A Data-Driven Air Quality Prediction Framework…

Air quality forecasting in semi-urban Indian regions remains significantly under-studied despite rising environmental risks and increasing public health concerns. Ahilyanagar, a rapidly developing district in Maharashtra, lacks transparent, downloadable, and continuous AQI data—resulting in decisions based on limited monthly classifications rather than granular pollutant measurements. In this study, I propose a hybrid analytical–predictive framework capable of utilizing categorical AQI records—representing the number of days per month falling under Low, Moderate, Poor, or Hazardous air-quality bands—from 2021 to 2025.

The model design includes three components: (1) extraction and structuring of historical categorical datasets, (2) visualization and seasonal pattern identification, and (3) development of a predictive engine using classical machine-learning techniques tailored for low-resolution data, including logistic regression, decision-tree classification, and baseline time-series models. Despite the absence of pollutant-level values (PM2.5, PM10, NO2, SO2, etc.), the proposed methodology demonstrates that useful trends and forecasts can still emerge from high-level classification data.

This work highlights the feasibility of deploying accessible forecasting tools for semi-urban municipalities, enabling early health advisories, better urban planning, and increased environmental awareness. The project also reinforces the role of student-led data science initiatives in bridging environmental information gaps in India. Future expansion—including API integrations, satellite-based feature engineering, and sensor-level monitoring—can further improve predictive reliability.