Recent progress and future prospects for traceable submicron aerosol measurements

Transcription

1 Recent progress and future prospects for traceable submicron aerosol measurements 9th June 2010 Richard Gilham

2 Summary Requirements for traceability Application to submicron aerosol measurements Current progress Future aspirations 2

3 Requirements for traceability metrological traceability (VIM) property of a measurement result whereby the result can be related to a reference through a documented unbroken chain of calibrations, each contributing to the measurement uncertainty SI Base Units Measurement Measurement Measurement Measurement Measurement 3

4 Requirements for traceability metrological traceability (VIM) property of a measurement result whereby the result can be related to a reference through a documented unbroken chain of calibrations, Comparability each contributing across time to the and measurement space uncertainty Reference to a common standard SI Base Units Firm basis for measurements with legal, financial or other implications Measurement Measurement Measurement Measurement Measurement 4

5 Requirements for traceability Rigorous measurements need: A measurand A value An uncertainty Traceability A definition of what we are measuring, hopefully defined by a well written ISO standard! Uses a defined procedure, preferably assessed and accredited to ISO or equivalent Achieved through a suitably designed and administered calibration procedure 5

6 Aerosol Measurement Measuring an ensemble of entities Measure abundance, or distribution of a property in the sample Abundance (concentration) Number Surface Area Mass Physical Attribute Size + = Shape Electrical charge Distribution Size Shape Charge 6

7 Abundance: Number Concentration Condensation Particle Counter (CPC) Heterogeneous nucleation & optical particle counting Aerosol Electrometer (AE) Filtration & electrical measurement Prospect for traceable calibration??? (we will assume we can calibrate flow rates) Unlike gas metrology, there are no particle abundance reference materials 7

8 CPC Transport efficiency? Nucleation efficiency? Counting efficiency? Friday, 16 July 2010 Optics Saturator Condenser 8

9 CPC Transport efficiency? Nucleation efficiency? Counting efficiency? Friday, 16 July 2010 Optics Detector Saturator Multiple particles in detection region at once Condenser Cannot detect 2nd particle until the first has cleared Frequency increases with concentration Time 9

10 AE Transport efficiency? Filtration efficiency? Particle charge? Electrometer calibration? Filter Electrometer 10

11 Size Distribution Utilise property that varies with particle size Electrical mobility Differential Mobility Analyser (DMA) Combine with particle detector to measure size distribution 11

12 DMA Z p = q c 4πVL ln r 4 r 1 Sample inlet Particle-free sheath air Friday, 16 July 2010 Calibration by characterising system parameters Comparison using reference materials 0 to 10 kv To CPC Axis of symmetry 12

13 Traceable measurement framework Non-aerosol measurements Aerosol Electrometer Differential Mobility Analyser Condensation Particle Counter More advanced aerosol measurements 13

14 Aerosol Electrometer- Calibration NPL Reference Low Current System Transfer Standard (Keithley 6430) Aerosol Electrometer- Direct Current Injection Zero Corrected AE Response (fa) IEEE Trans. Instrum. Meas., 2007, 56, (2), y = x R 2 = Reference Current (fa) 14

15 CPC- Calibration Soot Aerosol Generator Charge Neutraliser Differential Mobility Analyser Reference Electrometer Test Instrument Data Analysis Procedure 1.2E+04 FCE Response (cm -3 ) 1.0E E E E+03 y = x R 2 = E Particle Diameter (nm) Particle Diameter (nm) 0.0E E E E E E E E+04 CPC Response (cm -3 ) 15

16 DMA- Size Calibration Transfer Function DMA transfer function Centroid mobility/size Width Complete profile Electrical Mobility Analytical solution (Knutson & Whitby; Stoltzenburg) Numerical solution (Hagwood) Reference material (PSL) Tandem DMA Experiment & Monte Carlo 16

17 DMA- MCDMA Estimate uncertainty in centroid mobility & size Monte Carlo uncertainty estimation V; u(v) Flow; u(flow) L,r 1,r 4 ; u(l,r 1,r 4 ) Monte Carlo Slip Corr; u(slip Corr) Z p ; u(z p ) D p ; u(d p ) T,p; u(t,p) JCGM101:2008 Evaluation of measurement data - Supplement 1 to the Guide to the expression of uncertainty in measurement - Propagation of distributions using a Monte Carlo method 17

