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FARM / SURFPro Novità nei modelli Bologna, 4 marzo 2010

FARM / SURFPro Novità nei modelli Bologna, 4 marzo 2010. ENEA, Riunione plenaria MINNI - Bologna, 4 marzo 2010. FARM (Flexible Air quality Regional Model). Main fatures:

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FARM / SURFPro Novità nei modelli Bologna, 4 marzo 2010

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  1. FARM / SURFPro Novità nei modelli Bologna, 4 marzo 2010 ENEA, Riunione plenaria MINNI - Bologna, 4 marzo 2010

  2. FARM (Flexible Air quality Regional Model) • Main fatures: • Emission of pollutants from area and point sources, with plume rise calculation and mass assignment to vertical grid cells • 3D dispersion by advection and turbulent diffusion • flexible gas-phase chemical mechanisms configuration FCM Software (SAPRC-90, SAPRC-99, SAPRC-07, EMEP-acid – through FCM) • Treatment of PM10 and PM2.5 (aero0 inorganic equilibrium module, aero3 modal aerosol module) • Dry removal of pollutants dependent on local meteorology and land-use • Removal through precipitation scavenging processes • One- and two-way nesting on arbitrary number of grids • Treatment of additional inert tracers • Parallel processing using OpenMP paradigm • Inclusion of data assimilation techniques • Online calculation of photolysis rates using TUV model (Tropospheric Ultraviolet and Visible radiation model; Madronich et al, 1989); RADM method to correct for cloud cover (Chang et al., 1987) • Inclusion of map scale factors and different coordinate systems • SAPRC99 and POPs-Hg Gas-phase chemical mechanisms generated via KPP Software (LSODE/Rosenbrock solvers)

  3. Parallel processing using OpenMP paradigm

  4. Speed-Up 4 Processori 8 Processori

  5. Inclusion of data assimilation techniques

  6. Assessment under EU Air Quality Directives Combining models with measurement Zona di non conformità la qualità dell’aria ambiente è valutata tramite misurazioni in siti fissi. Tali misurazioni possono essere integrate da tecniche di modellizzazione e/o da misurazioni indicative (la modellazione è “secondaria” rispetto alle misurazioni) Valore limite (VL) Soglia di valutazione superiore (SVS) è possibile combinare le misurazioni in siti fissi con le tecniche di modellizzazione e/o le misurazioni indicative al fine di valutare la qualità dell’aria ambiente (la modellazione ha lo stesso valore delle misurazioni) Zona di conformità Soglia di valutazione inferiore (SVI) è possibile utilizzare solo tecniche di modellizzazione o di stima obiettiva al fine di valutare la qualità dell’aria ambiente (la modellazione è “alternativa” alle misurazioni)

  7. measurement modelling Assessment under EU Air Quality Directives Combining models with measurement 100% measurement Measurement, no interpretation Measurement+interpretation Measurement+interpolation Measurement+model fitted to measurement Data assimilation Model validated by measurement in the same zone Model validated elsewhere Unvalidated model 100% modelling …there is an almost continuous spectrum of combination of measurements and other assessment methods (mathematical techniques and models) From: Guidance on Assessment under the EU Air Quality Directives, EEA 2002

  8. Measurement no interpretation Model validated by measurement in the same zone Measurement Measurement and interpolation Proposta Integrazione dei dati sperimentali contenuti nel dataset BRACE per la produzione di campi di analisi sul territorio italiano. Data Assimilation Modelling

  9. Schemi di analisi oggettiva implementati in FARM • Observational Nudging/Newtonian Relaxation technique; • Optimal Interpolation; • Bratseth method of Successive Corrections.

  10. Schemi di analisi oggettiva implementati in FARM The time evolution of the i-th chemical species over the time step Δt is then computed as follow: where LN is the nudging operator. Including the obs nudging (1), the nudging operator has the form: Using the Optimal Interpolation (2) (or the Successive Correction Method / Bratseth scheme), LN has the form: where cAi is the gridded analysed concentration field.

  11. Rv = 100 m Rv = 100 m Upper layers weighting Let c(i,j,z=z0,t) the increment (or decrement) of surface concentration (z=z0) at grid cell (i,j) at time t due to observational nudging. The concentration at upper layers is then computed as follow:

  12. Observational Nudging/Newtonian Relaxation technique For observational nudging GA is given by the following expressions: i is the observational quality factor, ranging from 0 to 1, that takes into accounts for characteristic errors in measurements and representativeness

  13. Observational Nudging/Newtonian Relaxation technique Wj is given by: • Ris the specified obs radius of influence • D id the distance between obs and the grid point • z is the vertical distance and Rz the vertical scale lenght •  is the specified time window for obs

  14. Weights: Wj Wx,y Wt

  15. Optimal Interpolation The analysed state vector XA is given by: where: XG: background state vector Y: observation vector H: observation operator (model space to observation space) B: background error covariance matrix R: observation error covariance matrix K: gain matrix

  16. The Bratseth Method of Successive Corrections The Bratseth technique (Bratseth, 1986) is a successive correction scheme that converges to optimal interpolation due to the inclusion of background and observation error statistics. The analysis is initialised with a background field, or first guess, which is then modified by the analysis of local data onto the model grid.

