When presented with a multi-pollutant exposure problem, it is possible to adapt univariate methods by applying them independently to each pollutant. While these advanced statistical models for predicting pollution exposures can incorporate all important meteorological, geographical and land-use information and allow for spatiotemporal dependence between the pollution concentrations at close distances in space and time to obtain accurate exposure predictions for a single pollutant, applying the univariate models to each pollutant of the multi-pollutant scenario will ignore a potentially important source of information that lies in the correlations between the pollutants. We develop novel unified exposure prediction approaches for multi-pollutant data based on the idea of linking models in a chain. In the proposed approaches, we apply univariate models sequentially and the predicted exposures from each model are used in the subsequent models as an input. We also incorporate dimension reduction and variable selection techniques before applying each model. The methods are applied to simulated data with different covariance structures and to monitoring data from U.S. EPA.