Emission reductions were mandated in the Clean Air Act Amendments of 1990 with the expectation of concomitant reductions in ambient concentrations of atmospherically-transported pollutants. To evaluate the effectiveness of the legislated emission reductions using monitoring data, this paper proposes a two-stage approach for the estimation of regional trends and their standard errors. In the first stage, a generalized additive model (GAM) is fitted to airborne sulfur dioxide (SO 2 ) data at each of 35 sites in the eastern United States to estimate the form and magnitude of the site-specific trend (defined as percent total change) from 1989 to 1995. This analysis is designed to adjust the SO 2 data for the influences of meteorology and season. In the second stage, the estimated trends are treated as samples with site-dependent measurement error from a Gaussian random field with a stationary covariance function. Kriging methodology is adapted to construct spatially-smoothed estimates of the true trend for three large regions in the eastern U.S. Finally, a Bayesian analysis with Markov Chain Monte Carlo (MCMC) methods is used to obtain regional trend estimates and their standard errors, which take account of the estimation of the unknown covariance parameters as well as the stochastic variation of the random fields. Both spatial estimation techniques produced similar results in terms of regional trend and standard error.