Air quality management increasingly relies on science-based services alongside decision-support tools to address environmental and health challenges. Yet decision-makers frequently lack access to adequate tools and services to support effective responses. While existing air quality services employ diverse methods, including satellite observations, air quality models, air sensors, as well as artificial intelligence and machine learning, these efforts often operate in parallel, with limited awareness of each other’s practices, tools, and challenges. Effective air quality services require: i) bridging disciplinary boundaries between atmospheric science, public health policy, urban planning, and community engagement; and ii) tailoring the services to the regional or local contexts.

This session aims to bring together diverse perspectives on how to approach the development of air quality services in support of effective air quality management strategies. We are particularly interested in lessons learned from transdisciplinary efforts that integrate multiple disciplinary paradigms or adopt participatory research practices that extend beyond academic boundaries to involve stakeholders, practitioners, and end users actively. By showcasing regional and local-level experiences, the session seeks to demonstrate how diverse data spaces facilitate downstream services and highlight the pathways towards more effective, accessible, and sustainable air quality services.