SILAM (System for Integrated Modeling of Atmospheric Composition) is a global-to-meso-scale atmospheric dispersion model developed by the Finnish Meteorological Institute (FMI).
It provides information on atmospheric composition, air quality, and wildfire smoke (PM<sub>2.5</sub>) and is also able to solve the inverse dispersion problem. It can take data from a variety of sources, including natural ones such as sea salt, blown dust, and pollen.
The FMI provides three datasets based on SILAM: a 4-day global air pollutant (SO<sub>2</sub>, NO, NO<sub>2</sub>, O<sub>3</sub>, PM<sub>2.5</sub>, and PM<sub>10</sub>) forecast based on TNO-MACC (global emission) and IS4FIRES (wildfire), a 5-day global wildfire smoke forecast based on IS4FIRES, and a 5-day pollen forecast for Europe.