Health and safety considerations of indoor occupants in enclosed spaces are crucial for building management whichinvolves the strict control and monitoring of carbon dioxide levels to maintain acceptable air quality standards. For this study, wedeveloped a wireless, noninvasive, and portable platform for the continuous monitoring of carbon dioxide concentration in enclosedenvironments, i.e., academic rooms. The system aimed to monitor and detect carbon dioxide using novel low-cost metal oxide-basedchemoresistive sensors, achieving sensing performance comparable to those of commercially available detectors based on opticalworking principle, e.g., nondispersive infrared sensors. In particular, a predictive study of carbon dioxide levels was performed byexploiting random forest and curve fitting algorithms on chemoresistive sensor data collected in an academic room, then comparingthe results with lab-based measurements. The performance of the models was evaluated with real environment conditions during 7weeks. The field measurements were conducted to validate and support the development of the system for real-time monitoring andalerting in the presence of relevant concentrations (above 1,000 ppm). Therefore, the study highlighted that the curve fitting modelobtained was able to recognize with an F1-score of 0.77 the presence of poor air quality, defined as concentration above 1,000 ppm ofcarbon dioxide as reported by the Occupational Safety and Health Administration.
Novel Chemoresistive Sensors for Indoor CO2 Monitoring: Validationin an Operational Environment
Marco MagoniPrimo
;Arianna Rossi
Secondo
;Francesco Tralli
;Paolo Bernardoni;Barbara Fabbri;Sandro Gherardi;Vincenzo GuidiUltimo
2024
Abstract
Health and safety considerations of indoor occupants in enclosed spaces are crucial for building management whichinvolves the strict control and monitoring of carbon dioxide levels to maintain acceptable air quality standards. For this study, wedeveloped a wireless, noninvasive, and portable platform for the continuous monitoring of carbon dioxide concentration in enclosedenvironments, i.e., academic rooms. The system aimed to monitor and detect carbon dioxide using novel low-cost metal oxide-basedchemoresistive sensors, achieving sensing performance comparable to those of commercially available detectors based on opticalworking principle, e.g., nondispersive infrared sensors. In particular, a predictive study of carbon dioxide levels was performed byexploiting random forest and curve fitting algorithms on chemoresistive sensor data collected in an academic room, then comparingthe results with lab-based measurements. The performance of the models was evaluated with real environment conditions during 7weeks. The field measurements were conducted to validate and support the development of the system for real-time monitoring andalerting in the presence of relevant concentrations (above 1,000 ppm). Therefore, the study highlighted that the curve fitting modelobtained was able to recognize with an F1-score of 0.77 the presence of poor air quality, defined as concentration above 1,000 ppm ofcarbon dioxide as reported by the Occupational Safety and Health Administration.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.