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Different measures to reduce PM10 concentrations –a model sensitivity analysisGunnar Omstedt, SMHI

•Introduction•model description•model sensitivity analysis -reducing studded tyres, sanding, street sweeping•conclusions

2000 2001 2002 2003 2004 2005 2006 2007 2008

År

0

40

80

120

160

PM

10

[µg

m-3

]

Stockholm, PM10

Hornsgatan (dygn)

Hornsgatan (m ånad)

Urban bakgrund

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Model description

)(** int, dustelfe erwrefPMfq

exhaustnonf

see earlier description for more details

ll SiSot

l

)(

gg SiSot

g

)(

fq is the source strength reduction function due to the moisture content, g, of the road (0-1) l is the amount of dust on the road (0-1)

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Sensitivity analysis

Case Description 1 baseline (studded tyres 75% and sanding) 2 baseline no sanding 3 keeping the road surface wet for 1 week 4 street sweeping March 1 5 street sweeping April 15 6 street sweeping March 1 and no sanding after that date 7 max studded tyres 50% + sanding 8 max studded tyres 25% + sanding 8 no studded tyres + sanding 10 max studded tyres 50% , no sanding 11 max studded tyres 25% , no sanding 12 no studded tyres , no sanding

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Sensitivity analysis

baselin

e

baselin

e no s

and

0

50

100

150

200

250

emis

sio

nfac

tor

(mg/

vkm

)no

n-e

xhau

sed

PM

10

baselin

ebase

line n

o sand

0

10

20

30

40

50

60

70

80

PM

10 (

µg/

m3)

M od e l se n sitiv ity 9 0 - p e rce n ti le s (d a ily m e a n )

EU d irective

Sw edish ob jective The effect of not using sand in the model for this case decrease the emission factor with about 45 mg/vkm and the PM10 concentration (90-p daily mean) with about 14 µg/m3

baseline: Hornsgatan 2000

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Sensitivity analysis

daily mean PM10 concentrations µg/m3

Case 3: keeping the road surface wet for 1 week

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Street sweeping

Why is it so difficult to clean streets?

PM10 Jagtvej year 2003PM10 Jagtvej year 2003

Ketzel, M., et al., 2007: Estimation and validation of PM2.5/PM10 exhaust and non-exhaust emissionfactorsfor practical street pollution modelling. Atmospheric Environment 41, 9370-9385.

kmvehmgeee exhaustnonf

exhaustf

totalf /129

Production and removal of road dust particles are in balance related directly to the traffic. After street sweeping a new balance will arise.

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Street sweeping

but how about Nordic winter conditions with strong seasonal variations in concentrations?

l is the baseline

1-Jan 16-Jan 31-Jan 15-Feb 1-M ar 16-M ar 31-M ar 15-Apr 30-Apr

0

0.2

0.4

0.6

0.8

1

dust

laye

r p

ara

me

ters

D u stla ye r PM 1 0lwlsl

1 M arch street sw eeping

case 4: street sweeping 1 March

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Street sweeping

so the timing is important!

case 5: street sweeping 15 April

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Street sweeping

1-Jan 16-Jan 31-Jan 15-Feb 1-M ar 16-M ar 31-M ar 15-Apr 30-Apr

0

0.2

0.4

0.6

0.8

1

dust

laye

r p

ara

me

ters

D u stla ye r PM 1 0lwlsl

1M ars street sw eepingand no sand ing

Case 6: street sweeping 1 March and no sanding after that

studded tyres is more important than sand

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Studded tyres

exhaustnonPM

exhaustPM

totalPM eee

externalPM

wearvehiclePM

wearroadPM

exhaustnonPM eeee

wearroadstudnoPM

wearroadPM estudae ,*

Emissionsfactors for PM10

[mg/vfkm]

wearvehiclePM10e 10

externalPM10e 0

wearroadstud noPM10,e 49

Norman, M., Johansson, C., 2006Atmospheric Environment 40, 6154-6164.

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Studded tyres

1-Jan 16-Jan 31-Jan 15-Feb 1-M ar 16-M ar 31-M ar 15-Apr 30-Apr

0

0.2

0.4

0.6

0.8

1

dust

laye

r p

ara

me

ters

D u stla ye r PM 1 0lwlsl

l m axstud=50%

Case 7: max stud=50% with sand

12-Feb 27-Feb 13-M ar 28-M ar 12-Apr 27-Apr 12-M ay

0

20

40

60

80

100

120

140

160

180

PM

10

stre

et (µ

g/m

3 )

