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

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 contributewith their own air qualitydata. However, low data quality is often an issue when using LCS and with it a risk of unrealisticexpectations of a higher degree of empowerment than what is possible. If the data quality andintended use of the data is not harmonized, conclusionsmay be drawn on the wrong basis anddata can be rendered unusable.

Ensuring high data quality is demanding in terms of labor andresources. The expertise, sensor performance assessment, post-processing, as well as thegeneral workload required will depend strongly on the purpose and intended use of the airquality data. It is therefore a balancing act to ensure that the data quality is high enough for thespecific purpose, while minimizing the validation effort. The aim of this perspective paper is toincrease awareness of data quality issues and provide strategies to minimizing labor intensityand expenses while maintaining adequate QA/QC for robust applications of LCS in citizenscience projects.

We believe that air quality measurements performed by citizens can be betterutilized with increased awareness about data quality and measurement requirements, incombination with improved metadata collection. Well-documented metadata can not onlyincrease the value and usefulness for the actors collecting the data, but it also the foundation forassessment of potential integration of the data collected by citizens in a broader perspective.

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