Calibr alibration ion and and Data a Fus Fusion ion of of - - PowerPoint PPT Presentation

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Calibr alibration ion and and Data a Fus Fusion ion of of - - PowerPoint PPT Presentation

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Calibr alibration ion and and Data a Fus Fusion ion of of PM2.5/ 2.5/PM10 10 sens ensor ors in in the he Net Nether herlands lands Joos oost Wes esseling eling Us Using ing AQ Q sens ensor ors What are we using the

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SLIDE 1

Calibr alibration ion and and Data a Fus Fusion ion

  • f
  • f PM2.5/

2.5/PM10 10 sens ensor

  • rs

in in the he Net Nether herlands lands

Joos

  • ost Wes

esseling eling

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SLIDE 2

Us Using ing AQ Q sens ensor

  • rs
  • What are we using the AQ sensors for?
  • When used in well organized projects, run by

professionals, using well known hardware, QA/ QC and calibration is usually addressed.

  • When you have hundreds of sensors producing

data every 5 minutes, some well managed, some not, all kinds of housings at different kinds of locations, QA/QC and calibration can be a challenge.

Here: on-the-fly “fusion” calibration of up to 750 PM sensors in the Netherlands of the type Nova SDS-011.

2 RIVM Sensors | FAIRMODE | Oct 2019

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SLIDE 3

Calibr alibration: ion: humidit humidity

  • All low-cost optical PM sensors are

very sensitive to relative humidity.

  • The effects of humidity are quite

similar for PM10 and PM2.5.

  • There is a large variation in the

effect of the humidity à more issues, effect location, housing, ...

Fit to the data:

RIVM Sensors | FAIRMODE | Oct 2019 3

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SLIDE 4

Calibr alibration: ion: Fus Fusion ion

RIVM Sensors | FAIRMODE | Oct 2019

  • In tests with groups of measurements in a number of

Dutch cities, we observed that the differences between hourly results of sensors in small areas are quite small, and the average PM gradients are small.

4

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SLIDE 5

Calibr alibration: ion: Fus Fusion ion

RIVM Sensors | FAIRMODE | Oct 2019

  • In tests with groups of measurements in a number of

Dutch cities, we observed that the differences between hourly results of sensors in small areas are quite small, and the average PM gradients are small.

  • Option: compare the average of all sensors located in a

short distance around an official measuring station to the official hourly results à local correction factor.

  • Interpolate the correction factors over the country.
  • Combine the calibrated sensor data with existing AQ

maps using the uncertainties as weights for the whole country to provide more details.

  • So, we do not work with individual pre-calibrated

sensors, but with the available overall combined data.

5

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SLIDE 6

Res esult ults of

  • f the

he fus usion-calibr ion-calibration ion

RIVM Sensors | FAIRMODE | Oct 2019

  • The fusion scheme was applied to all hourly sensor

data (Nova SDS-011) in the period Jan-Aug 2019.

  • Compare the results for sensors that are co-located
  • r are very close (<250 meter) to official stations.
  • For a clean test, the nearest official measurement

was not used in calibrating each sensor.

  • The 8-month average raw and fusion-calibrated

PM2.5 concentrations are quite similar.

PM2.5

6

Cavg = 10.7 ug/m3 Cavg = 10.7 ug/m3 Cavg = 11.8 ug/m3 Cavg = 10.8 ug/m3

Raw Calibrated

Small/blue markers represent data from co-located sensors and larger orange markers represent sensors that are up to 250 meter from the official location.

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SLIDE 7

Res esult ults of

  • f the

he fus usion-calibr ion-calibration ion

RIVM Sensors | FAIRMODE | Oct 2019

  • The hourly scatter of the calibrated

sensor data around the nearest

  • fficial is much smaller than that of

the raw data.

  • During hours with high relative

humidity the effects of the calibration are very prominent.

  • The raw data severely over-estimate

the nearby official data.

  • After the calibration, the data scatters

around official data, although with a relatively large uncertainty.

PM2.5

7

Small/blue markers represent data from co-located sensors and larger orange markers represent sensors that are up to 1000 meter from the official location.

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SLIDE 8

RIVM da data a por portal al

RIVM Sensors | FAIRMODE | Oct 2019

Presently up to 750 sensors every hour in the data portal.

8

Development and Implementation of a Platform for Public Information on Air Quality, Sensor Measurements, and Citizen Science, Atmosphere 2019, 10(8), 445

https://samenmeten.rivm.nl/

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SLIDE 9

RIVM Sensors | FAIRMODE | Oct 2019 9

https://samenmeten.rivm.nl/

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SLIDE 10

Fus Fusion-calibr ion-calibration ion in in pr pract actice ice

RIVM Sensors | FAIRMODE | Oct 2019

  • RIVM is currently involved in several

projects with/for/by citizens.

  • It is important to present data that are

properly calibrated.

  • One project is near a large steel plant that is

quite close to the coast.

  • In practice the PM2.5/PM10 ratios are more

complicated than in an average urban environment.

  • Using the available local official data in the

calibration of the sensors is important.

Blue: raw sensor data, Purple: calibrated sensor data, Red: official result (@160 meter)

10

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SLIDE 11

Data a Fus Fusion ion of

  • f sens

ensor

  • rs and

and ma maps ps

  • Look at the data of September

26, 2019, 09:00 (UTC).

  • There were significant effects
  • f the high humidity,

especially at the coast and in the middle of the country.

  • 90% ≤ RH ≤ 97%
  • After the calibration there is

much less variation in sensor values.

PM2.5 ug/m3 Raw sensor data

11

Calibrated sensor data Correction field

RIVM Sensors | FAIRMODE | Oct 2019

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SLIDE 12

Data a Fus Fusion ion of

  • f sens

ensor

  • rs and

and ma maps ps

RIVM Sensors | FAIRMODE | Oct 2019

  • The structure of the PM2.5

concentration map changes slightly due to the sensor data.

  • There are more detailed sub

structures in the map.

PM2.5 ug/m3 Background map Background /calibrated sensor map

12

Official data Official map + uncertainties Calibrated sensors + uncertainties

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SLIDE 13

Eas aster er fir ires es

Effect of traditional Easter fires in Germany.

PM10, from 20 April 2019, 23:00 until 21 April 2019, 14:00, Steps of 3 hours.

23:00 hours 02:00 05:00 08:00 hours 11:00 14:00

www.youtube.com/watch?v=dJveRLMRaiA

RIVM Sensors | FAIRMODE | Oct 2019 13

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SLIDE 14

Conc

  • ncluding

luding remar emarks ks

RIVM Sensors | FAIRMODE | Oct 2019 14

  • Data from low-cost PM10/PM2.5 sensors (SDS-011) can be combined with that
  • f nearby sensors and nearby official measurements to yield a correction field.
  • The calibration algorithm does not need much information about the sensors.
  • It takes all effects into account, not only humidity.
  • Tests of the data fusion calibration scheme show it performs at least as good as

using local humidity correction for the sensors.

  • The local correction factors for sensors can be used to calculate a sensor-data

field, including uncertainties, for the whole country.

  • The sensor-data field can be combined with official hourly back ground map in a

data fusion approach, yielding new and more detailed maps.

  • All the information can be used for a QA/QC scheme.
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SLIDE 15

Ques Questions ions ?

RIVM Sensors | FAIRMODE | Oct 2019 15