Abstract
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.
Highlights
The ongoing rapid development of smaller, cheaper sensors is radically expanding possibilities for air quality monitoring, and this new sensor technology has gained strong interest for air quality in many different fields of science as well as among the general public (Lewis et al, 2018; Lukeville, 2020; Peltier et al, 2021)
The quality assurance and quality control (QA/QC) needs and required level of expertise will depend on the level of data quality, it is a key to define a clear goal with the data collection
To ensure a robust data analysis that meets the project aim, it is important to clearly define the purpose of the data collection and level of data quality needed in an initial stage of the project
Summary
The ongoing rapid development of smaller, cheaper sensors is radically expanding possibilities for air quality monitoring, and this new sensor technology has gained strong interest for air quality in many different fields of science as well as among the general public (Lewis et al, 2018; Lukeville, 2020; Peltier et al, 2021). LCS are often used, for example, to engage citizens, to increase spatial coverage of measurements, or to assess hyper-local pollution variability 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 directives specify the metadata needed to document, for example, siting, maintenance and calibration procedures This is to ensure high data quality and comparability of measurements (European Parliament, Council of the European Union, 2008).
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