SILAM

It provides information on atmospheric composition, air quality, and wildfire smoke (PM_2.5) 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.^[1]

The FMI provides three datasets based on SILAM: a 4-day global air pollutant (SO₂, NO, NO₂, O₃, PM_2.5, and PM₁₀) 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.^[2]