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

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

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

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

Some examples… January 2008

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

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

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

Climate induced change in total-SOX deposition (total-, wet- drydeposition) 2021-2050 minus 1961-1990. January 2008

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

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

January 2008

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

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

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

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

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

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

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

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

Visual inspection of model results Timeseries January 2008

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.

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

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

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

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

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

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

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 12-24 UTC during 10 September 2007 January 2008

“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

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.

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

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

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

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.

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

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

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 January 2008

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.

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

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

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.

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.).

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

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

An example from real life: January 2008

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

Chernobyl accident (cont’d) January 2008

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

Different types of models… January 2008

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

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

Gaussian model January 2008

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

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.

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

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

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

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

Eulerian models (or gridpoint models) January 2008

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

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

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

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

Horizontal scale of various air pollution problems X ‘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

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

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

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 0.0018% Helium 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 <0.0000001% [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%) January 2008

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

The Earth radiation balance January 2008

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

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

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

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

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”

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

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

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

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

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

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

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

Surface analysis January 2008

The end… January 2008