Indoor Air-Quality Data-Monitoring System: Long-Term Monitoring Benefits
Abstract
:1. Introduction
2. Materials and Methods
2.1. Framework
- IoT. The term "Internet of things" was coined by Kevin Ashton of Procter & Gamble in 1999, when he viewed Radio-frequency identification (RFID) as essential to the IoT, allowing computers to manage all individual things (all existing things). Presently, the IoT concept is that the pervasive presence of a variety of things or objects—such as RFID tags, sensors, actuators, mobile phones, etc.
- Low-cost IoT-based sensing. The low-cost IoT sensors enable the use of wireless communications and computing for interacting with the physical world. The relevant sensors could sense indoor environmental parameters such as IAQ, comfort, lighting, and acoustic conditions. Several systems [17,18,19] have been developed for monitoring indoor environmental conditions with low-cost sensors. The data quality generated by these sensors are often of questionable. The performance of different low-cost air-quality sensors vary from unit to unit, spatially and temporally, as it relies on different algorithms, the meteorological conditions and atmospheric composition [20]. The IAQ data-monitoring platform implemented in this study is low-cost sensor-based considering that high accuracy is not the top requirement for the targeted applications of this study. Instead, this platform is developed for purposes such as awareness raising and recommendation of sampling period selection for OSH legal compliance, which only demand the pollution level on a coarse scale. In addition, as shown in Figure 2, the accuracy of the proposed platform can be improved through data adjustment with professional instrument at each OSH regulatory spot-check in long periods, just by observing potential bias or sensor saturation.
- Network. The network e.g., IoT gateway, bridges sensor networks with the traditional communication networks. It settles the heterogeneity between various sensor networks, mobile communication networks, and the Internet (all computer networks) [21,22]. A single-board computer (SBC), such as Raspberry Pi, could provide low-cost and efficient gateway services based on emerging IoT standards.
- DLT. Blockchain, as the first DLT, was invented by Satoshi Nakamoto in 2008 to serve as the public transaction ledger of the cryptocurrency Bitcoin [23]. The main component of DLT is a distributed ledger, which is used as a distributed database maintained by a consensus protocol run by nodes in a peer-to-peer network. This consensus protocol replaces a central administrator, since all peers contribute to maintaining the integrity of the database [24]. With a decentralized and consensus-driven nature, DLT could provide reliable solutions, such as blockchain [23], Ethereum [25] and IOTA Tangle [26], to enable secure and tamper-resistant data storage and sharing.
- IOTA and the Tangle. IOTA is a tangle-based cryptocurrency designed specifically for the IoT industry. The IOTA tangle naturally succeeds the blockchain as its next evolutionary step by overcoming some of its fundamental limitations, such as scalability, high transaction fees, and vulnerability to quantum attack [26]. The main feature of the tangle is that it uses a Directed Acyclic Graph (DAG) for storing transactions instead of sequential blocks. In the Tangle, users need to perform a small amount of computational work to approve two previous transactions to issue a new transaction. This new transaction will be validated by subsequent transactions [27].
- Masked Authenticated Messaging (MAM). The main data communication protocol used for data sharing in IOTA is MAM. It enables clients to emit and access an encrypted data streams over the IOTA Tangle, regardless of the size or cost of a device [28]. MAM uses channels (Public/Private/Restricted) for message spreading. IOTA users can create a channel and publish a message of any size, at any time. A small amount of proof-of-work is required to allow the data to propagate through the network and to prevent spamming. Other users can subscribe to this channel through its address, and receive a message that is published by the channel owner.
