RECEPTOR MODELLING OF UK ATMOSPHERIC AEROSOL

Roy M. HarrisonUniversity of Birmingham

and National Centre for Atmospheric Science

RECEPTOR MODELLING TECHNIQUES1. Multicomponent analysis in many samples followed by

factor analysis (usually PMF)- we have applied to PAH and to particle number size distributions

2. Use of chemical tracers, including organic molecular markers and Chemical Mass Balance modelling- we have applied to urban and rural PM2.5

3. Targeted studies- e.g. work on brake dust particles

4. Aerosol mass spectrometry

ROADSIDEURBAN

BACKGROUND RURAL

PM10

(BROS)PM10

(BCCS)PM10

(CPSS)

Major Component Composition of PM10

Receptor Modelling Using Organic Molecular Source Tracers

• Uses approaches developed in California and mostly US source profiles

• Considers atmospheric PM chemical composition to be a linear sum of relevant source emission profiles (Chemical Mass Balance model)

• Two sites: Urban background Rural

Chemical Mass Balance Study using Molecular Markers

• PM2.5 samples were collected and analysed for

n-alkanes from C24 – C36

9 specific hopanes 13 PAH 14 carboxylic acids levoglucosan cholesterol inorganic marker elements (Si, Al)

CMB Model Results

• Model used to apportion sources of organic carbon to:

diesel engine exhaust gasoline engines smoking gasoline engines vegetative detritus dust and soil wood smoke coal combustion natural gas combustion

Source Contributions to OC at Urban Background Site

Summer Winter Annual0.0

0.7

1.4

2.1

2.8

3.5

EROS

Other OC

Dust/Soil

Coal

Smoking Engines

Gasoline Engines

Diesel Engines

Natural Gas

Woodsmoke

VegetationOC

con

trib

uti

ons

( m

g m

-3)

Source Contributions to OC at Rural Site

Summer Winter Annual0.0

0.5

1.0

1.5

2.0

2.5

3.0

CPSS

Other OC

Dust/Soil

Coal

Smoking Engines

Gasoline Engines

Diesel Engines

Natural Gas

Woodsmoke

Vegetation

OC

con

trib

uti

ons

( m

g m

-3)

EROS

y = 1.01x + 0.34

R2 = 0.92

0.0

2.0

4.0

6.0

0.0 1.0 2.0 3.0 4.0 5.0

Other OC

Sec

OC

Relationship of “Other OC” from CMB Model with Secondary OC from Graphical Method

(µg m-3)

(µg

m-3)

Main Conclusions from CMB Model

• Road traffic contribution to primary OC is dominant.

• Split between diesel, gasoline and gasoline smoker emissions requires further study.

• Vegetative detritus is significant at the rural site.

• Small contributions from coal and natural gas combustion, very small from meat cooking.

• “Other” OC correlates highly with secondary OC estimated by the method of Castro et al. (1999).

• Wood smoke contribution is small, but studies at other sites using a multi-wavelength aethalometer show substantial concentrations.

Sources of particles from a vehicle

Emissions dependent upon• vehicle speed (resuspension, tyre and road surface wear)• engine revs and load (exhaust)• driving mode (exhaust, brake, tyre, road surface)• materials (brakes, tyres, road surface)• fuel and lubricant (exhaust)• vehicle weight and aerodynamics (resuspension)• road surface silt loading (resuspension)

exhaust

resuspension

tyre wear

road surface wear

brake wear

Median Concentrationof PM10

Studies of Non-Exhaust Particles at Marylebone Road – Chemical Composition

as a Tracer

Ba and Cu are clear tracers of brake wear particles

Al appears most plausible tracer for resuspension, but this appears difficult

Tyre wear remains a problem

Size Distribution ofBa, Cu, Fe, and Sbat (a) Marylebone Roadand (b) Regent’s Park

a

b

Inorganic coarse particles rich in Fe and Ba, Specific fingerprint for Brake wear

154

13863

5656392723

88

72

43

60

35

32

2617

19

0 50 100 150 200 250

Da = 1.25 µm

m/z

Barium

Aluminium

Iron

Iron Oxide

Single Particle Mass Spectrometry

CONCLUSIONS• Receptor modelling techniques are a blunt tool but nonetheless

can identify components which emissions inventories are poor at quantifying.

• To a large extent receptor modelling techniques (especially CMB) will only find what you tell them to look for.

• There is much scope for extending receptor modelling methods to reveal more, especially by exploiting newer techniques (e.g. high resolution aerosol mass spectrometry) and by using techniques in combination. This will be expensive.

ACKNOWLEDGEMENTS – to collaborators who collected the data,especially Dr Jianxin Yin, Dr David Beddows and Dr Johanna Gietl.