Artificial Intelligence Review,
Journal Year:
2024,
Volume and Issue:
57(3)
Published: Feb. 15, 2024
Abstract
With
massive
population
growth
and
a
shift
in
the
urban
culture
smart
cities,
constant
generation
of
waste
continues
to
create
unsanitary
living
conditions
for
city
dwellers.
Overflowing
solid
garbage
rapid
non-degradable
produce
slew
infectious
illnesses
that
proliferate
throughout
ecosystem.
Conventional
management
systems
have
proved
be
increasingly
harmful
densely
populated
areas
like
cities.
Also,
such
require
real-time
manual
monitoring
garbage,
high
labor
costs,
maintenance.
Monitoring
on
timely
basis
reducing
costs
is
scarcely
possible,
realistically,
municipal
corporation.
A
Smart
Dustbin
System
(SDS)
proposed
implemented
ensure
hygiene.
This
paper
undertakes
comprehensive
analysis
application
dustbin
systems,
following
an
extensive
literature
review
discussion
recent
research
expected
help
improve
systems.
current
SDS
used
with
most
advances
from
deep
learning,
computer
vision,
Internet
Things.
The
system
day-to-day
life
minimizes
overloading
bins,
lowers
saves
energy
time.
It
also
helps
keep
cities
clean,
lowering
risk
disease
transmission.
primary
users
are
universities,
malls,
high-rise
buildings.
evolution
over
years
various
features
technologies
well
analyzed.
datasets
Waste
Management
benchmark
image
presented
under
AI
perception.
results
existing
works
compared
highlight
potential
limitations
these
works.
PLoS ONE,
Journal Year:
2025,
Volume and Issue:
20(1), P. e0308367 - e0308367
Published: Jan. 15, 2025
The
composition
of
solid
waste
affects
technology
choices
and
policy
decisions
regarding
its
management.
Analyses
studies
are
almost
always
made
on
a
parameter
by
basis.
Multivariate
distance
techniques
can
create
wholisitic
determinations
similarities
differences
were
applied
here
to
enhance
series
comparisons.
A
set
New
York
City
residential
conducted
in
1990,
2004,
2013,
2017
compared
EPA
data
88
other
US
jurisdictions
from
1987–2021.
total
stream
the
disposed
wastes
NYC
found
be
similar
nature,
very
different
out
for
recycling.
Disposed
more
across
five
boroughs
single
year
than
one
borough
over
28-year
time
period,
but
recyclables
14
years
year.
Food
plastics
percentages
streams
increased
time,
paper
fell.
food
disposal
rate
much
less
show.
decreased.
largely
conformed
trends
did
not
generally
agree
with
sets.
use
novel-to-waste
multivariate
analyses
offers
promise
simplifying
identification
overall
studies,
so
improving
management
planning
waste.
Waste Management,
Journal Year:
2025,
Volume and Issue:
196, P. 60 - 70
Published: Feb. 19, 2025
Managing
construction
and
demolition
waste
(CDW)
poses
serious
concerns
regarding
landfilling
recycling
because
of
the
potential
release
hazardous
elements
after
leaching.
Ceramic
materials
such
as
bricks,
tiles,
porcelain
account
for
more
than
70%
CDW.
Fourteen
samples
different
CDW
products
from
Ferrara
(Northeast
Italy)
were
subjected
to
geochemical
analyses,
including
leaching
tests,
in
accordance
with
UNI
EN
12457-2.
The
interaction
between
ceramics
concrete
was
examined,
highlighting
influence
mixed
environments
on
behavior.
Results
compared
an
extensive
database
150
collected
literature
types
worldwide.
Multivariate
statistical
analysis
machine
learning
used
classify
compositions
based
bulk
chemical
data.
Various
metrics-contaminant
factors
(Cf
Cd)
quotients
(HQ
HQm)-were
introduced
quantify
key
environmental
hazards
leachates.
results
this
study
underscore
proposed
approaches
automating
classification
predicting
Cf
HQ
using
only
starting
composition.
findings
enhance
management
practices
support
sustainability
efforts
industry.
