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A new method for estimating time-varying fluxes of atmospheric trace gases using an atmospheric transport model and observed concentrations is presented. Specifically Kaiman filtering is used to estimate inputs from a state-space model identified using unit-pulse response functions from a transport model. The method is new in that no assumptions about initial concentrations in the model are required, although this in turn means that all flux processes must be explicitly modeled as inputs linearly related to concentrations. This also means that at least one extra measuring-site or other measurement variable (e.g. a linear combination of emissions) than the number of input-fluxes being estimated, is required to ensure a stable Kaiman filter.