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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.



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