JoWUA

Abstract

Air pollution is a significant issue, particularly in urban areas, and many people are unaware of the air quality, which has adverse health effects. Therefore, countries must establish air quality standards. Monitoring air quality is expensive but necessary since pollution impacts daily life and the environment. Introducing an IoT-based air quality monitoring model that tracks pollution in various areas, such as industrial and residential zones. It collects and analyses data in the cloud to display pollution levels. The paper aims to develop a low-cost and very effective air quality monitoring and prediction model using the NodeMCU ESP8266 microcontroller with sensors for temperature, Humidity, and pollutants like CO2, NO2, PM2.5, and other harmful pollutants. Real-time data is sent to the cloud for analysis. Machine learning models, such as linear Regression and XGBoost, are used to predict the Air Quality Index (AQI) and provide current and future pollution levels by capturing the location, benefiting individuals with health concerns and urban planners, and highlighting that simple solutions can tackle serious air pollution challenges. The main aim is to develop a system capable of predicting air quality and forecasting upcoming pollution patterns, enabling users to take preventative measures. The emphasis is on creating a system that is cost-effective, portable, precise, and user-friendly, allowing it to be used in various contexts, including residences and public areas.