Identifying efficient and effective air pollution and climate change mitigation policy requires approaches that connect emissions sources to impacts across scales. This session invites contributions on new computational methods for designing and evaluating potential interventions, as well as actionable findings from such analyses. Approaches may include: satellite imagery, machine learning and AI foundation models, data assimilation, low-cost or real-time monitoring networks, inverse and process-based modeling, optimization, and decision support. We welcome studies across diverse geographies and spatial scales that link policies to outcomes associated with greenhouse gases, air pollutants, and climate extremes (e.g., wildfires, mortality, morbidity, sea level rise, heat stress). Contributions may emphasize methodological advances, real-world applications, or societal relevance, including mitigation portfolio design, tradeoff analysis, and the identification of robust strategies under uncertainty. We especially encourage work that translates scientific innovation into actionable co-benefits for communities, regulators, and environmental managers.

• A - Atmospheric Sciences
• GC - Global Environmental Change