re d u ctio n o f stu d d e dtyre s a n d sa n d in g

baseline

m ax stud 50%w ith sand ing

no stud no sand ing

Cases: 7 and 12

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Summary of results

baselin

e

baselin

e no s

and

stre

et wet 1

week

1 Marc

h stre

et sweep in

g +sa

nd

15 April

stre

e t sweepin

g + s

and

1 Marc

h stre

et sweepin

g and n

o sand a

fter t

ha t date

max

studded ty

res

50% +

sand

max

studded ty

res

25%+ s

and

no stu

dded tyre

s +sa

nd

max

studded ty

res

50% n

o sand

max

studded ty

res

25% n

o sand

no stu

dded tyre

s no s

and

0

50

100

150

200

250

em

issi

on

fact

or

(mg

/vkm

)n

on

-exh

ause

d P

M1

0

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Summary of results

baselin

e

baselin

e no s

and

stre

et wet 1

week

1 Marc

h stre

et sweepin

g +sa

nd

15 April

stre

e t sweepin

g + s

and

1 Marc

h stre

et sweepin

g and n

o sand a

fter t

hat date

max

studded ty

res

50% +

sand

max

studded ty

res

25%+ s

and

no stu

dded tyre

s +sa

nd

max

studded ty

res

50% n

o sand

max

studded ty

res

25% n

o sand

no stu

dded tyre

s no s

and

0

10

20

30

40

50

60

70

80

PM

10 (

µg/

m3)

M o d e l se n sitiv ity 9 0 - p e rce n tile s (d a ily m e a n )

EU d irective

Sw edish ob jective

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Conclusions

The model seems to response qualitatively rather realistic to different measures for reducing PM10 concentrations

Studded tyres is the most important parameter Sanding can increase the emissions Street sweeping can decrease emissions but the effectiveness is strongly

dependent of timing Street sweeping, doing it at the right time, can be as effective as not using sand A combination of different measures will probably be the best solution

This is only a model sensitivity study so the conclusions are first of all related to the model!

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Applications of the models: discussion on the suitability of the models for assessment/ planning/ management

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PM10 90-percentil> 50 µg/m3

40-50 µg/m3

35-40 µg/m3

30-35 µg/m3

<35 µg/m3

Norrköpings kommunMiljö- och hälsoskyddskontoretRobert Sandsveden

Åtgärdsprogram-PM10http://www.norrkoping.se/trafik/partikelhalter/

Norrköping nuläge

Example

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Norrköping scenario 2015 med ökad trafikgenomförda åtgärder

PM10 90-percentil> 50 µg/m3

40-50 µg/m3

35-40 µg/m3

30-35 µg/m3

<35 µg/m3

Norrköpings kommunMiljö- och hälsoskyddskontoretRobert Sandsveden

TrafikuppräkningDammbindnig med CMA, tidigare samt upprepad vårrengörning, Fysiska åtgärder hamnbron, SjötullsgatanSöderleden+ ny NorrledBeteendeändringar: minskad dubbdäck,mer kollektiv trafik

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Type of stations 19 streets and close to roads stations 21 urban background stations

Andersson, S. och Omstedt, G., 2009: Validering av SIMAIR mot mätningar av PM10, NO2 och bensen. Utvärdering för svenska tätorter och trafikmiljöer avseende år 2004 och 2005. SMHI Meteorologi, Nr. 137.

Model validation of SIMAIRroad

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Results- streets and roads

The uncertainty of modelling estimation is defined as the maximum deviation between the measured and calculatedconcentration levels for 90 % ofindividual monitoring points, without taking into account the timing of the events. The average annual modelling uncertaintyfor PM10 is defined as ±50%

Comparison with EU Air Quality Directive targets

MRPE annual mean=0.38 ; MRDE annual mean=0.24MRPE 90-percentile =0.42

p

pp

O

MORPEMRPE

max)max(

Fairmode http://fairmode.ew.eea.europa.eu/

LV

MORDEMRDE LVLV

max)max(

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Uncertainties in models

SIMAIR: MRPE annual mean=0.38 and MRPE 90-percentile =0.42 this means that the model is OK ?, but how should we communicate such results?

Example: calculated yearly mean PM10 concentration is 25 µg/m3 and calculated 90-percentil is 40 µg/m3 then the uncertainties is:

Yearly mean: 25 +/-9.5 µg/m3 i.e. between 15.5-34.5 µg/m3

90-percentil(daily mean): 40 +/- 16.8 µg/m3 i.e. between 23.2-56.8 µg/m3

Fairmode http://fairmode.ew.eea.europa.eu/

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Improvements/ decreasing uncertainties

inputdata

traffic, geometric configurations, uncertain information about studded tyres, sanding/salting, cleaning etc., background concentrations, meteorological data

model description

simple dispersion concept such as street canyon and open roads

meteorological uncertainties

measurements: representatives, errors