Bioremediation has been argued to be one of the most cost-effective remediation technologies available to reduce soil, sediment, or groundwater contamination, particularly because this approach may allow for the implementation of in-place strategies. Recent trends have advocated the application of innovative sediment stabilization strategies through placement of (reactive) capping material to allow long-term biodegradation of contaminants in these complex biogeochemical environments. The potential long-term risk reduction associated with this approach requires a demonstration of causal relationships between sediment or contaminant stability on the one hand, and microbial reactivity on the other. The spatial analysis needed to fully understand and quantify these correlations requires sensitive probabilistic techniques. Geostatistics has been used for the characterization of multi-scale spatial patterns for the last few decades, and the analysis of microbial attributes has shown significant spatial structures on microbial abundance and activity. However, there is a dearth of information on the applicability of geostatistics to quantitatively describe the interaction between the microorganisms and their environment. Using the Passaic River (NJ) dioxin data as a model dataset, multiple scaling models were applied to scale and interpolate sampled dioxin data and derive dechlorination signatures in sediments. Unlike conventional geostatistic tools that are based on the point-to-point spatial structures, the new multi-scale model (M-Scale) introduces a new framework for spatial analysis in which regional values at different scales are anchored by the correlations to each other. Spatial dioxin distributions and microbial dechlorination signatures were used as benchmarks for comparison of M-Scale to ordinary kriging. The results from cross-validation and jackknifing approaches applied to these datasets were analyzed and compared using Quantile-Quantile (Q–Q) plots and reproduction coefficients. These plots indicated that the M-Scale better preserves the local features of hotspots during data interpolation to a basin-wide scale. Current efforts focus on mapping microbial abundance and respiratory competence in the Anacostia River, based on measurements at three different scales. The outcomes of this work will be used to develop an uncertainty-based spatial decision tool for site remediation in this watershed using various capping strategies.