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Comparative Analysis of Indoor Air Quality (IAQ) Monitoring Devices and Sensors

Table 1 provides a summary of devices available in the market in the context of pollutant measurement. The comparative analysis is carried out by studying industry and academic literature and is cited in the table accordingly.

 

Key aspects for comparison include:

Device/Product/Company: Information about the specific devices or products and the companies that produce them.

 

Pollutant Measurement Supported & Sensors Used: Types of pollutants these devices can measure and the specific sensors utilised for this purpose. We also provide further analysis of these sensors in Table 2.

 

Range & Accuracy: The measurement range of the devices and their accuracy levels in detecting pollutants.

 

Level of Access to Data: How accessible the data is to users and developers, which could imply anything from open access to restricted or proprietary data.

 

Calibration Techniques: Methods used for calibrating these devices to ensure accurate readings.

 

Licensing: Information regarding the licensing agreements or requirements for using these devices.

 

In-built Behavioural Models: Whether these devices and associated apps include integrated behavioural models, and if so, what those models entail.

 

Cost (£): The pricing of these devices or products.

 

Analysis from Use in Academic Research or Commercial Projects: Insights or outcomes derived from the usage of these devices in academic or commercial settings, focusing on their effectiveness, reliability, and overall impact.

Table 1 Comparative Analysis of IAQ Monitoring Devices

An IAQ (Indoor Air Quality) device is a tool designed to monitor and report on the quality of air within indoor environments. These devices can be composed of a number of sensors, each designed to detect and measure specific pollutants or air quality indicators such as particulate matter.  Table 1 covers a comparison of devices.

Sensors, on the other hand, are individual components within IAQ devices. Each sensor is specialized to monitor a particular pollutant or environmental parameter. Table 2 provides a comparative analysis of such sensors.

 

Table 2 Comparative Analysis of IAQ Sensors

In conducting a critical analysis of the devices, we observe several key dimensions for comparison: API access, calibration techniques, licensing restrictions, in-built behavioural models, and additional features.

API Access and Calibration Techniques: The majority of the devices, including Kaiterra Sensedge, Awair, Netatmo, Airgradient, Airthinx, Davis AirLink, Atmotube Pro, and Airthings View Plus, exhibit a trend towards private and restricted API access. This limitation indicates a proprietary approach to data management and a potential barrier to integration with broader smart home or environmental monitoring systems. The absence of disclosed calibration techniques across these devices raises concerns regarding the transparency and reliability of the data provided. In contrast, with its open API access and published open-source calibration techniques, the CARING device demonstrates a more user-centric and transparent approach, potentially enhancing reliability and user trust.

Licensing Restrictions and Customization: The restrictive licensing observed in most devices implies limited development customisation, which could impact the utility of these devices in diverse application scenarios. This aspect is crucial for users seeking to integrate these monitors into customised environmental monitoring solutions. The CARING device, offering more open licensing, emerges as a more adaptable option in this regard.

In-Built Behavioral Models: The absence of in-built behavioural models in all devices except the CARING device suggests a gap in advanced features. In-built behavioural models are precious in devices that aim to monitor and actively contribute to improving the conditions they are monitoring. They represent a blend of data science, user experience design, and sound behaviour modelling.

 

Additional Features and Pricing: The other features/issues, such as connectivity issues (Kaiterra Sensedge, Awair), size considerations (Netatmo), assembly requirements (Airgradient), and design aspects (Davis AirLink) contribute to the usability and aesthetic appeal of these devices. The pricing, ranging from £138 to £930, indicates a significant variance, with the CARING device positioned as a cost-effective solution, potentially due to its reliance on low-cost sensors.

The analysis reveals a predominant industry trend towards proprietary systems with limited user accessibility and customisation. The exception, the CARING device, with its open-source approach, offers greater transparency, adaptability, and potential for integration, presenting a more user-centric alternative. The absence of in-built behavioural models across all devices highlights a potential area for future development, aiming to enhance air quality monitors' predictive capabilities and overall utility. This comparative analysis underscores the need for a balanced approach that integrates technical robustness, user accessibility, and adaptability to meet diverse user needs in air quality monitoring. In conclusion, while these devices may find utility in commercial and enterprise environments, their application within academic research is severely hampered by their closed data ecosystems, lack of API access, subscription-based models, and, in some cases, questionable accuracy. Adopting these devices within an academic context would require a thorough cost-benefit analysis, ensuring that their limitations do not undermine the scientific integrity of the research conducted.