2.2. Standards and Guidelines for OSH Assessment
2.3. IoT and DLT-Based Data-Monitoring System
2.4. Case Studies
3. Results and Discussion
3.1. Long-term Monitoring Benefits for OHS Assessment
3.2. Long-Term Monitoring Benefits for Regulating Working Conditions
3.3. Long-Term Monitoring Benefits for OSH Transparency
3.4. Long-Term Monitoring Benefits for Data Sharing by IOTA
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
DLT | Distributed Ledger Technologies |
GDPR | General Data Protection Regulation |
IAQ | Indoor Air Quality |
IoT | Internet of Things |
IIoT | Industrial Internet of Things |
OSH | Occupational Safety Health |
TLV | Threshold Limit Value |
TVOC | Total Volatile Organic Components |
Appendix A
Study | P | T | S | D | Tr |
---|---|---|---|---|---|
[3] | NO2, TVOC, PM0.3-10 | 8h working time in five working days, September 2015 | No | Aeroqual 200 (NO2, TVOC), Extech VPC300 (PM0.3-10) | No |
[10] | PM2.5, HCHO, CO2 | PM2.5 and CO2 entire year, HCHO in four seasons (sampling time: 20 min) | Yes | A: on-line monitoring system with Ikair ( CO2) and Yun (PM2.5) sensors B: on-site measurement for HCHO by spectrophotometry | No |
[43] | HCHO, CO2 | 4 h between 08:00 AM and 12:00 AM | No | A real-time occupational exposure monitoring system with Grove-HCHO and T6613C (CO2) | No |
[8] | NO2, O3 and 29 VOCs | One week between 20 and 27 December 2012 | No | Diffusive samplers | No |
[11] | 34 VOCs, NO2,O3 | Summer: 24 and 28 May 2010; Winter: February 21 and 25, 2011 | Yes | Passive samplers | No |
[12] | Temperature, humidity, HCHO, C6H6, C2HCL3, Pinene, Limonene, NO2, CO2, CO, PM2.5, VOCs, Radon, O3 | Monday to Friday, in both non-heating (26/09/2011-14/10/2011) and heating (23/01/2012-10/02/2012) | Yes | Diffusive samplers (HCHO, C6H6, C2HCl3, Pinene, Limonene, NO2, O3); Telair 7001 (CO2), aeroQUAL (CO), PM2.5 (Derenda LVS3.1/PMS3.1-15) | No |
[9] | PM2.5, PM10, CO2, CO, HCHO, and VOCs, O3 | 1 h | No | Lighthouse handheld 3016 (PM, temperature, humidity), WolfSense (CO2, CO, VOC and O3), htV-M (HCHO) | No |
[44] | PAHs | One month in April | No | Passive sampler | No |
[45] | VOCs, HCHO, acetone and O3 | During 4 h with a 40-m frequency | No | PRO-EKOS AT. 401X (HCHO, O3), gas chromatograph Voyager (VOCs and acetone) | No |
[46] | temperature, humidity, CO, CO2, PM10, NO2, HCHO, C6H6 and toluene, bacteria and fungi | 3–10 December | No | Passive bubblers (HCHO), passive bubbler (NO2), SKC passive sampler (VOCs) | No |
[47] | PM, noise, temperature, humidity | May 2009 (hot season) and February 2010 (cold season) | Yes | – | No |
[48] | Bacteria, fungi, dust, ammonia, and HCHO | 2 h | No | Passive sampler | No |
[49] | Eighteen PAHs | 28 days (May–June 2014) | No | Passive sampler | No |
[50] | PM | Pre-winter (November and early December 2013) and winter season (January and early February 2014) | Yes | MOUDI | No |
[51] | 17 VOCs | May 2015 | No | Passive sampler | No |
[52] | TVOC, 13 VOCs, PM2.5, NOx, O3 | Two weeks (working and non-working days) which starts from early morning (08:00 a.m.) to late evening (20:00 p.m.)during winter season of 2014 | No | Model EC 9810 series (O3), Model Ecotech Sernious 40 (NOx), Micro IV Single Gas Detector (CO), MiniVol™ TAS (PM2.5), PhoCheck 5000 photo-ionization detector (PID) (TVOC), NIOSH method (VOCs) | No |
[53] | benzene, toluene, ethylbenzene m,p-xylene and o-xylene (BTEX) | Winter (from 9 December 2013 to 17 January 2014) and Spring (from 24 March to 17 April 2014 | Yes | Passive sampler | No |
