Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Modelling of air pollution -Why? Magnuz Engardt January 2008 Swedish Meteorological and Hydrological Institute Instruments in air pollution assessments Air quality / deposition measurement programmes Emission inventories Effect studies January 2008 → Atmospheric transport and dispersion models 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 January 2008 their full potential -and to increase the quality of each other 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 January 2008 •Check emission inventories •Verify measurements •Etc… January 2008 Some examples… January 2008 ~1000 km Origin of total non-seasalt sulphur deposition in Sweden during 1998 as deduced by the MATCH-model Source-receptor calculations for Southeast Asia National emissions (Q) and depositions (D) in nine Southeast Asian countries during 2000 500 Q 450 Q 400 D Gg sulphur per year 350 D 300 250 200 Q 150 D D D 100 Q Q D Q Q Q D Q D D Receptor country na m Vi et nd ai la Th or e ap Si ng nm ar M ya sia ay M al os La ia es on In d C am bo di a ei 0 Br un January 2008 50 Boundary Shipping China Vietnam Thailand Singapore Myanmar Malaysia Laos Indonesia Cambodia Brunei 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. Average summer near-surface ozone concentration in southern Sweden under different emission scenarios Decrease VOC or/and NOX emissions with ~50% ~100 km January 2008 Other studies include different NO/NO2 ratio of NOX-emissions or different speciation of VOC emissions. Global distribution of methane -why does it look like this? January 2008 Weekly measurements of “marine boundary layer” CH4. Data processed by an interpolating and “smoothing” program. January 2008 What is a model ? Mathematical relations based on empirical or physical laws Models are used everywhere in society In our field we have, for example, Economical models Numerical weather forecast models Population models Climate models Technological models Emission inventories … Integrated Assessment Models Dispersion models including emissions, transport, deposition, chemical conversion etc. January 2008 ... Estimated change in the global population. Quality of model output never better than the input to the model Meteorology Atmospheric Concentration Emission Inventory January 2008 Surface Deposition Various Parameters 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.) January 2008 ►Surface characteristics ►Various assumptions ►Etc. 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) January 2008 •Etc. 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 Note the difference • Errors in the input to the model • Errors in measurements Note the difference • Non-representative measurements January 2008 • Etc., … Model verification “Objective” statistics, using other measures than mean and standard deviation often used Mean error (Bias), MBE RMS-error, Correlation r Ci = simulated value Mi = measured value January 2008 N = Number of data points X = standard deviation of X X = average of X 1N C - Mi N i1 i A measure of over- or under estimation. 1 N 2 (Ci - Mi) N i1 Gives the magnitude of the error. 1 N (Mi - M)(Ci - C) N i1 CM A measure on how well the results co-vary. 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. January 2008 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 Model verification (cont’d) Visual inspection of results “Subjective” inspection of the results by plotting them should also be performed. Methods include: • Timeseries • Scatterplots January 2008 • Maps Visual inspection of model results Timeseries Ozone daily max concentration at Diabla Gora (Polen) 90 Observed daily max Model daily max 80 70 c(O3) / ppb(v) 60 50 40 30 20 January 2008 10 0 00-02-01 00-03-01 00-04-01 00-05-01 00-06-01 00-07-01 00-08-01 00-09-01 00-10-01 00-11-01 00-12-01 Visual inspection cont’d 25 Scatterplots 2 -3 MATCH NONO 2 [gm (µg/m3)] MATCH All 20 1:1 Skåne Allerum Arkelstorp Klintaskogen Tunby 15 10 5 0 0 5 10 15 3) Mätdata NO2 (µg/m-3 20 25 January 2008 NO2 [gm ] Observations 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 January 2008 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] 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 January 2008 Note the problem of unrepresentative measurements Keep uncertainty in input data in mind (model results could not be better than the input) 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 January 2008 periods If you have discrepancies, try to understand what they are caused by! 