AI-Driven Air Quality Analytics and Decision Support Framework for Dhaka City

This work is among the first to combine AI-based ward-level air quality prediction with an actionable, tiered decision-support and control layer specifically for Dhaka — closing the gap between just 3 physical monitoring stations covering 111 wards, and translating raw predictions into concrete, low-cost, institutionally realistic intervention priorities rather than simply reporting pollution numbers.

More specifically, the contribution operates on three levels:

Technical contribution: Combines sparse ground-truth data (3 CAMS stations) with satellite-derived aerosol data (Sentinel-5P, MODIS) and an LSTM-based spatiotemporal model to estimate hyperlocal AQI across all 111 wards of Dhaka — extending air quality visibility far beyond what existing station-level or city-level reporting can offer.
Decision-support innovation: Moves beyond prediction alone by introducing a rule-based priority-tier system (Critical/High/Moderate/Safe) that maps each ward to specific, evidence-grounded control actions — informed by real Bangladesh-specific field evidence (Brooks et al., 2024 RCT on brick kilns), making the recommendations institutionally realistic rather than idealized.
Scalability and replicability: Relies entirely on publicly available data with no new hardware deployment required, making the framework low-cost and directly transferable to other pollution-burdened Bangladeshi cities (Narayanganj, Gazipur, Chattogram) and comparable South Asian urban contexts.