2025

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Air Pollution Mitigation in Islamabad: A Data-Driven Approach Using Air Quality Index (AQI) And Climate Trends
(UMT, Lahore, 2025) MUQADDAS SATTAR
This study examines air pollution outcomes from an analytics framework and specifically looks at air quality index (AQI) from 2020-2024, one of the climate variables, temperature, and humidity. The primary pollutants specialist study was identified as PM2.5- and NO₂- and three models were developed using machine learning (random forest, ARIMA, and LSTM) to forecast air quality. The exploratory data analysis identified seasonal increase in pollution with climate variability, winter, and higher pollution levels due to temperature inversions. The optimal long-term prediction based AQI model was LSTM, although a kind of random forest model added a few predictor variables. This research found that combining air quality data with meteorological data did improve forecasting and potentially improved policies. The study also procured recommendations for real-time monitoring, sustainable transport, and greener public urban planning design to mitigate changes in air quality. The intent of this research was to generate a pragmatic model for operationalizing the way environmental scientists could align both predictive modelling and practice strategies into a sensible approach to better address urban air pollution challenges as demonstrated in regard to Islamabad and beyond.