Between 2015 and 2017, action research was conducted by an experimental citizen monitoring network in the city of Nijmegen as part of the Smart Emission project. The citizen monitoring network used new small sensors to map environmental pollution in the city in detail. A group of citizens actively contributed ideas about where to measure and which local issues, known as “use cases”, to address. Organising “participatory sense-making” meetings for citizens and experts stimulated a joint learning process (Volten et al., 2016). Citizen science requires a considerable time investment from all those involved, but the dialogue process can also be of great added value in building mutual understanding between citizens and professionals from government and knowledge institutions.
Joint learning
In Nijmegen, the alderman responsible for the Smart Emission project concluded that it is a better investment to free up time and money for citizen science than to engage in protracted legal battles between citizens and the local government about environmental pollution and perceived nuisance.
The main question was: what is the value of a citizen monitoring network from a social innovation perspective? The short answer is that the direct effects of gaining a more detailed and dynamic insight into air quality and noise are limited. Such detail requires better technology and more sensors. However, the indirect effects in terms of dialogue between the government and citizens, and between experts and citizens, are significant. During the project, expectations and positions converged. The various parties involved in the project entered into a joint dialogue. There was a great deal of “joint learning”. The living lab with monthly meetings was very important for the mutual dialogue and, together with the sensor network, formed the core of the project.
Considerable time investment
An important lesson is that citizen science requires a significant investment of time from those involved. This gives the dialogue process great added value by building understanding and trust and creating a common “language” between the participating citizens, the local government and knowledge parties (Carton et al., 2017; Klein Gunnewiek, 2018). Being able to analyse their own “use case” gives citizens a sense of empowerment. It allows them to “check” the statements made by professionals themselves. This has helped to reduce the knowledge gap and misunderstanding between science, citizens and government policy. In addition to short-term motives for investigating a specific situation, citizens also have latent and long-term motivations, such as raising awareness about environmental quality and putting the importance of the environment on the policy agenda (Posthumus et al., 2022).
Results
We return to the central research question: what is the value of a citizen monitoring network from a social innovation perspective? We arrive at six learning outcomes, three more social (about the living lab) and three more technical (about environmental monitoring).
Learning outcome 1: Dialogue processes provide participants with insight into air quality and noise
Joint learning was perhaps the greatest added value of the Smart Emission project. Participants in the meetings, especially those who had attended the “evening lectures”, were more aware of the complexity of air quality as an environmental phenomenon. The spatial-temporal dynamics of pollutants depend on the atmosphere and air layers, wind direction, dispersion and dilution. Noise can also “rise” in one place and “fall” in another. There was a lot of enthusiasm for learning about this during the evening lectures on air quality and noise. The dialogue process yielded a number of good discussions about the interpretation of sensor data in a number of periods: Was locally elevated pollution measured, and if so, where did that pollution come from? Or did the pollution come from further away? Discussions focused on reaching consensus on atmospheric conditions and the interpretation of local use cases such as the Four Days Marches or traffic on Groeneweg.
Learning outcome 2: Different motivations, but still speaking the same language
An important question during the Smart Emission project was why people participated. Greater insight into the quality of their immediate living environment was at the top of the list. Other motives included learning about environmental issues such as air quality and noise.
Indirect goals were also mentioned: to raise environmental quality higher on the political and social agenda and to work on environmental awareness in general. Apparently, participation in citizen science, through debate, dialogue and now also (helping with) measurement, is a means of working on social awareness. Did social learning take place? Klein Gunnewiek's master's thesis (2018) confirms that a shared identity and language were formed during the project. According to this research, “shared meanings” were developed based on the “shared values” of the participants.
Learning outcome 3: Citizen monitoring network as a building block for social change
In line with social learning, participants expressed great appreciation for the joint search for truth. It was not the loudest voices that were listened to most, but rather the substantive dialogue and discovering “what the sensors tell us”, as one student assistant put it. A group of citizen scientists has a different character than an action group. The emphasis is on conducting research to gain insight. Social change requires new narratives and new ways of organising and framing issues. Citizens can use citizen science to give urban environmental quality greater visibility in the public debate. However, this is where the reality of a short-term project clashes with the need for commitment, time and money for a long-term transition process. A living lab approach with a sensor network can serve as a means to gain more insight, but a one- or two-year project is relatively short to put the environment higher on the social agenda.
Learning outcome 4: The concept works, but more measuring points are needed
In addition to the social lessons, we have three lessons for environmental monitoring. First of all, peak values. An initial concern of the municipality was the possible strong reaction of citizens to the high peak values sometimes recorded by sensors. Although citizens were generally able to accurately estimate peak values, a denser monitoring network is desirable. With the principle of redundancy (extra measuring points that create overlap) in a sensor monitoring network, measurements can be verified and calibrated, but this requires many more sensors at shorter distances in a single use case.
Learning outcome 5: A detailed picture of air quality at street level has not yet been achieved
In response to the follow-up question of whether low-cost sensors can provide a detailed picture of air quality at street level, the answer is that this type of air quality imaging was not yet sufficiently developed at the time of the study. This requires further technical development and optimisation of the measuring sensors and data processing. There appeared to be no sensor locations where unexpectedly high levels of air pollution or noise were concentrated. The lesson here is to provide participants with methods to conduct further research themselves. This alone gives citizens a form of empowerment.
Learning outcome 6: Combinations of measurement and calculation are the way forward
Some residents were disappointed in their expectations, as they had hoped to be able to compare the measurement results with national models and discover major gaps, errors or differences.
Gradually, however, a picture emerged that the RIVM's traffic models actually calculate traffic emissions quite accurately. That in itself is a clear result. Having observed this for themselves, some citizens said they felt reassured that local air pollution in Nijmegen is not as bad as they had thought, now that it has been measured at many busy locations in the city.
