Beyond deterministic air quality modeling: a probabilistic screening approach for emission inputs in AERMOD.

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Computational Modeling

Background:

• Traditional air dispersion models often overestimate pollutant concentrations by using maximum emission rates.
• This overestimation does not reflect typical industrial operating conditions, especially for variable emission sources.
• There is a need for more realistic and data-driven modeling approaches.

Purpose of the Study:

• To develop and demonstrate a novel probabilistic modeling framework for estimating industrial air pollutant concentrations.
• To integrate emission rate variability into air dispersion modeling.
• To compare probabilistic modeling results with traditional deterministic approaches.

Main Methods:

• A probabilistic framework combining Monte Carlo screening with AERMOD was developed.
• The framework was applied to model nitrogen oxides (NOx) emissions from a virtual kraft pulp mill.
• Emission rates were derived using a data-driven approach, incorporating variability.

Main Results:

• A baseline AERMOD simulation using maximum emission rates predicted the highest ambient nitrogen dioxide (NO2) concentrations (worst-case scenario).
• The probabilistic framework, incorporating emission variability, yielded substantially lower estimated ambient NO2 concentrations.
• The results highlight the significant impact of emission variability on modeled pollutant levels.

Conclusions:

• Probabilistic air dispersion modeling provides a more flexible and accurate alternative to traditional deterministic methods.
• Integrating emission variability leads to more realistic assessments of industrial pollutant concentrations.
• The developed framework can be enhanced by including additional variability sources and applied to other pollutants.