![]() ![]() Furthermore, interpolation techniques used in air quality modeling are usually country dependent, and not open enough, thus acting as “black boxes” with results that are very difficult, if at all possible, to evaluate and reproduce. It is thus difficult to combine observation data from different channels. At the same time, stations belonging to existing authoritative air quality networks are not dense equipment is expensive and the majority of available information technology solutions for data collection and exchange is vendor specific. ![]() That is why we consider that observation data collected by citizens, without a means to estimate the quality of observation data and/or compare to existing authoritative sources of information, should not be used as input to modeling and/or for decision making. While those activities achieve very good results in raising visibility and engaging citizens on the importance of air quality, they are still not able to provide sufficient quality for observation data. The approaches that those projects adopt are different, but they all rely on inexpensive hardware and establish a community of volunteers who are engaged in collecting observation data. In particular, in the field of air quality, many recent citizen science initiatives, such as, aim to complement and/or substitute official measurement networks in their attempt to monitor the quality of ambient air. Volunteers, also referred to as citizen scientists, empowered by inexpensive and readily available technology, are increasingly engaged in collecting and processing heterogeneous data, which has traditionally been collected by authoritative sources. The number of devices, interconnected into the Internet of Things (IoT) is expected to reach 50 billion in 2020. For Earth sciences, this is similar to the revolution caused by the use of remote sensing data during the 1970s. The ways in which we create, manage and make use of data is fundamentally changing under the influence of several interdependent factors. AirSensEUR is described from the perspective of interoperable data management with emphasis on possible use case scenarios, where reliable and timely air quality data would be essential. Within this context, the manuscript provides an overview of the AirSensEUR project, which establishes an affordable open software/hardware multi-sensor platform, which is nonetheless able to monitor air pollution at low concentration levels. There can be major benefits from the deployment of the IoT in smart cities and environmental monitoring, but to realize such benefits, and reduce potential risks, there is an urgent need to address current limitations, including the interoperability of sensors, data quality, security of access and new methods for spatio-temporal analysis. This in turn is creating both opportunities and challenges for policy-making and science. ![]() The widespread diffusion of sensors, mobile devices, social media and open data are reconfiguring the way data underpinning policy and science are being produced and consumed. ![]()
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