assessment of low-cost sensors for air quality in real-world ......pod (aqmesh): pm 1, pm 2.5 and pm...

Assessment of low-cost sensors for

air quality in real-world conditions

María Cruz Minguillón, Mar Viana, Cristina Reche, Fulvio Amato, Xavier Querol

Meeting on sensor technology for air quality Bilthoven, 13 February 2017

Institute of Environmental Assessment and Water Research (IDAEA), CSIC

What is a low-cost air quality sensor?

An instrument to measure air quality that…

- is small

- is low-cost

- can be deployed outdoors or indoors

- can be deployed for a reasonable amount of time

• They are complementary to air quality networks

• Laboratory performance is satisfactory, although their validation in real-world

conditions still has limitations

cheap enough to be replaced rather than fixed if it fails (Morawska, 2016)

<1 kg?

1 month?

• OBJETIVE: assess the performance of air quality sensors in real-world

conditions

Bilthoven, 13 February 2017

Methodology

Urban background site in Barcelona, with pollutant concentrations

typical of the urban background in the Mediterranean region


Palau Reial, IDAEA-CSIC

PM: Optical particle counter (GRIMM 180)

O3: Ultraviolet absorption SIR S-5014

NO and NO2: Chemiluminescence SIR S-5012

PM: High volume captor and gravimetric determination


Methodology

ELM / CANARIT O3 Metal oxide

POD (AQMesh) NO, NO2, O3 Electrochemical

PerkinElmer (before AirBase)

CAPTOR O3 Metal oxide

Particulate matter

DYLOS

MICROPEM AIRBEAM

Tested sensors

POD (AQMesh)


Gaseous pollutants

X stopped

Pod (AQMesh): NO, NO2, O3

y = 0.69x - 6.9R² = 0.90

0

50

100

150

200

250

300

0 200 400

NO

(µ

g/m

3)

AQ

Me

sh

NO (µg/m3) Reference

y = 0.82x + 1.1R² = 0.99

0

100

200

300

400

0 200 400

NO

(µ

g/m

3)

AQ

Me

sh

NO (µg/m3) Reference

Modification

Equipped with a solar roof to reduce temperature effect



Modification

Improve in technology

and data treatment

results in an increased R2



Modification

ATTENTION!

They can vary over time.

Worse correlations in

summer than winter

R2 = 0.72

Improve in technology

and data treatment

results in an increased R2


Captor: O3

H2020 project


Inter instrument

comparison

Captor: O3

H2020 project


Field deployment

Example 1

Captor: O3

H2020 project


y = 0.588x + 10.881R² = 0.4715

0

20

40

60

80

100

120

140

160

180

200

0 50 100 150 200

Cap

tor

O3

(µg/

m3)

Reference O3 (µg/m3)

Field deployment

Example 2

Dylos: N>0.5 and N>2.5

• 3 units

• >1 year


• N0.5-2.5 calculated

• Proxy for PM2.5

• Time res: 1min

• Averaged to

5min or

30min

Inter instrument comparison

2 months

Dylos: N>0.5 and N>2.5


y = 16859x - 144570R² = 0.7047

0

100000

200000

300000

400000

500000

600000

0.0 10.0 20.0 30.0 40.0D

ylo

s_1

(#/

ft3)

Ref PM (µg/m3)

d1_aprmay

RH= 67,74%T=17,18°C

y = 29573x - 305052R² = 0.7052

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

0.0 10.0 20.0 30.0 40.0 50.0

Dyl

os

(#/f

t3)

Ref PM (µg/m3)

d1_mar

RH= 64,13%T=12,69°C

Comparison with reference

y = 38856x - 373608R² = 0.8546

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

0.0 10.0 20.0 30.0 40.0 50.0

Dyl

os_

1 (

#/ft

3)

Ref PM (µg/m3)

d1_janfeb

RH= 65,21%T=13,64°C

Pod (AQMesh): PM1, PM2.5 and PM10


• Time res:

1 min every 15 min

• Averaged to 1h

• 1 unit

• 5.5 months

0

20

40

60

80

100

17/01/2016 06/02/2016 26/02/2016 17/03/2016 06/04/2016 26/04/2016 16/05/2016 05/06/2016 25/06/2016

POD_PM2.5

REF_PM2.5

0

25

50

75

100

125

150

175

17/01/2016 06/02/2016 26/02/2016 17/03/2016 06/04/2016 26/04/2016 16/05/2016 05/06/2016 25/06/2016

Pod_PM10

REF_PM10Comparison with

reference



• Time res:

1min every 15 min

• 3 units

• 1 month

PM10

PM2.5

PM1

Inter instrument

comparison



• Time res:

1min every 15 min

Matched to 5min reference average PM10

PM2.5

PM1


• 3 units

• 1 month

Airbeam: PM2.5


0

10

20

30

40

08/08/2016 0:00 13/08/2016 0:00 18/08/2016 0:00 23/08/2016 0:00 28/08/2016 0:00 02/09/2016 0:00

Airbeam_1 Airbeam_2

• PM2.5

• Time res: 5min

• Averaged to 30min

• 1 month


Airbeam: PM2.5


• PM2.5

• Time res: 5min


• 3 months


0

5

10

15

20

25

30

35

40

45

50

09

/08

/16

14

/08

/16

19

/08

/16

24

/08

/16

29

/08

/16

03

/09

/16

08

/09

/16

13

/09

/16

18

/09

/16

23

/09

/16

28

/09

/16

03

/10

/16

08

/10

/16

13

/10

/16

18

/10

/16

23

/10

/16

PM

2.5

(μg

m-3

)

Airbeam IDAEA

Reference

Airbeam: PM2.5


• PM2.5

• Time res: 5min


• 3 months


Airbeam: PM2.5


Application:

Awareness raising,

teaching in schools

MicroPEM: N<2.5 or N<10



MicroPEM: N<2.5 or N<10


0

20

40

60

80

10/mar 15/mar 20/mar 25/mar 30/mar

MicroPEM PM10

Reference PM10

Comparison

with reference

Intercomparison exercise 1st EuNetAir Air Quality Joint Intercomparison Exercise Aveiro (Portugal) 13-27 October 2014

Aveiro

Measured parameters: • CO, NOx, O3, SO2, • PM10, PM2.5, • temperature, relative

humidity

IDAD-Institute of Environment and Development Air Quality Mobile Laboratory


- 15 participating teams - 130 microsensors - 27 working sensors

Conclusions

Some nodes provide satisfactory or acceptable results

Usually good agreement between different nodes of the same type

Changes over time: required frequent validation with reference

instrumentation

Influence of temperature for gaseous pollutants measurements

Optical particle counters valid as proxy for fine particles (<2.5 µm)

Most particle nodes not adapted for outdoor deployment

More tests and improvement of technology and data processing required

The possibility to use sensors for air quality assessment can be a fact provided

some improvements and validations are applied


Thank you for

your attention

Acknowledgements

[email protected]

Anna Ripoll, Marc Padrosa, Francesco Miacola, Giorgos Spais

assessment of low-cost sensors for air quality in real-world ......pod (aqmesh): pm 1, pm 2.5 and pm...

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