Dynamic multivariate regression for on-field calibration of high speed air quality chemical multi-sensor systems

In the last few years, the interest in the development of new pervasive or mobile implementations of air quality multi-sensor devices, has significantly grown. New application opportunities appeared together with new challenges due to limitations in dealing with rapid pollutants concentrations transients. In this work, we propose a Dynamic Neural Network (DNN) approach to the stochastic prediction of air pollutants concentrations by means of chemical multi-sensor devices. DNN architectures have been devised and tested in order to tackle the cross sensitivities issues and sensors inherent dynamic limitations. Testing have been performed using an on-field recorded dataset from a pervasive deployment in Cambridge (UK), encompassing several weeks. Results have been compared with those obtainable by static models showing the performance advantages of on-field dynamic multivariate calibration in a real world air quality monitoring scenario.