Transcript Application of back trajectory technique in air pollution

Back Trajectory
Techniques in Air Pollution
Farhan Akhtar
Benton Whitesides
Bo Yan
11/19/2003
EAS 6792
Definition
 Trajectories: the paths of infinitesimally small
particles of air as they move through time and
space.
 Such fluid particles, ‘marked’ at a certain point in
space at a given time, can be traced forward or
backward in time along their trajectory.
 Backward (back) trajectories:
 indicate the past path of a particle
 Forward trajectories:
 indicate the future path of a particle
Example Back Trajectory
receptor
7-day Back trajectories from the
ship
(receptor)
have
been
calculated using the HYSPLIT 4
model (HYbrid Single-Particle
Lagrangian Integrated trajectory).
Applications of Back Trajectories

Synoptic meteorology
 Investigate air mass flow around mountains (Steinacker,
1984)

Climatology
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Environmental Sciences
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Identify pathways of water vapor transport (D’Abreton and
Tyson, 1996) or desert dust (Chiapello et al., 1997)
Establish source-receptor relationships of air pollutants
(Stohl, 1996a)
Law Enforcement

Combine with pollen measurements to find possible
locations of marijuana cultivation (Cabezudo et al.,1997)
Calculation of the Back Trajectory
dX
V
dt
X - the position vector during a time step dt resulting from the
wind v;
V - mean wind velocity vector (no consideration the turbulent
mixing in atmosphere)
X(t)  X(Xo , t)
Calculation of the Back Trajectory
(cont’d)
If known x0 at t0 :
X(t)  X(X o , t)
2
dX
2 d X
1
X(t)  X(t 0 )  ( t)
t o  2 ( t)
dt
dt 2
 X(t)  X(t o )  (t)V(t o )
to

Error Sources in the Computation
of Back Trajectories
 Wind field errors

In many cases, they are the largest single source of errors
for back trajectory calculations. Wind field errors can be
due to either analysis or forecast errors.
 Starting position errors and
amplification of errors



The starting positions of the trajectories are often not
exactly known
Difficult to select start positions due to the differences
between the model topography and the real topography
Back trajectory position errors can be strongly amplified in
convergent flow.
Error sources for the computation
of back trajectories (con’t)
 Truncation errors
 They come from the trajectory equation solution, which is approximated by a
finite-difference scheme that neglects the higher order terms of Taylor series.
In order to keep truncation errors negligible, a numerical scheme of high
order using very short time steps is needed.
 Interpolation errors
 Due to the limit available wind data, wind speed must be estimated at the
trajectory position. The interpolation errors will be caused during the
process.
 Errors resulting from assumptions regarding
the vertical wind
 Because there are no routine observation of vertical wing
component w, Wind field of w can only gotten from meteorological
model. So, it is less accurate than the horizontal wind fields.
Lagrangian Particle Dispersion
Models (LPDM)
X(t  t)  X(t)  t [v(t)  v(t)]


