1. 1
Polluscope project from the sensors selection to their deployment during field campaigns involving volunteers
B. Languille1, V. Gros1, N. Bonnaire1, C. Pommier1, P. Masson1, C. Honoré2, C. Debert2, L. Gauvin2, S. Srairi3, B. Chaix4, I. Annesi-Maesano4, K. Zeitouni5 2 3 4 5

2. Outline Context and project presentation
Sensors selection and assessment
Feasibility campaign

3. Context and project presentation

4. Air quality monitoring in Île-de-France

5. Air quality monitoring in Île-de-France
Today: Airparif
70 static stations (PM, O3, NO2, benzene, etc.)
Very efficient temporal monitoring for outdoor air
Interpolation and modelling used for mapping and forecast air quality
Modelling NO2 map
Source: Airparif

6. Air quality monitoring in Île-de-France
Today: Airparif
But… limitations
70 static stations (PM, O3, NO2, benzene, etc.)
Very efficient temporal monitoring for outdoor air
Interpolation and modelling used for mapping and forecast air quality
Maps and daily reports: outdoor measurements only
Interpolation errors/lack of spatial gradient measurements
Etc.
 Personal exposure is not precisely estimated

7. Air quality monitoring in Île-de-France
Today: Airparif
But… limitations
70 static stations (PM, O3, NO2, benzene, etc.)
Very efficient temporal monitoring for outdoor air
Interpolation and modelling used for mapping and forecast air quality
Maps and daily reports: outdoor measurements only
Interpolation errors/lack of spatial gradient measurements
Etc.
 Personal exposure is not precisely estimated
Small sensors as a solution?
Small size and weight: wearable all day long
Low cost: possibility to buy several units and thus to monitor on a wide area
 Closer to “real” personal exposure?

8. Air quality monitoring in Île-de-France
Today: Airparif
But… limitations
70 static stations (PM, O3, NO2, benzene, etc.)
Very efficient temporal monitoring for outdoor air
Interpolation and modelling used for mapping and forecast air quality
Maps and daily reports: outdoor measurements only
Interpolation errors/lack of spatial gradient measurements
Etc.
 Personal exposure is not precisely estimated
Sensors drawback
Small sensors as a solution?
Not as accurate as reference instruments
Interferences with other pollutants or humidity have to be checked
 Need to strictly assess the sensors performances
Small size and weight: wearable all day long
Low cost: possibility to buy several units and thus to monitor on a wide area
 Closer to “real” personal exposure?

9. project presentation Funded project (2016 - 2021)
Main objective: design a participatory observatory of air quality
Campaigns:
80 volunteers wearing sensors
1 week for each volunteer
2 campaigns/year (first one in winter 2018)
Personal exposure measurement
Link between health and pollution levels
General public information
Source: Benh Lieu Song

10. project presentation Funded project (2016 - 2021)
Main objective: design a participatory observatory of air quality
Campaigns:
80 volunteers wearing sensors
1 week for each volunteer
2 campaigns/year (first one in winter 2018)
Personal exposure measurement
Link between health and pollution levels
General public information
Source: Benh Lieu Song
Metrology – Sensors selection and assessment

11. project presentation Funded project (2016 - 2021)
Main objective: design a participatory observatory of air quality
Campaigns:
80 volunteers wearing sensors
1 week for each volunteer
2 campaigns/year (first one in winter 2018)
Personal exposure measurement
Link between health and pollution levels
General public information
Source: Benh Lieu Song
Metrology – Sensors selection and assessment
Health – Health study conducted by experts

12. project presentation Funded project (2016 - 2021)
Main objective: design a participatory observatory of air quality
Campaigns:
80 volunteers wearing sensors
1 week for each volunteer
2 campaigns/year (first one in winter 2018)
Personal exposure measurement
Link between health and pollution levels
General public information
Source: Benh Lieu Song
Metrology – Sensors selection and assessment
Health – Health study conducted by experts
Data processing – Platform developed by IT engineers

13. project presentation Funded project (2016 - 2021)
Main objective: design a participatory observatory of air quality
Campaigns:
80 volunteers wearing sensors
1 week for each volunteer
2 campaigns/year (first one in winter 2018)
Personal exposure measurement
Link between health and pollution levels
General public information
Source: Benh Lieu Song
Metrology – Sensors selection and assessment
Health – Health study conducted by experts
Data processing – Platform developed by IT engineers

14. Sensors selection and assessment

15. Early stage (year 2017) Technical specifications requirements
Reliability
Weight (< 2 kg)
Price
Battery life (> 12 h)
Connectivity
Etc.
Bibliographic survey
Sensors discussed amongst hundreds
(Almost) commercially available only
8 tested sensors
7 measured pollutants
Particulate matter (PM10, PM2.5, PM1)
Ozone (O3)
Nitrogen oxides (NO, NO2)
VOC
Black carbon (BC)
Formaldehyde
Tested sensors

