It is difficult to have missed the recent ‘Volkswagon scandal’. The US Environmental Protection Agency claimed that Volkswagen rigged emissions tests by fitting a device that reduced emissions of Nitrogen Oxides (NOx) during testing. By law, all vehicles have to abide to local environmental emission standards. Since direct measures of air pollution health effects are difficult to measure, it is common to use thresholds limits of harmful pollutants used as proxies in regulations.
The problem facing researchers and governments is one of making air pollution visible. This can be done indifferent ways: from scientific measurements, government data and published indexes, through to political statements by ‘the public’, (like images of pollution during the recent Saharan Dust episode). Indeed exactly ‘what’ is made visible also functions to make air pollution into a political, environmental or public health concern. The invisible nature of air pollution means that these processes of making it visible are complex and multi-layered. Indeed, it is the nature of invisibility which means it these processes of ‘making visible’ have to operate through and across different kinds of visual, material and discursive fields.
Questions of what can and can’t be made visible are central to this. Attempts to ‘re-measure’ the health effects of air pollution suggest current approximations are under estimating air pollution. This has given rise to a series of alarming statistics on the ‘actual’ number of deaths resulting from air pollution. The World Health Organisation has claimed that air pollution is a ‘public health emergency’ which is killing millions and threatening to overwhelm health services across the globe. Yet, these ways of measuring and making air quality visible are always going to ‘miss’ certain relations. For example, NOx emissions are linked to the production of ground level Ozone, another potent irritant. As one researcher on an air pollution project explained, if the emissions data are wrong then all the air pollution models are wrong. The scale of the problem is not just the recording of air pollution emissions, but also estimates of other pollutants and how these effect each other. These in turn have an influence human health and any future policy initiatives.
To return to the Volkswagon scandal, what is most significant is the way in which ‘the problem’ has shifted the ‘politics of air’. The different visualisations of air pollution have moved and challenged the idea of air pollution. The image of air pollution has moved beyond ‘the laboratory’, where it is constructed, stabilised and measured (e.g. in car manufacturer vehicle emissions tests), into the ‘real world’ where air pollution is materialised and made visible as part of ‘everyday’ practices and processes (e.g. ‘real world’ driving ‘on the road’ and through self-monitoring/ tracking technologies). This shift is ambiguous, for what these data represent and how they are to be used remains rather unclear.
This uncertainty opens up a space for research to focus on the politics of air. Indeed, the Volkswagon scandal has meant that driving a car at a certain speed or buying a specific model becomes a data practice itself – it shapes how air pollution is measured and made visible. In this way, rather than focusing on how scientists and technicians collect data, we could ask what in fact gets counted as data practices in the first place.
Questions around what gets counted as data and data practices are significant, but so are inquiries into the politics of and within data which also shape the ‘mattering of air’. We need to accept that measurements of air pollution are inherently unstable and uncertain, so that our research remits include processes of verification and computation as well as sites of data production and use. These are opportunities to critically explore the playing out of the social relations and political framings of knowledge about health and the environment. As Upritchard has argued, big data are also sites where social inequality gets (re)produced by omission, through analytics and by unequal data access and production, and, so, ‘questions about who benefits from big data descriptions may need to be explored together’. Making air pollution visible through data silences some dimensions (its inscription in objects outside the lab) and obscures others (inequalities within the data). That the problem of air pollution is being materialised in objects outside the laboratory (through how people drive their cars), and in practices which don’t get counted as ‘data’ (like citizen science projects) means that the air pollution problem is being constructed in particular ways, and not being constructed in other ways.
Finally, this problem of ‘where’ to measure air pollution has always been internal to the scandal itself within which dichotomies of ‘on the road’ versus ‘in the lab’ are frequently drawn upon. A more encompassing notion of air, environments and bodies would perhaps enable a more extensive consideration of the politics of air beyond this current binary. Shapiro suggests the concept of air entanglements as a more productive way of studying scientific configurations of chemical processes, as well as individuals’ sensing of air through bodies. Air entanglements, then, are a means of moving between the different scales and spaces of air pollution in contemporary environmental health. Air entanglements, permit the studying of data to the data set, as well as many kinds of sensing practices, which can help us multiply and diversify our approaches to data practices of air pollution.
Studying the problem of making air pollution visible is also a process of opening up dialogue and extending what gets counted in the practice of measuring and responding to air. Researchers (from sociologists to GIS modellers) could then use data – their diversity and their omissions – to complicate not only what counts as a measure, but to also extend our understanding about what is and should be measured. Such considerations offer new modes of enquiry, for example: what can these different data practices learn from one another? And, in what ways are data re-configuring oppositions between, say, ‘reality’ and the ‘laboratory’? Posing questions such as these orientates our focus to the kinds of environments which are being made visible through difference and multiplicity, whilst at the same time paying heed to ‘the shadows’ and exclusions which inevitably emerge as a result.
About the authors: Emma Garnett is a Research Fellow at the London School of Hygiene & Tropical Medicine. Her research interests include the Sociology of science, public health and knowledge making (see her on twitter @Emmargarnett). Nerea Calvillo is Assistant Professor at the Centre for Interdisciplinary Methodologies, University of Warwick. Her research interests include environmental monitoring, science and technology studies and architecture.