Development of low-cost ammonia gas sensors and data analysis algorithms to implement a monitoring grid of urban environmental pollutants

Maria Chiesa; Federica Rigoni; Maria Paderno; Patrizia Borghetti; Giovanna Gagliotti; Maurizio Bertoni; Antonio Ballarin Denti; Lorenzo Schiavina; Andrea Goldoni; Luigi Sangaletti

doi:10.1039/C2EM30102D

Development of low-cost ammonia gas sensors and data analysis algorithms to implement a monitoring grid of urban environmental pollutants

Maria Chiesa,^ab Federica Rigoni,^b Maria Paderno,^a Patrizia Borghetti,^b Giovanna Gagliotti,^ab Maurizio Bertoni,^b Antonio Ballarin Denti,^ab Lorenzo Schiavina,^bc Andrea Goldoni^d and Luigi Sangaletti*^b

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^a Università Cattolica del Sacro Cuore, CRASL, Via dei Musei 41, 25121 Brescia, Italy
E-mail: maria.chiesa@unicatt.it
Fax: +39 030 2406742
Tel: +39 030 2406755

^b Dipartimento di Matematica e Fisica and Interdisciplinary Laboratories for Advanced Materials Physics, Università Cattolica del Sacro Cuore, Via dei Musei 41, 25121 Brescia, Italy
E-mail: sangalet@dmf.unicatt.it
Fax: +39 030 2406742
Tel: +39 030 2406709

^c EDOR M.Q. S.r.L., Italy
E-mail: lorenzo@edor.it

^d Sincrotrone Trieste, S.c.p.A., Basovizza, Trieste, Italy

Abstract

The present study is focused on the implementation of a novel, low cost, urban grid of nanostructured chemresistor gas sensors for ammonia concentration ([NH₃]) monitoring, with NH₃ being one of the main precursors of secondary fine particulate. Low-cost chemresistor gas sensors based on carbon nanotubes have been developed, their response to [NH₃] in the 0.17–5.0 ppm range has been tested, and the devices have been properly calibrated under different relative humidity conditions in the 33–63% range. In order to improve the chemresistor selectivity towards [NH₃], an Expert System, based on fuzzy logic and genetic algorithms, has been developed to extract the atmospheric [NH₃] (with a sensitivity of a few ppb) from the output signal of a model chemresistor gas sensor exposed to an NO₂, NO_X and O₃ gas mixture. The concentration of these pollutants that are known to be the most significant interfering compounds during ammonia detection with carbon nanotube gas sensors has been tracked by the ARPA monitoring network in the city of Milan and the historical dataset collected over one year has been used to train the Expert System.

Journal of Environmental Monitoring

Development of low-cost ammonia gas sensors and data analysis algorithms to implement a monitoring grid of urban environmental pollutants

Abstract

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