V - mean wind vector obtained directly from
meteorological model
V’ - turbulent wind vector describing the turbulent
diffusion of the tracer in the PBL.
Lagrangian box models
•
•
•
•
Similar to LPDM, changes in the
concentrations in the box caused by chemical
reactions and deposition are calculated.
No boundary conditions are required.
Applicable only at higher levels of the
atmosphere
The most important boundary layer processes,
such as the formation of nighttime reservoir
layers or the rapid growth of the mixed layer
depth in the morning, can be described with
such models (Hertel et al., 1995),
Statistical analyses of trajectories
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Flow Climatologies
Cluster analysis
Residence time analysis and conditional
probability
Concentration fields
Redistributed concentration field
Inverse modeling
Accuracy
 Measure of the integral effect of all errors
 Determined by following the movement of
conserved tracers:
 Balloons
 Stay at a constant pressure height
 Do not measure vertical errors
 Material Tracers
 Conservative species are monitored.
 Compare results with Meteorological measurements
 Dynamical Tracers
 Attempt to model vertical movement in the atmosphere
 Potential temperature, isentropic potential vorticity
Examples Applications of
Back-Trajectory Techniques
Determination of Regional Sources of Winter Smoke Pollution in New Zealand
Tajectory analysis of particulates in Big Bend national park
Determination of Regional Sources
of Smoke Pollution in Winter
• Night time burning of wood and coal in
domestic fires created smoke pollution for
the town of Christchurch, New Zealand.
• In the evening, temperature inversions
trap pollution close to the surface.
• Burning created high concentrations of
particulate matter from the ground to 10m.
• Used back-trajectory models to determine
origin and pathways of polluted parcels.
PM10 and CO Concentrations
• Winter 1988-1999 averaged concentrations in
Christchurch for a 24 hour period.
• Reveals Diurnal cycle of PM10 peaking over
night.
Region of Interest
City of Cristchurch New Zealand
• Plains to the North and West
• Hills to the South
• Water to the East
Complexities
• Terrain creates complexity in low level
flow.
• On clear calm nights, radiative cooling of
hill slopes causes cold air drainage into
the region of interest.
Techniques
• Only enough data to use simple backtrajectory techniques.
• Lagrangian Kinematic Back-Trajectory
Modeling techniques.
• Regional Atmospheric Modelling System
(RAMS) based on averaged nocturnal
wind fields typically associated with high
pollution events in the city (1995-2000).
Nested Grid Model
• RAMS is a 3-D
Nested Grid Model
allowing focus on
specific regions.
• No vertical grid
nesting- focus on
lowest km of
atmosphere (damping
applied to higher
altitudes).
Techniques
• Models air flow of 4 Cases:
No initial wind
Weak NW wind
Strong SW wind
Moderate NE wind
• Resolution: (Spatial 500m) (Temporal 15
mins)
• Run times = 3pm to 3am
• 2nd Order Turbulence Closure
Model Vs. Observations
• Model recreation of the horizontal wind field compares
well to actual observations.
• Other methods of comparison included standard
deviation & root-mean square.
• Using these wind fields, back trajectories plotted for
given endpoints.
Problems
• Ignored particle settling rates.
• Vertical velocities neglected (though
realistic for night)
• No examination of concentration changes
in parcels during transport.
• No consideration of sources of sinks
during transport (chemical &
photochemical reactions).
• Synoptic events not considered
Back Trajectory Plots
• Trajectory plots show
parcel path across
grid space from
surrounding regions.
• Urban area of
Christchurch is
represented by grid
dots.
Results: No Initial Wind
• Surface airflow
dominated by local
effects (cold air
drainage from hills).
• Air originates in plains
and moves towards
the city, except for
near the hills where
cold air drainage
occurs.
Results: Strong SW Wind (10 m/s)
• Gradient wind:
-dominates transport,
-turbulent mixing and
-inhibits inversion
• No impact from cold air
drainage.
• Air travels much farther.
Results: Light NW Wind
• Similar to what happens with no initial wind.
• Terrain dominates transport (cold air
drainage).
• Transport almost independent from wind.
• Parcels move from hills into city.
(As expected from cold air drainage)
Results: Moderate NE Wind
• Very different from
other cases
• Air blowing on shore.
• Seabreeze &
orographic wind
switch direction as
drainage develops.
• Air re-circulates over
city allowing evening
pollution buildup.
• Hills less important.
3 Back Trajectories From NE Wind:
• Note recirculation of parcels over the city with
changing winds.
• Grey dots indicate endpoints of each trajectory.
Implications & Conclusions
• Cold Air Drainage allows leakage of
southern hill pollutants into city and
northern valleys overnight.
• Drainage can be inhibited by stability.
• Burning during winter (problematic
months) should be restricted.
Particulates in Big Bend National
Park
Background
• Park has registered the poorest visibility in the
western United States.
• Since 1988, fine particulate matter and optical
data has been collected in the park
• The majority of the visibility degradation is due to
sulfate particles.
• Large coal-fired power plants are located over
the border into Mexico.
• Use a LPDM to determine the sources for these
particulates
LPDM Inputs
 The depth of the transport zone is set at the
lowest inversion layer which meets these
criteria:
 height is at least 300 m above the ground
 Potential temperature lapse rate of at least 5 K/km
 Potential temperature is 2K greater at the top than at
the bottom
 If no inversion exists, 3000m is assumed
 Horizontal winds are linearly interpolated from
rawinsonde measurements
 Computed backward in 6h time steps for a
maximum of 120h (5 days)
Accuracy and Errors
• Rainout and especially low inversion
layers are not accounted for
• Trajectories are aggregated over long time
periods to attempt to minimize errors
Results
• If over 80% of the
trajectories calculated for
a day came from one
country, the day was
assigned to that country.
• 935 days from 10 years
were analyzed
• The model indicates that
most particles (59%)
came from Mexico.
Results by season
Overall source attribution from 1989-1998
Results by season
Overall source attribution for fine sulfur from 1989-1998
Results by season
Overall source attribution for organic carbon from 1989-1998
References
Gebhart, Kristi A., et al., Back-trajectory analyses of fine particulate matter
measured at Big Bend National Park in the historical database and
the 1996 scoping study. The Science of the Total Environment. Vol
276. Elsevier 2001. pp.185-204.
Stohl, A. Computation, Accuracy and Applications of Trajectories- A Review
and Bibliography. Atmospheric Environment. Vol 32. Pergamon 1998.
pp. 947-966.
Stohl, A., et. al. A replacement for simple back trajectory calculations in the
interpretation of atmospheric trace substance measurements.
Atmospheric Environment. Vol 36. Pergamon 2002. pp. 4635-4648.
Sturman, A., P. Zawar-Reza. Aplication of back-trajectory techniques to the
determination of urban clean air zones. Atmospheric Environment.
Vol 36. Pergamon 2002. pp. 3339-3350.