Published Paper Details:

It is increasingly being recognized that indoor air quality has an impact on human health. The increasingly serious indoor air pollution caused by PM, VC, and CO in recent years has attracted plenty of attention, which the world had not known before because of the politics, industry, and network caused by the urbanization development and industrialization. A statistical method to predict IAQ has been developed in this work, and an easy-to-use website-based framework, including the statistical model, was implemented. The system can provide air quality predictions from one to seven days in advance by integrating statistical forecasting methods with a stable IAQI calculation process. A flexible pipeline for data processing that supports different CSV-based inputs constitutes the basis of the approach. It is based on statistical forecasting with residual correction, scaling of pollutants, and calculation of the IAQI according to the Central Pollution Control Board (CPCB). The predictions are subsequently served to the users via a modern Flask application, enabling dataset uploads and visualisation of both numerical and categorical predictions. The system is an economical instrument that transforms statistical modeling information into a helpful decision-making framework for a policymaker with an emphasis on accessibility, reproducibility, and application.