Irregularly emitting air pollution sources: acute health risk assessment using AERMOD and the Monte Carlo approach to emission rate

Abstract

Intermittent irregular sources of air pollution, when used in dispersion modeling, can exaggerate the acute health risk due to improbable coincidence of release with the worst-case meteorological conditions. This problem is alleviated by randomizing the moments of emission and applying the Monte Carlo method to obtain the realistic expected yearly maxima of hourly concentrations/risks. Emissions are modeled as irregular “pulses,” possibly with additional constraints on timing. Such are major emission sources in important industries: oil refineries, gas extraction, cement production, etc. We have tested the approach in ~100 projects for industrial plants in Russia and obtained considerable reductions in estimated acute health risks: up to two orders of magnitude, depending on the level of intermittency of sources. These corrections to unrealistically high worst-case concentration values at nearby populated areas are, in many cases, a key to obtaining reasonable exclusion/protection zones for plants. To our knowledge, such a body of results on intermittent irregular sources is unique, and it can be useful especially for developing countries where the exact timeline of emissions is often unknown. Taking intermittency into account is also known to be an important step toward compliance with 1-h US National Ambient Air Quality Standard. We provide a detailed description of the Monte Carlo algorithm used. We compare Monte Carlo with the usual quantile-based approach to peak values; they agree when quantile is dependent on intermittency. The non-linearity of maximum function gives rise to some counterintuitive phenomena, which call for refinement of risk definition for intermittent irregular sources.

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