Data reliability and fault diagnostic for air quality monitoring station based on low cost sensors and active redundancy

Abstract

Air pollution is both an environmental and societal issue. Conventional air quality monitoring involves costly measuring stations that demand specialized personnel. A more affordable and accessible alternative approach consists in the use of low-cost sensor networks (LCS). However, LCS have drawbacks such as variable component quality, unreliable measurements, and short lifespan. Previous research has addressed these problems through redundant LCS and expand measurement capabilities. Nevertheless, challenges remain regarding reliability. Specifically, the use of median aggregations tends to hide errors and failures, especially when most sensors are faulty. This paper proposes a new method to make data reliable and generate diagnostic through three indicators: Synthesis, Detection, and Indexes. These indicators enable the prediction of the station’s health status, identify faulty sensors, and assess the quality of the Synthesis with a confidence index. This approach ensures consistent data collection despite disturbances and malfunctions in the LCS and can be used for informed decisions and actions to mitigate air pollution. The initial implementation of this approach involves deploying the first station in the Communauté de Communes Pyrénées Vallée des Gaves (CCPVG) territory. The obtained results showed the adaptability of the proposed approach for future LCS implementations and long-term air quality monitoring.