8%,
15.9%,
respectively,
while
PM10,
contributions
30.6%,
21.6%,
29.9%,
17.9%
respectively.
An
analysis
δ18O-NO3-
values
indicated
N2O5
hydrolysis
over
year
was
64.0%
75.6%,
highlighting
predominant
role
nitrate
formation.
Nevertheless,
gas-phase
reaction
NO2
with
·OH
radicals
notably
more
pronounced
summer.
Compared
contributes
PM2.5.
These
offer
vital
foundation
further
research
into
provide
scientific
support
measures
prevent
control
pollution.
Nature Communications,
Год журнала:
2024,
Номер
15(1)
Опубликована: Сен. 13, 2024
Microbial
carbon
use
efficiency
(CUE)
affects
the
fate
and
storage
of
in
terrestrial
ecosystems,
but
its
global
importance
remains
uncertain.
Accurately
modeling
predicting
CUE
on
a
scale
is
challenging
due
to
inconsistencies
measurement
techniques
complex
interactions
climatic,
edaphic,
biological
factors
across
scales.
The
link
between
microbial
soil
organic
relies
stabilization
necromass
within
aggregates
or
association
with
minerals,
necessitating
an
integration
processes
approaches.
In
this
perspective,
we
propose
comprehensive
framework
that
integrates
diverse
data
sources,
ranging
from
genomic
information
traditional
assessments,
refine
cycle
models
by
incorporating
variations
CUE,
thereby
enhancing
our
understanding
contribution
cycling.
Soil Biology and Biochemistry,
Год журнала:
2024,
Номер
195, С. 109458 - 109458
Опубликована: Май 6, 2024
The
fate
of
soil
organic
matter
(OM)
is
determined
by
its
microbial
use
for
growth
or
respiration.
Many
environmental
factors
influence
OM
use,
including
the
presence
contaminants
and
toxins
in
environment,
such
as
heavy
metals.
We
evaluated
short-
long-term
responses
processes
to
metal
contamination
estimating
biomass
concentrations
rates
bacteria
fungi,
respiration,
resulting
carbon-use
efficiencies
(CUE),
turnover
times.
sampled
O-horizon
from
a
gradient
boreal
forest
soils
exposed
arising
industrial
point
source
since
1930s
assess
long
term
effects
on
microorganisms.
To
estimate
short-term
exposure,
additions
Cu
were
used.
Bacterial
respiration
decreased
response
contamination,
while
fungal
unaffected,
without
changes
CUEs.
independent
total
decreased.
Thus,
times
slowed
accelerated
pollution.
was
inhibited
stimulated
experimental
additions,
with
bigger
effect
sizes
contaminated
sites.
interpreted
low
but
high
collected
samples
indicate
that
community
included
large
mycorrhizal
fraction.
Although
overall
OM-use
(i.e.,
sum
bacterial
respiration),
they
also
increased
CUE.
In
conclusion,
less
sensitive
than
pollution
CUE
unaffected.
Microbial
decomposer
communities
able
maintain
higher
when
challenged
new
additions.
Our
results
imply
align
their
trait
compositions
challenges,
this
can
mitigate
reduction
often
expected
occur
stress.
Journal of Sustainable Agriculture and Environment,
Год журнала:
2025,
Номер
4(1)
Опубликована: Фев. 5, 2025
ABSTRACT
High
microbial
carbon
use
efficiency
(CUE)
in
agricultural
soils
can
limit
the
return
of
atmospheric
dioxide
(CO
2
)
from
organic
matter
mineralisation
and
potentially
increase
soil
(SOC)
accumulation
through
formation
biomass
necromass.
Therefore,
management
practices
that
CUE
are
relevant
for
sustainable
agriculture
climate
change
mitigation.
We
conducted
an
exploratory
literature
review
evidence
synthesis
to
compare
between
conventional
tillage
(CT)
low‐intensity
systems
(reduced
tillage,
RT
no‐tillage,
NT).
The
50
paired
observations
11
studies
showed
overall
12%
under
compared
CT
(
p
=
0.02).
Separate
contrasts
NT
versus
(i.e.,
RT/CT
NT/CT)
also
higher
with
0.06
0.05,
respectively.
is
likely
due
improved
substrate
availability
growth
and/or
changes
community
induced
by
contrasting
systems.
However,
limited
quantitative
data
linking
tillage‐induced
these
drivers
constrains
further
analysis.
extracted
available
SOC
eligible
studies,
but
this
did
not
provide
increases
were
correlated
content.
Future
should
extend
emerging
empirical
set
clarify
abiotic
biotic
which
be
refined
better
mitigation
strategies.
Further
aim
understand
link
dynamics,
important
representation
global
models.
Global Change Biology,
Год журнала:
2024,
Номер
30(8)
Опубликована: Авг. 1, 2024
Soil
microbial
traits
and
functions
play
a
central
role
in
soil
organic
carbon
(SOC)
dynamics.
However,
at
the
macroscale
(regional
to
global)
it
is
still
unresolved
whether
(i)
specific
environmental
attributes
(e.g.,
climate,
geology,
types)
or
(ii)
community
composition
drive
key
directly.
To
address
this
knowledge
gap,
we
used
33
grassland
topsoils
(0-10
cm)
from
geoclimatic
gradient
Chile.
First,
incubated
soils
for
1
week
favorable
standardized
conditions
quantified
wide
range
of
such
as
biomass
(MBC),
enzyme
kinetics,
respiration,
growth
rates
well
use
efficiency
(CUE).
Second,
characterized
climatic
physicochemical
properties
bacterial
fungal
soils.
We
then
applied
regression
analysis
investigate
how
strongly
measured
were
linked
with
setting
versus
composition.
show
that
(predominantly
amount
matter)
determined
patterns
MBC
along
gradient,
which
turn
explained
respiration
rates.
normalized
(i.e.,
growth)
more
than
attributes.
Notably,
both
followed
distinct
trends
related
different
parts
community,
resulted
strong
effects
on
CUE.
conclude
even
macroscale,
CUE
result
physiologically
decoupled
aspects
metabolism,
partially
by
The
affect
functions,
therefore
factors
need
be
considered
context
SOC
Sustainability,
Год журнала:
2024,
Номер
16(16), С. 6849 - 6849
Опубликована: Авг. 9, 2024
Monitoring
and
estimating
spatially
resolved
changes
in
soil
organic
carbon
(SOC)
stocks
are
necessary
for
supporting
national
international
policies
aimed
at
assisting
land
degradation
neutrality
climate
change
mitigation,
improving
fertility
food
production,
maintaining
water
quality,
enhancing
renewable
energy
ecosystem
services.
In
this
work,
we
report
on
the
development
application
of
a
data-driven,
quantile
regression
machine
learning
model
to
estimate
predict
annual
SOC
plow
depth
under
variability
climate.
The
enables
analysis
content
levels
respective
probabilities
their
occurrence
as
function
exogenous
parameters
such
monthly
temperature
precipitation
endogenous,
decision-dependent
parameters,
which
can
be
altered
by
use
practices.
estimated
quantiles
trends
indicate
uncertainty
ranges
likelihoods
plausible
content.
used
reduced-form
scenario
generator
stochastic
scenarios.
It
integrated
submodel
Integrated
Assessment
models
with
detailed
sectors
GLOBIOM
analyze
costs
find
optimal
management
practices
sequester
fulfill
food–water–energy–-environmental
NEXUS
security
goals.