16. Sensors tests summary

17. Sensors tests summary

18. Sensors tests summary 3 selected sensors AE51 – BC Canarin – PM
Cairsens – NO2

19. selection step : 1 – Static measurements
Reference instruments: SIRTA station (part of the ACTRIS infrastructure)

20. selection step : 1 – Static measurements
Reference instruments: SIRTA station (part of the ACTRIS infrastructure)
Very accurate
 Retained for the next steps

21. selection step : 1 – Static measurements
Reference instruments: SIRTA station (part of the ACTRIS infrastructure)
Very accurate
 Retained for the next steps
Medium and inhomogeneous results
 Necessity to assess more robustly and objectively: SET

22. sensor evaluation toolkit (SET) results
 Designed by Fishbain et al., 2017
 Integrated performance index (0 < IPI < 1): average of 7 parameters
Sensor
Measure
Mean
Match
RMSE
Pearson
Kendall
Spearman
Presence
LFE
IPI A
BC (ng/m3)
1077
0,88
268,16
0,98
0,85
0,96
0,97
1,00
0,91 B
O3 (ppb) 8
0,30
15,20
0,70
0,60
0,80
0,75
0,46
NO2 (ppb) 11
5,33
0,94
0,72
0,89
0,78
0,76 C 20
0,35
13,24
0,04
0,08
0,12
0,42 D 47
0,24
36,72
0,54
0,73
0,67
0,56
PM10 (µg/m3)
104
0,43
111,81
0,18
0,20
0,26
0,81
0,99
0,40 E
535
0,37
1819,14
0,06
0,38
0,52
0,69
0,07 F 21
0,63
15,94
0,84
0,33
0,64
PM2.5 (µg/m3)
136
184,98
0,45
0,49 18
9,83
0,66
0,82
PM1 (µg/m3) 43
32,75
0,62
0,65 13
0,77
8,50 !
Sensor excluded

23. sensor evaluation toolkit (SET) results
 Designed by Fishbain et al., 2017
 Integrated performance index (0 < IPI < 1): average of 7 parameters
Sensor
Measure
Mean
Match
RMSE
Pearson
Kendall
Spearman
Presence
LFE
IPI A
BC (ng/m3)
1077
0,88
268,16
0,98
0,85
0,96
0,97
1,00
0,91 B
O3 (ppb) 8
0,30
15,20
0,70
0,60
0,80
0,75
0,46
NO2 (ppb) 11
5,33
0,94
0,72
0,89
0,78
0,76 C 20
0,35
13,24
0,04
0,08
0,12
0,42 D 47
0,24
36,72
0,54
0,73
0,67
0,56
PM10 (µg/m3)
104
0,43
111,81
0,18
0,20
0,26
0,81
0,99
0,40 E
535
0,37
1819,14
0,06
0,38
0,52
0,69
0,07 F 21
0,63
15,94
0,84
0,33
0,64
PM2.5 (µg/m3)
136
184,98
0,45
0,49 18
9,83
0,66
0,82
PM1 (µg/m3) 43
32,75
0,62
0,65 13
0,77
8,50 !
Sensor excluded
Robust and objective sensors assessment

24. sensor evaluation toolkit (SET) results
 Designed by Fishbain et al., 2017
 Integrated performance index (0 < IPI < 1): average of 7 parameters
Sensor
Measure
Mean
Match
RMSE
Pearson
Kendall
Spearman
Presence
LFE
IPI A
BC (ng/m3)
1077
0,88
268,16
0,98
0,85
0,96
0,97
1,00
0,91 B
O3 (ppb) 8
0,30
15,20
0,70
0,60
0,80
0,75
0,46
NO2 (ppb) 11
5,33
0,94
0,72
0,89
0,78
0,76 C 20
0,35
13,24
0,04
0,08
0,12
0,42 D 47
0,24
36,72
0,54
0,73
0,67
0,56
PM10 (µg/m3)
104
0,43
111,81
0,18
0,20
0,26
0,81
0,99
0,40 E
535
0,37
1819,14
0,06
0,38
0,52
0,69
0,07 F 21
0,63
15,94
0,84
0,33
0,64
PM2.5 (µg/m3)
136
184,98
0,45
0,49 18
9,83
0,66
0,82
PM1 (µg/m3) 43
32,75
0,62
0,65 13
0,77
8,50 !
Sensor excluded
Robust and objective sensors assessment
First estimation of the sensors quality

25. selection step 2 – Mobility tests
3 units of each sensor are worn by 3 people for 1 day
Route previously set: different environments, transient standing periods close to reference stations