18 DMA- MCDMA Particle Size (nm) For TSI 3081 DMA running at 15 lpm & 1000V ~2% uncertainty (k=2) in absolute diameter <20% of non-diffusing transfer function width ~1/2 SMPS size bin (64 channels/decade) 18

19 DMA- Trajectory Analysis Using either plug or parabolic flow does not affect the nondiffusion transfer function DMA residence time is affected by the flow model, even when ignoring diffusion Frequency Denisty DMA Residence Time (s) Next steps: Combine with MCDMA and include diffusion: Complete time-dependent DMA simulation Real flow and electric fields 19

20 Size Distribution Measurement- SMPS EURAMET 1027 Field Measurements North Kensington :1 SMPS (particles / cm 3 ) CPC (particles / cm 3 ) Same DMA (TSI 3081) Different CPCs (TSI- various) Different flow rates Same software (TSI- AIM) Size OK; concentration poor 20

21 Transfer function: describes the narrow size distribution actually selected at each nominal particle size for a given DMA geometry. Ω 1 q + s 1 2 qa q s Min 1, q a q + s q a 2π Raw data: Particles counted at each DMA voltage Detector efficiency Friday, correction: 16 July 2010 Detectors are less efficient at small sizes. Detection Efficiency (%) 120% 100% 80% 60% 40% 20% Detection Efficiency Curve for the TSI 3022A CPC 0% Particle Diameter (nm) q + 4π c q m q + 2π a q s Charge distribution: describes the fraction of particles at each size carrying n elementary Approximation on Fuchs charge distribution: charges. J Aero Sci, Vol. 19, pp (1988) N = -2 N = -1 N = +1 N = +2 Z p φ ( r, z ) Instrument response function: expected response at a given DMA voltage Data inversion Diffusion loss correction: Smaller particles are more readily lost due to diffusion. Penetration Efficiency (%) 100% 80% 60% 40% SMPS Correction due to Diffusion Losses % Charging Probability Provisional particle size distribution 0% Particle Diameter (nm) Diameter (nm) Particle flight time: allows for the flight time of the particle through the DMA and to the detector. Conversion between particle size, electrical mobility: Z ( d ) = p. e 3πη. d S c Final particle size distribution 21

22 Size Distribution Measurement- SMPS Instrument hardware performs well CPC calibration vs electrometer DMA characterisation Disagreement between instruments and methods caused by available software Main culprits transfer function and flight time? Research and intercomparisons needed! 22

23 Surface Area Measurement of great interest 3-D analogue of the coastline paradox Adsorb species onto surface Count the number adsorbates Measure a property affected by the adsorbates 27 units 30 units 1 unit ruler 2 unit ruler 23

24 Surface Area Large number of weakly interacting adsorbates ( short ruler ) Hard to detect Neutral Species Surface Area Small number of strongly interacting adsorbates ( long ruler ) Easy to detect Charged Species Gas titration Radioactive species BET (bulk) Epiphaniometer Diffusion Charging Differential Mobility Comparability between techniques? 24

25 Diffusion Charging vs. Differential Mobility Aerosol Generator Charge Neutraliser Differential Mobility Analyser Diffusion Charger Differential Mobility Size Spectrometer Friday, 16 July Soot PSL Day 1 PSL Day 2 Integrate size distribution to obtain surface area DC/SMPS Particle Diameter (nm) 25

26 SMPS vs BET Fluidised bed aerosol generator CPC SMPS TEOM Bulk powder BET measurement (ZnO) Specific Surface Area (m 2 g -1 ) 26

27 6000 SMPS vs BET nm SMPS- lognormal fit and concentration correction dn/dlogdp GSD 1.53 ~50 hours continuous data Diameter (nm) 9.00E E+01 Aerosol SSA (m 2 g -1 ) BET SSA (m 2 g -1 ) /[W((Po/P)-1)] 7.00E E E E E E E E E E E E E E E E-01 Relative Pressure P/P0 27

28 Conclusions Non-aerosol measurements Friday, 16 July 2010 Aerosol Electrometer Differential Mobility Analyser Metrology for CPC and DMA progressing well CPC ripe for key comparison More work required for size distribution Integrated SMPS CPC Probably software Traceable aerosol surface area measurements some way off. Shape? Chemistry? Condensation Particle Counter More advanced aerosol measurements 28

29 Acknowledgements Co-workers Paul Quincey, Neil Harrison, Jordan Tompkins, Martin Milton, Ratna Tantra, Stephen Giblin, David Butterfield, Sonya Beccaceci Funding 29

30 Friday, 16 July 2010 Thank you 30