bridging the gap? Ernie Weijers, Hester Volten, Marita Voogt - - PowerPoint PPT Presentation

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bridging the gap? Ernie Weijers, Hester Volten, Marita Voogt - - PowerPoint PPT Presentation

Citizen science and regulatory monitoring: bridging the gap? Ernie Weijers, Hester Volten, Marita Voogt Department of Research and Innovation of Monitoring National Institute for Public Health and the Environment (RIVM), Bilthoven, The


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Citizen science and regulatory monitoring: bridging the gap?

Ernie Weijers, Hester Volten, Marita Voogt Department of Research and Innovation of Monitoring

National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands

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Outline

  • RIVM and Citizen Science
  • Examples and lessons learned
  • Potential benefits and challenges
  • Roadmap

ASIC, 13 September 2018, Oakland

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RIVM and Citizen Science

RIVM

  • ‘Dutch’ Environmental Protection Agency
  • Responsible for the National Air Quality Monitoring Network: 67 stations

measuring air quality in cities, rural areas and near industry

  • Dedicated research for the national and local government

2016: start program ‘Innovation of Monitoring‘ Why Sensors and Citizen Science?  advances in sensor technology  prospect of higher spatial and temporal data resolution  new ways to communicate with citizens needed

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NO2 map

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Participating in various projects

www.waag.org/en man.rivm.nl ispex.nl smartemission.ruhosting.nl Friends of the Earth

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Example 1: Simple sensors in an ammonia network

The Ammonia Measurement Network in Nature Reserves measures ammonia concentrations at 82 locations on a monthly basis  more than 250 tubes in total passive diffusion tube

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Lesson learned Trusting measurement devices to nonexperts is a cost- efficient way to build a monitoring network on a scale that would

  • therwise not be feasible.

Example 1: Simple sensors in an ammonia network

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Example 2: Amsterdam Smart Citizens Lab

Bottom up approach – De Waag Society Aim

  • Citizens develop simple tools to

measure and understand their living environment

  • Citizens involved in all steps of the

project: issue mapping sensor making sensing understanding comparison with official data decision making acting …

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Example 2: Amsterdam Smart Citizens Lab

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Air Quality (NO2) mapping using sensor kits built by citizens

Lessons learned:

  • Citizens want support and information from experts
  • Chance for success increases if experts participate
  • Timing is crucial: people need enough information

at an early stage

  • Citizens may have different goals
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Example 3 Firework experiment 2017/2018

Kits provided by RIVM Contribution of other initiatives Data portal was constructed …?

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Shinyei PPD42NJ NOVA SDS011

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DIY sensor housings

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Example 3 Firework experiment 2017/2018

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=> More than 130 sensors

  • nline
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What people do with it …

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Recurring themes

  • Citizens are motivated to contribute to air quality measurements
  • They want to compare with official data
  • CS offers a platform for a better communication between ‘official’ science

(monitoring) and citizens

  • Motivation is stronger when participants "invest" ...
  • Accessible expert information is needed to support citizens, e.g. by an

interactive knowledge- and dataportal

  • Increasing awareness of air quality as an environmental problem

 Research institutes can make CS more successful but ‘management

  • f expectations’ is needed

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RIVM and citizen science –

From contradiction to synergie?

National monitoring Citizen science Higher resolution Calibration

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Innovating a traditional measurement network towards a hybrid platform

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Example of Palmes diffusion Annual average NO2

Reference s=5% BI =10 % Sensor s=8% BI =16 % Model s=13% BI =26 %

Calibration points

Example: Modelcalibration with sensor data

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What's next?

  • but what about the other way round:
  • can science benefit fro CsS?
  • Environment protection agencies can benefit(?) from citizen science

using small sensor networks

  • lowcost sensors (low quality data) need to be improved, and

applications calibrated and validated (by reference data)

  • Data needs to be assimilated, for example, in models.

ASIC, 13 September 2018, Oakland