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Geochemical systems are known to exhibit highly variable spatiotemporalbehavior. This may be observed both in non-smooth concentration curves in spacefor a single sampling time and also in variability between samples taken fromthe same location at different times. However, most models that are designed tosimulate these systems provide only single-solution smooth curves and fail tocapture the noise and variability seen in the data. We apply a recentlydeveloped reactive particle-tracking method to a system that displayshighly-complex geochemical behavior. When the method is made to most closelyresemble a corresponding Eulerian method, in its unperturbed form, we seenear-exact match between solutions of the two models. More importantly, weconsider two approaches for perturbing the model and find that thespatially-perturbed condition is able to capture a greater degree of thevariability present in the data. This method of perturbation is a task to whichparticle methods are uniquely suited and Eulerian models are not well-suited.Additionally, because of the nature of the algorithm, noisy spatial gradientscan be highly resolved by a large number of mobile particles, and this incursnegligible computational cost, as compared to expensive chemistry calculations.

Reactive Particle-tracking Solutions to a Benchmark Problem on Heavy Metal Cycling in Lake Sediments