Atmospheric Inversions to Quantify Emissions at Different Scales

Dear Colleagues,

The accurate monitoring (detection, localization, and quantification) of greenhouse gas (GHG) emissions at different scales is essential for effective mitigation and control policies. Atmospheric inversion techniques based on atmospheric GHG concentration measurements have been developed and used for the monitoring of these emissions. Observations of atmospheric constituents are the first element in the development and application of these inversion techniques, and depending on the scale of study and the detection limit of the targeted emissions, these can come from a fixed network of sensors, ground mobile platforms, aircraft, unmanned vehicles, drones, flux towers, satellites, etc. Atmospheric transport models are another essential element of the development and application of most atmospheric inversion techniques, and depending on the specific measurements, scale, and characteristics of a study domain, a range of atmospheric dispersion models from simple Gaussian models to computational fluid dynamics (CFD) models, Lagrangian models, and numerical chemistry transport models have been developed and used in the inversions. Although significant progress has been made in the development and application of atmospheric inversions and data assimilation techniques, uncertainty quantification methods, measurement strategies, and atmospheric transport models, the inversions of GHG emissions at different spatial-temporal scale still pose many challenges and uncertainties. Thus, this Special Issue will be focused on original research related to atmospheric inversion methods and related topics as well as their application to estimating emissions at different spatial and temporal scales. We also welcome detailed review papers related to atmospheric inversions and related topics.

Relevant topics include, but are not limited to:

• Atmospheric inversions and data assimilation techniques and their applications to estimate emissions at local and regional scales. 
• New measurement datasets/strategies for atmospheric inversions.
• Uncertainty quantification methods and analysis. 
• Improved/new atmospheric dispersion models and new observational dispersion datasets.
• CFD modeling for atmospheric dispersion and inversions.
• Improved understanding of boundary layer parameters and their significance within atmospheric dispersion and inversion models.
• Public health concern and emergency impact assessment of CBR (chemical, biological and radiological) releases.

Dr. Pramod Kumar
Dr. Gavendra Pandey
Guest Editors

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• atmospheric inversions and data assimilations
• atmospheric dispersion
• CFD modeling
• air quality modeling
• uncertainty quantification