The Relationship Between Surface Meteorological Variables and Air Pollutants in Simulated Temperature Increase Scenarios in a Medium-Sized Industrial City
Atmosphere,
Journal Year:
2025,
Volume and Issue:
16(4), P. 363 - 363
Published: March 24, 2025
This
study
investigated
the
relationship
between
surface
meteorological
variables
and
levels
of
air
pollutants
(O3,
PM10,
PM2.5)
in
scenarios
simulated
temperature
increases
Rio
Grande,
a
medium-sized
Brazilian
city
with
strong
industrial
influence.
utilized
five
years
daily
data
(from
1
January
2019
to
31
December
2023)
model
atmospheric
conditions
two
pollutant
21
2021
20
simulate
how
would
respond
annual
°C
2
°C,
employing
Support
Vector
Machine,
supervised
machine
learning
algorithm.
Predictive
models
were
developed
for
both
averages
seasonal
variations.
The
predictive
analysis
results
indicated
that,
when
considering
averages,
concentrations
showed
decreasing
trend
as
temperatures
increased.
same
pattern
was
observed
scenarios,
except
during
summer,
O3
increased
rise.
greatest
reduction
occurred
winter
(decreasing
by
10.33%
12.32%
under
warming
respectively),
while
PM10
PM2.5,
most
significant
reductions
spring.
lack
correlation
levels,
along
their
other
variables,
explains
Grande.
research
provides
important
contributions
understanding
interactions
climate
change,
pollution,
factors
similar
contexts.
Language: Английский
A Pilot Study with Low-Cost Sensors: Seasonal Variation of Particulate Matter Ratios and Their Relationship with Meteorological Conditions in Rio Grande, Brazil
Climate,
Journal Year:
2025,
Volume and Issue:
13(4), P. 71 - 71
Published: March 30, 2025
(1)
Background:
This
study
investigated
seasonal
variations
in
particulate
matter
(PM)
ratios
(PM1/PM2.5,
PM2.5/PM10,
and
PM1/PM10)
their
relationship
with
the
meteorological
conditions
Rio
Grande,
Brazil.
(2)
Methods:
PM1,
PM2.5,
PM10
levels
were
collected
using
low-cost
Gaia
Air
Quality
Monitors,
which
measured
PM
concentrations
at
high
temporal
resolution.
Meteorological
variables,
including
atmospheric
pressure,
temperature,
relative
humidity,
wind
speed,
precipitation,
obtained
from
National
Institute
of
Meteorology
(INMET).
The
data
analyzed
through
multiple
linear
regression
to
assess
influence
factors
on
ratios.
(3)
Results:
results
show
that
highest
occurred
winter,
indicating
a
predominance
fine
ultrafine
particles,
while
lowest
observed
spring
summer.
Multiple
analysis
identified
maximum
temperature
as
key
drivers
distribution.
(4)
Conclusions:
highlights
importance
continuous
monitoring
ratios,
particularly
remains
underexplored
findings
underscore
need
for
targeted
air
quality
policies
emphasizing
mitigation
strategies
improved
pollution
control
minimize
health
risks
associated
exposure.
Language: Английский