- freely available
Sensors 2016, 16(3), 403; doi:10.3390/s16030403
Abstract: 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. This in turn is creating both opportunities and challenges for policy-making and science. 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. 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. 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.
2.1. Citizen-Based Systems
- Difficult discovery of environmental sensor devices and networks, due to the lack of metadata and services that expose them;
- Spatial/temporal mismatch of observations and measurements deriving data from unevenly-distributed monitoring stations that do not always form networks causing difficulties in data reuse for initially-unintended purposes;
- Lack of interoperability between components (e.g., measurement devices, protocols for data collection and services) of acquisition and dissemination systems;
- Information silos, created by the use of standalone vocabularies that are bound to particular environmental domains, such as hydrology and air quality;
- Proprietary solutions for logging sensor measurements, which require custom code to be wrapped around the manufacturer’s software development kit;
- Accuracy of the pollution sensors, which, as described in , is the major fault in any environmental network of sensors due to their low sensitivity to ambient levels of air pollutants.
2.2. International Standards
3. AirSensEUR: An Interoperable Plug-and-Play Sensor Node
3.1. Open Hardware
- the Sensoric model (diameter of 16 mm, mounted with a TO5 connector),
- and sensors with a 32-mm diameter: e.g., the 7 series of City Technology or SGX Sensortech, the Membrapor “Compact” sensor series or the “B” sensor series of Alphasense.
3.2. Open Source Software
3.2.1. Sensor Host
3.2.2. Server Components
4. Use Cases
4.1. Monitoring for Regulatory Purposes
- reference methods that can be applied everywhere and for all purposes with a maximum measurement uncertainty of 15% for O, NO, NO and CO;
- indicative methods that can be applied in areas where a defined level, the upper assessment threshold (UAT), is not exceeded, and they permit a reduction of 50% of the minimum reference measurements where the UAT is exceeded, thus allowing one to diminish the cost of monitoring by reducing the mandatory number of reference methods. Indicative methods are associated with a DQO of 25% for NO, NO and CO, 30% for O;
- objective estimation that can only be implemented in an area of low levels of air pollution with a DQO of 75% for O, NO, NO and CO.
4.2. Monitoring for Informative Purposes
4.2.1. Fixed Measurements
4.2.2. Mobile Measurements, Outdoor/Indoor Environments and Citizen Observatories
4.3. Strategies to Ensure Data Quality of AirSensEUR
- establishing a deterministic model based on laboratory and field experiments based on a strict protocol of the sensor test ;
- as the AirSensEUR includes seven sensors, cross sensitivities may be solved in a multivariate system of equations;
- design of an active sampling system on top of the sensors to easily control the humidity of the air beam and to filter the gaseous interfering compounds;
- calibration at the field monitoring station using co-located pair of reference and sensor data. The types of calibration methods can include linear, multi-linear equations, sensor cluster coupled with artificial neural network (ANN), etc. A good comparison of these techniques is given in . ANN was found to be the most effective technique though requiring additional metal oxide (MOx) sensors not yet present on the AirSensEUR shield;
- future development of calibration facilities (including zero and span) directly on the sensor platform can be imagined. This solution, likely expensive, may only be adopted in association with the active sampling system a few points above. Both of them would use the same pneumatic system. While designing zero air using selective chemical filters seems possible, for example thriethanolamine (TEA) for NO, 1,2-di(4-pyridil)-ethylene (DPE) or indigo for O, the development of a span gas generator appears quite challenging.
5. Discussion and Conclusions
- “Plug-and-play” architecture, which is transparent, allows configuration of each individual component and can be adapted to different mobile and in situ use cases;
- First low cost sensor, expected to provide indicative measures, which would be within the bounds of the requirements of European Union’s Air Quality Directive ;
- Interoperable architecture, which is aligned by design with the legal requirements of the European Union INSPIRE Directive , thus being able to “plug” data into the rapidly evolving pan-European spatial data infrastructure;
- Possibility for the establishment of an open software/hardware/data community around the project through the adoption of a transparent approach, combined with the broad use of well-established open source technology;
- Technical capability for the implementation of on-the-fly calibration through the possibility to push data directly from each sensor node to the “R” statistical package, where calibration curves and other post-processing can be done.
Conflicts of Interest
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|1. Host with CPU at the back side||2. Sensor shield|
|3. Control panel||4. Battery|
|5. GPS Antenna||6. USB for GSM/GPS key|
|7. USB WiFi key||8. On/Off switch|
|9. USB power supply and battery recharging||9a. Wall power supply (220 V)|
|10. USB to Linux console to control CPU|
|1. Web transactions||AirSensEUR SOS-T client||Java application, pushing data (JSON POST transactions) from the host to a server when an Internet connection is available.|
|2. Storage||sqlite3||Local data storage on the sensor host.|
|PostgreSQL/PostGIS||Server-side storage, with a database schema suitable for the SOS implementation|
|3. Web services||SOS||Implementation of an INSPIRE-compliant SOS|
|TimeSeriesAPI||RESTful interface on top of the SOS web service|
|4. Clients||SensorWeb client||Mobile-friendly web client for interaction with observation data|
|Geoserver||Mash-up with other geospatial data and implementation of INSPIRE discovery and view services|
|5. Visualization||R||Post-processing of data (e.g., for calibration or further statistical analysis)|
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