## 2 Implementation of the System
To illustrate, the BME680 (Bosch Sensortec, Kusterdingen, Germany) employs a metal-oxide gas sensor to respond to a diverse array of airborne pollutants, whereas the CCS811 gauges VOC levels without isolating specific compounds. Our objective was not to achieve selective detection of individual VOC, which is why we selected these specific sensors. Nevertheless, selective VOC detection can be integrated into our system by incorporating additional sensors designed for that purpose. The Arduino Uno microcontroller acts as the central point of the system, receiving and processing sensor data [31]. It uses its processing capabilities to execute complex algorithms and evaluate the gathered IAQ parameters, providing a detailed assessment of the air quality in the monitored environment. The NX3224T028 (ITEAD Intelligent Systems Co., Ltd., Shenzhen, China) display serves as a user-friendly interface [32], connecting directly to the Arduino Uno through the UART port, which includes both RXD and TXD functionalities [33]. This configuration enables the display to promptly present air quality data to users, empowering them to make informed decisions and actively contribute to enhancing IAQ [34]. Additionally, the system includes an audible warning feature. This feature is activated by a single input to the speaker, which emits audible alerts when air quality levels exceed predetermined limits. This critical function acts as an effective alert system, facilitating swift action to address air quality concerns and enhancing the safety and healthiness of indoor environments [35]. The air quality monitoring system has a color-coded display that intuitively shows important air quality metrics. This display gives users an immediate overview of the IAQ in the observed area, including CO\({}_{2}\) levels and VOC concentrations. The integration of the color-coded display significantly enhances user interaction and accessibility, making it an ideal tool for a wide range of users, regardless of their technical background. This feature encourages users to actively participate in monitoring and improving their IAQ. The system is designed to provide an accessible and straightforward means for users to assess air quality, as demonstrated by the combination of the color-coded display and the detailed air quality index in Table 1. This highlights the system's role in promoting informed and proactive IAQ management. Table 2 shows the maximum possible measurement deviations for various air quality indicators in the air quality monitoring system. These deviations are in line with the specifications provided by the sensor manufacturers, confirming the system's measurement precision and reliability. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Air Quality Index (Color)** & **CO\({}_{2}\), ppm** & **IAQ, a. u.** & **VOC, ppb** \\ \hline Good (Green) & \(\leq\)700 & \(\leq\)50 & \(\leq\)200 \\ Moderate (Yellow) & 701–1000 & 51–175 & 201–400 \\ Sensitive (Orange) & 1001–1500 & 176–200 & 401–600 \\ Unhealthy (Red) & 1501–2500 & 201–300 & 601–1000 \\ Hazardous (Purple) & \(>\)2500 & \(>\)300 & \(>\)1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The ranges for the color-coded air quality indicators. An air quality monitoring system was developed using a moving average filter algorithm to enhance data processing efficiency. The filter window was set to encompass 10 measurements, which effectively reduced temporal noise and deviations inherent in sensor-derived data. The system was designed based on empirical analysis to balance noise mitigation and preservation of significant data trends [36]. It includes a moving average filter that utilizes a cyclic buffer for sensor data management. This configuration enables real-time data processing by dynamically updating with new measurements while discarding the oldest [37]. This results in the calculation of an average based on the ten most recent readings, providing a more precise representation of IAQ. The use of a moving average filter improves data quality by reducing noise interference.