SILAM (System for Integrated Modeling of Atmospheric Composition) is a global-to-meso-scaleatmospheric dispersion model developed by theFinnish Meteorological Institute (FMI).
It provides information onatmospheric composition,air quality, andwildfire smoke (PM2.5) and is also able to solve the inverse dispersion problem. It can take data from a variety of sources, including natural ones such assea salt,blown dust, andpollen.[1]
The FMI provides three datasets based on SILAM: a 4-day global air pollutant (SO2,NO,NO2,O3,PM2.5, andPM10) 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]