Abstract. We investigate different methods for estimating anthropogenic CO 2 using modeled continuous atmospheric concentrations of CO 2 alone, as well as CO 2 in combination with the surrogate tracers CO, δ 13 C(CO 2 ) and 14 C(CO 2 ). These methods are applied at three hypothetical stations representing rural, urban and polluted conditions. We find that, independent of the tracer used, an observation-based estimate of continuous anthropogenic CO 2 is not yet feasible at rural measurement sites due to the low signal-to-noise ratio of anthropogenic CO 2 estimates at such settings. The tracers δ 13 C(CO 2 ) and CO provide an accurate possibility to determine anthropogenic CO 2 continuously, only if all CO 2 sources in the catchment area are well characterized or calibrated with respect to their isotopic signature and CO to anthropogenic CO 2 ratio. We test different calibration strategies for the mean isotopic signature and CO to CO 2 ratio using precise 14 C(CO 2 ) measurements on monthly integrated as well as on grab samples. For δ 13 C(CO 2 ), a calibration with annually averaged 14 C(CO 2 ) grab samples is most promising, since integrated sampling introduces large biases into anthropogenic CO 2 estimates. For CO, these biases are smaller. The precision of continuous anthropogenic CO 2 determination using δ 13 C(CO 2 ) depends on measurement precision of δ 13 C(CO 2 ) and CO 2 , while the CO method is mainly limited by the variation in natural CO sources and sinks. At present, continuous anthropogenic CO 2 could be determined using the tracers δ 13 C(CO 2 ) and/or CO with a precision of about 30 %, a mean bias of about 10 % and without significant diurnal discrepancies. Hypothetical future measurements of continuous 14 C(CO 2 ) with a precision of 5 ‰ are promising for anthropogenic CO 2 determination (precision ca. 10-20 %) but are not yet available. The investigated tracer-based approaches open the door to improving, validating and reducing biases of highly resolved emission inventories using atmospheric observation and regional modeling.