1. Modelling of air pollution -Why?
Magnuz Engardt Swedish Meteorological and Hydrological Institute
January 2008

2. Instruments in air pollution assessments
Air quality / deposition measurement programmes
Emission inventories
Effect studies
→ Atmospheric transport and dispersion models
Three types of instruments are used in assessment studies of the air pollution: emission inventories, atmospheric dispersion and transport models and air quality measuring programmes.
Before getting acquainted with the atmospheric pollution models let us first define and discuss models in a broader sense and environmental models in particular.
January 2008

3. 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
January 2008

4. 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…
January 2008

5. Some examples…
January 2008

6. Origin of total non-seasalt sulphur deposition in Sweden during 1998 as deduced by the MATCH-model
~1000 km
January 2008

7. Source-receptor calculations for Southeast Asia
National emissions (Q) and depositions (D) in nine Southeast Asian countries during 2000 Q D
January 2008

8. Annual total deposition of oxidised nitrogen in South Asia resulting from NOX emissions in Bangladesh.
~1000 km
January 2008

9. Climate induced change in total-SOX deposition (total-, wet- drydeposition) minus
January 2008

10. 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.
January 2008
~100 km

11. 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.
January 2008

12. January 2008

13. What is a model ? Mathematical relations based on empirical or physical laws
Models are used everywhere in society
Economical models
Population models
Technological models …
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.
...
Estimated change in the global population.
January 2008

14. Quality of model output never better than the input to the model
Various
Parameters
Meteorology
Emission
Inventory
Surface
Deposition
Atmospheric
Concentration
Dispersion model
January 2008

15. 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.
January 2008

16. 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.
January 2008

17. 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., …
Note the difference
Note the difference
January 2008

18. Model verification “Objective” statistics, using other measures than mean and standard deviation often used
Mean error (Bias),
A measure of over- or under estimation.
RMS-error,
Gives the magnitude of the error.
Correlation
A measure on how well the results co-vary.
Example
Ci = simulated value
Mi = measured value
N = Number of data points
January 2008
sX = standard deviation of X
= average of X

19. Different “objective” measures may give different scores for a model (
Identical meanvalues, no bias Poor correlation (r≈0) 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 < r < 1) Different mean values, high bias Large RMS-error
January 2008

20. Model verification (cont’d) Visual inspection of results
“Subjective” inspection of the results by plotting them should also be performed. Methods include:
Timeseries
Scatterplots
Maps
January 2008

21. Visual inspection of model results
Timeseries
January 2008

22. NO2 [gm-3] Observations
Visual inspection cont’d
Scatterplots
NO2 [gm-3] MATCH
NO2 [gm-3] Observations
January 2008
Comparison between calculated and observed monthly average concentrations of NO2 (g/m3) at four regional background stations. Correlation coefficient R=0,96.

23. Review of precipitation-chemistry data in India Data 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.
ammonium [μEq l-1]
sulphate [μEq l-1]
January 2008

24. Can you use a model of limited quality
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)
January 2008

25. Model quality (cont’d)
It’s 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!
January 2008

26. 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 doesn’t matter much
Analyse the robustness of your results through sensitivity tests
January 2008

27. Magnuz Engardt Swedish Meteorological and Hydrological Institute
Atmospheric dispersion modelling –basic concepts (Ch. 23 in Seinfeld and Pandis, 1998)
Magnuz Engardt Swedish Meteorological and Hydrological Institute
January 2008

28. Pollutants (gases and particles) are transported with the three-dimensional wind
t=t0
t=t0+Dt
January 2008

29. Note that mean wind and turbulence is not constant in time or space
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 UTC during 10 September 2007
January 2008

30. “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.
January 2008

31. Atmospheric “stability” and surface characteristics (“roughness” etc
Atmospheric “stability” and surface characteristics (“roughness” etc.) affects the turbulence
January 2008
Here the shape of a “plume” during different stabilities (vertical temperature variations) is illustrated.

