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Enhancements to AERMOD's Building Downwash Algorithms based on Wind-Tunnel and Embedded-LES Modeling.

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This study enhances the AERMOD model for industrial stack effluent dispersion, improving building downwash scenario accuracy. New modifications better predict ground-level concentrations near buildings under various conditions.

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Area of Science:

  • Environmental Science
  • Atmospheric Science
  • Chemical Engineering

Background:

  • Accurate industrial stack effluent dispersion modeling is crucial for human health impact assessment.
  • Gaussian plume models like AERMOD are used to predict pollutant fate, but building effects pose challenges.
  • Existing AERMOD algorithms require refinement for complex building downwash scenarios.

Purpose of the Study:

  • To improve AERMOD's accuracy in modeling complex building downwash scenarios for industrial stack emissions.
  • To enhance the physical basis and internal consistency of AERMOD's building downwash algorithm.
  • To refine AERMOD's representation of elongated buildings in oblique winds.

Main Methods:

  • Incorporation of data from wind tunnel studies and large eddy simulations.
  • Development of three modifications to AERMOD's building downwash algorithm.
  • Implementation of one modification to AERMOD's building pre-processor for elongated structures.

Main Results:

  • The study presents three key modifications to AERMOD's core downwash algorithm.
  • One modification enhances the pre-processor for elongated buildings under oblique winds.
  • These improvements demonstrably enhance AERMOD's ability to model ground-level concentrations near buildings.

Conclusions:

  • The modifications significantly improve AERMOD's predictive capability for building downwash effects.
  • Enhanced AERMOD provides more reliable assessments of industrial effluent impact on human health.
  • The study validates improved modeling through comparison with wind tunnel and numerical simulation data.