Modelling of air pollution -Why?

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Modelling of air pollution-Why?

• Magnuz EngardtSwedish Meteorological and
  Hydrological Institute

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Instruments in air pollution assessments

• Air quality / deposition measurement programmes
• Emission inventories
• Effect studies
• ? Atmospheric transport and dispersion models

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Measurements and Modelling
Measure or calculate concentrations and
depositions ?

• Models and measurements both have uncertainties
• Some features are particular to either method
• Models and measurements should be used together
  to explore their full potential -and to increase
  the quality of each other

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Why modelling?

• Mapping of remote regions (incl. areas without
  measurements)
• Source-Receptor calculations
• Environmental assessments (incl. future /
  history)
• Find location / consequences of emitters,
  receptors
• Combine with effect studies (health,
  acidification, crop yield, )
• Understand processes in the atmosphere
• Check emission inventories
• Verify measurements
• Etc

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Some examples
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Origin of total non-seasalt sulphur deposition in
Sweden during 1998 as deduced by the MATCH-model
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Source-receptor calculations for Southeast Asia
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Annual total deposition of oxidised nitrogen in
South Asia resulting from NOX emissions in
Bangladesh.
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Climate induced change in total-SOX deposition
(total-, wet- drydeposition) 2021-2050 minus
1961-1990.
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Average summer near-surface ozone concentration
in southern Sweden under different emission
scenarios
Decrease VOC or/and NOX emissions with
50 Other studies include different NO/NO2
ratio of NOX-emissions or different speciation of
VOC emissions.
100 km
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Global distribution of methane-why does it look
like this?
Weekly measurements of marine boundary layer
CH4. Data processed by an interpolating and
smoothing program.
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What is a model ?Mathematical relations based on
empirical or physical laws
In our field we have, for example, Numerical
weather forecast models Climate models Emission
inventories Integrated Assessment
Models Dispersion models including emissions,
transport, deposition, chemical conversion
etc. ...

• Models are used everywhere in society
• Economical models
• Population models
• Technological models

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Quality of model outputnever better than the
input to the model
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Input needed by dispersion models

• ?Emission data
• Magnitude (and speciation) how much is emitted?
• Location (latitude, longitude and height) where
  is it emitted?
• Temporal variation how do the emissions vary
  with time?
• ?Weather data
• Simple wind-mast or
• Time varying three-dimensional fields(historical
  weather, weather forecasts, weather from climate
  models, etc.)
• ?Surface characteristics
• ?Various assumptions
• ?Etc.

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Errors in model results typical due to

• Emissions wrong
• Meteorology wrong (or too simplified)
• Important processes or parameters are wrong, or
  omitted, in the model
• Bugs (errors) in the model-code or processing
  of input/output (including scaling errors)
• Etc.

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How good is a model?

• ?Model results must be evaluated in order to
  assess the accuracy of the model results
• ? Most common is to compare modelled values with
  observations
• ? Mismatch between calculated and observed values
  can be due to
• Errors in the model
• Errors in the input to the model
• Errors in measurements
• Non-representative measurements
• Etc.,

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Model verificationObjective statistics, using
other measures than mean and standard deviation
often used
A measure of over- or under estimation.
Mean error (Bias),
Gives the magnitude of the error.
RMS-error,
A measure on how well the results co-vary.
Correlation
Example
Ci simulated value
Mi measured value
N Number of data points
sX standard deviation of X
average of X
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Different objective measures may give different
scores for a model (!)
Identical meanvalues, no biasPoor correlation
(r0)Large RMS-error Very different standard
deviations.
Identical mean-values, no bias Identical standard
deviations Very poor correlation (r-1) Very
large RMS-error
Identical standard deviations Reasonable
correlation 0 lt r lt 1)Different mean values,
high biasLarge RMS-error
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Model verification (contd)Visual inspection of
results

• Subjective inspection of the results by
  plotting them should also be performed. Methods
  include
• Timeseries
• Scatterplots
• Maps

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Visual inspection of model results

• Timeseries

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Comparison between calculated and observed
monthly average concentrations of NO2 (?g/m3) at
four regional background stations. Correlation
coefficient R0,96.
Visual inspection contd
Scatterplots
NO2 ?gm-3 MATCH
NO2 ?gm-3 Observations
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Review of precipitation-chemistry data in
IndiaData from 100 stations overlaid MATCH
results
Underlined digits are suburban stations, others
are rural. Red digits are wet-only collectors,
black digits are bulk collectors.
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Can you use a model of limited quality?(How
bad performance is acceptable?)

