An Overview of Predictive Analysis for Air Quality Measurements and Visualization

Air quality is closely connected to the well-being of humans, ecosystems, and wildlife, making its continuous monitoring and preservation as a prime importance. Various causes contribute to air pollution, exerting a notable influence on the degradation of air quality. Recent research employs machine learning, AI, data analysis, Internet of things and microcontroller-based devices to construct resilient models for monitoring and collect the data for predicting air quality. The incorporation of diverse datasets enhances their capacity to provide well-informed predictions and judgments. This overview study systematically abstracts the significance of Air Quality Index (AQI) and India Meteorological Department (IMD) data, synthesizing contemporary study in the realm of air pollution. It emphasizes on an array of data sources, advanced monitoring methodologies, and the deployment of forecasting models, particularly highlighting the integration of Machine Learning (ML) techniques for effective AQI measurement. Furthermore, the paper presents an in-depth examination of the interdependencies among various parameters post data preprocessing. It features a detailed analysis of the interplay between various air pollutants and meteorological factors post data preprocessing. Moreover, this study introduces visualization techniques to effectively present the complex dynamics of air pollutants and weather conditions, offering a comprehensive perspective on air quality determinants. By aligning with Sustainable Development Goals, it advocates for public health and clean energy, aiming to contribute to sustainable urban development and mitigate air pollution challenges.