Random time series are found everywhere in nature.  The Brownian motion of small particles; the price of assets (stocks) in financial markets; the diffusion of individual molecules through a membrane; the ballistic deposition of nano-particles onto a lattice substrate; and the time-varying concentration fluctuations at a point downwind from a pollution source all have a common dynamic description.  All are stochastic processes where the local rate of change of the variable has a natural drift back to some equilibrium state, combined with a random fluctuating component.  We will explore how a stochastic model for concentration fluctuations can be constructed for a fixed receptor (your nose), and used to generate a large ensemble of simulated concentration time series.  As a concrete example, these concentration time series will be used to estimate the event-to-event variability in how far downwind you must stand to avoid being killed by exposure to "identical" ruptures of a sour gas pipeline transporting a mixture of natural gas and hydrogen sulfide.  Finally, we will demonstrate the advantages of using direct event simulation (rather than probability distributions) when non-linear processes such as toxicology are involved.  An essential element of this demonstration will require breaking up a large sheet of peanut-brittle with a hammer, and having the audience digest the experimental data.