CO, NO2 and NOx urban pollution monitoring with on-field calibrated electronic nose by automatic bayesian regularization

Low cost gas multisensor devices can represent an efficient solution for densifying the sparse urban air pollution monitoring mesh. In a previous work, we proposed and evaluated the calibration of such a device using short term on-field recorded data for the benzene pollution quantification. In this work, we present and discuss the results obtained for CO, NO2 and total NOx pollutants concentration estimation with the same set up. Conventional air pollution monitoring station is used to provide reference data. We show how a multivariate calibration can be achieved with the use of two weeks long on-field data recording and neural regression systems. Also for these pollutants, no significant performance boost was detectable when longer recordings were used. The influence of an appropriate feature selection for achieving optimal performances is also discussed comparing long term performance results of the obtained calibrations. Benefits and issues of multivariate correlation based calibration are evaluated during one year long measurement campaign.