Situating Air Quality: 

Three Experiments in Data Activation


Monday 22nd January 2018, first day of the Save Our Air data sprint. Researchers associated with the Public Data Lab gather at King’s College London eager to encounter a series of local actors engaged around the question of air quality. Among these actors are Rachel and Pamela, from Camden Air Action, a group fighting against air pollution in the streets of Camden, a borough within central and north London. One of their campaigns measured air pollution around the 104 schools of Camden. Volunteers installed diffusion tubes on lampposts and monitored the level of NO2 and polluting particles for three months. The data they collected was analysed and projected on a map of Camden to visualise the pollution children are exposed to, to localise air quality, and to compel authorities to take action. Citizen science at the service of political action. But is this what the project actually achieved ?

According to Rachel and Pamela’s own account, “participatory evidence-based policy-making” is a questionable label for the data collection efforts. Is the data granular enough given that pollution levels seem to vary completely from street to street and according to time? Are protocols and results robust enough to compare with other sources of data? Are the data rich enough to provide evidence of toxicity and of responsibilities? Did data collection lead to any actual policy change?

This is not to say, of course, that Camden Air Action’s data collection did not have positive consequences – it had quite a few! It succeeded in creating a community of concerned citizens; it improved social cohesion in the neighbourhood; it empowered the activists and helped them to gain credibility; and raised the question of air quality in the public agenda. Mapping air pollution in Camden, it seems, played a different role than is usually associated with urban data.

It has become commonplace in social and political science to critique the notions of “smart cities” and “evidence-based policymaking” on the account that data, no matter how abundant or granular, is always partial and problematic and therefore opens more problems than it solves. While this posture has its merits – notably to counterbalance the hype that often shrouds the discussions about data and governance – it also has the disadvantage of discouraging social and political scientists from contributing to such discussions.

In this project, we tried to take a different approach. We considered the possible uses of air-quality datasets in urban governance from the viewpoint of social and creative research, but instead of pointing at the obvious shortcomings of those datasets, instead of showing how they betray the complex relations between social groups and natural environments, we decided to play along and give a chance to the idea that more and better data can improve public debate.

Taking such attitude, we discovered that such improvement is often characterised, by the actors of the urban debate, in terms of a greater “localisation”. For data to be more useful, we were consistently told, they needed to be “more local”. But what does “local” mean when applied to socio-environmental data? Clearly the expression refers to a better geographical referencing. It is one thing when the discussion hinges on a whole city average and another when it draws on a street-by-street reading. Increasing the precision with which air data is geo-localised has a powerful political effect. Its power comes from its capacity to ground the air that floats through our cities down to specific places: our homes, our streets our schools.

But geographical pinpointing is only one of the many techniques in which data can be activated in urban discussions. Quality and quantity, after all, seem to be secondary compared to the capacity of data to act (i.e. to make a difference) in a specific political situation. Data is more useful when it is closely related to the specific public concerns. This is why, for example, some civic groups insist on inviting citizens to the collection of air-quality data. Not because they distrust official statistics or need more granular information, but because participating in the collection of data spreads awareness and puts civic groups on the radar of local authorities.

Through our three experiments we tried to explore a few of these situating techniques. In the “My Air” experiment we combined an air quality sensor and a mobile phone to trace the air inhaled by secondary school pupils in Copenhagen. Anchoring the data not only in space but also in time personalises air quality and situates it in personal biographies and in collective enquiries. In the “Mobilizing Our Air”, we situated the issue of air quality within a larger landscape of public concerns. Finally, in the “Hot Potato Machine” we traced the finger-pointing between different institutions revealing how they apportion responsibility for taking action to do address air pollution.

Testing a plurality of situating strategies is important because it opens the air quality debate to a greater diversity of data, actors and issues. When air datasets are projected on individual trajectories through the city, the notion of evidence and data quality are deeply redefined (e.g. averages lose much of their interests, but the precision of the time-space association becomes crucial). More radically, our experiments revealed that relevant information includes not only the physical and chemical composition of the air, but also the individual habits and transportation preferences as well as the social composition of activists and institutions mobilised by the question.

Situating differently opens questions connected to the balance of power in the city and the role of the urban publics, which cease to be passive and undifferentiated audience and become co-investigator in the collection and analysis of data. Already interested and active, urban publics do not need to be informed or sensitized (as in a classic “public understanding of science” model), but demand to be included in all steps of data collection and analysis, to make sure that their concerns and viewpoints are fairly accounted for in these measures.

By being situated, the question of air quality becomes active in several other social and environmental issues and vice versa. Connecting data to specific political situations reveals how the question is never just about atmospheric pollution, but always also about urban nature, housing offer, transportation models, social and economic asymmetries, and the many other conversations among different actors and in different sites that we ought to have about public life in the city.

Our work in the Save Our Air project led to the production of three prototypes: a digital platform/social network, a teaching toolkit/measuring device and an interactive/data collection protocol. All of them are available as a functional proof of concept, but none of them is a fully finished operational product. However, all of them could become one, if actors other than researchers decide to pick them up (for example the platform could be developed by a civic tech start-up; the teaching toolkit by science educators, the blame game by a game designer).

The approach of the Public Data Lab is to organise social research as an open process – a process in which the research methods are developed at the same time as their results (the prototypes). But neither the methods nor their results are the specific object of our research. Instead, what we hope to hatch through our interventions are new “data publics”: publics that are not just the passive object of commercial and institutional monitoring, but who produce their own data actively and “by design”.