The paper discusses an adaptive-grid algorithm used in air pollution models. The algorithm reduces errors related to insufficient grid resolution by automatically refining the grid scales in regions of high interest. Meanwhile the grid scales are coarsened in other parts of the domain that are of lesser interest. This ensures a near-optimal use of available computational resources at all times during the simulation. The movement of the grid is controlled by a weight function formed as a linear combination of errors in various pollutant species. The user defines which species should be included in the weight-function calculation. After a brief description of the adaptive-grid model, the paper discusses its performance in an initial application to an ozone episode that occurred in the Tennessee Valley during July 1995. A mesoscale model at 4-km resolution over the region provided the meteorological inputs. The estimated ozone levels are compared to the observations from the Aerometric Information Retrieval System (AIRS) network for verification of the model.