  17. Online calculation of photolysis rates using TUV model

  18. Calcolo dei ratei di fotolisi Approccio attuale (FCM) L’inserimento di un modulo di trasferimento radiativo per il calcolo dei ratei di fotolisi delle diverse specie in un modello di chimica dell’atmosfera determina un significativo incremento del tempo di calcolo. Per tale ragione nel modello FARM i ratei di fotolisi delle diverse specie chimiche vengono calcolati mediante l’utilizzo di “look-up tables” assumendo condizioni di cielo sereno. Tali ratei vengono stimati al livello del suolo e quindi corretti per le quote superiori mediante l’utilizzo di formule empiriche (Peterson, 1976).

  19. Calcolo dei ratei di fotolisi TUV http://cprm.acd.ucar.edu/Models/TUV/index.shtml Main fatures: Tropospheric Ultraviolet-Visible Model (TUV) has been developed by Madronich [1989]. TUV is a state-of-the-art radiation transfer model, and is widely used by the scientific community. TUV calculates spectral irradiance, spectral actinic flux, and photodissociation rates (J-values) for the wavelength range between 121 and 750 nm. References: Madronich, S., Photodissociation in the atmosphere 1. Actinic flux and the effect of ground reflections and clouds, J. Geophys. Res., 92, 9740-9752, 1989.

  20. Calcolo dei ratei di fotolisi TUV “j-Values”: Definition NO2 + h NO + O ( < 424 nm) “actinic” flux (photons cm-2 s-1 nm-1) absorption cross section (cm2) photolysis quantum yield (photons-1)

  21. Calcolo dei ratei di fotolisi TUV Factors Affecting Actinic Flux • solar zenith angle • observer altitude • ozone profile/amount • other absorbers/scatterers (O2, air) • surface reflectivity (albedo), Spectral albedo file to be used (as done in MODTRAN) • surface altitude • aerosol morphology/optical properties • cloud morphology/optical properties • atmospheric refraction

  22. Calcolo dei ratei di fotolisi in assenza di nuvole NO2 + h NO + O ( < 424 nm)

  23. Calcolo dei ratei di fotolisi in presenza di nuvole Approccio attuale

  24. Calcolo dei ratei di fotolisi in presenza di nuvole Chang et al., 1987

  25. Calcolo dei ratei di fotolisi in presenza di nuvole Al di sotto della “cloud base”

  26. Calcolo dei ratei di fotolisi in presenza di nuvole Al di sopra della “cloud base”

  27. Calcolo dei ratei di fotolisi in presenza di nuvole Confronto al di sotto della “cloud base” cldtop TCC = 1 cldtop -cldnùbot=1000 m cldbot

  28. Calcolo dei ratei di fotolisi in presenza di nuvole Confronto al di sotto della “cloud base”

  29. Calcolo dei ratei di fotolisi in presenza di nuvole Confronto al di sopra della “cloud base”

  30. Original data Interpolated data Cressmann Calcolo dei ratei di fotolisi (TUV) Columnar ozone - OMI Satellite data 24 June 2009

  31. Calcolo dei ratei di fotolisi (TUV) Columnar ozone - OMI Satellite data 3 January 2005 Original data Optimal Interpolation # of influential points = 50 correlation lenght=20°

  32. Calcolo dei ratei di fotolisi in presenza di nuvole Calcolo dello spessore ottico

  33. Calcolo dei ratei di fotolisi in presenza di nuvole Calcolo dello spessore ottico

  34. Inclusion of map scale factors

  35. Computing Scale Factors where m is the local scale factor, and the symbol dist stands for a small increment of distance on either the map or the earth accordingly.