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 January 2008 tests Atmospheric dispersion modelling –basic concepts (Ch. 23 in Seinfeld and Pandis, 1998) Magnuz Engardt January 2008 Swedish Meteorological and Hydrological Institute Pollutants (gases and particles) are transported with the three-dimensional wind t=t0 January 2008 t=t0+Dt Note that mean wind and turbulence is not constant in time or space ! (not even in the tropics) January 2008 Near-surface wind, pressure and temperature over Sweden 12-24 UTC during 10 September 2007 “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., … January 2008 The turbulence is varying over time and space. January 2008 Atmospheric “stability” and surface characteristics (“roughness” etc.) affects the turbulence 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 ”Closed Chamber experiment” – Molecular diffusion cause gases to mix. 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. . . . . . . . . . .. .. . . . . . . . . . .. . . . . .. .. . . . . .. . . .. . .. ... . . . .. . . .. .. . . . .. . . . . . . . . . .. .. . .. . . . . . .. January 2008 CO2 and other gases (O3 ,SO2 …) are taken up by vegetation. The transport through the stomata of the leaves occur through molecular diffusion. Mixed layer, boundary layer Height Tracer profile Windspeed profile Temperature profile 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. January 2008 Mixed layer or Boundary Layer Height. Typically ~1-2 km during day, 100m or less during night. Mixed layer height vary over time and space The depth of the mixed layer height greatly affects nearsurface concentrations Height January 2008 Tracer profile Windspeed profile Temperature profile A more shallow mixed layer cause near-surface tracer concentrations to be higher Fumigation (downwash) January 2008 -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. Local environmental and meteorological effects may interact with the dispersion of pollutants January 2008 The spread of a plume during very calm conditions 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 ~1000 km 100 km January 2008 Calculated plume of NO2 emitted in Tallinn, Estonia Dust from Sahara follows trade winds across the Atlantic 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 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] January 2008 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 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. Species Surface type Time of day SO2 Grass Grass Forest Forest Snow Water Grass Grass Water Day Night Day Night O3 NO NO2 January 2008 NH3 HNO3 Sea Land Sea Land Day Night Day Night Value (cm s-1) 1.2 0.3 0.5 0.2 0.1 1.0 1.0 0.1 0.01 Net source 0.1 0.5 0.1 1.0 5 2-20 January 2008 Drydeposition of particles is a strong function of particle size 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. January 2008 Scavenging of particles and gases depends on solubility and cloud and rain type. Wetdeposition can readily be measured through collecting and analysing rainwater. Species may undergo chemical or physical transformation QNOX NQNOX QNH3 (1-N)QNOX (1-S)QSOX k21•OH•NO2 kB•NO3- k11•O3•NO NO NO3- NO2 JNO2•NO2 k12•O3•NO2 NH3 HNO3 kA•HNO3 D D,W SQSOX kT•fCC SO2 SO42- Kp=HNO3•NH3 min( NH3 , SO42-) 1.5 Kp=f(RH, T) irreversible reversible NH4NO3 January 2008 QSOX D,W D,W SO2 kgas•OH • SO2 H0.5(NH4)1.5SO4 D,W D,W D,W D,W 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 January 2008 •Etc. Summary: Terms needed during modelling of pollutants: CONCENTRATION = CHANGE = EMIS + ADVXY + ADVZ + CONVZ + TURBZ + CHEM +PHYS + DRYDEP + WETDEP 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 January 2008 DRYDEP = Drydeposition of gases or particles WETDEP = Wetdeposition of gases or particles January 2008 An example from real life: The Chernobyl accident 25 April 1986 Trajectory calculations depicting the path of the first emitted cloud of radioactive particles from the exploded Chernobyl reactor. January 2008 Note that different levels of the cloud travelled different routes. January 2008 Chernobyl accident (cont’d) Chernobyl accident (cont’d) January 2008 Measured deposition of 137Cs and rain amount in Sweden January 2008 Different types of models… Box-model Concentrat ion proportion al to Emissions Mixed layer height Horizontal wind speed It’s possible to create air-pollution indexes or Calculate average concentration in a city if the area and total emissions are known January 2008 Boundary layer height Gaussian model (assume “normal distribution” of pollutants on average) Instantanoues extent of the plume at different times January 2008 When averaging over time the plume is approximately normally distributed in the horizontal and vertical along the “centre line” Gaussian model The Gaussian Plume model (no uptake at the ground at the ground): 2 2 2 1 y 1 z - H 1 z H exp - exp - c( x, y, z) exp - u2z y 2 z 2 y 2 z Q where Q is the source strength, H is the effective plume height, u the effective transport velocity,z and y are the vertical and horizontal dispersion parameters, z the height, y the crosswind distance. January 2008 z and y are function of stability and distance from the source 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 z and y. They can be estimated from wind-profile January 2008 data and surface characteristics CFD (Computational Fluid Dynamics) Cross-section of the plume. January 2008 A plume from a stack. 