Remote Calibration strategies for Low Cost Air Quality Multisensors: a performance comparison

Recently, the state of the art field calibration approach for low cost air quality sensors have been criticized fort its lack of scalability. Actually the need for co-locating with reference analyzers each and every multi-sensors for a significant amount of time is definitely unfeasible if a truly pervasive commercial deployment of this technology is pursued. Remote calibration using post-deployment data stream along with those coming from nearby reference stations open source data can be a viable solution. This methodology can actually cope with sensors accuracy issues and can limit the effects of concept and sensors drift by continuous iteration of the calibration procedure. However, the imperfect match of the data recorded in different places which is inherent of the air quality spatial variance poses serious doubt on the generalization of this procedure. Careful reference station selection and data management is needed to extract the beneficial informative content from its open source data stream. Using a multi-seasonal multi-unit muti-location dataset, this work compares the performance obtained by different remote calibration strategies comparing them with state-of-the-art approaches quantifying the benefit obtainable by such methodologies and the performance impact of the calibration design choices.