Calibration of a cluster of low-cost sensors for the ... 2014/90...2. Spinelle L, Aleixandre M,...
Transcript of Calibration of a cluster of low-cost sensors for the ... 2014/90...2. Spinelle L, Aleixandre M,...
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L. Spinelle1, M. Gerboles1, M.G. Villani2, Manuel Aleixandre3 and F. Bonavitacola4
1European Commission, JRC, Ispra (VA), Italy2ENEA, Ispra (VA), Italy3Instituto de Física Aplicada, Madrid, Spain4Phoenix Sistemi & Automazione s.a.g.l., Muralto (TI), Switzerland
IEEE SENSORS 2014 – Valencia, SpainNovember 2-5, 2014
Calibration of a cluster of low-cost sensors for the measurement of air pollution in
ambient air
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Spinelle L, Aleixandre M, Gerboles M. Protocol of evaluation and calibration of low-cost gas sensors for the monitoring of air pollution. EUR 26112. Luxembourg (Luxembourg): Publications Office of the European Union; 2013. JRC83791
Evaluation&
Validation Protocol
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Field calibration of a cluster of sensors
Sensors
Meteorological
mast
Inlet sampling
line
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Linear regression and multilinear regression
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Linear regression
- ≠
sensors- depend on the
exposure conditions- include all
interfering effects
Multilinear regression
based on laboratory experiments
- improve the quality of the data
- needs other variables (gaseous compounds,
temperature, humidity...)
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Linear regression and multilinear regression
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Artificial Neural Network
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O3
O3 3E1F
NO2 2710
http://midsizeinsider.com/en-us/article/googles-neural- network-makes-advances-i
CO TGS5210
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Artificial Neural Network
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Model Uncertainty O3 NO2 NO CO CO2
Algorithms Ambient parameters
Inputs
LM No Sensor
MLR No Sensor +Reference
ANN No Sensors
ANN+Std No Sensors
ANN+MLR No Sensors +Reference
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)2(2)( 22
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Model Uncertainty
Algorithms Ambient parameters
Inputs
LM No Sensor
MLR No Sensor +Reference
ANN No Sensors +Abs. Hum.
ANN+Std No Sensors +Abs. Hum.
ANN+MLR No Sensors +Reference
O3 NO2 NO CO CO2
iiiir yxbaxunRSSyU /])1([)(
)2(2)( 22
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Model Uncertainty - Target Diagram
- target cycle = model results are within the observation uncertainty range
- symbols out of the target circle = RMSE > s (standard deviation of reference measurements)
- ANN show a lower unbiased RMSE (called centered root-mean-square error, CRMSE) and a lower bias
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Algorithms Ambient parameters
Inputs
LM No Sensor
MLR No Sensor +Reference
ANN No Sensors +T. + Hum.
ANN+Std No Sensors +T. + Hum.
ANN+MLR No Sensors +Reference
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Model Uncertainty O3 NO2 NO CO CO2
iiiir yxbaxunRSSyU /])1([)(
)2(2)( 22
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Algorithms Ambient parameters
Inputs
LM No Sensor
MLR No Sensor +Reference
ANN No Sensors +T. + Hum.
ANN+Std No Sensors +T. + Hum.
ANN+MLR No Sensors +Reference
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Model Uncertainty O3 NO2 NO CO CO2
iiiir yxbaxunRSSyU /])1([)(
)2(2)( 22
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Model Uncertainty O3 NO2 NO CO CO2
iiiir yxbaxunRSSyU /])1([)(
)2(2)( 22
Algorithms Ambient parameters
Inputs
LM No Sensor
MLR No Sensor +Reference
ANN No Sensors
ANN+Std No Sensors
ANN+MLR No Sensors +Reference
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The DQO for indicative methods can be met for O3 , likely for NO2 . High uncertainty for NO and CO (>75%). For CO2 , low uncertainty down to about 5%.
Linear and Multilinear regression gives the highest U.
ANN methods: higher R² and lower CRMSE -> lower U; lower bias to reference data (slopes and intercept nearer to 1 and 0, respectively).
Reference data (meteo / gas) does decrease measurement uncertainty for the ANN methods.
ANN can solve cross sensitivity issues from which suffers the major part of sensors (gaseous interference, temperature/humidity dependence).
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Conclusion calibration methods for the whole cluster of sensors
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Thank You...M. Gerboles, M. Aleixandre, F. Lagler, M. G. Villani, F. Bonavitacola, M. Penza
N. R. Jensen, A. Dell’Acqua, C. Gruening, G. Manca,S. Martins Dos Santos
Reports at:ftp://ftp_erlap_ro:3rlapsyst3m@s-jrciprvm-ftp- ext.jrc.it/ERLAPDownload.htm
Or send a mail at
[email protected]@jrc.ec.europa.eu