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. January 2008 Time varying threedimensional wind field Lagrangian models (cont’d) January 2008 c ( x, y , z ) m 2 xy 2 Dz xy exp - 0.5 x 2 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. Lagrangian models (cont’d) January 2008 Typical regional spread from an instantaneous point-source located near the surface Lagrangian models (cont’d) Lagrangian models (SO 2 )new (SO 2 )old EMIS SO2 - DEPSO2 - CHEM SO2 May include emissions, deposition (SO 4)new (SO 4)old EMIS SO4 - DEPSO4 CHEM SO2 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 January 2008 meteorological data. January 2008 Eulerian models (or gridpoint models) 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. January 2008 Eulerian models are relatively timeconsuming on computers. Eulerian models (cont’d) Eulerian model can cover small areas (cities), regions, countries, and even the whole globe. January 2008 The resolution is the “size of the gridboxes” 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 January 2008 described needs to be “parameterised” Horizontal scale of various air pollution models January 2008 Model type Gaussian CFD Lagrangian Eulerian Macroscale Mesoscale Microscale Global Regional-to-cont. Local-to-reg. Local x x x x x x x x x x Horizontal scale of various air pollution problems Scale of dispersion phenomenon Environmental issue Local-to-reg. Local Climate change x X Ozone depletion x x Tropospheric ozone (x) x x Acidification (x) x (x) x x x Urban air quality x x Industrial emissions x x Corrosion January 2008 Global Regional-to-cont. Basic meteorology… Chapter 1. in Atmospheric Chemistry and Physics (Seinfeld and Pandis, 1998) January 2008 Magnuz Engardt 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? January 2008 What does meteorology has to do with air quality and air pollution? The atmosphere consists of a mixture of gases and particles (liquid and solid) January 2008 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%) The atmosphere divided into “spheres” depending on the temperature variation with height. The pressure is “the weight” of the air above a certain level. pV nRT p nRT V 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. January 2008 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 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. January 2008 The earth emits (longwave) radiation relatively uniformly. 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 January 2008 Rising air generates clouds Sinking air causes dry-up -> deserts. Global maps of surface winds and pressure during different seasons January Note the seasonal shift of the intertropical convergence zone, ITCZ January 2008 July January 2008 Annual average latitudinal distribution of precipitation, r (solid line) and evaporation, E (dashed line) Rotation of the earth affects wind-direction January 2008 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” 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”. 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) Warm air has lower density than cold air p Horizontal temperature variations cause horizontal pressure variations winds p-Dp p+Dp Early day (/summer) p-Dp p p+Dp Mid day (/summer) p Height January 2008 p-Dp p+Dp Water Land Height The sea-breeze (summer monsoon) circulation January 2008 Water Land Again, the Coriolis force (and mountain chains etc.) will deflect the wind from its “original” direction from high pressure to low pressure January 2008 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 January 2008 Local meteorology and surface characteristics determine the planetary boundary layer height. Various sources of information are used to describe the current state of the atmosphere January 2008 Weather radar Synop stations Weather satellite Ordinary physical laws can be used to create a threedimensional 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 January 2008 Etc. 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 January 2008 the atmosphere (weather forecasts) January 2008 Surface analysis January 2008 The end…