AQSens: An Interoperable Platform Integrating Citizen Science and Authoritative Air Quality Data

Over the last few years there have been many activities to evaluate and use air quality measurements gathered by lower cost devices. This is especially intended to complement the coverage of official air quality measurement networks that deliver authoritative air quality data. Examples of such activities include the AirSensEUR project of the Joint Research Centre (JRC), luftdaten.info, or hackAIR. Combining the data from multiple sources remains a challenge for utilising the full potential of those developments.With this presentation we aim to introduce the development of an interoperable data platform that allows to integrate both authoritative as well as citizen science air quality measurements. Our presentation will cover especially the following aspects:Interoperability: For sharing the collected data and to avoid the creation of isolated data silos, it is important to use open interfaces and data encodings. In case of the AQSens project, this comprises the provision of INSPIRE-compliant Download Services based on the SensorThings API (STA) and Sensor Observation Service (SOS) standards of the Open Geospatial Consortium.Data analytics: Besides providing access to the raw data, different types of data analysis are necessary. On the one hand this comprises the validation of incoming citizen science data in conjunction with corresponding authoritative data sources. On the other hand, the aim is to provide a tool for further data analysis on top of the collected data. For this purpose we show, how the R programming language can be linked to the Sensor Web Server via a dedicated R package (sos4R).Data visualisation: Finally, for enabling the visual exploration of the collected data, a Web-based client application will be provided. This allows users to connect to the published air quality Data Download Services (in this case the OGC SensorThings API) and to request graph-based time series visualisations combining data from potentially different sources.In summary, our presentation will show how existing interoperability standards as well as Web technologies can be used for building a Cloud-ready data platform (i.e. relying on Docker) that enables the collection, management, analysis, and visualisation of both Citizen Science and authoritative air quality data.

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Tackling Data Quality When Using Low-Cost Air Quality Sensors in Citizen Science Projects

Frontiers in Environmental Science ◽

10.3389/fenvs.2021.733634 ◽

2021 ◽

Vol 9 ◽

Author(s):

Ågot K. Watne ◽

Jenny Linden ◽

Jens Willhelmsson ◽

Håkan Fridén ◽

Malin Gustafsson ◽

...

Keyword(s):

Air Quality ◽

Data Quality ◽

Citizen Science ◽

Low Cost ◽

Quality Data ◽

High Data ◽

Science Projects ◽

Air Quality Measurements ◽

Air Quality Data ◽

Intended Use

Using low-cost air quality sensors (LCS) in citizen science projects opens many possibilities. LCS can provide an opportunity for the citizens to collect and contribute with their own air quality data. However, low data quality is often an issue when using LCS and with it a risk of unrealistic expectations of a higher degree of empowerment than what is possible. If the data quality and intended use of the data is not harmonized, conclusions may be drawn on the wrong basis and data can be rendered unusable. Ensuring high data quality is demanding in terms of labor and resources. The expertise, sensor performance assessment, post-processing, as well as the general workload required will depend strongly on the purpose and intended use of the air quality data. It is therefore a balancing act to ensure that the data quality is high enough for the specific purpose, while minimizing the validation effort. The aim of this perspective paper is to increase awareness of data quality issues and provide strategies to minimizing labor intensity and expenses while maintaining adequate QA/QC for robust applications of LCS in citizen science projects. We believe that air quality measurements performed by citizens can be better utilized with increased awareness about data quality and measurement requirements, in combination with improved metadata collection. Well-documented metadata can not only increase the value and usefulness for the actors collecting the data, but it also the foundation for assessment of potential integration of the data collected by citizens in a broader perspective.

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