Data quality evaluation of low-cost PM10 air quality sensors · Aerasense NanoMonitor PNMT 1000. O....

DATA QUALITY EVALUATION OF LOW-COSTPM10 AIR QUALITY SENSORSV.M. VAN ZOEST, N.A.S. HAMM, A. STEIN (ITC, UNIVERSITY OF TWENTE)G. HOEK (INSTITUTE OF RISK ASSESSMENT SCIENCE (IRAS), UTRECHT UNIVERSITY)R. OTJES (ENERGY RESEARCH CENTRE OF THE NETHERLANDS (ECN))

PROJECT DAMAST: “DEVELOPMENT OF AN AUTOMATIC SYSTEM FOR MAPPING AIR QUALITY RISKS IN SPACE AND TIME”

THE AIR QUALITY SENSOR NETWORK

Geospatial Sensor Webs Conference, 29-31 August 2016 2

www.aireas.com

DATA

Reference data:

• RIVM LML data (national ambient air quality monitoring network)

• 2 authorative sensors (BAMs) in Eindhoven, 3 in surrounding area


Variable Units Instrument

PM10 μg m-3 Shinyei PPD42 ECN revised

PM2.5 μg m-3 Shinyei PPD42 ECN revised

PM1 μg m-3 Shinyei PPD42 ECN revised

UFP # cm-3 Aerasense NanoMonitor PNMT 1000

O3 μg m-3 E2V MICS 2610

NO2 μg m-3 Citytech Sensoric NO2 3E50 ECN revised

Temperature °C Sensirion SHT75

Relative humidity % Sensirion SHT75

RESEARCH QUESTIONS

Data quality of low-cost sensors largely unknown. The followingquestions arise:

What is the precision of the sensors?

How do the low-cost sensors perform compared to the authorativeLML monitors (BAMs) in Eindhoven?


RESEARCH QUESTIONS





SENSOR PRECISION: METHODS

Co-locating sets of 12 airboxes at location RIVM LML monitor (BAM)

3 sets of airboxes, each 2 sessions of ~1 week

Calculate standard deviation (SD) and coefficient of variation (CV) across each set of sensors for each hour

𝐶𝐶𝐶𝐶 = 𝑆𝑆𝑆𝑆𝑚𝑚

× 100%


Calculate aggregatedstatistics on mean, SD andCV per session

Compare variability betweensensors when co-located andwhen at their usual locations

SENSOR PRECISION: RESULTS


Series Session Mean of mean (µg m-3)

Mean SD (µg m-3)

Median SD(µg m-3)

Mean CV (%)

Series 1 Session 1 9.7 1.3 1.2 13.1

Session 2 10.0 1.3 1.3 13.3

Series 2 Session 1 15.9 2.7 2.6 16.4

Session 2 12.7 1.9 1.6 14.0

Series 3 Session 1 13.2 2.7 2.3 18.8

Session 2 15.2 2.8 2.4 18.0

Precision of PM10 sensors August-December 2015



Comparison precision and spatial variation airboxes 2015 (hourly PM10 concentration values)

Serie & session

Period Mean of mean (µg m-3)

Mean SD(µg m-3)

Median SD(µg m-3)

Mean CV(%)

Serie 1 validation

15/08/2015 – 18/08/2015 +25/09/2015 – 04/10/2015 10.1 1.4 1.3 13.4

Serie 1 regular

01/01/2015 – 13/08/2015 +06/10/2015 – 31/12/2015 15.3 2.8 2.3 18.6

Serie 2 validation

08/10/2015 – 11/10/2015 +04/11/2015 – 12/11/2015 14.0 2.2 2.1 15.0

Serie 2 regular

01/01/2015 – 06/10/2015 +14/11/2015 – 31/12/2015 15.9 3.0 2.6 18.6

Serie 3 validation

17/11/2015 – 30/11/2015 +19/12/2015 – 27/12/2015 13.3 2.6 2.2 18.3

Serie 3 regular

01/01/2015 – 15/11/2015 +29/12/2015 – 31/12/2015 15.2 3.0 2.6 19.6

Regular Validation Maintenance Validation Regular

RESEARCH QUESTIONS





SENSOR ACCURACY: METHODS

2 airboxes permanently co-located with authorative sensors (BAMs of RIVM LML network)

Derive time plots, scatterplots, statistical indicators using dailyaverage concentrations over full year 2015

Calculate sensor uncertainty compared to BAM using Guide to theDemonstration of Equivalence


SENSOR ACCURACY: RESULTS


Con

cent

ratio

n(µ

g/m

3 )

1/1/2015 31/12/2015Date



Con

cent

ratio

n(µ

g/m

3 )

1/1/2015 31/12/2015Date

SENSOR UNCERTAINTY: METHODS

1. Uncertainty between airboxes

2. Uncertainty between LML monitoring stations

3. Calibration airbox to LML monitoring station

4. Uncertainty airbox (calibrated values) in relation to LML station (equivalence test)

5. Derive uncertainty at threshold concentration of 50 µg m-3

(uncertainty should be max. 25%)

17

EC WORKING GROUP ON GDE 2010. Guide to the Demonstration of Equivalence of Ambient Air Monitoring Methods. European Commission.

Geospatial Sensor Webs Conference, 29-31 August 2016

SENSOR UNCERTAINTY: RESULTS

18Geospatial Sensor Webs Conference, 29-31 August 2016

Uncertainty at 50 µg/m3: 27.2%

INFLUENCE OF WIND DIRECTION

19Geospatial Sensor Webs Conference, 29-31 August 2016

Con

cent

ratio

ndi

ffere

nce

(µg/

m3 )

Wind direction

CONCLUSION

Spatial variability is larger than the noise of the airbox sensors

Moderate correlation between airbox and LML monitor based on 24h average values

Uncertainty of airbox compared to LML just above threshold valuefor equivalence

Airbox underestimates values of LML, especially whenconcentrations peak (airbox does not detect particles <0.3µm)


CONCLUSION

Data quality of low-cost sensors largely influenced by:

Outliers

Erroneous values

Pollutant composition

There is a need for:

Automatic data quality evaluation

Outlier detection

Provision of data quality information to the users

Standard calibration methods on finer temporal resolutions


THANK YOU FOR YOUR ATTENTIONANY QUESTIONS?

Data quality evaluation of low-cost PM10 air quality sensors · Aerasense NanoMonitor PNMT 1000. O....

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