Statistical analysis of time series : application for the assessement of occupational exposure to nanomaterials

1. EXPLORATORY STUDY ON A STATISTICAL METHOD
TO ANALYSE TIME RESOLVED DATA OBTAINED
DURING NANOMATERIAL EXPOSURE
MEASUREMENTS
F. Clerc, O. Witschger
INRS - Pollutant Metrology Department

2. .2
INRS: French Occupational Safety and Health Institute
• Non profit organization funded by Health
Insurance
• 2 localizations in France : Paris (200
people) and Nancy (380 people)
• 4 missions
 Information : publication of flyers, multimedia,
journals…
 Training
 Assistance : helping French enterprises for
occupational safety
 Studies and Research

3. .3
• Assessing the exposures to nano particles
• The measurement devices are often not selective
 How to make the difference between potentially
harmful emitted particules and environmental particles?
• Measurement devices often produce
time resolved data

4. .4
• Academic research lab on coatings
• Lab-scale reactor (cold plasma-
deposition)
• Thin films (~150 nm) containing silver
nanoparticles in a polymeric matrix
(SiCxOyHz).
• Task: cleaning done manually every
day using a flexible abrasive (sand
paper)
Evaluating exposure of worker to nano particles during
specific tasks

5. .5
• Room located in the basement
 No ventilation
• Two measurement devices : nano particles counters
 Located at the source of emission
 Located at far field from emission
• 68 minutes operation
 No activity
 5 different tasks : A to F
> Different parts of reactor
source
far field

6. .6
2000
2500
3000
3500
4000
4500
5000
source
far field
A B C D E F
Nbofparticles
time (s)
noactivity
►Computing time series of emitted particles
►Issue : taking into account the background aerosol

7. .7
2000
2500
3000
3500
4000
4500
5000
source
far field
A B C D E F
Nbofparticles
time (s)
noactivity
►Computing time series of emitted particles
►Issue : taking into account the background aerosol
• Hypothesis
• Same background aerosol
 Far field and source
 Systematic difference measured because of devices
capabilities
• The source device also record the aerosol emitted
by the tasks.
• Same aerosol behaviour
 around both devices
 emitted particles remain near the source

8. .8
• Computing the time series of emitted particles : 4 steps
 1 - Estimating the systematic difference
2000
2500
3000
3500
4000
4500
5000
175
290
475
590
770
890
1175
1250
1370
1490
1610
1730
1850
2030
2150
2270
2390
2690
2870
2990
3110
3410
3530
3650
3770
3890
source
far field
A B C D E F
Nbofparticles
time (s)
noactivity

9. .9
• Computing the time series of emitted particles : 4 steps
 1 - Estimating the systematic difference
 2 - Removing it from the source time series
2000
2500
3000
3500
4000
4500
5000
175
290
475
590
770
890
1175
1250
1370
1490
1610
1730
1850
2030
2150
2270
2390
2690
2870
2990
3110
3410
3530
3650
3770
3890
far field
A B C D E F
Nbofparticles
time (s)
noactivity
source
: shift curve upwards

10. .10
• Computing the time series of emitted particles : 4 steps
 1 - Estimating the systematic difference
 2 - Removing it from the source time series : shift curve upwards
 3 - Removing the background : time series of emitted particles
-400
-200
0
200
400
600
800
175
290
475
590
770
890
1175
1250
1370
1490
1610
1730
1850
2030
2150
2270
2390
2690
2870
2990
3110
3410
3530
3650
3770
3890
Nbofparticles
A B C D E F
time (s)
noactivity

11. .11
• Computing the time series of emitted particles : 4 steps
 1 - Estimating the systematic difference
 2 - Removing it from the source time series : shift curve upwards
 3 - Removing the background : time series of emitted particles
 4 - Quantifying the emissions related with each task
-400
-200
0
200
400
600
800
175
290
475
590
770
890
1175
1250
1370
1490
1610
1730
1850
2030
2150
2270
2390
2690
2870
2990
3110
3410
3530
3650
3770
3890
Nbofparticles
A B C D E F
time (s)
noactivity

12. .12
2000
2500
3000
3500
4000
4500
5000
175
290
475
590
770
890
1175
1250
1370
1490
1610
1730
1850
2030
2150
2270
2390
2690
2870
2990
3110
3410
3530
3650
3770
3890
source
far field
• Systematic difference = Probabilistic difference between distributions
• Bayesian network
 Monte Carlo sampling
 Bayesian inference
• Estimating the systematic difference
A B C D E F
Nbofparticles
time (s)
noactivity
source
Mean: 3718.000 Dev: 43.116
Value: 3719.000
50.00% <=3705
25.00% <=3757
25.00% >3757
far field
Mean: 4070.000 Dev: 74.000
Value: 4077.000
50.00% <=4033
-0.00% <=4107
50.00% >4107
no activity
Mean: 348.022 Dev: 96.764
Value: 348.022
-0.00% <=-360
-0.00% <=-220
-0.00% <=-80
-0.00% <=60
4.09% <=200
43.55% <=340
44.90% <=480
7.46% <=620
-0.00% <=760
-0.00% >760
Systematic difference
(mean = 348 particles more at far field )

