Design and prototype implementation of indoor air quality monitoring using LonWorks technology

K.P, Yogalakshmi; Sudha, R; Selvam, Chenthamarai

doi:10.1109/tiar.2015.7358556

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…Recent research has shown clear evidence that airborne transmission plays a significant role in many nosocomial infections [19]. Air is considered the "most important carrier" of hospital contaminations, indoor environments are characterised by many as sources of pollutants, and health concerns associated with poor IAQ have become increasingly relevant in recent years especially for public health infrastructure [20,21]. Thus, IAQ contributed to public health by conveying modern expertise to patients, hospital staff and doctors [22]; however, air quality in indoor environment is a serious concern, particularly in hospitals given the numerous residents [1].…”

Section: Introductionmentioning

confidence: 99%

Development of a real-time monitoring and detection indoor air quality system for intensive care unit and emergency department

2022

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To develop an Indoor Air Quality (IAQ) monitoring and detecting system based on a new Internet of Thing (IoT) sensory technology device that incorporated nine recommended indoor pollutants by the academic literature and reliable organizations, such as World Health Organization (WHO), Environmental Protection Agency (EPA), and International Organization for Standardization (ISO). The pollutants include Carbon Monoxide (CO), Carbon Dioxide (CO2), Nitrogen Dioxide (NO2), Ozone (O3), Formaldehyde (HCHO), Volatile Organic Compounds (VOC), Particulate Matter 2.5 (PM2.5) as well as air humidity and temperature that are used to assess the variety of indoor pollutants and provide a new IAQ pollutants dataset. Besides, the newly developed system provides real-time air quality monitoring, reports the pollutants’ data to a cloud platform (i.e., ThingSpeak), and can trigger early warnings as a service when abnormalities occurred in the air quality index. The system was tested to ensure its conformance to the recommended pollutants by collaborating with surgeons and specializing in IAQ in a hospital surgical intensive care unit (SICU), emergency department (ED), and in the women’s ward, which accommodate patients who are either newly born mothers (in case they need that) or who have had an operation, as well as pregnant patients who need to stay in the hospital to be under the supervision of medical care. Nine pollutants were identified and collected the pollutants dataset and their thresholds that affect the air quality within the hospital facilities and services (SICU, ED) to be used for assessing the effectiveness of the amount, concentration, and diversity of the pollutants. In the SICU, the concentrations of some pollutants were high in the beginning due to the residues of the previous surgery and because of the frequent use of sterilizers to clean and prepare the surgery room. Then, the concentrations of pollutants were moderate, but minutes after the start of the surgical, an increase in CO2 and formaldehyde was observed, which exceeds the threshold limit because of the use of anesthetic gas and sterilization. In the women’s ward, was all concentrations generally moderate except for particles matter PM2.5, and the same context with the 3rd installed location in the pharmacy of ED, most concentrations were moderate, except formaldehyde which exceeded the threshold. “CO” was the highest positive correlated and strongly correlated to “NO2” and that was expected because CO influences the oxidation of NO to NO2. On the contrary, the “CO” had the highest negative correlation with “VOC”, and the “NO2” had the highest negative correlation with “VOC”, chemistry is part of the responsibility for the weak correlation observed between the pollutants.

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Section: Introductionmentioning

confidence: 99%

Development of a real-time monitoring and detection indoor air quality system for intensive care unit and emergency department

2022

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Indoor air quality pollutants predicting approach using unified labelling process-based multi-criteria decision making and machine learning techniques

et al. 2022

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Development of the Internet of Things sensory technology for ensuring proper indoor air quality in hospital facilities: Taxonomy analysis, challenges, motivations, open issues and recommended solution

et al. 2022

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Design and prototype implementation of indoor air quality monitoring using LonWorks technology

Cited by 4 publications

References 4 publications

Development of a real-time monitoring and detection indoor air quality system for intensive care unit and emergency department

Development of a real-time monitoring and detection indoor air quality system for intensive care unit and emergency department

Indoor air quality pollutants predicting approach using unified labelling process-based multi-criteria decision making and machine learning techniques

Development of the Internet of Things sensory technology for ensuring proper indoor air quality in hospital facilities: Taxonomy analysis, challenges, motivations, open issues and recommended solution

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