Abstract
Since
2006,
the
rapid
development
of
China’s
aviation
industry
has
been
accompanied
by
a
significant
increase
in
one
its
emissions,
namely,
PM2.5,
which
poses
substantial
threat
to
human
health.
However,
little
data
is
describing
PM2.5
concentration
caused
aircraft
activities.
This
study
addresses
this
gap
initially
computing
monthly
emissions
landing-take-off
(LTO)
stage
from
Jan.
2006
Dec.
2023
for
175
Chinese
airports,
employing
modified
BFFM2-FOA-FPM
method.
Subsequently,
uses
Gaussian
diffusion
model
measure
24-hour
average
resulting
flight
activities
at
each
airport.
mainly
draws
following
conclusions:
Between
and
2023,
highest
recorded
all
airports
was
observed
2018,
reaching
5.7985
micrograms
per
cubic
meter,
while
lowest
point
2022,
2.0574
meter.
Moreover,
with
higher
are
predominantly
located
densely
populated
economically
vibrant
regions
such
as
Beijing,
Shanghai,
Guangzhou,
Chengdu,
Shenzhen.
Environmental Science & Technology,
Год журнала:
2023,
Номер
57(24), С. 8954 - 8964
Опубликована: Июнь 5, 2023
In
response
to
the
severe
air
pollution
issue,
Chinese
government
implemented
two
phases
(Phase
I,
2013–2017;
Phase
II,
2018–2020)
of
clean
actions
since
2013,
resulting
in
a
significant
decline
fine
particles
(PM2.5)
during
2013–2020,
while
warm-season
(April–September)
mean
maximum
daily
8
h
average
ozone
(MDA8
O3)
increased
by
2.6
μg
m–3
yr–1
China
same
period.
Here,
we
derived
drivers
behind
rising
O3
concentrations
using
bottom-up
emission
inventory,
regional
chemical
transport
model,
and
multiple
linear
regression
model.
We
found
that
both
meteorological
variations
(3.6
m–3)
anthropogenic
emissions
(6.7
contributed
growth
MDA8
from
2013
2020,
with
changes
playing
more
important
role.
The
contributions
rise
2017–2020
(1.2
were
much
lower
than
2013–2017
(5.2
m–3).
lack
volatile
organic
compound
(VOC)
control
nitrogen
oxides
(NOx)
responsible
for
increase
due
VOC-limited
regimes
most
urban
areas,
synergistic
VOC
NOx
II
initially
worked
mitigate
2018–2020,
although
its
effectiveness
was
offset
penalty
PM2.5
decline.
Future
mitigation
efforts
should
pay
attention
simultaneous
improve
quality.
Frontiers in Environmental Science,
Год журнала:
2022,
Номер
10
Опубликована: Июль 22, 2022
Due
to
recent
developments
in
the
global
economy,
transportation,
and
industrialization,
air
pollution
is
one
of
main
environmental
issues
21st
century.
The
current
study
aimed
predict
both
short-term
long-term
Jiangsu
Province,
China,
based
on
Prophet
forecasting
model
(PFM).
We
collected
data
from
72
quality
monitoring
stations
forecast
six
pollutants:
PM
10
,
2.5
SO
2
NO
CO,
O
3
.
To
determine
accuracy
compare
its
results
with
predicted
actual
values,
we
used
correlation
coefficient
(R),
mean
squared
error
(MSE),
root
(RMSE),
absolute
(MAE).
show
that
PFM
R
values
0.40
0.52,
RMSE
16.37
12.07
μg/m
MAE
11.74
8.22
respectively.
Among
other
pollutants,
also
are
between
5
12
;
11
has
extensive
power
accurately
concentrations
pollutants
can
be
regions.
this
research
will
helpful
for
local
authorities
policymakers
control
plan
accordingly
upcoming
years.
Ecological Indicators,
Год журнала:
2023,
Номер
146, С. 109924 - 109924
Опубликована: Янв. 20, 2023
Rapid
urbanization
has
led
to
economic
growth
with
inevitable
air
pollution.
There
are
significant
differences
in
the
dominant
factors
of
different
pollutants.
However,
influencing
mechanism
on
pollutants
is
still
unclear.
Therefore,
exploring
differential
effects
various
great
significance
for
accelerating
local
collaborative
treatment
and
improving
regional
quality.
Based
analysis
spatial–temporal
pattern
evolution,
spatial
agglomeration,
internal
correlation
six
mainland
China
during
2013–2020,
namely
PM2.5,
PM10,
SO2,
NO2,
O3
CO,
we
combined
environmental
Kuznets
theory
build
a
panel
regression
model
Except
NO2
O3,
concentrations
other
four
all
decreased
degrees,
among
which,
SO2
concentration
most.
The
pollution
showed
that
typical
areas
significantly,
while
which
higher
population
density
or
development
was
relatively
higher.
As
key
factor
affecting
quality,
aspects
have
direction
intensity
effect
relationship
between
conforms
curve
(EKC).
nonlinear
relationships
CO
concentrations,
inverted
"U-shaped",
"N-shaped",
"U-shaped"
respectively.
In
addition,
spillover
whose
reflects
phased
change.
