Environmental Quality Management,
Journal Year:
2024,
Volume and Issue:
34(1)
Published: March 10, 2024
Abstract
African
cities
grapple
with
urban
air
pollution
from
traffic‐related
pollutants
(TRAPs).
This
study
investigated
TRAPs
concentration
variations
at
traffic
intersections
(TIs)
in
Ibadan,
Nigeria.
AERMOD
model
was
employed
to
examine
dispersion
25
selected
TIs,
considered
as
volume
sources.
Seasonal
distributions
of
six
(CO,
NO
2
,
SO
TVOCs,
PM
2.5
and
10
)
were
determined
using
meteorological,
topographical,
pollutants’
emission
rates
AERMOD.
Estimated
peak
concentrations
the
studied
generally
higher
rainy
season
than
dry
season,
surpassing
quality
standards
set
by
World
Health
Organization
(WHO)
Nigeria's
National
Environmental
Standards
Regulations
Enforcement
Agency
(NESREA)
during
seasons
except
(24
h)
which
did
not
exceed
NESREA
standard.
highlighted
TIs
significant
contributors
degradation
both
Ibadan
showed
AERMOD's
suitability
for
studies.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: March 21, 2024
Abstract
Industrial
advancements
and
utilization
of
large
amount
fossil
fuels,
vehicle
pollution,
other
calamities
increases
the
Air
Quality
Index
(AQI)
major
cities
in
a
drastic
manner.
Major
AQI
analysis
is
essential
so
that
government
can
take
proper
preventive,
proactive
measures
to
reduce
air
pollution.
This
research
incorporates
artificial
intelligence
prediction
based
on
pollution
data.
An
optimized
machine
learning
model
which
combines
Grey
Wolf
Optimization
(GWO)
with
Decision
Tree
(DT)
algorithm
for
accurate
India.
quality
data
available
Kaggle
repository
used
experimentation,
like
Delhi,
Hyderabad,
Kolkata,
Bangalore,
Visakhapatnam,
Chennai
are
considered
analysis.
The
proposed
performance
experimentally
verified
through
metrics
R-Square,
RMSE,
MSE,
MAE,
accuracy.
Existing
models,
k-nearest
Neighbor,
Random
Forest
regressor,
Support
vector
compared
model.
attains
better
traditional
algorithms
maximum
accuracy
88.98%
New
Delhi
city,
91.49%
Bangalore
94.48%
97.66%
95.22%
97.68%
Visakhapatnam
city.
