At the 2014 annual Dispersion Modellers user group meeting guest speaker Sean Beevers spoke on the topic: 'Update on progress with the development of a hybrid personal exposure model'
Call In girls Connaught Place (DELHI)⇛9711147426🔝Delhi NCR
Developing Hybrid Exposure Model in London
1. Developing
a
hybrid
exposure
model
in
London
Sean
Beevers
King’s
College
London
2. Traffic
Pollu8on
and
Health
in
London
project
http://www.erg.kcl.ac.uk/ResearchProjects/Traffic/Default.aspx?
DeptID=ResearchProjects&CategoryID=ResearchProjectsTraffic
3. WP2
modelling
Sources
Road
transport
Avia8on
sources
Passenger
and
commercial
shipping
Passenger
and
freight
rail
Large
regulated
industrial
processes
(Part
A)
Small
regulated
industrial
processes
(Part
B)
Gas
hea8ng
(domes8c
and
industrial-‐commercial)
Oil
combus8on
sources
(domes8c
and
large
boiler
plant),
Coal
combus8on
sources
(domes8c
and
commercial)
Construc8on
source
(NRMM,
construc8on/
demoli8on)
Others
(agricultural,
landfill,
waste
transfer,
accidental
fires
and
household
sources)
Biomass
burning
MRC-HPA Centre for Environment and Health
Imperial
College
London
Uses:
Conges8on
charging,
Low
Emissions
Zones,
Mayor's
Air
Quality
Strategy
(hMp://www.london.gov.uk/sites/default/files/Air%20Quality%20Strategy%20v3.pdf
)
Epidemiological
research
and
Health
Impact
assessments
London
air
quality
model
results
-‐
hMp://data.london.gov.uk/laei-‐2008
8. Coupled
local/regional
scale
modelling
(CMAQ-‐urban)
Model
outputs:
NOX,
NO2,
O3,
PM
components
Compu8ng
facili8es…..you
can’t
have
enough
Emissions
inventories
UK
Na8onal
Atmospheric
Emissions
Inventory
(NAEI)
King’s
Great
Britain
road
traffic
emissions
European
Monitoring
and
Evalua8on
Programme
(EMEP,
hMp://www.ceip.at/)
European
Pollutant
Release
and
Transfer
Register
(EPRTR)
Biogenic
Emission
Inventory
System
(BEIS
v3.14)
VOC
and
soil
NO
Eclipse
-‐
IIASA
Boundary
condi8ons
Beevers
SD
,
Kitwiroon
N,
Williams
ML,
Carslaw
DC.
2012.
One
way
coupling
of
CMAQ
and
a
road
source
dispersion
model
for
fine
scale
air
pollu8on
predic8ons.
Atmospheric
Environment
59,
pp
47-‐58
9. Hybrid
exposure
model
CMAQ-‐urban
air
quality
model
Micro-‐environmental
modelling:
in-‐vehicle
(bus,
car,
train,
tube),
cycle,
walk,
indoors
(I/O
exchange
-‐
J
Taylor
(UCL))
Oyster
card
data
London
Travel
Demand
Survey:
Trips
by
transport
mode:
Sex,
age,
gender
and
socio-‐economic
status
Detailed
human
exposure
J
Taylor
et
al.,
2014.
The
modifying
effect
of
the
building
envelope
on
popula8on
exposure
to
PM2.5
from
outdoor
sources.
Indoor
Air.
doi:10.1111/ina.
12116
10. The
London
Travel
Demand
Survey
(LTDS)
from
TfL
–
it’s
anonymised
Using the LTDS however, we have data on 85,000
people journeys for 24 hours
2005/2006 5008 households, 11583 people, 29,797 trips, 61,542 stages
2006/2007 8005 households, 18241 people, 47,029 trips, 95,930 stages
2007/2008 7873 households, 17926 people, 44,828 trips, 91,967 stages
2008/2009 8134 households, 18975 people, 43,076 trips, 89,701 stages
2009/2010 8290 households, 19187 people, 43,475 trips, 92,121 stages
2010/2011 Will get data soon
2011/2012 Will get data soon
Scaled up to the London population ~ 7 million people
11. MRC-HPA Centre for Environment and Health
Imperial College
London
London
journeys
12. Distance
travelled
by
popula8on
age
~
25%
of
people
stay
at
home
PM2.5
exposure
if
you
are
65
years
and
older
(most
important
mode)
Walk:
164,368,
Car:
123,043
Bus:
103,554,
Underground:
11,277
13. Geographic
dose
to
NOX
(top)
and
PM2.5
(boMom)
Static model Hybrid model
17. CMAQ-‐urban
model
evalua8on
No
sites
NO2
96
NOx
96
O3
41
PM10
106
PM2.5
68
SOA
~
1ug
m-‐3
(Derwent
and
c.f.
observa8ons)
Biomass
burning
~
1ug
m-‐3
(Fuller
2013/4)
Strongly
bound
water
-‐
(Franks
2006)
Fuller
GW
et
al,
2013.
Atmospheric
Environment
68,
295–296.
Fuller
GW
et
al,
2014.
Contribu8on
of
wood
burning
to
PM10
in
London.
Atmospheric
Environment
(accepted,
publica8on
pending).
Frank
N
2006
Retained
Nitrate,
Hydrated
Sulfates,
and
Carbonaceous
Mass
in
Federal
Reference
Method
Fine
Par8culate
MaMer
for
Six
Eastern
U.S.
Ci8es.
ISSN
1047-‐3289
J.
Air
&
Waste
Manage.
Assoc.
56:500–511
19. Uncertainty
Analysis
in
Atmospheric
modelling
Quan8fying
model
uncertainty
becomes
prohibi8ve
for
models
with
long
run
8mes.
Reduce
the
number
of
parameters
through
sensi8vity
analysis
(214
to
31)
Our
solu8on
is
to
build
an
‘emulator’
Model
input
Output
Training
run
Bayesian
Calibra?on
Each
Monte
Carlo
run
is
weighted
according
to
its
probability
of
corresponding
to
observa8onal
data
This
shivs
the
mean
of
the
uncertainty
distribu8on
towards
the
observed
value
and
reduces
its
standard
devia8on
20. The
future….
Indoor
model
-‐
important
addi8on
to
the
current
hybrid
model
Model
evalua?on
-‐
MRC
funded
project,
Characterising
COPD
Exacerba8ons
using
Environmental
Exposure
Modelling
(COPE)
project
Uncertainty
of
the
hybrid
approach
Develop
sources
within
the
model
that
are
currently
poorly
understood
-‐
non-‐exhaust
emissions,
biomass
burning,
cooking
etc
21. Thanks
for
your
aMen8on…
Thanks
to
colleagues
ERG
modelling:
NuMhida
Kitwiroon,
David
Dajnak,
Andrew
Beddows,
Chris8na
Mitsakou,
James
Smith,
Gregor
Stewart
and
Jonathon
Taylor
(UCL)
Transport
for
London
and
the
Greater
London
Authority
We
thank
the
Natural
Environment
Research
Council,
Medical
Research
Council,
Economic
and
Social
Research
Council,
Department
of
Environment,
Food
and
Rural
Affairs
and
Department
of
Health
for
the
funding
received
for
the
Traffic
Pollu8on
and
Health
in
London
project
(NE/I008039/1),
funded
through
the
Environmental
Exposures
and
Health
Ini8a8ve
(EEHI).
The
research
was
also
supported
by
the
Na8onal
Ins8tute
for
Health
Research
(NIHR)
Biomedical
Research
Centre
based
at
Guy’s
and
St
Thomas’
NHS
Founda8on
Trust
and
King’s
College
London.