###### Abstract
\begin{table}
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**Study** & **Parameters** & **Objective** & **Main Results** \\ \hline [PERSON] et al. [7], 2019 & PM\({}_{2.5}\), PM\({}_{10}\), CO\({}_{2}\), NO\({}_{2}\) & & PM\({}_{2.5}\) associated with \\ [PERSON] et al. [7], 2019 & Temperature, Relative & Analyze the concentration & infiltration from \\  & Humidity & and sources of air pollution & outdoor/traffic pollution \\  & & Humidity & PM\({}_{10}\) indoors was higher than \\  & & & outdoors. \\ \hline [PERSON] et al. [17], 2019 & PM\({}_{2.5}\), NO\({}_{2}\), CO, CO\({}_{2}\) & Measure indoor concentration & Outdoor conditions (incl. \\  & Temperature, Relative & Measure indoor concentration & seasons), facility conditions were found to contribute to \\  & Humidity & and determine factors that & were found to contribute to \\  & Humidity & affect these levels & indoor pollution \\  & & & concentrations. \\ \hline [PERSON] et al. [18], 2017 & PM\({}_{10}\), SO\({}_{2}\) & Measure PM\({}_{10}\) and SO\({}_{2}\) levels & Found a significant correlation \\  & & for schools at different & between PM\({}_{10}\) and indoor \\  & & distances from mines & dust. \\ \[PERSON] [19], 2016 & CO\({}_{2}\), CO, TVOCs, NO, NO\({}_{2}\), & & Natural and mixed ventilation \\  & SO\({}_{2}\), total suspended & Measure air quality & systems recorded higher \\  & particulate matter, PM\({}_{10}\), & parameters in relation to & levels of NO\({}_{2}\); mechanical \\  & PM\({}_{2.5}\) & different ventilation systems & systems recorded higher \\  & & & levels of CO\({}_{2}\) and lower levels \\  & & & of PM than the others. \\ \hline [PERSON] et al. [20], 2015 & CO\({}_{2}\), TVOCs, PM & & Mechanical ventilation \\  & & Compare natural and & superior for controlling TVOC \\  & & mechanical ventilation & and CO\({}_{2}\) levels regardless of \\  & 2015 & CO\({}_{2}\), TVOCs, PM & systems for controlling indoor \\  & & air quality & occupancy levels. PM\({}_{1}\)/O \\  & & & ratios were lower for \\  & & & mechanical ventilation than \\  & & & natural ventilation. \\ \hline \hline \end{tabular}
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Table 1: Recent air quality studies in the literature. In recent years, advances in Internet of Thing (IoT) technology have created the support for various IAQ monitoring studies with differing goals and objectives. Additionally, the barrier to purchase and adopt these technologies has gradually lowered to the point where virtually any entity may purchase a set of sensors off the shelf, install them in the desired location, and connect them with the chosen data management platform. Previous IAQ studies with a focus on Internet of Things (IoT) technology development are summarized in Table 2.  
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A number of studies have been enabled through such IoT technology in school settings. [PERSON] et al. [7], through the implementation of indoor and outdoor sensors, conducted a study on monitoring PM levels at a New Zealand primary school. The result was used to identify the potential sources of PM and their impact on and contribution to fluctuation levels identified in the data collected. The data showed significantly higher PM levels (both PM\({}_{2.5}\) and PM\({}_{10}\)) when school children were present. As expected, average CO\({}_{2}\) concentrations were also higher when school children were present. Regarding NO\({}_{2}\), measured levels were higher on weekdays than weekends. Furthermore, these levels began to rise at 6 am and peaked at 1 pm before gradually decreasing. [PERSON] et al. [17] monitored both indoor and outdoor PM, NO\({}_{2}\), CO, CO\({}_{2}\), temperature, and relative humidity levels of 16 schools using a sensing system. Furthermore, they conducted a pre-study evaluation of the sites to record extrinsic (i.e., types of nearby roads) and intrinsic factors (i.e., building types and their heating, ventilation, and air conditioning (HVAC) systems), which were later used as explanatory variables for the monitored data. [PERSON] et al.