###### Abstract
[18] conducted a study of PM\({}_{10}\) and SO\({}_{2}\) at schools in South Africa located 1-2 km and 5 km+ from a mine. Additional sensors were carried by participants to measure the local area of respirable dust. Their study primarily focused on the health of the participants. They found that the mean concentrations of PM\({}_{10}\), SO\({}_{2}\), and respirable dust were significantly higher in schools that were closer to mines. Accessible off-the-self IoT devices and systems are the key enablers for a wide range of users to conduct continuous data monitoring and analyses at a low cost. IAQ monitoring studies are not limited to school settings. Similar studies were conducted for office buildings. [PERSON] and [PERSON] [19] conducted a monitoring study in 11 buildings in Sydney, Australia. These buildings varied in their ventilation types, being either natural, mechanical, or mixed. The purpose of their study was to compare the different ventilation types through comparisons of the corresponding IAQ parameters. These parameters included PM\({}_{10}\), PM\({}_{2.5}\), TVOCs, CO\({}_{2}\), and CO which were measured through a combination of high-end professional-grade sensors. These sensors and the IAQ monitoring framework enabled the researchers to conclude that the ventilation types had significant impacts on the air pollutant data. However, the authors did not provide a replicable methodology to implement and verify their IAQ monitoring study. There is a clear lack of detail regarding the sensor selection, implementation of the IoT framework, and a central hub to aggregate the data. An office air quality study by [PERSON] et al. [20] used the measurements of IAQ sensors to compare natural and mechanical ventilation systems in terms of governing standards. The parameters monitored were CO\({}_{2}\), TVOCs, and PM. On the one hand, they found that mechanical ventilation was effective in controlling CO\({}_{2}\) and TVOC levels, regardless of occupancy levels. On the other hand, natural ventilation was associated with increased CO\({}_{2}\) and TVOC levels as occupancy levels increased. With regards to PM, the indoor-to-outdoor ratio of PM was found to be higher for natural ventilation than the mechanical one. The above studies provide information on which sensors were used as well as the calibration methods in some cases. However, given that the focus of their respective studies, whether it be on identifying sources of IAQ parameters or using these parameters to evaluate HVAC systems, little detail information was provided to readers to replicate or verify the results. Such detailed information includes the methods of data communication between sensors and the database; the visualization of collected data; or the criteria by which sensors are selected. Apart from designing and evaluating IoT communication modules for IAQ monitoring applications, [PERSON] et al. [21] provided an in-depth investigation on the performance of gas sensors in detecting different TVOCs in the air. While their work provided insights on the characteristics of two gas sensors in the market, details on the calculation of the proprietary VOC index were not provided, which makes it hard to apply the findings in real-life applications. [PERSON] et al. [22] also proposed an IoT-based platform for monitoringhuman and plants' health in a home gardening system. The adoption of an open-source database system, i.e., MySQL, allowed them to have full control and ownership of their data. However, MySQL is not an optimal solution for storing and processing real time-series data for IAQ monitoring applications. Similar to the study in [21], [PERSON] et al. [22] gave attention to the HVAC conditions of the worksite rather than the implementation of the IAQ monitoring system. A list of sensors was provided with their respective sampling intervals and sensor accuracy. However, readers seeking to replicate such a study without the requisite knowledge may face tough challenges.