32. Turbulence (and molecular diffusion) may also transport species in the absence of mean wind.
There is typically no mean vertical wind close to the ground, still does vertical transport to and from the surface occur. This is caused by turbulence.
”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.
January 2008

33. 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 layer or Boundary Layer Height.
Typically ~1-2 km during day,
100m or less during night.
January 2008

34. Mixed layer height vary over time and space The depth of the mixed layer height greatly affects near-surface concentrations
Height
Wind-
speed
profile
Tracer
profile
Temperature profile
January 2008
A more shallow mixed layer cause near-surface tracer concentrations to be higher

35. Fumigation (downwash) -caused by horizontal variations in near-surface turbulence (variations in surface roughness and atmospheric stability)
January 2008
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.

36. The spread of a plume during very calm conditions
Local environmental and meteorological effects may interact with the dispersion of pollutants
The spread of a plume during very calm conditions
January 2008
Even in a flat environment is the wind direction (and magnitude) changing with height

37. Changing wind direction -and speed- cause “plumes” not to be straight
100 km
Dust from Sahara follows trade winds across the Atlantic
~1000 km
Calculated plume of NO2 emitted in Tallinn, Estonia
January 2008

38. 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
NH 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
January 2008

39. Gases and particles may leave the atmosphere through drydeposition on various surfaces…
Drydeposition flux is often modelled as:
Fdrydep = vd(z) c(z) [ms-1×gm-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.
January 2008
Dry deposition can be measured through various more or less advanced methods. Not routinely done.
Most simple methods include “throughfall measurements.
Dry deposition can be estimated through measuring concentrations in the air and multiplying with relevant deposition velocities.

40. Typical drydeposition velocities (valid at 1 m) Uncertain to at least a factor of two.
January 2008

41. Drydeposition of particles is a strong function of particle size
January 2008

42. 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.
January 2008
Wetdeposition can readily be measured through collecting and analysing rainwater.

43. Species may undergo chemical or physical transformation
January 2008
Coupled nitrogen/sulphur chemistry in MATCH
Most reactions depends on ambient conditions (temperature, abundance of oxidants, solar radiation, humidity etc.).

44. Physical transformation:
Gas to particle conversion (or vice versa)
Particle-to-particle coagulation
Water condensing on existing particles
Etc.
In display mode there is “moving graphics”.
January 2008

45. Summary: Terms needed during modelling of pollutants:
CONCENTRATION
CHANGE =
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
January 2008
WETDEP = Wetdeposition of gases or particles

46. An example from real life:
January 2008

47. 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.
January 2008

48. Chernobyl accident (cont’d)
January 2008

49. Chernobyl accident (cont’d) Measured deposition of 137Cs and rain amount in Sweden
January 2008

50. Different types of models…
January 2008

51. It’s possible to create air-pollution indexes
Box-model
It’s possible to create air-pollution indexes or
Calculate average concentration in a city if the area and total emissions are known
Boundary layer height
January 2008

52. 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”
January 2008

53. Gaussian model
January 2008

54. 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
January 2008

55. CFD (Computational Fluid Dynamics)
Cross-section of the plume.
A plume from a stack.
January 2008
Near surface concentrations of pollutants in different industrial areas.

56. Lagrangian models
Consider an air-parcel that is travelling with the time-varying three-dimensional wind.
Time varying three-dimensional wind field
January 2008

57. Lagrangian models (cont’d)
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.
January 2008

58. Lagrangian models (cont’d)
Typical regional spread from an instantaneous point-source located near the surface
January 2008

59. Lagrangian models (cont’d)
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.
January 2008

60. Eulerian models (or gridpoint models)
January 2008

61. 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.
January 2008

62. Eulerian models (cont’d)
Eulerian model can cover small areas (cities), regions, countries, and even the whole globe.
The resolution is the “size of the gridboxes”
January 2008