13. .13
2000
2500
3000
3500
4000
4500
5000
175
290
475
590
770
890
1175
1250
1370
1490
1610
1730
1850
2030
2150
2270
2390
2690
2870
2990
3110
3410
3530
3650
3770
3890
far field
A B C D E F
Nbofparticles
time (s)
noactivity
source

14. .14
2000
2500
3000
3500
4000
4500
5000
175
290
475
590
770
890
1175
1250
1370
1490
1610
1730
1850
2030
2150
2270
2390
2690
2870
2990
3110
3410
3530
3650
3770
3890
far field
A B C D E F
Nbofparticles
time (s)
noactivity
source-400
-200
0
200
400
600
800
175
290
475
590
770
890
1175
1250
1370
1490
1610
1730
1850
2030
2150
2270
2390
2690
2870
2990
3110
3410
3530
3650
3770
3890

15. .15
> Compute 200 samples out of
“no activity” distribution
> Calculate point to point
difference D between
“source” and “far field”
> For each time step, compute
200 possibilities (D + sample)
Algorithm
For each time step t
For i=1 to 200
Sample di out of “no activity” distribution
Ci(t) = Csource(t) – Cfar field(t) + di
End for
End for

16. .16
-400
-200
0
200
400
600
800
175 890 1610 2270 3110 3890
Nbofparticles
A B C D E F
time (s)
noactivity
Task A
Mean: 459.073 Dev: 217.886
Value: 459.073
0.00% <=-610
0.00% <=-420
0.00% <=-230
0.54% <=-40
6.93% <=150
22.60% <=340
31.85% <=530
26.32% <=720
10.86% <=910
0.91% >910
Task B
Mean: 539.720 Dev: 145.505
Value: 539.720
0.00% <=-610
0.00% <=-420
0.00% <=-230
0.00% <=-40
0.24% <=150
8.20% <=340
35.80% <=530
47.73% <=720
8.04% <=910
0.00% >910
Task C
Mean: 244.126 Dev: 170.987
Value: 244.126
0.00% <=-610
0.00% <=-420
0.03% <=-230
3.87% <=-40
25.04% <=150
42.54% <=340
24.69% <=530
3.80% <=720
0.04% <=910
0.00% >910
Task D
Mean: 49.768 Dev: 249.684
Value: 49.768
0.10% <=-610
2.36% <=-420
11.69% <=-230
22.29% <=-40
26.95% <=150
24.30% <=340
10.85% <=530
1.44% <=720
0.02% <=910
0.00% >910
Task E
Mean: 23.257 Dev: 163.586
Value: 23.257
0.00% <=-610
0.04% <=-420
4.73% <=-230
30.26% <=-40
43.88% <=150
19.00% <=340
2.08% <=530
0.00% <=720
0.00% <=910
0.00% >910
Task F
Mean: -25.536 Dev: 192.620
Value: -25.536
0.00% <=-610
0.63% <=-420
13.03% <=-230
35.81% <=-40
32.05% <=150
15.62% <=340
2.86% <=530
0.01% <=720
0.00% <=910
0.00% >910
Task A
Mean: 459.073 Dev: 217.886
Value: 459.073
0.00% <=-610
0.00% <=-420
0.00% <=-230
0.54% <=-40
6.93% <=150
22.60% <=340
31.85% <=530
26.32% <=720
10.86% <=910
0.91% >910
Task B
Mean: 539.720 Dev: 145.505
Value: 539.720
0.00% <=-610
0.00% <=-420
0.00% <=-230
0.00% <=-40
0.24% <=150
8.20% <=340
35.80% <=530
47.73% <=720
8.04% <=910
0.00% >910
Task C
Mean: 244.126 Dev: 170.987
Value: 244.126
0.00% <=-610
0.00% <=-420
0.03% <=-230
3.87% <=-40
25.04% <=150
42.54% <=340
24.69% <=530
3.80% <=720
0.04% <=910
0.00% >910

17. .17
• Discussion
 A study not designed for statistical tests
 No information about silver itself
 “Easy” methodology :
> no specific tool (Excel)
> no specific expertise
> Graphical view
• Conclusion : Recommendations for IH
 several devices : same devices, same sampling frequency
 No artificial cutoffs during sampling
 Long time sampling : open to other statistical methods

18. Our job: making yours safer
Thanks for your attention