• Unrealistic data should never be accepted
• A factor of two is often regarded as a very
  good correspondence
• If there is little measured data available you
  may have to trust your model results even if the
  discrepancy is relatively large.
• Sometimes you are concerned with typical average
  levels, sometimes you want to capture diurnal or
  day-to-day or seasonal variations
• Note the problem of unrepresentative measurements
• Keep uncertainty in input data in mind (model
  results could not be better than the input)

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Model quality (contd)

• Its good to check the model in different ways
• Both atmospheric concentrations and surface
  depositions
• Study vertical profiles (although you very seldom
  have any data away from the surface)
• Test both inert and reactive species
• Both primary and secondary species
• Test the same model at different places and
  during different periods
• If you have discrepancies, try to understand what
  they are caused by!

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Error propagation

• Sometimes small errors in the input cause large
  errors in the output
• Sometimes it turns out that certain input data or
  model formulations doesnt matter much
• Analyse the robustness of your results through
  sensitivity tests

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Atmospheric dispersion modelling basic concepts
(Ch. 23 in Seinfeld and Pandis, 1998)

• Magnuz EngardtSwedish Meteorological and
  Hydrological Institute

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Pollutants (gases and particles) are transported
with the three-dimensional wind
tt0
tt0Dt
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Note that mean wind and turbulence is not
constant in time or space ! (not even in the
tropics)
Near-surface wind, pressure and temperature over
Sweden 12-24 UTC during 10 September 2007
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Turbulence cause pollutants to mix and dilute
in the atmosphere (Cf. the widening of the
plume).

• Turbulence is stochastic wind elements (eddies)
• There are a number of reasons for turbulence to
  occur ? atmospheric (in-) stability ? surface
  roughness ? vertical wind change ? etc.,
• The turbulence is varying over time and space.

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Atmospheric stability and surface
characteristics (roughness etc.) affects the
turbulence
Here the shape of a plume during different
stabilities (vertical temperature variations) is
illustrated.
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Turbulence (and molecular diffusion) may also
transport species in the absence of mean wind
Closed Chamber experiment Molecular diffusion
cause gases to mix.
CO2 and other gases (O3 ,SO2 ) are taken up by
vegetation. The transport through the stomata of
the leaves occur through molecular diffusion.
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Mixed layer, boundary layer
Height
The boundary layer is the part of the atmosphere
that is influenced by surface friction. Here the
atmosphere is neutrally stratified and tracers
are well mixed. The wind-speed increases with
height wind-direction also change with height.
Wind- speed profile
Tracer profile
Temperature profile
Mixed layerorBoundary Layer Height. Typically
1-2 km during day, 100m or less during night.
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Mixed layer height vary over time and spaceThe
depth of the mixed layer height greatly affects
near-surface concentrations
Height
Wind- speed profile
Tracer profile
Temperature profile
A more shallow mixed layer cause near-surface
tracer concentrations to be higher
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Fumigation (downwash)-caused by horizontal
variations in near-surface turbulence (variations
in surface roughness and atmospheric stability)
Mixed layer height and temperature profile can be
different over different surfaces due to
different head capacities (land/water) and/or due
to different roughness of the surface.
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Local environmental and meteorological effects
may interact with the dispersion of pollutants
Even in a flat environment is the wind direction
(and magnitude) changing with height
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Changing wind direction -and speed- cause
plumes not to be straight
Calculated plume of NO2 emitted in Tallinn,
Estonia
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Different species have different lifetime in the
atmosphere

• Species Lifetime (Effect in the atmosphere)
• radicals (OH, H2O2, ) seconds Oxidants
• Large particles minutes-hours (Health,) staining
  of materials
• PM10 a few hours Health
• PM2.5 a few days Health, Climate
• NH3 2-3 days Acidification, Eutrophication
• VOCs hours-days-weeks- Health, Near surface
  ozone
• SO2, NOX, O3, 3-5 days Acidification,
  Climate, Crops
• CH4, CO a few months Climate, near surface
  ozone
• CO2 several years Climate
• CFCs several decades Climate, stratospheric
  ozone

