AMGI
Air quality modeling –
Neighborhood/urban scales
Darko Koracin
Desert Research Institute, Reno, Nevada, USA
Vlad Isakov
NOAA/EPA, Research Triangle Park, North Carolina
Air Quality Management, Monitoring, Modeling, and Effects
AMGI – EURASAP; Zagreb, Croatia, 24-26 May 2007
1
Land-use
Air Quality
Model
Meteorology
Emissions
Air Pollutant
Concentrations
2
Main concepts in air quality modeling
3
Air Quality Modeling
• Meteorological Models – Provide input meteorology for dispersion
and photochemical models.
• Dispersion Models - These models are typically used in the
permitting process to estimate the concentration of pollutants at
specified ground-level receptors surrounding an emissions source.
• Photochemical Models - These models are typically used in
regulatory or policy assessments to simulate the impacts from all
sources by estimating pollutant concentrations and deposition of both
inert and chemically reactive pollutants over large spatial scales.
• Receptor Models - These models are observational techniques which
use the chemical and physical characteristics of gases and particles
measured at source and receptor to both identify the presence of and
to quantify source contributions to receptor concentrations.
4
Air Quality
Model
Land-use
Climate
Change
Meteorology
Air Quality
Emissions
Health
5
Spatial scales and relevant pollutants
• Microscale (10 to 100 m) and Middle-scale (100 to
500 m) – odors, dust, traffic, hazardous pollutants.
• Neighborhood scale (500 m to 4 km) – vehicle
exhaust, residential heating and burning, primary
industrial emissions.
• Urban scale (4 to 100 km) – ozone, secondary
sulfates and nitrates, forest fires, regional haze.
• Continental scale (1,000 to 10,000 km) – Asian and
Saharan dust, large scale fires.
• Global scale (> 10,000 km) – greenhouse gases,
halocarbons, black carbon.
6
The simplest dispersion modeling – Gaussian approximation for
the plume spread
Not applicable to regional scales – complex terrain,
convective conditions, and ground-level sources.
7
Limitations of Gaussian-plume models
•
Causality effects
Gaussian-plume models assume pollutant material is transported in a straight line instantly
(like a beam of light) to receptors that may be several hours or more in transport time away
from the source.
•
Low wind speeds
Gaussian-plume models 'break down' during low wind speed or calm conditions due to the
inverse wind speed dependence of the steady-state plume equation, and this limits their
application.
•
Straight-line trajectories
In moderate terrain areas, these models will typically overestimate terrain impingement effects
during stable conditions because they do not account for turning or rising wind caused by the
terrain itself. CTDM and SCREEN are designed to address this issue.
•
Spatially uniform meteorological conditions
Gaussian steady-state models have to assume that the atmosphere is uniform across the
entire modelling domain, and that transport and dispersion conditions exist unchanged long
enough for the material to reach the receptor.
•
Convective conditions are one example of a non-uniform meteorological state that Gaussianplume models cannot emulate.
•
No memory of previous hour's emissions
In calculating each hour's ground-level concentration the plume model has no memory of the
contaminants released during the previous hour(s).
8
Advanced dispersion models (I)
•
Puff models
Pollutant releases can also be represented by a series of puffs of material
which are also transported by the model winds. Each puff represents a discrete
amount of pollution, whose volume increases due to turbulent mixing. Puff
models are far less computationally expensive than particle models, but are not
as realistic in their description of the pollutant distribution.
•
Eulerian grid models
Pollutant distributions are represented by concentrations on a (regular) threedimensional grid of points. Difficulties arise when the scale of the pollutant
release is smaller than the grid point spacing. The simulation of chemical
transformations is most straightforward in a Eulerian grid model.
•
Lagrangian particles
Pollutant releases, especially those from point sources, are often represented
by a stream of particles (even if the pollutant is a gas), which are transported by
the model winds and diffuse randomly according to the model turbulence.
Particle models are computationally expensive, needing about millions or so
particles to represent a pollutant release, but may be the best type to represent
pollutant concentrations close to the source.
9
Lagrangian particle
dispersion models
Puff models
Eulerian Chemical Model:
o-1 3 4
2
1
8
o-3
7
9
[O 3]
[NO 2]
o-2
5
6
o-4
[NO]
[VOC]
[HN 4NO3 ]
...
o-1 3 4
2
1
8
o-3
o-2
6
7 o-4
9 o-6 o-5
5
•Chemical transformations
will be made on a Eularian
grid.
•Enables interactions
between emissions from
different sources.
•Includes gas and aqueous
phase chemistry and
secondary aerosol
formation.
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However, real conditions are quite complex. First: Need to know
wind aloft – virtually no continuous measurements
Complex horizontal, vertical, and temporal wind structure
11
Next: In most of cases we are not dealing with flat terrain –
topographic complexity
Complex horizontal, vertical, and temporal dispersion
12
Next: Topographic complexity induces local flows and circulations
Complex horizontal, vertical, and temporal dispersion
13
Building downwash for two identical plumes emitted at different
locations
The stack on the left is located on top of a building and this
structure impacts on the wind-flow which, in turn, impacts upon
the plume dispersion, pulling it down into the cavity zone behind
the building. The stack on the right is located far enough
downwind of the building to be unaffected by the wake effects14and
is not as dispersed in the near field.