References:

 

[1]          J. Zhou, H. Wang, G. Huebner, Y. Zeng, Z. Pei, and M. Ucci, "Short-term exposure to indoor PM2. 5 in office buildings and cognitive performance in adults: An intervention study," Building and Environment, vol. 233, p. 110078, 2023.

[2]          S. R. Iyer et al., "Modeling fine-grained spatio-temporal pollution maps with low-cost sensors," npj Climate and Atmospheric Science, vol. 5, no. 1, p. 76, 2022/10/12 2022, doi: 10.1038/s41612-022-00293-z.

 

[3]          Y. Zhang et al., "Efficiency of portable air purification on public buses: A pilot study," Environmental Pollution, vol. 329, p. 121696, 2023/07/15/ 2023, doi: https://doi.org/10.1016/j.envpol.2023.121696.

 

[4]          J. Li, S. K. Mattewal, S. Patel, and P. Biswas, "Evaluation of nine low-cost-sensor-based particulate matter monitors," Aerosol and Air Quality Research, vol. 20, no. 2, pp. 254-270, 2020.

 

[5]          I. Demanega, I. Mujan, B. C. Singer, A. S. Anđelković, F. Babich, and D. Licina, "Performance assessment of low-cost environmental monitors and single sensors under variable indoor air quality and thermal conditions," Building and Environment, vol. 187, p. 107415, 2021.

 

[6]          S. Yun, "Assessment of Monitoring Strategies for Inhalation Exposure and Occupancy in Office Environments," EPFL, 2023.

 

[7]          S. Nyenhuis et al., "Utilizing Real-time Technology to Assess the Impact of Home Environmental Exposures on Asthma Symptoms: Protocol for an Observational Pilot Study," JMIR Research Protocols, vol. 11, no. 8, p. e39887, 2022.

 

[8]          W. Mueller et al., "Urban greenspace and the indoor environment: Pathways to health via indoor particulate matter, noise, and road noise annoyance," Environmental research, vol. 180, p. 108850, 2020.

 

[9]          M. S. Rahaman et al., "An Ambient–Physical System to Infer Concentration in Open-Plan Workplace," IEEE Internet of Things Journal, vol. 7, no. 12, pp. 11576-11586, 2020, doi: 10.1109/JIOT.2020.2996219.

 

[10]        N. Gao, M. Marschall, J. Burry, S. Watkins, and F. D. Salim, "Understanding occupants’ behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables," Scientific Data, vol. 9, no. 1, p. 261, 2022.

 

[11]        L. Chatzidiakou et al., "Schools’ air quality monitoring for health and education: Methods and protocols of the SAMHE initiative and project," Developments in the Built Environment, p. 100266, 2023.

 

[12]        M. L. Zamora, J. Rice, and K. Koehler, "One year evaluation of three low-cost PM2. 5 monitors," Atmospheric environment, vol. 235, p. 117615, 2020.

 

[13]        M. L. Zamora, J. Rice, and K. Koehler, "One year evaluation of three low-cost PM2.5 monitors," Atmospheric Environment, vol. 235, p. 117615, 2020/08/15/ 2020, doi: https://doi.org/10.1016/j.atmosenv.2020.117615.

 

[14]        A. Sedaghat et al., "Exploring energy-efficient building solutions in hot regions: A study on bio-phase change materials and cool roof coatings," Journal of Building Engineering, vol. 76, p. 107258, 2023.

 

[15]        K. Y. A. Albarracín, A. A. Consuegra, and J. Aguilar-Arias, "Particulate matter 10 µm (PM10), 2.5 µm (PM2. 5) datasets gathered by direct measurement, low-cost sensor and by public air quality stations in Fontibón, Bogotá DC, Colombia," Data in Brief, vol. 49, p. 109323, 2023.

