| Literature DB >> 29690534 |
Gonçalo Marques1, Cristina Roque Ferreira2, Rui Pitarma3.
Abstract
Occupational health can be strongly influenced by the indoor environment as people spend 90% of their time indoors. Although indoor air quality (IAQ) is not typically monitored, IAQ parameters could be in many instances very different from those defined as healthy values. Particulate matter (PM), a complex mixture of solid and liquid particles of organic and inorganic substances suspended in the air, is considered the pollutant that affects more people. The most health-damaging particles are the ≤PM10 (diameter of 10 microns or less), which can penetrate and lodge deep inside the lungs, contributing to the risk of developing cardiovascular and respiratory diseases, as well as of lung cancer. This paper presents an Internet of Things (IoT) system for real-time PM monitoring named iDust. This system is based on a WEMOS D1 mini microcontroller and a PMS5003 PM sensor that incorporates scattering principle to measure the value of particles suspended in the air (PM10, PM2.5, and PM1.0). Through a Web dashboard for data visualization and remote notifications, the building manager can plan interventions for enhanced IAQ and ambient assisted living (AAL). Compared to other solutions the iDust is based on open-source technologies, providing a total Wi-Fi system, with several advantages such as its modularity, scalability, low cost, and easy installation. The results obtained are very promising, representing a meaningful tool on the contribution to IAQ and occupational health.Entities:
Keywords: healthy buildings; indoor air quality (IAQ); internet of things (IoT); occupational health; particulate matter (PM); real-time monitoring
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Substances:
Year: 2018 PMID: 29690534 PMCID: PMC5923863 DOI: 10.3390/ijerph15040821
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
A short summary of similar type of research on IoT platform for real-time indoor air quality monitoring.
| MCU | Sensors | Architecture | Low Cost | Open-Source | Connectivity | Data Access | Easy Installation | |
|---|---|---|---|---|---|---|---|---|
| D. Lohani and D. Acharya [ | Arduino UNO, ESP8266 | Temperature, Relative Humidity, CO2 | IoT | √ | √ | Wi-Fi, BLE | Mobile | × |
| P. Srivatsa and A. Pandhare [ | Raspberry Pi | CO2 | WSN/IoT | √ | √ | Wi-Fi | Web | × |
| F. Salamone et al. [ | Arduino UNO | CO2 | WSN | √ | √ | ZigBee | × | × |
| S. Bhattacharya et al. [ | Waspmote | CO, CO2, PM, Temperature, Relative Humidity | WSN | × | √ | ZigBee | Desktop | × |
| F. Salamone et al. [ | Arduino UNO | Temperature, Relative Humidity, CO2, Ligth, Air velocity | IoT | √ | √ | ZigBee/BLE | Mobile | × |
MCU: microcontroller; √: apply; ×: not apply.
Figure 1PM types.
Figure 2iDust system architecture.
Figure 3iDust Connection Diagram.
Figure 4iDust installation schema.
Figure 5iDust Web application.
Figure 6Results of particulate matter concentrations obtained in the experiments conducted in a real environment: (a) PM10 (µg/m3); (b) PM2.5 (µg/m3); (c) PM1.0 (µg/m3).
Figure 7iDust Notification Architecture.
Figure 8iDust Wi-Fi configuration.
Figure 9iDust Notifications.