Frontiers in Public Health,
Год журнала:
2023,
Номер
11
Опубликована: Авг. 10, 2023
Background
People
usually
spend
most
of
their
time
indoors,
so
indoor
fine
particulate
matter
(PM
2.5
)
concentrations
are
crucial
for
refining
individual
PM
exposure
evaluation.
The
development
concentration
prediction
models
is
essential
the
health
risk
assessment
in
epidemiological
studies
involving
large
populations.
Methods
In
this
study,
based
on
monitoring
data
multiple
types
places,
classical
linear
regression
(MLR)
method
and
random
forest
(RFR)
algorithm
machine
learning
were
used
to
develop
hourly
average
models.
Indoor
data,
which
included
11,712
records
from
five
obtained
by
on-site
monitoring.
Moreover,
potential
predictor
variable
derived
outdoor
stations
meteorological
databases.
A
ten-fold
cross-validation
was
conducted
examine
performance
all
proposed
Results
final
variables
incorporated
MLR
model
concentration,
type
place,
season,
wind
direction,
surface
speed,
hour,
precipitation,
air
pressure,
relative
humidity.
results
indicated
that
both
constructed
had
good
predictive
performance,
with
determination
coefficients
(R
2
RFR
72.20
60.35%,
respectively.
Generally,
better
than
(RFR
developed
using
same
as
model,
R
=
71.86%).
terms
predictors,
importance
suggested
speed
important
variables.
Conclusion
research,
places
first
time.
Both
easily
accessible
indicators
displayed
promising
domain
outperformed
result
suggests
application
algorithms
pollutant
prediction.
Environmental Research Communications,
Год журнала:
2024,
Номер
6(1), С. 012001 - 012001
Опубликована: Янв. 1, 2024
Abstract
Understanding
of
the
various
sources
indoor
air
pollution
requires
quality
(IAQ)
data
that
is
usually
lacking.
Such
can
be
obtained
using
unobtrusive,
low-cost
sensors
(LCS).
The
aim
this
review
to
examine
recent
literature
published
on
LCS
for
IAQ
measurements
and
determine
whether
these
studies
employed
any
methods
identify
or
quantify
pollution.
Studies
were
reviewed
in
terms
source
apportionment
employed,
as
well
microenvironment
type,
geographical
location,
several
metrics
relating
contribution
outdoor
pollutant
ingress
versus
potential
sources.
We
found
out
60
relevant
studies,
just
four
apportionment,
all
which
utilised
receptor
models.
Most
undertaken
residential
educational
environments.
There
a
lack
other
types
microenvironments
locations
outside
Europe
North
America.
are
inherent
limitations
with
producing
This
applies
external
data,
however
even
more
challenging
measure
due
its
characteristics.
environment
heterogeneous,
significant
variability
within
space
between
different
locations.
Sensor
placement,
occupancy,
activity
reports,
locations,
contribute
understanding
variability.
Outdoor
pollutants
into
via
building
envelope,
measurement
environmental
conditions,
recording
details
fabric
ventilation
help
apportion
contributions.
Whether
not
models
from
LCS,
there
parameters
which,
if
carefully
considered
during
campaigns,
aid
identification
pollutants.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Янв. 13, 2025
Abstract
The
rapid
development
of
low-cost
sensors
provides
the
opportunity
to
greatly
advance
scope
and
extent
monitoring
indoor
air
pollution.
In
this
study,
calibrated
particle
matter
(PM)
a
non-negative
matrix
factorisation
(NMF)
source
apportionment
technique
are
used
investigate
PM
concentrations
contributions
across
three
households
in
an
urban
residential
area.
NMF
is
applied
combined
data
from
all
houses
generate
profiles
that
can
be
understand
how
characteristics
similar
or
differ
between
different
same
2.5
10
were
greater,
more
variable,
significantly
ambient
recorded
at
nearby
site.
Concentrations
also
houses,
with
World
Health
Organisation
24-h
guideline
limits
for
breached
one
household.
methodology
was
highly
successful
modelling
(R
2
$$\ge$$
≥
0.983),
finding
I/O
(indoor
outdoor
sources
ratio)
lowest
1
(down
0.08),
greatest
(up
4.93).
