Having tried to solve the problem with only public- and private-sector measures, cities are beginning to realise that success is unattainable unless citizens are involved, too. COMPAIR’s three-year-long experience of running citizen science in Athens, Berlin, Flanders, Plovdiv and Sofia shows that achieving progress is possible but requires simultaneous action in several areas.

Recommendations

Urban value chain: Bring together members of the quadruple helix community (research, society, policy, industry) to co-create effective place-based solutions to reduce air pollution and related urban challenges e.g. congestion.

Quality assurance: Use your multi-stakeholder panel to define the project's scope, target locations, and participants. Develop research protocols for stakeholder engagement and data collection to ensure your findings are representative, ethically robust, scientifically sound, and policy-ready.

Policy relevance: It would be a mistake to ask “How can policy makers use our results?” after the data has been collected. This question should be explored early on together with public authorities. If policy impact is the goal, they should be the ones informing data collection.

Sensors: Decide which pollutants you want to measure and where, and then select measurement devices accordingly. In a citizen science project, sensors should be affordable and easy-to-use as much as possible. It’s important to check network connectivity in the area/country of interest prior to making any orders. Failure to do so may lead to unpleasant surprises. For instance, you may discover that the national telecoms infrastructure doesn’t support IoT standards used in your devices. This would render data transfer from sensor to cloud impossible.

Data quality: Use tried-and-tested calibration methods to increase trust in citizen science data. The improvement will make citizen science results more appealing to local decision makers, who could use them in combination with other data sources to enact better policies.

Digital innovation: Air pollution is an invisible threat whose presence and impact becomes more apparent when digital tools are used. Recent innovations go beyond mere visualisations of pollution hotspots. Advanced apps and dashboards, such as those developed by COMPAIR, 1) provide a comparative analysis of policy impact on air quality, 2) simulate actions that individuals and governments can take to reduce carbon footprint, 3) visualise dynamic exposure to air pollution while people are on the move, and 4) show air pollution in people’s immediate surroundings as floating particles in Augmented Reality.

Inclusive engagement: Expand the traditional pool of volunteers to allow different social groups to benefit from citizen science. Marginalised communities may be hard to engage, especially through unsolicited outreach, so it is best to use trusted intermediaries (e.g. charities) that already work with them to establish contact.

Behaviour change: Stimulate behavioural change by increasing environmental awareness among participants. Use science communication and digital tools to explain air pollution and its impacts, to show how people’s action and inaction contributes to, or helps mitigate, the problem. Information provision combined with gamification can be a powerful tool for nudging people towards sustainable lifestyles.

Sustainability: From day one of your experiment start thinking about what is going to happen to the project when the funding is over and/or data collection stops. Past research shows that many volunteers, including first-timers, are eager to continue their involvement after the first iteration (provided that their experience is rewarding and meaningful). Assess this motivation to plan ahead. Supply-side measures include building capacity among volunteers to work on their own or with minimal oversight by project staff, and identifying ‘super-volunteers’ who can act as community champions to inspire, mobilise and support future participants. On the demand side, it’s important to generate a need for citizen science in different institutional settings by showing how it can help to, for example, evaluate policies (government) or improve STEM education (schools).

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