The degree of certainty in epidemiological studies is probably limited more by estimates of exposure than by any other component. We present a methodology for computing daily pollutant concentration fields that reduces exposure uncertainty and bias by taking account of spatial variation in air quality. This approach, using elliptical influence functions, involves the optimum blending of observations from a monitoring network with gridded pollution fields predicted by the complex air quality model TAPM. Such fields allow more information to be incorporated in the exposure fields used in epidemiological studies, rather than having to assume that ambient exposure is the same across a whole city and/or that individuals remain at the one location for the duration of a study.