1. RECEPTOR MODELLING OF AIRBORNE PARTICULATE MATTER
Roy M Harrison
Division of Environmental Health & Risk Management
The University of Birmingham
United Kingdom

2. RECEPTOR MODELLING OF PARTICULATE MATTER IN EUROPE BACKGROUND (1)
CAFE Position Paper reports only results of studies based upon major component composition
Historically there have been a number of studies which have used multi-elemental analyses and multivariate statistics to apportion PM mass to major source categories. These studies are mostly over five years old and the results reflect emissions at the time of air sampling

3. RECEPTOR MODELLING OF PARTICULATE MATTER IN EUROPE BACKGROUND (2)
The use of sophisticated receptor modelling methods is hampered by
- a lack of multi-element datasets of sufficient length
- adequate information on source profiles of inorganic constituents (for CMB)
- the absence of a road traffic marker since the removal of lead from gasoline
- a shortage of high quality EC/OC data
- a lack of adequate information on the abundance of organic tracers in relevant source material

4. EUROPEAN STRENGTHS AND WEAKNESSES
Extensive high quality measurements of physical properties of airborne particles
Good international collaboration in data assessment
Scarcity of detailed information on aerosol chemical composition

7. RECEPTOR MODELLING OF PARTICULATE MATTER IN THE UNITED KINGDOM (1)
You can only use the data that you have!
QUALITY OF URBAN AIR REVIEW GROUP (1996)
- estimated road traffic-derived particles from correlations with NOx and CO
- estimated secondary inorganic PM from mass of sulphate and estimated nitrate/ sulphate ratios
- assumed coarse particles (PM2.5 – 10) to be resuspended dust and sea salt

8. RECEPTOR MODELLING OF PARTICULATE MATTER IN THE UNITED KINGDOM (2)
THE AIRBORNE PARTICLES EXPERT GROUP (1999) developed a quantitative three component model
PM10 (TEOM) = a.BS + b.SO42- + c
where BS = black smoke
a and b are coefficients determined by regression;
c is an intercept representing “other”, mainly coarse particles
The model was subsequently refined (John Stedman, AEA Technology)
- to estimate a road traffic contribution from national maps of nitrogen oxides
- to include dispersion model results for emissions from non-traffic point and area sources

9. RECEPTOR MODELLING OF PARTICULATE MATTER IN THE UNITED KINGDOM (3)
The “pragmatic mass closure model” of Harrison et al (2003) offers greater refinement.
- nitrate is included as well as sulphate
- measurements of OC and EC allow estimation of primary and secondary organic carbon
- coarse dusts are quantified through analysis of Cl- (sea salt), Fe and Ca
- the method is directly applicable to manual gravimetric samples
Weaknesses include
- inability to apportion the primary carbonaceous component
- limited information on the sources of coarse “mineral” dusts

10. URBAN AIR QUALITY: PARTICULATE MATTER
Which sources are important?
Source Chemical tracer (bulk samples)
Local traffic exhaust elemental carbon and organic compounds
Long range transport
- SO2 oxidation sulphates
- NOx oxidation nitrates
- ammonia emissions ammonium
Soil and road dusts iron
Construction and demolition calcium
Sea salt sodium chloride

11. Adjustment Factors used in Mass Closure
* After subtraction of sulphate derived from gypsum (CaSO4.2H2O)
MISC48

12. Relationship of Reconstructed Mass and Gravimetric Particulate Matter Mass at the Roadside Sites
MISC49

13. Composition of PM in the Urban
Atmosphere(London and Birmingham )
All sites - Background PM10
Samples = 151; Mean = 24.7 µg m-3
ammonium
bound water
sulphate 8%
14%
dust (Fe)
19%
ammonium and
sodium nitrate
14%
calcium salts 5%
sodium chloride 8%
elemental carbon
organic
10%
compounds
22%

14. Composition of PM in the Urban
Atmosphere (London and Birmingham )
All sites - Roadside PM10
Samples = 101; Mean = 35.7 µg m-3
ammonium
bound water
sulphate 6%
10%
ammonium and
dust (Fe)
sodium nitrate
17% 9%
sodium chloride 5%
calcium salts 3%
organic
elemental carbon
compounds
27%
23%

15. Composition for Days with
Comparison of PM
Composition for Days with 10 -3 PM
> 50 µg m
with all Days: Urban background 10 ] 70 -3 60 50 40
Days PM10 > 50 µg m-3 [n = 10]
Mean Concentration [µg m 30
All days [n = 151] 20 10
Sodium
nitrate
Sodium
Bound
water
chloride
Calcium
sulphate
Iron rich
dust
Elemental
carbon
Mean mass
Organic
compounds
Ammonium
nitrate
Ammonium
sulphate

16. Composition for Days with
Comparison of PM
Composition for Days with 10 PM
> 50 µg m -3
with all Days: Urban background 10 ] -3 14
Days PM10 > 50 µg m-3 [n = 10] 12
All days [n = 151] 10 8
Mean Concentration [µg m 6 4 2
Sodium
nitrate
Bound
water
Sodium
chloride
Calcium
sulphate
Iron rich
dust
Elemental
carbon
Organic
compounds
Ammonium
nitrate
Ammonium
sulphate

17. RECEPTOR MODELLING IN THE UNITED KINGDOM
Clearly the eight-component model is not in itself
sufficient to allow prediction of future PM
concentrations.
Additional refinements should include:
Estimation of secondary organic carbon and modelling of source-receptor relationships for SOC
Estimation of contributions to primary EC and OC
Using dispersion modelling
Understanding more fully the sources of the “iron-rich” and “calcium –rich” dust components.
Modelling source-receptor relationships for sulphate and nitrate, including possible effects of changing ammonia emissions.

18. RECEPTOR MODELLING OF AIRBORNE PARTICULATE MATTER
Use of the TSI AEROSOL TIME-OF-FLIGHT MASS SPECTROMETER
Advantages
Comprehensive information on single particle composition
Disadvantages
May not ionise the entire particle
Illustrates the immense complexity of urban particles
Post-emission particles processing makes source recognition difficult
Some sources show immense diversity of composition when analysed as single particles

19. The ATOFMS (Aerosol Time-of-Flight Mass Spectrometer)
Laser based system.
Measures the size and composition
of individual particles.
Transportable.
Runs off 240VAC

20. The ATOFMS (Aerosol Time-of-Flight Mass Spectrometer)
Atmospheric
Particles
0.68m
Particle Detectors
measures the speed
and hence the size
of the individual particles.
Dual Ion Mass Spectrometer
measures the composition of
the individual particles.
+ ions
- ions
ZAP!

21. Example of ATOFMS Particle Characterisation Results (1)

22. Example of ATOFMS Particle Characterisation Results (2)

23. Example of ATOFMS Particle Characterisation Results (3)

24. SUMMARY AND CONCLUSIONS
Limited progress in source apportionment in the UK.
Receptor models based on major components.
Three component model now being superceded by eight component model.
Combination of receptor model and dispersion model more powerful.
Single particle mass spectrometry (ATOFMS) is powerful but poses series interpretational problems.