Ensuring Data Quality in FIWARE-Based Air Quality Monitoring Systems: A Step-by-Step Approach

Ensuring high-quality data is essential for effective air quality monitoring, especially in smart urban environments, where real-time insights drive health and policy decisions. This paper presents a structured, step-by-step approach for developing FIWARE-based air quality monitoring systems with a strong focus on data quality. Addressing challenges, such as sensor heterogeneity, calibration, real-time data processing, and semantic interoperability, the paper outlines best practices for achieving accurate and reliable environmental data. The system design leverages open-source FIWARE components integrated with NGSI-LD Smart Data Models to ensure standardized data flows. Furthermore, the TwinAIR project is explored as a real-world implementation that enhances data quality through validation and digital twins for air quality trend prediction. The proposed architecture demonstrates how high data integrity can be maintained across the entire data life-cycle — from sensor deployment to actionable analytics. This work is a practical guide for system designers and a benchmark to achieve scalable, interoperable, and reliable air quality monitoring solutions.