Issues Related to Plume Meander in the AERMOD System

Issues Related to Plume Meander in the AERMOD System
Overview of Issues
AERMOD accounts for plume meander (i.e., the slow lateral back and forth shifting of the plume from
low frequency, non-diffusing eddies) as the plume travels downwind from the source. This is one of
many formulation enhancements to dispersion over AERMOD's predecessor, the Industrial Source
Complex (ISC) model. Meander decreases the likelihood of observing a coherent plume after long travel
times and results in a greater plume spread and increased dispersion downwind. Currently, plume
meander is only applied to point and volume sources within AERMOD and is not applied to area sources,
though an area source plume is expected to exhibit similar behavior downwind of the source.
In addition, under the current default options, AERMOD has shown a tendency to overpredict in low
wind conditions for some sources, especially during nighttime stable conditions. There is a need to
better understand how plume meander is affected in low wind conditions and its potential influence in
situations where overprediction occurs. As discussed in more detail in the next section, plume meander
in AERMOD consists of two limiting components: a coherent plume and a random plume (i.e., pancake
plume). The random plume results in some amount of the plume dispersed upwind of the source,
whereas the coherent plume maintains the entire mass of the plume downwind of the source.
EPA first provided beta options to address model overprediction for low wind conditions within
AERMOD version 12345. These beta options included:
• ADJ_U* which adjusts the surface friction velocity (u*) during stable, low wind conditions;
• LOWWIND1 which increases the minimum value of the lateral turbulence intensity (sigma-v)
from the default value of 0.2 m/s to 0.5 m/s; sigma-v is used to determine the lateral plume
dispersion coefficient (sigma-y); and
• LOWWIND2 which increases the minimum value of sigma-v to a value of 0.3 m/s.
A fourth beta option, LOWWIND3, was included in the release of AERMOD version 15181. LOWWIND3
also increases the minimum value of sigma-v to 0.3 m/s. In addition to modifying minimum sigma-v,
LOWWIND1, LOWWIND2 and LOWWIND3 each include changes from the default implementation of
plume meander that is applied when the AERMOD is run in the default regulatory mode. Meander was
not a consideration in the ADJ_U* option that was promulgated as a regulatory option in the release of
AERMOD version 16216r. LOWWIND1, LOWWIND2, and LOWWIND3, however, remain beta options.
With regards to the LOWWIND options and meander, LOWWIND1 turns off the horizontal meander
component altogether, whereas LOWWIND2 incorporates meander with an adjustment on the default
upper limit of the meander factor (FRAN) from 1.0 to 0.95. LOWWIND2 also includes an adjustment to
the default time scale at which the mean wind is assumed to no longer be correlated with the location
of plume material at a downwind receptor. The time scale was changed from the default value of 24
hours to 12 hours for LOWWIND2. LOWWIND3 uses the default time scale of 24 hours. LOWWIND3
includes the same adjustment to the upper limit on FRAN as used for LOWWIND2 but eliminates upwind
dispersion of the plume.
EPA is focused on the following two issues related to plume meander in the AERMOD dispersion model:
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1) Meander is only applied to point and volume sources such that we intend to pursue adding
meander for area sources.
2) Understanding the appropriate response of the plume in low wind conditions with regard to
meander and the effect meander has on concentrations in low wind conditions. The influential
aspects of meander that EPA has identified to date include the upper limit of FRAN, the time
scale for which there is no correlation between the location of the plume near the source and
downwind of the source, the degree to which upwind dispersion should be applied or
eliminated, or whether meander should be eliminated altogether.
Current Implementation in AERMOD
AERMOD accounts for plume meander by interpolating between two concentration limits: the coherent
plume limit (which assumes that the wind direction is distributed about a well-defined mean direction
with variations due solely to lateral turbulence) and the random plume limit, (which assumes an equal
probability of any wind direction).
For the coherent plume, the horizontal distribution function (FyC) has the familiar Gaussian form:
where oy is the lateral dispersion parameter. For the random plume limit, the wind direction (and plume
material) is uniformly distributed through an angle of In. Therefore, the horizontal distribution function
FyR takes the simple form:
where xr is the radial distance to the receptor. Although the form of the vertical distribution function
remains unchanged for the two plumes, its magnitude is based on downwind distance for the coherent
plume and radial distance for the random plume.
Once the two concentration limits (CCh - coherent plume; CR - random plume) have been calculated, the
total concentration for stable or convective conditions (Cc,s) is determined by interpolation. Interpolation
between the coherent and random plume concentrations is accomplished by assuming that the total
horizontal "energy" is distributed between the wind's mean and turbulent components. That is,
where Oh2 is a measure of the total horizontal wind energy and a 2 is a measure of the random
component of the wind energy. Therefore, the ratio or2/ah2 is an indicator of the importance of the
random component and can therefore be used to weight the two concentrations as done in eq. 3.
