The air we breathe is a natural resource whose composition is critical for human and ecosystem health. Atmospheric particulate matter is an important driver for both air quality and global climate, as well as a vehicle for the spreading of contaminants in the environment. While significant effort has been put into understanding the sources and atmospheric processing of these aerosol particles, the processes through which they are removed from the atmosphere are much more poorly understood. The proposed research program addresses this currently overlooked topic in an innovative way, with a focus on atmospheric organic compounds. We will constrain the removal of organic particulate pollutants and their precursors by clouds, precipitation and wet surfaces, using a combination of molecular scale simulations and modeling of the dynamic cloud-formation processes. We will develop novel computational approaches to simplify these non-linear processes and better represent them in state-of-the-art air quality and climate models with reasonable computational cost, leading to improved estimates of the amount and properties of atmospheric particulate matter and its effects on air quality, pollutant deposition and climate. These improvements are necessary for evaluating the consequences of different political choices regarding e.g. emission standards, energy policies, land-use and urban planning.