The term ''nanomaterial'' describes a preparation in which the particle size is on the order of 10 to 100 nm in diameter. Such particles have the ability to form suspensions in fluid media such as air and water that can dramatically increase the environmental transport potential in comparision with like materials of larger particle sizes. Quantifying such transport requires an ability to predict the stability of such suspensions as to their tendency to aggregate or interact with other environmental constituents. In this paper, we present a method for predicting the magnitude and uncertainty associated with nanoparticle suspension stability. The critical buoyancy properties are predicted using the Boltzmann equation. The rates of aggregation are then predicted on the basis of molecular collision and adhesion coefficients. The progress of particle growth is simulated across all potential pathways probabalistically using the Gillespie model to characterize the uncertainty. Discussion is provided regarding potential environmental applications and further potential development in predicting particle behavior and effects on the environment.

The term "nanomaterial" describes a preparation in which the particle size is on the order of 10 to 100 nm in diameter. Such particles have the ability to form suspensions in fluid media such as air and water that can dramatically increase the environmental transport potential in comparision with like materials of larger particle sizes. Quantifying such transport requires an ability to predict the stability of such suspensions as to their tendency to aggregate or interact with other environmental constituents. In this paper, we present a method for predicting the magnitude and uncertainty associated with nanoparticle suspension stability. The critical buoyancy properties are predicted using the Boltzmann equation. The rates of aggregation are then predicted on the basis of molecular collision and adhesion coefficients. The progress of particle growth is simulated across all potential pathways probabalistically using the Gillespie model to characterize the uncertainty. Discussion is provided regarding potential environmental applications and further potential development in predicting particle behavior and effects on the environment.

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