Near-field dispersion modeling for regulatory applications

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Air Quality Modeling

Background:

• Near-field pollutant dispersion modeling is crucial for assessing industrial emissions.
• Existing models like ISCST3 show limitations in accurately predicting pollutant concentrations, especially at lower levels.

Purpose of the Study:

• To evaluate the performance of dispersion models in estimating near-field pollutant concentrations.
• To investigate methods for improving the accuracy of near-source air quality predictions.

Main Methods:

• Case studies in Barrio Logan, San Diego, and a tracer experiment at UC Riverside were used.
• Industrial Source Complex Short-Term Model (ISCST3) and other regulatory models were evaluated.
• A diagnostic study with a Gaussian dispersion model incorporating site-specific meteorology and lateral meandering was performed.

Main Results:

• ISCST3 underestimated low hexavalent chromium concentrations in Barrio Logan.
• Other models overestimated high concentrations and underestimated low ones.
• Incorporating lateral plume meandering in Gaussian models improved concentration estimates.

Conclusions:

• Accurate near-source concentration estimates require precise emission data, onsite micrometeorology, and accounting for lateral meandering.
• Lateral meandering is a key factor for improving dispersion model accuracy in the near field.