The horizontal wind is composed of a mean component tl, and random components ou and ov. Thus, a
measure of the total horizontal wind "energy" (given that the along-wind and crosswind fluctuations are
assumed equal i.e., ou = ov), can be represented as
eq. 2
Cc,s = Qh(l - 0V2M?) + CR(o?/ol) eq. 3
Oh = 2dy + u2 eq. 4
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where u = (u2 - 2av2)1/2. The random energy component is initially 2a v2 and becomes equal to Oh2 at
large travel times from the source when information on the mean wind at the source becomes
irrelevant to the predictions of the plume's position. The evolution of the random component of the
horizontal wind energy can be expressed as
Ok = 2dy + u2(l — exp(—xr/uTr)) eq. 5
where Tr is a time scale (= 24 hrs) at which mean wind information at the source is no longer correlated
with the location of plume material at a downwind receptor. Analyses involving autocorrelation of wind
statistics (Brett and Tuller 1991) suggest that after a period of approximately one complete diurnal cycle,
plume transport is "randomized." Equation 5 shows that at small travel times, or2 = 2av2, while at large
times (or distances) a 2 = 2av2 + u2, which is the total horizontal kinetic energy (oh2) of the fluid.
Therefore, the relative contributions of the coherent and random horizontal distribution functions (eq.
3) are based on the fraction of random energy contained in the system (i.e., a 2/Oh2)-
Summary of Current Literature or Research
Mortarini et al., 2016
Mortarini et al. studied meander during low-wind cases from field campaigns in Italy and Brazil.
Meander and non-meander cases were identified using Eularian autocorrelation functions (EAF) of the
horizontal wind-velocity components and temperature. The study concluded that meander does not
depend on stability; however, meander does depend on wind speed and is further influenced by the
presence of buildings. The standard deviation of the horizontal wind speed is generally large during low
wind conditions. The researchers demonstrate that meander and non-meander cases can be identified
based on the ratio of the standard deviations of the vertical and horizontal velocity components. Non-
meander cases exhibit a larger ratio than meander cases.
Moreira et al., 2013
This work resulted in a new formulation for the parameterization of turbulence associated with
meander in a shear driven stable boundary layer. The formulation is based on a relationship between
turbulence and the meander period in which patterns of movement are characterized by a weighting of
turbulence and meander. The formulation was tested with a Lagrangian stochastic dispersion model
against field observations at the Idaho Engineering Laboratory (INEL). Results are presented which
demonstrate good performance.
Hiscox et al., 2010
Hiscox et al. used aerosol lidar measurements from the JORNADA field campaign in the New Mexico
desert to study plume spread and meander. The turbulent scale was separated from the submesoscale
using multiresolution decomposition, and durations of turbulent kinetic energy (TKE) stationarity and
wind steadiness were used to characterize the local scale and submesoscale turbulence. The researchers
found that in strong stability during weak and variable winds, horizontal plume spread was primarily
from plume meander caused by submesoscale motion, and small scale turbulence had little influence.
During periods of higher wind speeds and weaker stability, meander was still dominant but the ratio of
the meander to small scale turbulence decreased. The study concluded that measure of wind steadiness
and the turbulence stationarity are closely related and could be viable parameters to describe plume
diffusion and meander in the stable boundary layer.
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Considerations for Updates in AERMOD Model System
The EPA welcomes input from the community on possible implementations of meander for area
sources.
In terms of the influence of meander during low wind conditions, the EPA is currently focusing its
examination on the following parameters:
1) upper limit on the meander fraction (FRAN);
2) time scale at which there is assumed to be no correlation with the location of the plume near
the source and a downwind receptor; and
3) degree or existence of upwind dispersion from the random or pancake plume.
EPA expects that the use of beta options as part of future releases of AERMOD will provide the ability to
adjust, at a minimum, a subset of parameters through user input for research and experimental
purposes. The EPA plans to engage with the community and welcomes input that can lend additional
insight on the appropriate role of plume meander, particularly under low wind conditions. The ultimate
goal for the treatment of meander is a robust beta option with values of relevant parameters set that
best or most appropriately reflect the role of meander in low wind conditions.
References
Brett, A. C. and S. E. Tuller. (1991). Autocorrelation of hourly wind speed observations. Journal of
Applied Meteorology, 30, 823-833.
Hiscox, A. L., Miller, D. R., and C. J. Nappo. (2010). Plume meander and dispersion in a stable boundary
layer. Journal of Geophysical Research, Vol. 115, D21105. doi:10.1029/2010JD014102.
Moreira, V. S., Degrazia, G., Timm, A. U., Roberti, D. R., and S. Maldaner. (2013). Connecting Turbulence
and Meandering Parameterization to Describe Passive Scalars Dispersion in Low Wind Speed
Conditions. ISRN Atmospheric Sciences, Volume 2013, Article ID 738024.
http://dx.doi.org/10.1155/2013/738024.
Mortarini, L., Stefanello, M., Degrazia, G., Roberti, D., Castelli, S. T., and Domenico Anfossi. (2016).
Characterization of Wind Meandering in Low-Wind-Speed Conditions. Boundary-Layer Meteorology,
161:165-182. DOI 10.1007/sl0546-016-0165-6.
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