Chronic epidemiological studies of airborne particulate matter (PM) have typically characterized the chronic PM exposures of their study populations using city- or countywide ambient concentrations, which limit the studies to areas where nearby monitoring data are available and which ignore within-city spatial gradients in ambient PM concentrations. To provide more spatially refined and precise chronic exposure measures, we used a Geographic Information System (GIS)-based spatial smoothing model to predict monthly outdoor PM(10) concentrations in the northeastern and midwestern United States. This model included monthly smooth spatial terms and smooth regression terms of GIS-derived and meteorological predictors. Using cross-validation and other pre-specified selection criteria, terms for distance to road by road class, urban land use, block group and county population density, point- and area-source PM(10) emissions, elevation, wind speed, and precipitation were found to be important determinants of PM(10) concentrations and were included in the final model. Final model performance was strong (cross-validation R(2)=0.62), with little bias (-0.4 mug m(-3)) and high precision (6.4 mug m(-3)). The final model (with monthly spatial terms) performed better than a model with seasonal spatial terms (cross-validation R(2)=0.54). The addition of GIS-derived and meteorological predictors improved predictive performance over spatial smoothing (cross-validation R(2)=0.51) or inverse distance weighted interpolation (cross-validation R(2)=0.29) methods alone and increased the spatial resolution of predictions. The model performed well in both rural and urban areas, across seasons, and across the entire time period. The strong model performance demonstrates its suitability as a means to estimate individual-specific chronic PM(10) exposures for large populations.