1 1 H U Dr Chris Stopford Prof. Paul Kaye Dr Edwin Hirst Dr Richard Greenaway Dr Warren Stanley

2 The University of Hertfordshire  Located 30 minutes north of London  25,000 students, 3000 staff  History of engineering and scientific research  Post-1992 university  Top 100 Universities under 50 in the world (according to the Times Higher)

3 Particle Instruments Research Group  Latest is the AIITS instrument, built for NASA’s Global Hawk atmospheric research UAV. The first UK-built instrument to fly on that aircraft.

4 The PI group has developed particle characterization technology in areas where the detection of specific hazardous airborne particles is essential. For example, in the detection of:  Volcanic ash  Airborne bio-organisms (spores, bacteria, Legionella, etc.)  Respirable Crystalline Silica (Fracking sand)  Low-cost PM measurement  Airborne asbestos fibres Hazardous Particle Detection

5 Recent Recent Sponsors UK Research Councils Met Office Home Office NASA NCAR (National Center for Atmospheric Research), USA US National Science Foundation German Institute for Meteorology Leipzig Institute for Tropospheric Research Canadian Met Service Japan Agency for Marine-Earth Science and Technology, Tokyo Universities and companies in UK, Europe, and the USA Defence Science and Technology Laboratory

6 Pioneered by University of Hertfordshire in the 1990’s... When a particle is illuminated with a beam of light, it will scatter the light in a pattern dependent on the particle’s size, shape, and structure. Particle inlet Scattering pattern detector Optical assembly Particles pass through beam in single file and scatter light Outflow Laser beam Background Research - Spatial light scattering This ‘scattering pattern’ is like a ‘thumbprint’ by which the particle may be classified or identified.

7 Ambient air scattering patterns The movie shows scattering patterns recorded using a lab system with an intensified CCD camera as the detector. Each image is the pattern produced by a single particle passing through the laser beam. Although the patterns appear rapidly, the speed of capture (~30 per second) is slow.

8 Examples of particle classification NaCl crystal (~3um) Water droplet (~4um) Hematite ellipsoid (~2um x 1um) Copper flake (~2um) NaCl crystal (~3um) Irregular cubic particle (~4um) Irregular cubic particle (~1.5um) Crocidolite asbestos fibre (~5um x 0.5um) 15um water droplet with oleic acid inclusion Silicon carbide fibre cluster Previous UH research has investigated patterns from known particle types to aid the identification of ‘unknown’ particles.

9 It turns out that fibre particles, such as asbestos, have very characteristic scattering patterns:- Background particles Crocidolite fibres Chrysotile fibres Background Research – Asbestos detection

10 N S Asbestos – magnetic properties….. Asbestos is virtually unique amongst fibrous materials in having a weak magnetic property. On its own, asbestos is non-magnetic, but when placed in a magnetic field it tries to align with the magnetic field. (Some types of asbestos try to align at right-angles to the field). Timbrell V., Ann. Occup. Hyg., 18, , If the field is strong enough, an asbestos fibre in air can be made to rotate through ~30º in 1ms. We could use this to try to differentiate asbestos fibres from non-asbestos fibres.

11 Dual-beam system - more accurate rotation assessment x1x1 x2x2 Rotation of fibre is simply proportional to x 1 – x 2 N S

12 ALERT Dual-Laser system ALERT DL Make it smaller, add a second laser and some magnets…

13 Let’s have a demonstration…

14 Laboratory trials Asbestos containment Three-level containment facility…

15 Laboratory Results Two-beam Prototype Change in angle distribution of crocidolite with & without magnetic field Fiber angle change during transit between beams (º) If a fibre is rotated >20° is 36x more likely to be asbestos than non-asbestos fibre such as gypsum

16 Where have we used it so far?

17 Field Trials  ALERT prototype tested ‘back-to-back’ against standard statutory filter testing using phase contrast microscopy and x-ray analysis.  So far, 100% success rate in determining presence or absence of asbestos. 17

18 What comes next? We can detect fibres in the air We can tell if there’s asbestos in the air We can do this in real-time We can monitor Superfund sites We can automate the system We can alert the authorities to come and inspect the site

19 Thanks for listening! Any questions?