From Measurement to Model: Monte Carlo, AERMOD and CALPUFF

Exposure monitoring is the foundation of occupational hygiene, but a finite set of samples can only describe the conditions that existed while you were measuring. Exposure modelling lets us reason about the conditions we did not sample — other shifts, other weather, other production scenarios — and to quantify the uncertainty around our estimates.

Probabilistic exposure with Monte Carlo

Real exposures are distributions, not single numbers. Monte Carlo simulation treats each input — emission rate, ventilation, task duration, near-field and far-field mixing — as a probability distribution, then runs thousands of iterations to produce a full picture of likely exposure. Instead of a single estimate, we report the probability of exceeding an occupational exposure limit, which is exactly the question a risk decision needs answered.

A 95th-percentile estimate with a stated confidence interval is worth more than a single 'representative' sample.

Regulatory dispersion: AERMOD and CALPUFF

When exposure leaves the fence line, atmospheric dispersion models predict how emissions travel to surrounding communities. AERMOD is the steady-state regulatory workhorse for near-field dispersion over relatively simple terrain. CALPUFF is a non-steady-state puff model suited to longer ranges, complex terrain and calm or variable wind conditions — the situations where AERMOD's assumptions break down.

• AERMOD — near-field, steady-state regulatory dispersion
• CALPUFF — long-range, complex-terrain, variable meteorology
• Monte Carlo — probabilistic workplace exposure under uncertainty

Used together, these tools turn a static survey into decision-grade intelligence — the difference between a report that describes the past and a model that informs the future.