RECEPTOR MODELLING OF UK ATMOSPHERIC AEROSOL

1. RECEPTOR MODELLING OF UK ATMOSPHERIC AEROSOL Roy M. Harrison University of Birmingham and National Centre for Atmospheric Science

2. RECEPTOR MODELLING TECHNIQUES • 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

3. Major Component Composition of PM10 URBAN BACKGROUND RURAL ROADSIDE PM10 (BROS) PM10 (BCCS) PM10 (CPSS)

4. 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

5. 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)

6. 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

7. Source Contributions to OC at Urban Background Site

8. Source Contributions to OC at Rural Site

9. Relationship of “Other OC” from CMB Model with Secondary OC from Graphical Method (µg m-3) (µg m-3)

10. 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.

11. 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 brake wear resuspension tyre wear road surface wear

12. Median Concentration of PM10

13. 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

14. a Size Distribution of Ba, Cu, Fe, and Sb at (a) Marylebone Road and (b) Regent’s Park b

15. Single Particle Mass Spectrometry Aluminium Iron Barium Iron Oxide m/z Inorganic coarse particles rich in Fe and Ba, Specific fingerprint for Brake wear

16. 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.