A multivariate geostatistical technique was presented in the first part of this paper (Rouhani et al. , 1992, Atmospheric Environment 26A , 2603–2614) with the objective of addressing two key issues of trend detection and network evaluation of the acid deposition data. The investigated data include weekly reported SO 4 2− concentrations and depositions from 34 level-1 stations of the NADP/NTN. The duration of the available data ranges from 6 to 10 years. In order to extract the maximum amount of information from these relatively short time series, it is imperative to avoid any data reduction, such as integrating the data into seasonal or annual series. Direct quadratic spectrum estimation is applied to the data, which clearly indicates the presence of annual cycles in a large number of stations. Based on these preliminary results, measurement stations are grouped into three main eco-regions, including the Northeast/central, the West and the Midwest/Gulf regions. While the first two regions exhibit significant annual periodicities, the latter region does not show any significant cyclic characteristics. This analysis is followed by multi-scale temporal variography that further confirms the presence of periodic trends. Two types of time series are generated by co-kriging: (1) the non-periodic components at each station, and (2) the non-periodic regionalized factors for each region. Kendall's test for trend detection is applied to all generated time series, as well as to the original data. The results indicate that by geostatistical filtering of periodic components, the proposed procedure offers an efficient technique for trend detection. Using the estimated coregionalization matrices, spatial correlograms are generated for various temporal scales and regions. Viewing the estimated integral scale of each spatial correlogram as an indicator of the radius of information-influence of each measurement station, the NADP/NTN network is evaluated for its adequacy of coverage under different temporal scales.