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Gases and particles may leave the atmosphere
through drydeposition on various surfaces
Drydeposition flux is often modelled as Fdrydep
vd(z) c(z) ms-1gm-3 gm-2s-1 vd(z) is
the drydeposition velocity and c(z) the
concentration of a species at z meters above
surface. vd(z) is dependent on surface type,
atmospheric stability and is species dependent.
Dry deposition can be estimated through measuring
concentrations in the air and multiplying with
relevant deposition velocities.
Dry deposition can be measured through various
more or less advanced methods. Not routinely
done. Most simple methods include throughfall
measurements.
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Typical drydeposition velocities (valid at 1
m)Uncertain to at least a factor of two.
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Drydeposition of particles is a strong function
of particle size
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Pollutants can be incorporated in clouds and
eventually be deposited to the ground by
precipitation
Scavenging of particles and gases by rain and
clouds takes place during cloud formation, inside
clouds and under precipitating clouds.
Scavenging of particles and gases depends on
solubility and cloud and rain type.
Wetdeposition can readily be measured through
collecting and analysing rainwater.
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Species may undergo chemical or physical
transformation
Coupled nitrogen/sulphur chemistry in MATCH Most
reactions depends on ambient conditions
(temperature, abundance of oxidants, solar
radiation, humidity etc.).
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Physical transformation

• Gas to particle conversion (or vice versa)
• Particle-to-particle coagulation
• Water condensing on existing particles
• Etc.

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SummaryTerms needed during modelling of
pollutants
EMIS Emission release of pollutants into the
atmosphere
ADV Advection transport with mean wind
CONV Convective transport subgrid vertical
transport in convective clouds
TURB Turbulent transport subgrid vertical
(near-surface) transport due to turbulence
CHEM Chemical formation/destruction
PHYS Physical formation/destruction
DRYDEP Drydeposition of gases or particles
WETDEP Wetdeposition of gases or particles
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An example from real life
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The Chernobyl accident 25 April 1986
Trajectory calculations depicting the path of the
first emitted cloud of radioactive particles from
the exploded Chernobyl reactor. Note that
different levels of the cloud travelled different
routes.
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Chernobyl accident (contd)
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Chernobyl accident (contd)Measured deposition
of 137Cs and rain amount in Sweden
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Different types of models
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Box-model
Its possible to create air-pollution
indexes or Calculate average concentration in a
city if the area and total emissions are known
Boundary layer height
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Gaussian model (assume normal distribution of
pollutants on average)
Instantanoues extent of the plume at different
times
When averaging over time the plume is
approximately normally distributed in the
horizontal and vertical along the centre line
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Gaussian model
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Statistical Gaussian models

• ? Calculate the dispersion from a number of
  Gaussian plumes.
• ? Run the model for a number of wind- speeds and
  directions.
• ? Add all plumes together.
• ? The turbulent mixing comes from sz and sy.
  They can be estimated from wind-profile data
  and surface characteristics

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CFD (Computational Fluid Dynamics)
Cross-section of the plume.
A plume from a stack.
Near surface concentrations of pollutants in
different industrial areas.
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Lagrangian models
Consider an air-parcel that is travelling with
the time-varying three-dimensional wind.
Time varying three-dimensional wind field
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Lagrangian models (contd)
Puff model Simulate dilution (turbulent mixing)
through making the airparcel larger. E.g. Double
the volume will half the concentration.
Particle model Simulate dilution (turbulent
mixing) through follow a number of particles
which are spread randomly according to stability
etc. Each particle carries a certain mass
(which decreases every time new particles are
emitted). After a number of timesteps it is
possible to add up the particles in a certain
volume to get the concentration.
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Lagrangian models (contd)
Typical regional spread from an instantaneous
point-source located near the surface
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Lagrangian models (contd)

• Lagrangian models
• May include emissions, deposition and simple
  chemistry. More difficult, however, to include
  chemistry where several simulated species
  interact.
• Lagrangian models are relatively fast on a
  computer. Need access to meteorological data.

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Eulerian models (or gridpoint models)
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Eulerian models
Eulerian models divide the atmosphere into a
number of gridboxes and treat advective and
turbulent transport between boxes, chemistry
between species, emission depositions etc. The
driving data (emissions meteorology, boundary
conditions etc. varies in time and
space. Eulerian models are relatively
time-consuming on computers.
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Eulerian models (contd)

• Eulerian model can cover small areas (cities),
  regions, countries, and even the whole globe.
• The resolution is the size of the gridboxes

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Eulerian models (contd)

• Not straightforward to construct advection and
  chemistry schemes that are shape and mass
  conservative etc.
• A number of processes, that can not be explicitly
  described needs to be parameterised

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Horizontal scale of various air pollution models
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Horizontal scale of various air pollution
problems
X
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Basic meteorologyChapter 1. in Atmospheric
Chemistry and Physics (Seinfeld and Pandis, 1998)

• Magnuz Engardt

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Do you know?

