On dispersion above a forest-Measurements and methods

Area of Science:

• Atmospheric Science
• Environmental Engineering
• Meteorology

Background:

• Traditional atmospheric dispersion modeling often relies on indirect measurements and generalized stability schemes.
• The Pasquill-Gifford stability scheme, while widely used, faces ongoing discussions regarding its applicability, especially in complex environments like forests.
• Accurate dispersion modeling is crucial for predicting downwind pollutant concentrations and ensuring regulatory compliance.

Purpose of the Study:

• To evaluate the efficacy of on-site, real-time turbulence measurements for atmospheric dispersion modeling in a mixed forest environment.
• To propose an improved methodology for dispersion modeling that utilizes direct turbulence data from sonic anemometers.
• To provide a more rational approach for dispersion modeling in forested areas, moving beyond legacy stability schemes.

Main Methods:

• Collected on-site meteorological data in a mixed conifer/deciduous forest using sonic anemometry.
• Measured velocity component standard deviations (σv and σw) in real-time.
• Compared the utility of direct turbulence measurements (σv, σw) against conventional parameters (σθ, σϕ) for dispersion modeling inputs.

Main Results:

• On-site, real-time measurements of velocity component standard deviations (σv and σw) are found to be preferable for dispersion modeling in forest settings.
• Directly measured turbulence data from cost-effective sonic anemometers facilitate improved extrapolation of release conditions.
• The uncertainties associated with extrapolating measured turbulence data are comparable to plume spread uncertainties, highlighting the importance of direct measurements.

Conclusions:

• Regulatory agencies and the applied dispersion modeling community should transition from traditional Pasquill stability classes to direct input of measured turbulence data.
• The adoption of three-component sonic anemometers offers an affordable and reliable method for meteorological monitoring, supporting enhanced dispersion predictions.
• Utilizing real-time turbulence data enhances the accuracy of predicting downwind concentrations and assessing exposure risks in forested environments.