Behind the scenes of planning for public participation: planning for air-quality monitoring with low-cost sensors

Abstract

We report from an environmental monitoring project planning for public participation to evaluate low-cost air pollution sensors. With an ethnographic approach, we studied how challenges were expressed and negotiated in discussions among project members when planning to involve the public in monitoring with low-cost sensors. Data was collected through participant observation of project meetings. Our analysis shows that perceived challenges involve data quality (i.e. reliability and validity), support, creating a sense of ownership and trust, as well as how to handle a possible rearrangement of power between authorities and the public. In order for the project to have control over different parts of the process when planning for public participation, they cannot stay true to all of the goals. This study contributes to the understanding of factors that foster the use of community-based data, and on the barriers for engaging the public in policy issues.

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

“…This highlights an important point: issues of power and empowerment are important to consider when LCS are used by municipalities in citizens science projects (Ekman and Weilenmann, 2021). It can be challenging for municipality when citizens expect a higher degree of empowerment by the collected data than what is the actual case (Ekman and Weilenmann, 2021), and often will data collected by citizens to document pollution be disregarded due to suspected bias (Garby, 2020). This issue can be avoided if the expectations on the data is harmonized with the possible use of the data.…”

“…Importantly, however, this will require a clear communication from the municipality on the expectations citizens can have on the data that is collected. This highlights an important point: issues of power and empowerment are important to consider when LCS are used by municipalities in citizens science projects (Ekman and Weilenmann, 2021). It can be challenging for municipality when citizens expect a higher degree of empowerment by the collected data than what is the actual case (Ekman and Weilenmann, 2021), and often will data collected by citizens to document pollution be disregarded due to suspected bias (Garby, 2020).…”

“…LCS are often used, for example, to engage citizens, to increase spatial coverage of measurements, or to assess hyper-local pollution variability. Although this low-cost sensor (LCS) technology have great potential for new strategies in air quality control (Peltier et al, 2021), the use of LCS data has been proven difficult due to inadequate data quality, caused by, for example, technical accuracy limitations, biases, and failing of measurement procedures to meet standardized regulation (Ekman and Weilenmann, 2021).…”

The grand challenges facing environmental citizen science

“…In these examples, the quality of the observations depends partly on the proper use of the sensor but much more on the quality of the actual sensing device. Whereas some devices and sensors used by citizens have had problems in the past and have led to inaccurate information, for example, on air quality (Ekman and Weilenmann, 2021), the reliability of these types of low-cost air quality sensors is increasing rapidly (Watne et al, 2021). In general, the cost of sensors as well as wearables has decreased dramatically, and many types of sensors (e.g., air quality sensors, temperature sensors, water quality sensors, etc.)…”

Wireless Sensor Network Dependable Monitoring for Urban Air Quality

“…Therefore, the needs and preferences of citizens should be considered in public-involved programs to implement of LWSN for environment monitoring. As reported in [11], residents may raise concerns on data quality, system reliability, operating support, ownership and the arrangement of power supply for monitoring systems installed at some locations in suburbs. Moreover, network security and data preservation should be taken into account when crowdsourcing data, e.g.…”