Whilst
could
not
clearly
distinguished
further
than
being
outdoors
indoors,
clear
insights
variability
within
monitored
houses.
These
results
highlight
importance
pollution
improve
exposure
estimates,
as
whilst
people
may
live
areas
acceptable
quality,
they
exposed
unhealthy
their
own
homes.
This
method
future
studies
extended
periods
influence
seasonality
on
scaled
up
larger
geographical
areas.
Indoor Environments,
Год журнала:
2024,
Номер
1(2), С. 100008 - 100008
Опубликована: Март 5, 2024
Exposure
to
ambient
and
household
fine-particulate
matter
is
identified
as
a
substantial
contributor
premature
mortality
in
India,
according
the
Global
Burden
of
Disease
Studies.
This
study
examines
impacts
typical
Indian
cooking
practices
on
indoor
air
quality
characteristics
by
monitoring
evolution
fine
ultrafine
particle
(UFP)
concentration
dining
facility
residential
educational
institute
India.
The
area
was
spread
across
kitchen
(zone1)
hall
(zone2).
A
combination
validated
low-cost
PM
sensors
(LCS),
DustTrak8433,
Scanning
Mobility
Particle
Sizer
(SMPS)
utilized
for
real-time
data
acquisition
while
using
Liquefied
Petroleum
Gas
(LPG)
fuel.
PM2.5
UFP
concentrations
were
monitored
at
1.3
m
1.8
from
floor
assess
vertical
variation
pollutants
during
activities,
including
breakfast,
lunch,
dinner,
processes
such
preheating,
reheating,
stir-frying,
deep-frying.
It
found
that
prolonged
durations
involved
high-heat
methods
like
stir-frying
deep-frying
resulted
rise
coarser
(300-550
nm)
PM2.5,
causing
higher
exposure
concentration.
levels
are
upper
heights
because
temperature-driven
convection
currents
hygroscopic
growth
particles
due
high
humidity
levels.
Air
exchange
rates
(AER)
considerably
varied
chimneys
low
controlled
(closed
doors)
compared
mixed
ventilation
(opened
conditions.
maximum
AER
obtained
lunch
(4.3
9.9
h-1)
breakfast
(-7.8
6.8
dinner
(0.55
7.9
h-1).
decrement
rate
inside
zone
1
highest
(126µgm-3h-1),
coinciding
with
ventilation.
recommended
improving
better
design
can
reduce
commercial
rural
kitchens.
Environment International,
Год журнала:
2024,
Номер
193, С. 109069 - 109069
Опубликована: Окт. 11, 2024
Currently,
methodologies
for
the
identification
and
apportionment
of
air
pollution
sources
are
not
widely
applied
due
to
their
high
cost.
We
present
a
new
approach,
combining
mobile
measurements
from
multiple
sensors
collected
daily
walks
citizen
scientists,
in
population
density
area
Birmingham,
UK.
The
methodology
successfully
pinpoints
different
affecting
local
quality
using
only
handful
measurements.
It
was
found
that
regional
were
mostly
responsible
PM
Journal of Physics Conference Series,
Год журнала:
2025,
Номер
2942(1), С. 012042 - 012042
Опубликована: Фев. 1, 2025
Abstract
Air
pollution,
particularly
particulate
matter
(PM),
poses
significant
health
risks
and
environmental
challenges.
Real-time
air
quality
monitoring
is
crucial
for
effective
management
mitigation
strategies,
especially
in
personal
exposure
contexts.
Wearable
devices,
commonly
utilizing
low-cost
optical
PM
sensors,
offer
a
promising
solution.
However,
these
sensors
often
require
recalibration
to
ensure
accuracy
reliability.
This
study
focuses
on
calibrating
the
SPS30
sensor,
popular
improve
its
performance
wearable
applications.
The
calibration
process
was
conducted
using
decay
method
an
aerosol
chamber,
aligning
SPS30’s
readings
with
those
of
reference
sensor
(HT-9600).
results
demonstrated
excellent
correlation
between
both
PM2.5
PM10
measurements.
confirms
that
proper
can
enhance
accuracy,
making
it
reliable
tool
real-time,
monitoring,
which
essential
public
management.
