MAESTRO - Air Quality Modeling and Forecasting: A Multi-Hazard Early Warning System and Policy Evaluation Framework

This project aims to advance the understanding and management of air pollution in Qatar and the region by developing a comprehensive modeling framework that integrates regional and local scales. The research is structured into four work packages (WPs): WP1 focuses on the operational forecasting of dust and air quality, combining high-resolution meteorology and chemistry models to predict pollutant levels and dust storms. This forecasting system will deliver actionable insights to support public health and policymaking. WP2 investigates the photochemical processes driving air quality, with sensitivity studies on emissions, meteorological factors, and secondary pollutant formation. By identifying key drivers, this work will guide strategies for reducing harmful pollutants like ozone and secondary aerosols. WP3 develops high-resolution spatial maps of primary pollutants, using detailed emission inventories. These maps will identify pollution hotspots, enabling targeted interventions to improve air quality. WP4 employs micro-scale dispersion modeling to assess pollutant behavior in urban environments. By simulating localized pollution dynamics in areas like traffic corridors and densely populated neighborhoods, this work will inform urban planning and mitigation strategies. The project will produce tools and datasets for air quality forecasting, mapping, and exposure assessment, contributing to sustainable urban development and public health protection. This research aligns with Qatar’s environmental priorities and offers a scalable framework for other regions facing similar challenges.

Hamad Bin Khalifa University (HBKU)