  36. Advection-diffusion operators where Lx, Ly are advection-diffusion operators along the two horizontal axes, Lz is the vertical operator taking into account transport, diffusion, source injection Ei and dry deposition Ri processes. ci is i-th gas-phase average species concentration, u, vand w are the components of wind velocity vector, KH and KV the lateral and vertical diffusivities and m is the map scale factor (ratio of the length of a path on the map to the length of the path that it represents on the earth)

  37. Map factor analysis GEMS Domain

  38. Map factor analysis MINNI Domain

  39. POPs and Hg gas-phase chemical mechanism

  40. KPP species #include atoms #DEFVAR {------------------------------------- Inorganics -------------------------------------} NO = N + O; NO2 = N + 2O; NO3 = N + 3O; HNO3 = H + N + 3O; N2O5 = 2N + 5O; PAN = 2C + 3H + 5O + N; SO2 = S + 2O; H2SO4 = 2H + S + 4O; {------------------------------------- PAHs -------------------------------------} PAH1 = IGNORE; {B[a]P, Benzo[a]pyrene} PAH2 = IGNORE; {B[b]F, Benzo[b]fluorene} PAH3 = IGNORE; {B[k]F, Benzo[k]fluorene} I_P = IGNORE; {indeno[1,2,3-cd]pyrene} {------------------------------------- Dioxins -------------------------------------} PCDD1 = IGNORE; {2,3,7,8-TeCDD} PCDD2 = IGNORE; {1,2,3,7,8-PeCDD} PCDD3 = IGNORE; {1,2,3,4,7,8-HxCDD} PCDD4 = IGNORE; {1,2,3,6,7,8-HxCDD} PCDD5 = IGNORE; {1,2,3,7,8,9-HxCDD} PCDD6 = IGNORE; {1,2,3,4,6,7,8-HpCDD} OCDD = IGNORE; {OCDD}

  41. KPP species {------------------------------------- Furans -------------------------------------} PCDF1 = IGNORE; {2,3,7,8-TeCDF} PCDF2 = IGNORE; {1,2,3,7,8-PeCDF} PCDF3 = IGNORE; {2,3,4,7,8-PeCDF} PCDF4 = IGNORE; {1,2,3,4,7,8-HxCDF} PCDF5 = IGNORE; {1,2,3,6,7,8-HxCDF} PCDF6 = IGNORE; {1,2,3,7,8,9-HxCDF} PCDF7 = IGNORE; {2,3,4,6,7,8-HxCDF} PCDF8 = IGNORE; {1,2,3,4,6,7,8-HpCDF} PCDF9 = IGNORE; {1,2,3,4,7,8,9-HpCDF} OCDF = IGNORE; {OCDF} {------------------------------------- PCBs -------------------------------------} PCB1 = IGNORE; {PCB-28} PCB2 = IGNORE; {PCB-105} PCB3 = IGNORE; {PCB-118} PCB4 = IGNORE; {PCB-153} PCB5 = IGNORE; {PCB-180} {------------------------------------- Pesticides -------------------------------------} gHCH = IGNORE; {gamma-Hexachlorocyclohexane} HCB = IGNORE; {C6Cl6} {------------------------------------- Mercury -------------------------------------} Hg = Hg; {Mercury elemental} HgO = Hg + O; {Mercury oxide} HgAER = IGNORE; {Mercury in particulate form}

  42. KPP species #DEFFIX O3 = 3O; OH = H + O; CCO_O2 = 2C + 3O; H2O = 2H + O; H2O2 = 2H + 2O;

  43. KPP reactions #Equations {Inorganic EMEP Acid Reactions} {1} NO2 + hv = NO : phk(1); {fcm_saprc99_phk('NO2_____',1e0,zenith);} {2} O3 + NO = NO2 : ARR(1.80e-12,1370.0e0,0.0e0); {3} O3 + NO2 = NO3 : ARR(1.40e-13,2470.0e0,0.0e0); {4} OH + NO2 = HNO3 : FALL(2.43e-30, 0.0e0,-3.10e0,1.67e-11,0.0e0,-2.10e0,0.60e0); {5} CCO_O2 + NO2 = PAN : FALL(2.70e-28,0.0e0,-7.10e0,1.20e-11,0.0e0,-0.90e0,0.30e0); {6} PAN = NO2 : FALL(4.90e-3,12100.0e0,0.0e0,4.0e+16,13600.0e0,0.e0,0.3e0); {7} OH + SO2 = H2SO4 : FALL(4.00e-31,0.0e0,-3.30e0,2.00e-12,0.0e0,0.0e0,0.45e0); {8} NO3 + hv = NO : phk(2); {fcm_saprc99_phk('NO3NO___',1e0,zenith);} {9} NO3 + hv = NO2 : phk(3); {fcm_saprc99_phk('NO3NO2__',1e0,zenith);} {10} NO2 + NO3 = N2O5 : FALL(2.80e-30,0.0e0,-3.50e0,2.00e-12,0.0e0,0.20e0,0.45e0); {11} N2O5 = NO2 + NO3 : FALL(1.e-3,11000.0e0,-3.5e0,9.7e+14,11080.0e0,0.1e0,0.45e0); {12} N2O5 + H2O = 2HNO3 : (2.60e-22); {13} NO + NO3 = 2NO2 : ARR(1.80e-11,-110.0e0,0.0e0); {PAHs: Meylan and Howard, 1993 cited in SRC PhysProp Database} {14} PAH1 + OH = PROD : (5.000e-11); {15} PAH2 + OH = PROD : (1.860e-11); {16} PAH3 + OH = PROD : (5.360e-11); {17} I_P + OH = PROD : (6.447e-11);

