Probabilistic Screening Method for Variable Emission Inputs to AERMOD

Abstract Description: Traditional air dispersion modeling relies on conservative assumptions, often overestimating atmospheric pollutant concentrations by using maximum emission rates and other worst-case conditions. This approach may not accurately reflect real-world situations, particularly for sources with variable emissions. Incorporating the variable emission rates directly into AERMOD is infeasible due to calculation costs and nature of the regulatory default model code. To address this, we compared results from a novel probabilistic Monte Carlo screening method with those from traditional maximum emission rates to better understand how real-world emission variability affects modeled pollutant concentrations. This study evaluates the concentration impacts of nitrogen oxides (NOx) emissions from a virtual kraft pulp mill located in the United States, with multiple emission sources, including a recovery furnace and lime kiln. A base regulatory-default application of an AERMOD simulation, using maximum emission rates, predicted a maximum concentration of 50.4 ppb for ambient nitrogen dioxide (NO₂), representing a worst-case scenario. In contrast, using emission rates derived from the 95th and 99th percentile screening concentration impacts, estimated maximum ambient NO₂ concentrations ranged from 31.4 to 34.4 ppb. These results are lower than the concentration estimated by the base AERMOD simulation, indicating that accounting for emission variability provides a more realistic estimate of concentration ranges.