IoT-Based Air Quality Monitoring System Design and Development Using ESP32

The objective of this study is to develop and construct an ESP32-based Internet of Things (IoT) air quality monitoring system. As part of the study technique, hardware and software that facilitate the monitoring of air quality metrics like $\mathrm{PM}_{2.5}, \mathrm{PM}_{10}, \mathbf{C O}, \mathrm{NO}_{2}, \mathrm{SO}_{2}$, and O<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>. Through an intuitive interface, users may monitor air quality thanks to the hardware’s ability to identify air contaminants and communicate data in real-time to a web-based platform. The results indicate that the system can reliably monitor air quality and supply values for the Air Pollution Index (API). A positive association is seen in the outcomes of sensor testing done using common equipment. According to the results of the regression study, the DFRobot Air Quality sensor for measuring $\mathbf{P M}_{2.5}$ parameters has an $R^{2}$ value of 0.9402 and for $P_{10}$ parameters has an $R^{2}$ value of 0.9175. The MiCS-4514 sensor for measuring CO parameters has an $R^{2}$ value of 0.8315, and NO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> parameters has an $R^{2}$ value of 0.8778. The MQ-136 sensor for measuring the SO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> parameter has an $R^{2}$ value of $\mathbf{0. 8 2 4 3}$, and the MQ-131 sensor for measuring the $O_{3}$ parameter has an $R^{2}$ value of 0.7838. This study presents a novel approach by integrating a wider range of pollutants into a single monitoring system and ensuring high accuracy through calibration and validation, thereby addressing existing gaps in current IoT air quality monitoring systems and enhancing the reliability and comprehensiveness of air quality assessments.