Next: Interaction between plumes of different buoyancy and an
inversion layer
Complex horizontal, vertical, and temporal dispersion
15
Sea and land breezes
(Left): The sea breeze where the air flows from the ocean towards
the warm land during the day with warmed air from above the land
recirculating back over the ocean.
(Right): The land breeze at night where cool air drifts from the
land towards the ocean, where it is warmed and recirculated back
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over the land.
Transport and dispersion of inert chemical
tracers in a coastal urban region
Example
• To examine the extent to which
Mesoscale Model (MM5) and a
Lagrangian Random Particle (LAP)
Model can be used for studies of
atmospheric transport and dispersion on
a sub-kilometer scale.
• Environmental Justice (?)
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Geographical setup – Western U.S. – Southern California
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Mesoscale Model 5 (MM5) Overview
• Limited-area nonhydrostatic, terrain-following
sigma-coordinate model for simulations or
forecasts of mesoscale and regional-scale
circulations.
• Continuous development since the early 70’s
(Penn State & NCAR).
• Community model – more than 100
registered users worldwide (universities,
government, private enterprise).
• More than 200 peer-reviewed publications
focusing on model development, evaluation,
and applications.
• Web page: http://www.mmm.ucar.edu/mm5
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MM5 Overview – Main features
• Prognostic equations
for:
– U (west-east), V (southnorth), W (vertical) wind
components
– Temperature
– Humidity
– Pressure
– Turbulence kinetic
energy
• Multiple model domains
(nests).
• Nonhydrostatic dynamics
allow high horizontal
resolutions (1 km or less).
• Multi-tasking capability on
shared- and distributedmemory machines.
• Four-dimensional data
assimilation capability.
• Advanced options for
physical parameterizations
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Field program
• Tracer experiment conducted in August 2001
in the SE San Diego area - Bario Logan.
• SF6 tracer released during daytime (10am to
7pm) on 21, 23, 25, 29, and 31 August.
• Tracer sampling stations - 50 locations
arranged in four arcs (250 m, 500m, 1km, and
2km).
• Additional meteorological measurements acoustic sounder (range at 200m, resolution
5m); six sonic anemometers (surface winds
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and turbulence).
Map of Barrio Logan - tracer experiment, August 2001
BL school:
5 sonics,
mini-sodar
NASSCO:
tracer release,
sonic (roof)
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Concept of the tracer
experiment
• Tracer was released over the land near the
shore during daytime (10am to 7pm) with
prevailing sea breeze (onshore flow).
• The tracer sampling stations were arranged
in four arcs to assure plume sampling during
the onshore flow.
• Tracer emission rate was generally constant
at about 5 g/s.
• Tracer concentrations were averaged hourly.
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Microscale Tracer Experiment at Barrio Logan
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Meteorological model (MM5) setup
Setup of the MM5 modeling domains with horizontal resolutions of 12 km (D01),
4 km (D02), 1.333 km (D03), and 0.444 km (D04).
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MM5 provides meteorological input (winds, stability, turbulence) to a dispersion model
MM5(20m) vs. SODAR(20m) and Surface st.(4m) validation (I)
Time series – “U” (west-east) wind component
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Lagrangian Random Particle (LAP)
Dispersion Model - Main principles
• Numerical model which uses a large number of
hypothetical particles to simulate the transport and
dispersion of atmospheric pollutants.
• Particles are subjected to 3D atmospheric fields.
• Dispersion of the simulated plume is directly linked
to the turbulence structure without the Gaussian
assumption.
• Typically, 500 particles per minute are emitted
from each source.
• The particles are continuously traced in time and
space and their population represents the plume
structure.
Koracin et al. 2007 (Atmospheric Environment)
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Complexity of plume(s)
Simulated and measured
hourly concentrations (ppt)
at each of the 50 receptors
at a particular hour.
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Complexity of plume(s)
Frequency distribution
Simulated and measured
hourly SF6 concentrations
(ppt) at all 50 receptors at all
times.
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San Diego – 23 Aug 2001
Measured concentrations
Simulated concentrations
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Top-view of distribution of particles –
San Diego harbor
1 km horizontal resolution
444 m horizontal resolution
Meteorology: MM5 model; Dispersion: Lagrangian Random Particle Dispersion Model
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Summary and concluding remarks
• Small-scale transport and dispersion of
tracers in coastal urban conditions
represents a significant challenge for
modeling.
• 50 receptors were located in four arcs within
the 2.5 x 2.5 km area.
• SF6 tracer was released during daytime with
on-shore wind conditions.
• Inhomogeneous (non-Gaussian) structure of
the plume(s) was observed /complexity of 32
meteorology & dispersion/.
Dispersion driven by models and/or
measurements
DRI – MM5 real-time weather forecasting
Emission input (tracer, pollutants, etc.)
Lagrangian random particle dispersion model
Individual element of the
plume
Synoptic
forecast
input 1
now
Source
Source
Winds and
turbulence
determine position
at each time step
before
The composition of
the individual
elements makes the
plume
Model simulations: 3D transport
and dispersion of the plume
Measurements
only input 2
Plume: NTS – 13 July 2005 – 1500 PDT
Measurement input
dispersion simulations
Mobile sodar – RASS measurement system
Synoptic input
dispersion simulations
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