As shown in the previous lesson, the EU's efforts to mitigate air pollution go back decades. It's an area for which continuous reporting has provided advanced sets of environmental data. However, the level of detail in the data sometimes falls short of what is needed for in-depth policy analysis at a local level. This challenge arises partly because the distribution of official air pollution monitoring stations is sparse. Government-owned reference stations provide a good regional average but with this data it's difficult to obtain representative coverage of air quality at a neighbourhood level.

Furthermore, reference stations used for official reporting have traditionally relied on sophisticated, costly stationary equipment. This approach has required that data collection and maintenance tasks are handled by individuals with specialised scientific background. While necessary for ensuring accuracy, this requirement has limited the opportunities for the wider public to contribute to air quality monitoring.

Citizen science helps to overcome limitations of regulatory efforts by empowering local communities to collect high-resolution data on their environment. This grassroots approach supports the provision of detailed spatio-temporal insights in urban micro-environments, complementing the broader but less granular data from official government sources.

Compared to reference stations, citizen science sensors are less accurate at the level of individual devices, a shortcoming they compensate for with scale. Aggregated data from numerous sensors in a citizen science network minimises the influence of individual inaccuracies. Outliers and errors encountered during measurements can be identified using statistical techniques, such as z-scores and the interquartile range method, to minimise their impact on a dataset.

Spatial distribution of reference stations (left) compared to a network of citizen sensors (right)

Still, data quality is an object of constant criticism. Policy makers, who are usually the intended users of citizen science results, more often than not feel reluctant to become them due to a lack of trust in findings. And it’s not just because the information can be erroneous or inaccurate; the whole experiment is often seen as unscientific. Anecdotal evidence encountered during the COMPAIR project shows why policy makers are sceptical. Many see citizen science projects as all about people having fun. There is too much focus on engagement at the expense of scientific rigour. What is often perceived as missing is evidence of quality protocols being followed to guide the whole experiment end-to-end.

Concerns are not limited to data quality; engagement too can be prone to biases as, traditionally, more privileged demographics, such as those with high-levels of education and social status, tend to be over-represented among citizen science participants.

Finally, with a few exceptions, citizen science projects are usually short-lived. Community-managed CS projects usually have less funding available than those managed by universities or research institutes. They run while the funding lasts or a few enthusiastic individuals continue to provide support. Without a credible sustainability pathway, citizen science projects risk undermining both interest and uptake by potential adopters, who might wonder - why should we use something that is temporary and is going to be discontinued after a short while?

Opportunities and challenges in citizen science

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