26. selection step 2 – Mobility tests
3 units of each sensor are worn by 3 people for 1 day
Route previously set: different environments, transient standing periods close to reference stations

27. selection step 2 – Mobility tests
3 units of each sensor are worn by 3 people for 1 day
Route previously set: different environments, transient standing periods close to reference stations
Sensors C
Good reproducibility
Environmental changes monitored

28. selection step 2 – Mobility tests
3 units of each sensor are worn by 3 people for 1 day
Route previously set: different environments, transient standing periods close to reference stations
Sensors C
Good reproducibility
Environmental changes monitored
Sensors B
Poor reproducibility
Average-like values

29. selection step 2 – Mobility tests
3 units of each sensor are worn by 3 people for 1 day
Route previously set: different environments, transient standing periods close to reference stations
Sensors C
Good reproducibility
Environmental changes monitored
Sensors B
Poor reproducibility
Average-like values
Reference comparison
Good agreement and dynamic
Poor dynamic, large gap between units

30. Conclusion of the selection step
Selection: 2 kinds of tests
1 – Static measurements
3 – Mobility tests
3 selected sensors
AE51 – black carbon
Canarin – PM10, PM2.5, PM1
Cairsens – NO2
Pant et al., 2017
Ezani et al., 2018
Velasco & Tan, 2016
New prototype
Jiao et al., 2016
Spinelle et al., 2013

31. Conclusion of the selection step
Selection: 2 kinds of tests
1 – Static measurements
3 – Mobility tests
To be assessed
3 selected sensors
AE51 – black carbon
Canarin – PM10, PM2.5, PM1
Cairsens – NO2
Pant et al., 2017
Ezani et al., 2018
Velasco & Tan, 2016
New prototype
Jiao et al., 2016
Spinelle et al., 2013

32. Assessment of the 3 selected sensors

33. Assessment of the 3 selected sensors

34. Assessment of the 3 selected sensors

35. Assessment of the 3 selected sensors

36. Assessment of the 3 selected sensors

37. Assessment step 1 – reproducibility, static measurements
Mean IPI = 0,71
Range = 23 ppb = 57 %
Satisfying results:
Good accuracy
Good reproducibility
Mean IPI = 0,81
Range = 616 ng.m-3 = 61 %
Mean IPI = 0,67
Range = 7 µg.m-3 = 44 %

38. Assessment step 2 – controlled Chamber test
NO2 variations
Good sensors reactivity
Medium accuracy
Good repeatability

39. Assessment step 2 – controlled Chamber test
NO2 variations
Good sensors reactivity
Medium accuracy
Good repeatability
Humidity variations (40 %, 80 % RH)
PM: no influence
BC: “weak” artefact
NO2: important artefact (≈ 20 ppb)

40. Assessment step 2 – controlled Chamber test
NO2 variations
Good sensors reactivity
Medium accuracy
Good repeatability
To be taken into account for the coming campaigns
Humidity variations (40 %, 80 % RH)
PM: no influence
BC: “weak” artefact
NO2: important artefact (≈ 20 ppb)

41. Feasibility campaign

42. Feasibility campaign (June 2018)
15 volunteers wearing the sensors for a week
Most of the time indoor
Highest values during commuting
Car trip
At home
Polluted indoor
PM2,5
5 µg.m-3
48 µg.m-3
NO2
23 ppb
9 ppb
4 ppb BC
4895 ng.m-3
610 ng.m-3
1390 ng.m-3

43. Feasibility campaign - specific episode
Tobacco smoke
Indoor, open windows
Huge PM values
No influence on NO2
 Attention for the coming campaigns

44. Conclusion A new methodology for sensors assessment
3 selected and assessed sensor
1 feasibility campaign with promising results
1 submitted article (Aerosol and Air Quality Research – AAQR)
× 6
× 15
× 15

45. Future works

46. Future works “Real” campaigns to come this winter
15 volunteers × 5 weeks × 2 seasons × 2 years
3 sensors + health measurements
Source: Mir

47. Future works “Real” campaigns to come this winter
15 volunteers × 5 weeks × 2 seasons × 2 years
3 sensors + health measurements
Data processing platform
Source: Mir

48. Future works “Real” campaigns to come this winter
Pollutants spatial gradients
Levels quantification for different environments
Future works
“Real” campaigns to come this winter
15 volunteers × 5 weeks × 2 seasons × 2 years
3 sensors + health measurements
Data processing platform
Source: Mir

49. Future works “Real” campaigns to come this winter
Pollutants spatial gradients
Levels quantification for different environments
Future works
“Real” campaigns to come this winter
15 volunteers × 5 weeks × 2 seasons × 2 years
3 sensors + health measurements
Data processing platform
Links between health and pollution levels
Source: Mir

50. Thank you