International Research Journal of Education and Technology

It is predicted that the massive Internet of Things (IoT) would bring a plethora of applications for a completely connected world. The deteriorating state of the air in many biosphere zones can be attributed to human challenges such as growing globalization and urbanization. Many cities have air pollution levels that are higher than permitted by law and the World Health Organization (WHO) for gaseous and particle pollutants, which are present in amounts that are harmful to human health. Frequent exposure to high pollution intensities raises the death rate and increases the number of people suffering from respiratory conditions including asthma and chronic obstructive lung disease. A parameter can be used to measure the air quality called the Index of Air Quality. Particulate matter (PM2.5 and PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) are the main pollutants used to construct the air quality index. In this article, the deployment of a cloud-based Internet of Things (IoT) system for air quality monitoring is discussed. The sensors are utilized to determine the levels of CO, PM2.5 and PM10, O3, SO2, and NOx pollution together with ambient parameters like humidity and temperature. In this research, an Internet of Things (IoT) air quality index (AQI) monitoring system built on the ESP32 microcontroller platform is presented. The system gathers real-time data on different air pollutants, including particulate matter, nitrous oxide, carbon monoxide, ozone, and sulfur dioxide, using a network of sensors. In order to precisely anticipate air quality levels, the gathered sensor data is processed and analyzed using deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The ESP32 functions as a sturdy platform for data capture and preprocessing, making it easier to gather and process sensor data initially before incorporating it into deep learning models. The accuracy of air quality predictions is increased by the system's ability to extract spatial and temporal patterns from the sensor data through the incorporation of deep learning.

Keywords—WHO,ESP32,air quality