Abstract
This special issue addresses emerging technologies and future directions in air quality research by integrating spaceborne observations with in situ measurements, data fusion frameworks, and advanced computational techniques. The collective findings of the contributing studies offer a valuable resource for researchers, practitioners, and policymakers seeking to understand and quantify air pollution across diverse environments. The methodologies presented across these papers establish a foundation for identifying pollution sources and characterizing pollutant transport and transformation processes at the regional scale, supporting the stabilization of air quality management systems. To reduce the health burden of ambient air pollution, the contributing authors collectively underscore the need to raise awareness around reducing anthropogenic emissions and to advance space-driven data fusion systems, including expanding monitoring infrastructure, operationalizing AI/ML-driven analytical pipelines, implementing science-informed emission control policies, and fostering meaningful community engagement.
| Original language | English |
|---|---|
| Journal | International Journal of Remote Sensing |
| DOIs | |
| State | Accepted/In press - 2026 |
Bibliographical note
Publisher Copyright:© 2026 Informa UK Limited, trading as Taylor & Francis Group.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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SDG 16 Peace, Justice and Strong Institutions
Keywords
- air quality
- particulate matter, AI/ML, anthropogenic emissions
- Remote sensing
ASJC Scopus subject areas
- General Earth and Planetary Sciences
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