Non-road
construction
equipment
(NRCE)
is
an
important
non-road
source
of
air
pollution,
and
it
crucial
to
fully
understand
the
impact
NRCE
on
atmospheric
PM2.5
O3
pollution.
However,
systematic
assessment
emissions
atmosphere
lacking,
especially
with
latest
implementation
Stage
IV
Standard,
current
research
progress
insufficient
for
development
effective
control
measures.
This
study
estimated
emission
inventories
at
different
standard
stages
their
atmosphere,
using
“2+26”
cities
as
case
area.
The
results
show
that
total
CO,
NOx,
HC,
were
387,
418,
82,
24
kt
in
2015
319,
262,
62,
15
2020
are
predicted
be
270,
226,
48,
10
2025,
respectively.
Simulation
contribution
ratio
NO3-,
NO2,
PM2.5,
was
16.7%,
18.9%,
7.7%,
8.2%
13.6%,
18.4%,
6.5%,
6.7%
2020,
Southern
affect
urban
agglomerations
more
than
northern
do.
local
from
each
city
ranged
30%
59%,
O3,
13%–39%.
sensitivity
by
HDDM
illustrates
nonlinear
characteristics
highlight
importance
coordinated
NOx
VOC
can
inspire
post-processing
technology
electricity
substitution.
belt-like
area
connecting
Zhengzhou
Beijing
has
a
higher
exposure
concentration
population
distribution,
areas
significantly
rural
other
areas.
environmental
provides
guidance
its
management
development.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Апрель 8, 2025
Abstract
Under
the
“dual
carbon”
goals,
heavily
polluting
enterprises
face
dual
pressures
to
reduce
both
pollution
and
carbon
emissions,
necessitating
urgent
exploration
of
effective
pathways
for
coordinated
emission
reductions.
This
study
investigates
potential
digital
transformation
in
achieve
synergistic
First,
entropy
method
is
employed
measure
enterprise
digitalization
pollutant
levels,
spatial–temporal
evolution
characteristics
regional
reductions
are
analyzed.
Subsequently,
using
panel
data
from
Yangtze
River
Economic
Belt,
examines
impact
on
reduction,
its
underlying
mechanisms,
moderating
effects
environmental
policies
these
relationships.
Robustness
tests
confirm
synergy
between
emissions.
The
findings
reveal
that
contributes
reduction
emissions
enterprises,
primarily
through
two
pathways:
integration
internal
innovation
resources
collaborative
engagement
external
networks.
Furthermore,
air
control
low-carbon
city
initiatives
significantly
enhance
digitalization.
Interestingly,
located
downstream
regions
River,
those
with
smaller
operational
scales,
or
facing
strong
financing
constraints,
demonstrate
more
pronounced
transformation.
Based
conclusions,
we
recommend
governments
focus
strengthening
either
“pollution
reduction”
“carbon
policies,
as
alone
can
yield
benefits.
Additionally,
tailoring
local
conditions
maximize
economic
Chinese Journal of Mechanical Engineering,
Год журнала:
2024,
Номер
37(1)
Опубликована: Май 22, 2024
Abstract
Fine
particulate
matter
produced
during
the
rapid
industrialization
over
past
decades
can
cause
significant
harm
to
human
health.
Twin-fluid
atomization
technology
is
an
effective
means
of
controlling
fine
pollution.
In
this
paper,
influences
main
parameters
on
droplet
size,
range
and
sound
pressure
level
(SPL)
a
twin-fluid
nozzle
(TFN)
are
investigated,
in
order
improve
performance,
multi-objective
synergetic
optimization
algorithm
presented.
A
multi-physics
coupled
acoustic-mechanics
model
based
discrete
phase
(DPM),
large
eddy
simulation
(LES)
model,
Ffowcs
Williams-Hawkings
(FW-H)
established,
numerical
results
method
verified
via
experimental
comparison.
Based
analysis
results,
effects
water
flow
characteristics
distribution
were
obtained.
result
was
employed
establish
orthogonal
test
database,
adopted
optimize
key
TFN.
The
optimal
as
follows:
gas
0.94
m
3
/h,
0.0237
orifice
diameter
self-excited
vibrating
cavity
(SVC)
1.19
mm,
SVC
depth
0.53
distance
between
outlet
5.11
3.15
mm.
particle
size
field
significantly
reduced,
spray
improved
by
71.56%,
SPL
data
at
each
corresponding
measurement
point
decreased
average
38.96%.
conclusions
study
offer
references
for
future
TFN
research.
Toxics,
Год журнала:
2025,
Номер
13(5), С. 327 - 327
Опубликована: Апрель 23, 2025
To
address
the
performance
degradation
in
existing
PM2.5
prediction
models
caused
by
excessive
complexity,
poor
spatiotemporal
efficiency,
and
suboptimal
parameter
optimization,
we
employ
stacking
ensemble
learning
for
feature
weighting
analysis
integrate
ant
colony
optimization
(ACO)
algorithm
model
optimization.
Combining
meteorological
collaborative
pollutant
data,
a
(namely
stacking-ACO-LSTM
model)
with
much
shorter
consuming
time
than
that
of
only
long
short-term
memory
(LSTM)
networks
suitable
concentration
is
established.
It
can
effectively
filter
out
variables
higher
weights,
thereby
reducing
predictive
power
model.
The
hourly
trained
tested
using
real-time
monitoring
data
Nanchang
City
from
2017
to
2019.
results
show
established
has
high
accuracy
predicting
concentration,
compared
same
without
considering
space
efficiency
defective
mean
square
error
(MSE)
decreases
about
99.88%,
coefficient
determination
(R2)
increases
2.39%.
This
study
provides
new
idea
cities.
PLoS ONE,
Год журнала:
2024,
Номер
19(3), С. e0299865 - e0299865
Опубликована: Март 4, 2024
Understanding
air
quality
requires
a
comprehensive
understanding
of
its
various
factors.
Most
the
association
rule
techniques
focuses
on
high
frequency
terms,
ignoring
potential
importance
low-
terms
and
causing
unnecessary
storage
space
waste.
Therefore,
dynamic
genetic
mining
algorithm
is
proposed
in
this
paper,
which
combines
improved
with
to
realize
terms.
Firstly,
chromosome
coding
phase
algorithm,
an
innovative
multi-information
strategy
proposed,
selectively
stores
similar
values
different
levels
one
unit.
It
avoids
storing
all
at
once
facilitates
efficient
valid
rules
later.
Secondly,
by
weighting
evaluation
indicators
such
as
support,
confidence
promotion
mining,
new
index
formed,
avoiding
need
set
minimum
threshold
for
high-interest
rules.
Finally,
order
improve
performance
rules,
crossover
rate
mutation
are
search
efficiency
algorithm.
In
experimental
stage,
paper
adopts
2016
annual
data
Beijing
verify
effectiveness
unit
point
reducing
air,
formed
low
item
set,
combining
swarm
intelligence
optimization
time
convergence.
above
three
aspects.
The
reduced
consumption
50%,
can
mine
more
interesting
whose
interest
level
be
up
90%,
while
lower
time,
we
it
about
20%
compared
some
meta-heuristic
algorithms,
improving
