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Chemical data assimilation is the process by which models use mea-1 surements to produce an optimal representation of the chemical composition of 2 the atmosphere. Leveraging advances in algorithms and increases in the available 3 computational power, the integration of numerical predictions and observations 4 has started to play an important role in air quality modeling. This paper gives an 5 overview of several methodologies used in chemical data assimilation. We discuss 6 the Bayesiandoi:10.3390/atmos2030426 fatcat:mntu64jmgndorb525ha4zv7qom