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.
Dumka et al. (Mon,) studied this question.