Development of an automatic low-cost air quality control system: A radon application

Air pollution is the fourth-largest overall risk factor for human health worldwide. Ambient air pollution (outdoors) and household air pollution (indoors) cause about 6.5 million premature deaths. The World Health Organization has established that between 3% and 14% of lung cancer cases are due to r...

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Detalles Bibliográficos
Autores: Alvarellos González, Alberto José, López Chao, Andrea María, Rabuñal Dopico, Juan Ramón, García Vidaurrázaga, María Dolores, Pazos Sierra, Alejandro
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Consejo General de la Arquitectura Técnica de España (CGATE)
Repositorio:RIARTE
OAI Identifier:oai:www.riarte.es:20.500.12251/2540
Acceso en línea:http://hdl.handle.net/20.500.12251/2540
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102571924&doi=10.3390%2fapp11052169&partnerID=40&md5=58585e99fb0c0362967ce2a146ce03f0
Access Level:acceso abierto
Palabra clave:Calidad del aire interior
Gas radón
Contaminación
Sistemas dinámicos y control
Algoritmos
Ventilación (Construcción)
3308.01 Control de la Contaminación Atmosférica
3311.02 Ingeniería de Control
1207.02 Sistemas de Control
3108.04 Control Ambiental de Enfermedades
Descripción
Sumario:Air pollution is the fourth-largest overall risk factor for human health worldwide. Ambient air pollution (outdoors) and household air pollution (indoors) cause about 6.5 million premature deaths. The World Health Organization has established that between 3% and 14% of lung cancer cases are due to radon gas, making it the most important cause of lung cancer after smoking. This work presents a fully automated, low-cost indoor air quality control system that can monitor temperature, pressure, humidity, total volatile organic compounds (TOVC), and radon concentration. Using the radon concentration as an air quality measure, we created a prediction algorithm. The system uses those predictions to control a ventilation system automatically. We tested the algorithm for different prediction windows and compared the results with those without the ventilation system in a radon research room. In this room, the radon concentration is high 100% of the time, reaching a level eleven times higher than the recommended limit. The results show that the system can achieve an 86% reduction of the radon concentration, maintaining it low 90% of the time while having the ventilation system on during only 34% of the time. This work demonstrates that we can control air quality using low-cost resources, keeping a household or workplace safe but comfortable. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.