Multivariate soil fertility relationships for predicting the environmental persistence of 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-tricyclohexane (RDX) among taxonomically distinct soils.

After nearly a century of use in numerous munition platforms, TNT and RDX contamination has turned up largely in the environment due to ammunition manufacturing or as part of releases from low-order detonations during training activities. Although the basic knowledge governing the environmental fate of TNT and RDX are known, accurate predictions of TNT and RDX persistence in soil remain elusive, particularly given the universal heterogeneity of pedomorphic soil types. In this work, we proposed overcoming this problem by considering the environmental persistence of these munition constituents (MC) as multivariate mathematical functions over a variety of taxonomically distinct soil types, instead of a single constant or parameter of a specific absolute value. To test this idea, we conducted experiments where the disappearance kinetics of TNT and RDX were measured over a >300 h period in taxonomically distinct soils. Classical fertility-based soil measurements were log-transformed, statistically decomposed, and correlated to TNT and RDX disappearance rates (k-TNTand k-RDX) using multivariate dimension-reduction and correlation techniques. From these efforts, we generated multivariate linear functions for k parameters across different soil types based on a statistically reduced set of their chemical and physical properties: Calculations showed that the soil properties exhibited strong covariance, with a prominent latent structure emerging as the basis for relative comparisons of the samples in reduced space. Loadings describing TNT degradation were largely driven by properties associated with alkaline/calcareous soil characteristics, while the degradation of RDX was attributed to the soil organic matter content - reflective of an important soil fertility characteristic. In spite of the differing responses to the munitions, batch data suggested that the overall nutrient dynamics were consistent for each soil type, as well as readily distinguishable from the other soil types used in this study. Thus, we hypothesized that the latent structure arising from the strong covariance of full multivariate geochemical matrix describing taxonomically distinguished "soil types" may provide the means for potentially predicting complex phenomena in soils. Published by Elsevier Ltd.