  44. KPP reactions {PCDDs: Brubaker and Hites, 1997} {18} PCDD1 + OH = PROD : (1.05e-12); {19} PCDD2 + OH = PROD : (5.60e-13); {20} PCDD3 + OH = PROD : (2.70e-13); {21} PCDD4 + OH = PROD : (2.70e-13); {22} PCDD5 + OH = PROD : (2.70e-13); {23} PCDD6 + OH = PROD : (1.30e-13); {24} OCDD + OH = PROD : (5.00e-14); {PCDFs: Brubaker and Hites, 1997} {25} PCDF1 + OH = PROD : (6.10e-13); {26} PCDF2 + OH = PROD : (3.00e-13); {27} PCDF3 + OH = PROD : (3.00e-13); {28} PCDF4 + OH = PROD : (1.40e-13); {29} PCDF5 + OH = PROD : (1.40e-13); {30} PCDF6 + OH = PROD : (1.40e-13); {31} PCDF7 + OH = PROD : (1.50e-13); {32} PCDF8 + OH = PROD : (6.00e-14); {33} PCDF9 + OH = PROD : (6.00e-14); {34} OCDF + OH = PROD : (3.00e-14);

  45. KPP reactions {PCBs: Anderson and Hites, 1996; Beyer and Matthies, 2001} {35} PCB1 + OH = PROD : ARR(2.70e-10,1650.0e0,0.0e0); {36} PCB2 + OH = PROD : ARR(6.15e-11,1554.0e0,0.0e0); {37} PCB3 + OH = PROD : ARR(6.15e-11,1554.0e0,0.0e0); {38} PCB4 + OH = PROD : ARR(8.12e-11,1850.0e0,0.0e0); {39} PCB5 + OH = PROD : ARR(1.40e-10,2146.0e0,0.0e0); {Pesticides: Brubaker and Hites, 1997} {40} gHCH + OH = PROD : ARR(6.00e-11,1708.0e0,0.0e0); {41} HCB + OH = PROD : ARR(4.90e-10,2923.0e0,0.0e0); {Mercury: Xie et al., 2008 and Jung et al., 2009, AER from CMAQ} {42} Hg + O3 = 0.5HgO + 0.5HgAER: ARR(8.43e-17,1407.0e0,0.0e0); {43} Hg + OH = 0.5HgO + 0.5HgAER: ARR(3.55e-14,-294.0e0,0.0e0); {44} Hg + H2O2 = HgO : (8.50e-19); {44} Hg + NO3 = HgO + NO2: (4.00e-15);

  46. POPs processes

  47. Degradation in air • Degradation process of POPs in the atmosphere is considered as the gas-phase reaction of pollutants with hydroxyl radicals and all other reactions are neglected. The degradation process in the atmosphere is described by the equation of the second order: • dC/dt=-kair ·C ·[OH] • where: • Cis the pollutant concentration in air (gaseous phase), ng/m3; • [OH] is the concentration of OH radical, molec/cm3; • kairis the degradation rate constant for air, cm3/(molec s).

  48. Gas/particle partitioning • POP partitioning between the gaseous and particulate phase is performed using the Junge-Pankow model [Junge, 1977; Pankow, 1987] based on subcooled liquid vapour pressure pOL(Pa). According to this model the POP fraction  adsorbed on tropospheric aerosol particles equals to: •  = c· / (pOL + c·) • where: • cis the constant dependant on the thermodynamic parameters of the adsorption process and on the properties of aerosol particle surface; it is assumed c=0.17 Pa·m [Junge, 1977] for background aerosol; • θ is the specific surface of aerosol particles, m2/m3.

  49. Dry deposition Dry deposition flux of the gas-phase is not considered. Dry deposition flux of the particulate phase Fpdry(ng/m2/s) is a product of dry deposition velocity vd(m/s) and air concentration CP(ng/m3) of a pollutant in the particulate phase taken at an air reference level coinciding with the middle of the lowest atmospheric layer: Fpdry = vd· CP

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