Development of a multi-module data-driven integrated framework for identifying drivers of atmospheric particulate nitrate and reduction emissions: An application in an industrial city, China
Environment International,
Год журнала:
2025,
Номер
unknown, С. 109394 - 109394
Опубликована: Март 1, 2025
Atmospheric
particulate
nitrate
(pNO3-),
a
crucial
component
of
fine
matter,
significantly
contributes
to
haze
pollution.
The
formation
pNO3-
is
driven
by
multiple
factors
including
meteorology,
emissions,
and
atmospheric
chemistry.
Understanding
the
key
drivers
developing
an
accurate
physically
meaningful
method
for
timely
assessment
direct
causes
pollution
are
essential.
In
this
study,
we
propose
multi-module
data-driven
integrated
framework
that
incorporates
improves
four
distinct
machine
learning
modules.
This
enhances
physical
interpretability
statistical
outcomes
driving
pNO3-,
quantifies
impacts
on
reveals
emission
reduction
trends.
Our
findings
show
meteorology
emissions
affect
35.3
%
64.7
%,
respectively,
while
chemistry
(48.0
%)
humidity
(17.1
its
formation.
Photochemistry
promotes
in
summer,
whereas
liquid-phase
reactions
dominate
winter
at
higher
levels
(>60
%).
industry
source
(IS)
(14.3
%),
combustion
(CS)
(12.8
transportation
(TS)
(11.8
main
sources.
primary
transformation
NOx
emitted
from
CS
TS
more
sensitive
changes
meteorological
conditions,
controlling
has
greater
benefits
reduce
pNO3-.
proposed
could
provide
reliable
identifying
different
events,
supporting
formulation
control
measures.
Язык: Английский
Data Factor Marketization and Urban Industrial Land Use Efficiency: Evidence from the Establishment of Data Trading Platforms in China
Sustainability,
Год журнала:
2025,
Номер
17(6), С. 2753 - 2753
Опубликована: Март 20, 2025
Enhancing
urban
industrial
land
use
efficiency
(UILUE)
is
critical
for
addressing
human–land
conflicts
and
promoting
sustainable
development.
However,
the
role
of
data
trading
in
influencing
UILUE
remains
insufficiently
examined
literature.
This
study
explores
effect
factor
marketization
(DFM)
on
its
underlying
mechanisms.
Using
from
284
Chinese
cities
between
2006
2022,
this
treats
establishment
platforms
as
a
quasi-natural
experiment.
A
multi-period
difference-in-differences
(DID)
model
applied
to
evaluate
causal
impact
DFM.
The
findings
indicate
that
DFM
significantly
improves
UILUE.
improvement
mainly
occurs
through
technological
innovation
reduced
resource
misallocation.
Furthermore,
positive
more
pronounced
with
lower
levels
market
segmentation,
stricter
environmental
regulations,
those
located
eastern
region.
offers
valuable
theoretical
insights
practical
implications
optimizing
advancing
Язык: Английский