 

[16]        S. Masri, J. Rea, and J. Wu, "Use of low-cost sensors to characterize occupational exposure to PM2. 5 concentrations inside an industrial facility in Santa Ana, CA: results from a worker-and community-led pilot study," Atmosphere, vol. 13, no. 5, p. 722, 2022.

 

[17]        G. Maguire, H. Chen, R. Schnall, W. Xu, and M. C. Huang, "Smoking Cessation System for Preemptive Smoking Detection," IEEE Internet of Things Journal, vol. 9, no. 5, pp. 3204-3214, 2022, doi: 10.1109/JIOT.2021.3097728.

 

[18]        M. R. Laurent and J. Frans, "Monitors to improve indoor air carbon dioxide concentrations in the hospital: A randomized crossover trial," Science of the Total Environment, vol. 806, p. 151349, 2022.

 

[19]        C. Quin, "Reviews-Consumer Technology. Gadgets: Wunda WundaSmart; Airthings View Plus; Keen Howser II; Harman Kardon Radiance 2400; ParcelHome; SLiDEE," Engineering & Technology, vol. 16, no. 10, pp. 68-69, 2021.

 

[20]        R. R. Kureshi et al., "Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring," Sensors, vol. 22, no. 3, p. 1093, 2022. [Online]. Available: https://www.mdpi.com/1424-8220/22/3/1093.

 

[21]        R. R. Kureshi, D. Thakker, B. K. Mishra, and J. Barnes, "From Raising Awareness to a Behavioural Change: A Case Study of Indoor Air Quality Improvement Using IoT and COM-B Model," Sensors, vol. 23, no. 7, p. 3613, 2023.

 

[22]        D. A. Hapidin, C. Saputra, D. S. Maulana, M. M. Munir, and K. Khairurrijal, "Aerosol chamber characterization for commercial particulate matter (PM) sensor evaluation," Aerosol and Air Quality Research, vol. 19, no. 1, pp. 181-194, 2019.

 

[23]        N. H. Nguyen, H. X. Nguyen, T. T. Le, and C. D. Vu, "Evaluating low-cost commercially available sensors for air quality monitoring and application of sensor calibration methods for improving accuracy," Open Journal of Air Pollution, vol. 10, no. 01, p. 1, 2021.

 

[24]        G.-H. Hong et al., "Long-term field calibration of low-cost metal oxide VOC sensor: Meteorological and interference gas effects," Atmospheric Environment, vol. 310, p. 119955, 2023/10/01/ 2023, doi: https://doi.org/10.1016/j.atmosenv.2023.119955.

 

[25]        K. Kaur and K. E. Kelly, "Laboratory evaluation of the Alphasense OPC-N3, and the Plantower PMS5003 and PMS6003 sensors," Journal of Aerosol Science, vol. 171, p. 106181, 2023.

 

[26]        N. Searle, K. Kaur, and K. Kelly, "Technical note: Identifying a performance change in the Plantower PMS 5003 particulate matter sensor," Journal of Aerosol Science, vol. 174, p. 106256, 2023/11/01/ 2023, doi: https://doi.org/10.1016/j.jaerosci.2023.106256.

 

[27]        M. Tagle et al., "Field performance of a low-cost sensor in the monitoring of particulate matter in Santiago, Chile," Environmental Monitoring and Assessment, vol. 192, no. 3, p. 171, 2020/02/10 2020, doi: 10.1007/s10661-020-8118-4.

 

[28]        M. F. R. Al-Okby, T. Roddelkopf, H. Fleischer, and K. Thurow, "Evaluating a Novel Gas Sensor for Ambient Monitoring in Automated Life Science Laboratories," Sensors, vol. 22, no. 21, p. 8161, 2022.

 

[29]        A. Božilov et al., "Performance assessment of NOVA SDS011 low-cost PM sensor in various microenvironments," Environmental Monitoring and Assessment, vol. 194, no. 9, p. 595, 2022.

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