Joint Statement on new opportunities for air quality sensing - lower-cost sensors for public authorities and citizen science initiatives

Abstract

Low-cost air quality sensors continue to spread. While their measurement quality does not compete with high-end instrumentation deployed in official air quality monitoring stations, they have a great potential to complement existing air quality assessments. However, we still see challenges related to data quality, data interoperability, and for collaborating on data assimilation and calibration. In order to move ahead we gathered as a group of 38 organisations from 14 different countries, including governmental authorities, network operators, citizen science initiatives, environmental Non-Governmental Organisations (NGOs), and academic researchers to explore how we can collaborate and better leverage each other’s work. This statement captures our joint findings and recommendations. Our key observations include: Co-operation between official monitoring networks (reference quality data) and lower-cost sensor operators is a key to make air quality data more usable. To be able to combine forces and benefit from each other’s expertise, the different perspectives of all stakeholders should be taken into account. There is a need to ensure that all users understand the possibilities and the limitations of making sense out of observations from different sensors. It is not realistic to expect that in the near future the data quality of lower-cost sensors will be as good as that of the official data. A way to make use of data that is of lower accuracy is by employing them in air quality modelling. Transparency about data quality is important to build more trust in the data, and to avoid unrealistic expectations. The need for interoperability should be clearly articulated and promoted by potential data users. There a need (and an opportunity) to provide guidance and standard operating procedures for the deployment and calibration of lower-cost sensors in order to increase the data quality delivered by participants of citizen science projects. Presently, we prefer to consider fixed-stationary sensors in a network instead of mobile sensor data. Furthermore, stationary data should not be aggregated with data from mobile sensors. Publishing and sharing this statement is only small step in the right direction and further actions have to be taken, inlcuding more in-depth discussions of the recommendations in smaller groups and follow-up meetings on dedicated topics. Co-operation between official monitoring networks (reference quality data) and lower-cost sensor operators is a key to make air quality data more usable. To be able to combine forces and benefit from each other’s expertise, the different perspectives of all stakeholders should be taken into account. There is a need to ensure that all users understand the possibilities and the limitations of making sense out of observations from different sensors. It is not realistic to expect that in the near future the data quality of lower-cost sensors will be as good as that of the official data. A way to make use of data that is of lower accuracy is by employing them in air quality modelling. Transparency about data quality is important to build more trust in the data, and to avoid unrealistic expectations. The need for interoperability should be clearly articulated and promoted by potential data users. There a need (and an opportunity) to provide guidance and standard operating procedures for the deployment and calibration of lower-cost sensors in order to increase the data quality delivered by participants of citizen science projects. Presently, we prefer to consider fixed-stationary sensors in a network instead of mobile sensor data. Furthermore, stationary data should not be aggregated with data from mobile sensors. Publishing and sharing this statement is only small step in the right direction and further actions have to be taken, inlcuding more in-depth discussions of the recommendations in smaller groups and follow-up meetings on dedicated topics.

Advances in air quality research – current and emerging challenges

“…These developments point to a new era in detecting the quality of air which we breathe (Munir et al, 2019;Schade et al, 2019;Schäfer et al, 2021) where virtually everybody can measure air pollutants. Following this potential high number of sensors, fine-granular assessment of air quality in urban areas is possible at lower costs.…”

Citizen Science for Transformative Air Quality Policy in Germany and Niger

“…This represents both a more enabling and strategic involvement of CS researchers and CS associations to directly link to relevant agencies, actors and frameworks, ideally around SDGs indicators. With this, we emphasize the link between environmental agencies and CS projects [25][26][27], but urge alignment with SDG-specific monitoring processes while considering the risks of misuse, underuse and abuse of (environmental) indicators through an integrative perspective [28]. We are only aware of one similar attempt in the Polish context [29] that proposes the use of the SDG indicators framework, together with sensor-based distributed input, to map urban air quality and health through development of a new indicator on pollen.…”

“…Additional details about CS linkages with the SDGs can be found [52][53][54][55]. Many different groups and campaigns use low-cost air sensors for environmental monitoring [25][26][27]; however, there are no known instances of CS projects substantially influencing SDG reporting or implementation at the national level. In Germany, we mapped and contacted all major participatory air quality monitoring initiatives and either undertook document analysis of their policy impact or conducted interviews with their organizers.…”

Advances in Air Quality Research – Current and Emerging Challenges

“…These developments point to a new era in detecting the quality of air which we breathe (Munir et al, 2019, Schade et al, 2019, Schäfer et al, 2021 where virtually everybody can measure air pollutants. Following this potential high number of sensors, https://doi.org/10.5194/acp-2021-581 Preprint.…”