Description:

There is a growing wealth of multi-platform observations in the field of atmospheric chemistry, including ground based remote-sensing and surface measurement networks and an increasing number of satellite remote sensing instruments. A lot of work is also being done to develop multi-scale chemistry-transport models for near-real-time forecasts and reanalyses of air pollution. Methodologies are also being developed including data assimilation of different types of observations, to improve initialization of air quality forecasting models and build long-term chemical reanalyses. Downscaling and attribution methods are also developed in different regions of the world, in order to analyze and identify the origin of atmospheric pollutants at a region- or city-level scale.

These modeling studies require a good knowledge of surface emissions of all atmospheric compounds, at a high spatial and temporal resolution. These anthropogenic emissions are either provided by inventories, emission models, or are optimized through inverse modeling.

We welcome papers describing advances in the development of emission datasets, chemistry-transport models, air quality forecasting, data assimilation techniques, inverse modeling, attribution techniques, atmospheric composition reanalysis as well as studies using observation and simulation results to advance our understanding of the composition of the atmosphere.