[54] | PM | Three weeks during the summer, autumn, and winter in 2014 and 2015 | Yes | OPS; TSI model 3330 | No |
[55] | HCHO and C6H6 | 45 min | No | Passive samplers | No |
[56] | HCHO | Second semester of 2010 and first semester of 2011 | No | Passive samplers | No |
[57] | VOCs | 24 h | No | Passive sampling | No |
[58] | Temperature, humidity, fungi, dust, endotoxins, CHO, VOCs, CO2, NO2 | Two seasons: October–Match; April–September | Yes | Radiello passive sampler (CHO and VOCs), Passam Ag passive sampler (NO2), Q-Trak (Temperature, humidity, CO2) | No |
[59] | PM2.5, PM10 | During rush hours (8:00 a.m.–12:00 p.m.) for one week per each season from June 2015–June 2016 | Yes | Dust-Trak | No |
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Pollutant | STEL (15 min) | Average over 24 h |
---|---|---|
CO2 | 30,000 ppm (54,000 mg/m3) STEL | |
CO | 200 pm (229 mg/m3) ceiling | |
Benzene | 1 ppm (3.2 mg/m3) ceiling (15 min) | |
Formaldehyde | 0.1 ppm (0.12 mg/m3) ceiling (15 min) | |
NO2 | 1 ppm (0.18 mg/m3) STEL | |
O3 | 0.1 ppm (0.2 mg/m3) ceiling | |
PM2.5 | 50 μg/m3 (EPA) | |
PM10 | 50 μg/m3 from EU air-quality standards |
Sampling Duration Time | Number of Samples |
---|---|
10 s | 30 |
1 min | 20 |
5 min | 12 |
15 min | 4 |
30 min | 3 |
1 h | 2 |
2 h | 1 |
No. | Sensor Name | Model | Functions | Range |
---|---|---|---|---|
1 | PM | KG-PM2 | PM2.5, PM10 Concentration Monitor | 0–1000 μg/m3 |
2 | HCHO | KG-HO2 | HCHO Concentration Monitor | 0–7 mg/m3 |
3 | TVOC | KG-TV2 | TVOC Concentration Monitor | 0–3 mg/m3 |
4 | C6H6 | KG-C62 | C6H6 Concentration Monitor | 0–320 mg/m3 |
5 | CO2 | KG-C22 | CO2 Concentration Monitor | 0–0.5% |
6 | CO | KG-C12 | CO Concentration Monitor | 0–500 ppm |
7 | NO2 | KG-N22 | NO2 Concentration Monitor | 0–20 ppm |
8 | O3 | KG-O32 | O3 Concentration Monitor | 0–20 ppm |
9 | T.H.I.N | KG-TN2 | Comfort Monitor (Temperature, humidity, illumination and noise) | T: −40–80°; H: 0–99.0% RH; I: 0–2000 Lux; N: 0–120 dB |
Characteristic | Site 1 | Site 2 |
---|---|---|
Section | workshop section | office section |
Year of construction | 35 | 46 |
Floor | 1 | 1 |
Number of occupants | 12 | 8 |
Total area (m2) | 200 | 100 |
Heating | No | Yes |
Ventilation | Natural | Ventilation System |
Windows | Single Glazing | Single Glazing |
Floor covering | Coating | Coating |
Facilities | One solvent printing machine, two caving machine, computers, furniture | Computers, furniture |
Cleaning schedule | Once a week | Everyday |
Working schedule | Flexible, 24 h, including weekends | Two shifts: 06:00–14:00; 14:00–22:00, only business days |
Smoking | Yes | No |
Nearby potential pollutant sources | No | No |
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Sun, S.; Zheng, X.; Villalba-Díez, J.; Ordieres-Meré, J. Indoor Air-Quality Data-Monitoring System: Long-Term Monitoring Benefits. Sensors 2019, 19, 4157. https://doi.org/10.3390/s19194157
Sun S, Zheng X, Villalba-Díez J, Ordieres-Meré J. Indoor Air-Quality Data-Monitoring System: Long-Term Monitoring Benefits. Sensors. 2019; 19(19):4157. https://doi.org/10.3390/s19194157
Chicago/Turabian StyleSun, Shengjing, Xiaochen Zheng, Javier Villalba-Díez, and Joaquín Ordieres-Meré. 2019. "Indoor Air-Quality Data-Monitoring System: Long-Term Monitoring Benefits" Sensors 19, no. 19: 4157. https://doi.org/10.3390/s19194157