• What the atmosphere is?
• Why the is wind blowing?
• Why does it rain?
• Why is it colder at night than during day
• Why do different regions have different climate?
• Why is the sky blue?
• How can it be possible to calculate what the
  weather will be like tomorrow?
• Why are the forecasts not always right?
• What does meteorology has to do with air quality
  and air pollution?

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The atmosphere consists of a mixture of gases and
particles (liquid and solid)

• The main constituents of the dry atmosphere
  (volume )
• Nitrogen N2 78.1 Oxygen O2 20.9 Argon Ar
  0.93 Carbon dioxide CO2 0.04 380
  ppm(v)Neon Ne 0.0018Helium He 0.00052
  Methane CH4 0.00018 1.8 ppm(v)Krypton Kr 0
  .00011 Near-surface Ozone O3 0.000005
  50 ppb(v)
• Sulphur dioxide SO2 lt0.0000001 1 ppb(v)
• The atmosphere also contains 0-30 g H2O vapour
  m-3 (0-3) and 0-1 g H2O particles m-3 (0-0.1)

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The atmospheredivided into spheres depending
on the temperature variation with height.
The pressure is the weight of the air above a
certain level.
The pressure at a certain level is proportional
to the number of molecules per volume of air. ?
99 of the atmosphere resides under 30 km.
Virtually all weather (clouds, rain, monsoon
circulation, tropical and extratropical cyclones,
etc.) occur in the troposphere.
Long-lived gases (N2, O2, Ar, (CFCs, N2O, CO2,
CH4),) are well mixed up to ca. 100 km.
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The Earth radiation balance
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The driving force of weather, (ocean currents,)
and climate
The earth has an energy surplus around the
equator and a deficit near the poles.
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General circulation (distributes heat (energy)
from lower latitudes towards the poles)
Warm air rises near the equator, Colder air is
being sucked in ITCZ (the Intertropical
Convergence Zone) follows the sun between the
tropical circles ? rainy seasons The earth
rotation deflects the airs movement ? the trade
winds ?West wind belt at the mid-latitudes. Mou
ntain chains and land/sea differences also have
an influence on the circulation Rising air
generates clouds Sinking air causes dry-up -gt
deserts.
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Global maps of surface winds and pressure during
different seasons
January
Note the seasonal shift of the intertropical
convergence zone, ITCZ
July
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Annual average latitudinal distribution of
precipitation, r (solid line) and evaporation, E
(dashed line)
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Rotation of the earth affects wind-direction
Where the surface pressure is low, the air
converges and is forced upwards.
In high pressure systems, air diverges, this
cause sinking motion, i.e. subsidence.
The driving force of winds is pressure
differences. The rotation of the Earth deflect
the air to the right (on the N. Hemisphere) ?The
Coriolis force The wind blows roughly parallel
to the isobars in the free atmosphere
When surface friction is apparent (i.e. close
to the ground) the wind has a component cross the
isobars
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Generation of sea-breeze (and monsoon
circulation) ((and global general circulation))
Warm air has lower density than cold
air Horizontal temperature variations cause
horizontal pressure variations ?winds
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The sea-breeze (summer monsoon) circulation
Again, the Coriolis force (and mountain chains
etc.) will deflect the wind from its original
direction from high pressure to low pressure
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Local topographical, or physical properties may
influence wind direction and speed.
Obstacles can affect wind direction as well
as enhance or decrease the wind speed
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Local meteorology and surface characteristics
determine the planetary boundary layer height.
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Various sources of information are used to
describe the current state of the atmosphere
Weather radar
Synop stations
Weather satellite
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Ordinary physical laws can be used to create a
three-dimensional picture of the state of the
atmosphere

• Fmg (Newtons second law)
• pVnRT (ideal gas law)
• Radiation laws (I?T4, etc.)
• RHw/wmax
• Conservation of mass
• Etc.

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Analysis and Forecast models

• Models are used to fill the gaps between the
  observations Analysis
• Models can also be used to calculate the future
  state of the atmosphere (weather forecasts)

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Surface analysis
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The end