63. Eulerian models (cont’d)
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”
January 2008

64. Horizontal scale of various air pollution models
The spatial (and temporal) scale of atmospheric processes influences air pollution dispersion. We distinguishing between the following scales:
Macroscale (exceeding 1,000 km); at this scale, the atmospheric flow is mainly associated with synoptic phenomena, i.e. the geographical distribution of pressure systems. Such phenomena are mainly due to large-scale inhomogeneities of the surface energy balance.
Microscale (characteristic lengths below 1 km); Although thermal effects may contribute to the generation of these flows, they are mainly determined by hydrodynamic effects (e.g. flow channelling, roughness effects).
Mesoscale (characteristic lengths between 1 and 1000 km); the flow configuration in the mesoscale is depending both on hydrodynamic effects and inhomogeneities of the energy balance.
Models describing the dispersion and transport of air pollutants in the atmosphere can be distinguished on many grounds. This is just a compilation of the most common model types used in air pollution studies.
Among these models, Lagrangian and Eulerian are applicable on several scales.
January 2008

65. Horizontal scale of various air pollution problemsX
‘Traditional’ air pollution problems are the local scale ones. More recently, environmental policy is to a large extent confronted with global scale problems (climate change, ozone depletion). Other important policy issues related to the environment are acidification, photo-oxidant formation, urban air pollution and the problem of air toxics.
Air pollution was considered as a local phenomenon until the middle of the 1960s. In the late 1960s Swedish scientists were the first to suggest a link between the large sulphur emissions in Central Europe, the high acidity of European precipitation and the harmful effects on Scandinavian freshwater ecosystems.
January 2008

66. Basic meteorology… Chapter 1
Basic meteorology… Chapter 1. in Atmospheric Chemistry and Physics (Seinfeld and Pandis, 1998)
Magnuz Engardt
January 2008

67. 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?
January 2008

68. 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 O % Argon Ar 0.93% Carbon dioxide CO2 ~0.04% [380 ppm(v)] Neon Ne % Helium He % Methane CH4 ~ % [1.8 ppm(v)] Krypton Kr % … … … Near-surface Ozone O3 ~ % [50 ppb(v)]
Sulphur dioxide SO2 < % [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%)
January 2008

69. The atmosphere divided 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.
January 2008

70. The Earth radiation balance
January 2008

71. The driving force of weather, (ocean currents,) and climate
Low latitudes receive more solar energy per area unit than high latitudes.
The earth has an energy surplus around the equator and a deficit near the poles.
The earth emits (longwave) radiation relatively uniformly.
January 2008

72. 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 air’s movement
→ the trade winds
→“West wind belt” at the mid-latitudes.
Mountain chains and land/sea differences also
have an influence on the circulation
Rising air generates clouds
Sinking air causes dry-up -> deserts.
January 2008

73. Global maps of surface winds and pressure during different seasons
January
Note the seasonal shift of the intertropical convergence zone, ITCZ
July
January 2008

74. Annual average latitudinal distribution of precipitation, r (solid line) and evaporation, E (dashed line)
January 2008

75. 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”
January 2008
When “surface friction” is apparent (i.e. close to the ground) the wind has a component cross the “isobars”

76. Generation of sea-breeze (and monsoon circulation) ((and global general circulation))
Morning (/spring)
Early day (/summer)
Mid day (/summer)
Water
Land
Height
p+Dp
p-Dp p
Warm air has lower density than cold air
Horizontal temperature variations cause horizontal pressure variations
winds
January 2008

77. The sea-breeze (summer monsoon) circulation
Water
Land
Height
January 2008
Again, the Coriolis force (and mountain chains etc.) will deflect the wind from its “original” direction from high pressure to low pressure

78. Local topographical, or physical properties may influence wind direction and speed.
January 2008
Obstacles can affect wind direction as well as
enhance or decrease the wind speed

79. Local meteorology and surface characteristics determine the planetary boundary layer height.
January 2008

80. Various sources of information are used to describe the current state of the atmosphere
Weather radar
January 2008
Synop stations
Weather satellite

81. Ordinary physical laws can be used to create a three-dimensional picture of the state of the atmosphere
F=mg (Newton’s second law)
pV=nRT (ideal gas law)
Radiation laws (I=T4, etc.)
RH=w/wmax
Conservation of mass
Etc.
January 2008

82. 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)
January 2008

83. Surface analysis
January 2008

84. The end…
January 2008