Portable Sensors for Dynamic Exposure Assessments in Urban Environments: State of the Science
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sensor System Selection
- commercially available
- wireless/power solution (battery or via car)
- weatherproof housing
- data transmission/logging solution (internal, USB, Bluetooth, LTE-M, LoRA, wifi)
- sensor capability to monitor PM, NO2, and/or BC
- availability of particle mass concentration (µg/m3; instead of particle number concentration)
- power autonomy (battery instead of car-powered systems)
- GPS localization (internal or via smartphone)
2.2. Benchmarking Protocol
2.2.1. Laboratory Test Protocol
- Lack-of-fit (linearity) at setpoints 0, 30, 40, 60, 130, 200, and 350 µg/m3 (PM10, dolomite dust). This concentration range can be considered representative for exhibited PM levels in typical urban environments [2,36,37,38,39,40,41,42]. A Palas Particle dispenser (RBG 100) system connected to a fan-based dilution system and aluminum PM exposure chamber was used.
- Sensitivity of PM sensor to the coarse (2.5–10 µm) particle fraction. We dosed, sequentially, 7.750 µm and 1.180 µm-sized monodisperse dust (silica nanospheres with density of 2 g/cm3) using an aerosolizer (from the Grimm 7.851 aerosol generator) system connected to a fan-based dilution system and an aluminum PM exposure chamber with fans to have homogeneous PM concentrations. This testing protocol is currently considered to be included in the CEN/TS 17660-2 (in preparation) on performance targets for PM sensors.
- Lack-of-fit (linearity) at setpoints of 0, 40, 100, 140, and 200 μg/m3.
- Sensor sensitivity to relative humidity at 15, 50, 70, and 90% (±5%) during stable temperature conditions of 20 ± 1 °C.
- Sensor sensitivity to temperature at −5, 10, 20, and 30 °C (±3 °C) during stable relative humidity conditions of 50 ± 5%.
- Sensor cross-sensitivity to ozone (120 µg/m3) at zero and 100 µg/m3.
- Sensor response time under rapidly changing NO2 concentrations (from 0 to 200 µg/m3).
2.2.2. Mobile Field Test
2.2.3. Field Co-Location Campaign
- Hourly data coverage (%)
- Timeseries plot: RAW & LAB CAL
- Scatter plot: RAW & LAB CAL
- Comparability between sensors: between-sensor uncertainty (BSU)
- Comparability with reference (hourly): R2, RMSE, MAE, MBE
- Expanded uncertainty (non-parametric): Uexp (%)
3. Results
3.1. Laboratory Test
3.1.1. PM
- Relative change (%) in fractional (coarse vs. fine) sensor/REF ratio during respective fine and coarse test conditions:
- Relative change (%) in PM10 sensor/REF ratio between fine and coarse test conditions:
3.1.2. NO2
3.2. Mobile Field Test
3.3. Field Co-Location Campaign
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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SENSOR SYSTEM | Accuracy (%) | MAE | R2 | Uexp | BSU | |||
---|---|---|---|---|---|---|---|---|
PM1 | PM2.5 | PM10 | µg/m3 | - | % | µg/m3 | ||
PM | ATMOTUBE (3) | 84 | 65 | 29 | 10.0 | 0.98 | 47 | 1.5 |
OPEN SENECA (3) | 83 | 54 | 22 | 12.6 | 0.99 | 55 | 1.2 | |
TERA (3) | 18 | 79 | 47 | 5.2 | 1.00 | 25 | 1.6 | |
SODAQ Air (3) | 64 | 70 | 31 | 8.9 | 0.99 | 40 | 4.0 | |
SODAQ NO2 (3) | 68 | 52 | 21 | 10.9 | 0.99 | 45 | NA | |
GeoAir (3) | NA | NA | NA | NA | NA | NA | NA | |
PAM (1) | 63 | 29 | 13 | 17.3 | 0.96 | 79 | NA | |
SENSOR SYSTEM | Accuracy | Stability | MAE | R2 | Uexp | BSU | ||
% | µg/m3 | µg/m3 | - | % | µg/m3 | |||
NO2 | SODAQ NO2 (3) | −166 | 51 | 270.3 | 0.11 | 304 | 124.7 | |
PAM (1) | 72 | 27 | 49.5 | 0.13 | 110 | NA | ||
Observair (1) | 0 | 0 | 79.0 | 0.98 | 112 | NA |
SENSOR SYSTEM | Data Coverage | MAE | R2 | Uexp | BSU | |
---|---|---|---|---|---|---|
% | µg/m3 | - | % | µg/m3 | ||
PM2.5 | ATMOTUBE (3) | 76 | 4.3 | 0.88 | 48 | 0.6 |
OPEN SENECA (3) | 100 | 3.7 | 0.90 | 35 | 0.3 | |
TERA (3) | 17 | 4.4 | 0.87 | 64 | 0.1 | |
SODAQ Air (3) | 44 | 3.1 | 0.68 | 16 | 0.7 | |
SODAQ NO2 (3) | 44 | 3.8 | 0.67 | 40 | 0.4 | |
AIRBEAM (3) | 53 | 3.9 | 0.87 | 36 | 0.7 | |
GeoAir (3) | 96 | 3.0 | 0.89 | 28 | 0.6 | |
PAM (1) | 100 | 4.7 | 0.89 | 66 | NA * | |
NO2 | SODAQ NO2_raw (3) | 44 | 190.3 | 0.42 | 614 | |
SODAQ NO2_cal (1) | 44 | 27.1 | 0.62 | 108 | ||
SODAQ NO2_mlcal (1) | 44 | 5.6 | 0.83 | 37 | ||
PAM (3) | 100 | 84.1 | 0.55 | 284 | ||
PAM_cal (1) | 100 | 349.0 | 0.55 | 1225 | ||
PAM_calml (1) | 100 | 44.2 | 0.75 | 44 | ||
Observair_raw | 78 | 28.4 | 0.38 | 111 | ||
Observair_cal | 78 | 28.8 | 0.38 | 95 | ||
Observair_mlcal | 78 | NA | NA | NA | ||
BC | Observair | 78 | 0.3 | 0.82 | ||
BCmeter | 78 | 0.2 | 0.83 |
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Hofman, J.; Lazarov, B.; Stroobants, C.; Elst, E.; Smets, I.; Van Poppel, M. Portable Sensors for Dynamic Exposure Assessments in Urban Environments: State of the Science. Sensors 2024, 24, 5653. https://doi.org/10.3390/s24175653
Hofman J, Lazarov B, Stroobants C, Elst E, Smets I, Van Poppel M. Portable Sensors for Dynamic Exposure Assessments in Urban Environments: State of the Science. Sensors. 2024; 24(17):5653. https://doi.org/10.3390/s24175653
Chicago/Turabian StyleHofman, Jelle, Borislav Lazarov, Christophe Stroobants, Evelyne Elst, Inge Smets, and Martine Van Poppel. 2024. "Portable Sensors for Dynamic Exposure Assessments in Urban Environments: State of the Science" Sensors 24, no. 17: 5653. https://doi.org/10.3390/s24175653