Meta‐Analysis of Urban Non‐Point Source Pollution From Road and Roof Runoff Across China
Earth s Future,
Год журнала:
2025,
Номер
13(3)
Опубликована: Март 1, 2025
Abstract
Urban
non‐point
source
(NPS)
pollution
has
become
an
important
issue
affecting
water
quality,
but
current
research
focused
mainly
on
local
scales
and
lacked
systematic
evaluations
at
large
spatial
scales.
Here,
a
meta‐analysis
was
conducted
to
explore
the
characteristics
of
runoff
indicators
(TSS:
total
suspended
solids,
TN:
nitrogen,
TP:
phosphorus,
COD:
chemical
oxygen
demand)
roads
roofs
in
41
Chinese
cities,
boosted
regression
tree
model
used
reveal
geographical
differences
levels
contribution
rates
their
influencing
factors.
The
results
revealed
that
average
event
mean
concentrations
(EMCs)
TSS
(326
mg/L),
TP
(0.6
COD
(160
mg/L)
were
significantly
greater
road
than
roof
runoff.
Among
them,
nearly
four
times
those
runoff,
whereas
3.2
2.3
greater,
respectively.
NPS
is
severe
China,
pollutants
far
exceed
USA,
Germany,
France.
There
significant
urban
due
influences
air
quality
(35%
relative
contribution),
climate
conditions
(15%),
human
activities
(45%).
Prominent
from
observed
Central
region,
more
Northern
relatively
light
Southern
North‐Eastern
regions.
This
study
provides
first
synthesis
cities
scale,
resulting
scientific
guidance
for
stormwater
management
control.
Язык: Английский
Enhanced Accessibility to Park Cooling Services in Developed Areas: Experimental Insights on the Walkability in Large Urban Agglomerations
Building and Environment,
Год журнала:
2025,
Номер
272, С. 112665 - 112665
Опубликована: Фев. 4, 2025
Язык: Английский
Identifying the determinants of natural, anthropogenic factors and precursors on PM1 pollution in urban agglomerations in China: Insights from optimal parameter-based geographic detector and robust geographic weighted regression models
Environmental Research,
Год журнала:
2025,
Номер
unknown, С. 121817 - 121817
Опубликована: Май 1, 2025
Язык: Английский
Boosting Winter Green Travel: Prioritizing Built Environment Enhancements for Shared Bike Users Accessing Public Transit in the First/Last Mile Using Machine Learning and Grounded Theory
Sustainability,
Год журнала:
2024,
Номер
16(22), С. 9843 - 9843
Опубликована: Ноя. 12, 2024
Shared
bikes
are
widely
used
in
Chinese
cities
as
a
green
and
healthy
solution
to
address
the
First/Last
Mile
issue
public
transit
access.
However,
usage
declines
cold
regions
during
winter
due
harsh
weather
conditions.
While
climate
factors
cannot
be
changed,
enhancing
built
environment
can
promote
travel
even
winter.
This
study
uses
data
from
Shenyang,
China,
investigate
how
attributes
impact
satisfaction
of
shared
bike
users
who
utilize
access
cities.
By
employing
machine
learning
algorithms
combined
with
Asymmetric
Impact-Performance
Analysis
(AIPA)
grounded
theory,
we
systematically
identify
key
rank
them
based
on
their
asymmetric
urgency
improvement.
The
analysis
revealed
19
attributes,
17
which
related
environment,
underscoring
its
profound
influence
satisfaction.
Notably,
such
profile
design
cycling
paths
safety
facilities
along
routes
were
identified
high
priorities
for
improvement
significant
potential
enhance
Meanwhile,
features
like
barrier-free
street
greenery
offer
substantial
opportunities
more
modest
efforts.
Our
research
provides
critical
insights
into
nuanced
relationship
between
users.
highlighting
priority
improvements,
urban
planners
policymakers
framework
creating
livable,
sustainable
environments
that
support
Язык: Английский