Air
pollution
greatly
impacts
economic
development
and
is
of
common
concern
to
all
sectors
society.
However,
the
discussion
on
interrelationships
between
air
pollutants
effect
socio-economic
indicators
remain
lacking.
This
study
systematically
analyzes
spatiotemporal
characteristics
drivers
four
major
based
a
panel
data
199
districts
counties
in
Beijing–Tianjin–Hebei
region
from
2013
2020.
The
results
showed
that
concentrations
PM2.5,
PM10
NO₂
decreased
by
48.87%,
48.54%
29.33%,
whereas
O₃
increased
24.78%,
making
it
concern.
Moreover,
demonstrated
an
overall
positive
spatial
correlation.
Among
factors,
GDP
per
capita
total
social
retail
goods
mitigated
pollution,
secondary
industry
was
biggest
cause
pollutant
concentrations.
increase
electricity
consumption
unit
alleviated
south-central
Beijing–Tianjin–Hebei.
Furthermore,
ecological
conservation
areas
represented
Zhangjiakou
Chengde
tended
exacerbate
as
level
increased.
study's
comprehensive
analysis
provides
theoretical
support
for
targeted
control
measures
policies
sustainable
different
regions.
Environment International,
Journal Year:
2025,
Volume and Issue:
195, P. 109251 - 109251
Published: Jan. 1, 2025
The
rapid
urbanization
in
China
has
brought
about
serious
air
pollution
problems,
which
are
likely
to
persist
for
a
considerable
period
as
the
process
continues.
In
urban
areas,
spatial
distribution
of
pollutants
represented
by
PM
Atmospheric Environment,
Journal Year:
2024,
Volume and Issue:
323, P. 120390 - 120390
Published: Feb. 3, 2024
The
Yangtze
River
Economic
Belt
(YREB)
in
China
is
one
of
the
most
populated
regions
world
with
fast
growth
economy
and
pollutant
emissions
from
human
activity,
which
has
contributed
to
severe
air-quality
issues
detrimental
impact
on
health.
China's
measures
control
spread
COVID-19
appear
have
resulted
reductions
energy
consumption
air
emissions.
However,
there
a
scarcity
studies
that
examine
how
natural
meteorological
socioeconomic
factors
collectively
influence
temporal
spatial
variations
pollution
YREB
before
during
lockdown.
In
this
study,
we
analyze
spatiotemporal
evolution
pollutants
period
2015–2022,
estimate
their
dependence,
clustering
characteristics,
spillover
effects,
response
multiple
variables.
Significant
decreasing
trends
were
observed
for
major
except
O3.
Although
24.44%
reduction
PM2.5
exposure
outbreak
7.60%
due
clean
measures,
O3
warm
seasons
(April–September)
increased
by
13.74%
9.29%
outbreak.
At
level,
highest
concentrations
six
recorded
urban
agglomerations
lower
or
middle
reaches
River.
A
significant
autocorrelation
annual
average
was
detected
Meteorological
conditions
activities
potentially
had
impacts
concentrations.
Prior
implementation
both
dominated
quality
changes
YREB.
following
epidemic,
far
outweighed
index.
finding
study
profound
implications
formulating
emission
policies
customized
China.
Environment International,
Journal Year:
2024,
Volume and Issue:
185, P. 108539 - 108539
Published: March 1, 2024
Exposure
scenario
and
receptor
behavior
significantly
affect
PM2.5
exposure
quantity
of
persons
resident
groups,
which
in
turn
influenced
indoor
or
outdoor
air
quality
&
health
management.
An
Internet
Things
(IoT)
system,
EnvironMax+,
was
developed
to
accurately
conveniently
assess
residential
dynamic
state.
A
university
community
"QC",
as
the
application
area,
divided
into
four
scenarios
five
groups
residents.
Low-cost
mobile
sensors
indoor/outdoor
pollution
migration
(IOP)
models
jointly
estimated
multi-scenario
real-time
concentrations.
Questionnaire
used
investigate
residents'
activity
characteristics.
Mobile
(app)
"Air
management
(AHM)"
could
automatic
collect
trajectory.
At
last,
daily
concentrations
each
residents-group
were
obtained.
The
results
showed
that
most
important
one,
where
residents
spend
about
60
%
their
time.
Closing
window
significant
affecting
contamination.
annual
average
concentration
studied
scenarios:
(RS)
<
public
(PS)
(OS)
catering
(CS).
Except
for
CS,
other
higher
than
by
5–10
μg/m3.
population
weighted
37.1
μg/m3,
78
concentration.
5
groups:
cooks
>
workers
students
elderly,
related
time
proportion
different
scenario.
