FAIRness and data quality assessment of urban air quality monitoring datasets: Perspective on insights from F-UJI evaluation.

Abstract

Advancements in information technology have supported the open availability of environmental monitoring datasets to aid global initiatives such as the United Nations Sustainable Development Goals (UN SDGs). Despite these efforts, challenges concerning data quality and adherence to FAIR (Findable, Accessible, Interoperable, Reusable) principles continue to restrict the effective reuse of such datasets, particularly for secondary applications. This study uses the F-UJI assessment tool and a set of eight established DQ dimensions to evaluate the FAIRness and Data Quality (DQ) of four publicly available urban air quality monitoring datasets from international agencies. Each dataset was assessed against 17 FAIR metrics and scored accordingly. The FAIR assessments revealed moderate to low levels of compliance across datasets, with Reusable scores ranging from 2 to 3 out of 10, and Interoperability often being the weakest dimension. DQ analysis showed recurring issues in consistency, completeness, interpretability, and traceability, particularly where metadata was poorly structured or lacked semantic depth. While the scope is limited to four datasets, the results highlight common structural and semantic deficiencies hindering data reuse. Based on these findings, the study offers targeted recommendations to support improved metadata practices and better alignment with FAIR principles within the air quality monitoring subdomain.