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NINFA: Air quality forecast over the Po Valley Basin .

NINFA: Air quality forecast over the Po Valley Basin . Marco Deserti , Enrico Minguzzi, Michele Stortini, Giovanni Bonafè Regione Emilia-Romagna ARPA-SIM, Area Meteorologia Ambientale. The NINFA modelling system 1 year hindcast: apr 2003 – mar 2004 (model verification and scenarios)

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NINFA: Air quality forecast over the Po Valley Basin .

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  1. NINFA: Air quality forecast over the Po Valley Basin. Marco Deserti, Enrico Minguzzi, Michele Stortini, Giovanni Bonafè Regione Emilia-Romagna ARPA-SIM, Area Meteorologia Ambientale

  2. The NINFA modelling system 1 year hindcast: apr 2003 – mar 2004 (model verification and scenarios) Model intercomparison 4 year hindcast: 2003-2006 (interannual variability) Contents

  3. NINFA modelling system (1)Northern Italian Network to Forecast photochemical and Aerosol pollution Orography height (m) • CTM: Chimere (dust & sea salt included) • Meteorological input : COSMO- IT (7 km horizontal resolution, under test 2.8 km) • NINFA BPA (operational): • 10 km horizontal resolution, 8 vertical levels up to 500 hPa (next 5 km) • Emissions: adapted from Corinair 2000 Italy + EMEP • Boundary conditions: Prev’air (0.5°*0.5°) • NINFA ER (not operational): • 5 km horizontal resolution, • Emissions: from ER 2003 (next INEMAR) • Boundary conditions: NINFA BPA

  4. NINFA modelling system (2) NOx gridded emissions, year 2000 annual total The Chimere CTM has been adapted to Northern Italy: • interface with COSMO meteorological fields • modification of MH and Kz evaluation • more urban corrections to meteorological input • evaluation of plume rise for point sources The NINFA system is used for: • operational air-quality forecasts and hindcast (started in October 2005, available at www.arpa.emr.it/sim) • Long-term simulations for air-quality assessment and scenario evaluation (4-year hindcast simulation (apr 2003 – mar 2007), Meteorological input from COSMO-IT re-analysis) Ongoing: Upgrade with the new Chimere version, from 10 to 5 km horizontal resolution

  5. The Meteorological model • COSMO-IT (formerly Lokal Modell - LAMI) • Multi-scale non-hydrostatic meteorological model (Steppler et al., 2003) • Clouds and precipitation micro-physics • Convection, radiation, turbulence, interaction between Earth surface, soil and atmosphere • Re-analysis mode • Forecast runs have not enough parameters => re-analysis • 12 hours run chain • same rotated grid of the forecast model, 7 km grid pace, 35 vertical levels • BC: ECMWF analysis (every 6 hours) • IC first level: ECMWF analysis (to avoid deviation) • IC upper levels: previous LAMI run • Hourly nudging (Schraff and Buchold, 1999)towards the measured Synop data during the model run in order to find the sweet spot between coherence and realism see (http://cosmo-model.cscs.ch/public/various/operational/arpa/operationalAppsARPA.htm

  6. Mixing height • Marco Deserti: • Modifications to Hmix: disabled enhancement below clouds, modified nocturnal scheme (now Mahrt 1981, function of U* only), increased minimum value in urban cells, introduced a maximum value of 2500 m • Plume rise scheme: taken from CAMx model (Turner 1986, modified ) Average mixing height during winter months, estimated by Chimere pre-processor. Default configuration (left) and setup adapted to Northern Italy (right) Modifications to Hmix: disabled enhancement below clouds, modified nocturnal scheme (now Mahrt 1981, function of U* only), increased minimum value in urban cells, introduced a maximum value of 2500 m PM10 annual average winter: + 5 - 7 mg/m3 Summer: + 4 - 5 mg/m3

  7. NINFANorthern Italian Network to Forecast photochemical and Aerosol pollution EMISSIONS: CTN_ACE METEO: COSMO IT/LAMA OUTPUT: O3, NO2, SO2, PM10 CHIMERE LANDUSE: CORINE2000+GLC2000 BOUNDARY CONDITIONS: (Prev'air) The modelling suite • Run every day on a Linux work station. Start at 4:00 GMT, output available at 09:00 GMT. • NINFA is based on the regional version of photochemical model CHIMERE developed at Ecole Polytechnique, Paris. • Boundary conditions by Prev'air data (www.prevair.org). • Emission input data from the Italian National Inventory (year 2000) adapted for the species required by the MELCHIOR photochemical mechanism. • point source emissions: a plume-rise module has been added to CHIMERE pre-processor. • Land use: detailed Italian Corine2000 and European GLC2000. • A suitable interface was constructed, to build CHIMERE meteorological input files starting form LAMI output. • fields from COSMO assimilation cycle (LAMA) are used for NINFA long-term analysis.

  8. NINFA BPA 10 km ris. NINFA ER 5 km ris NINFA: Northern Italian Network to Forecast photochemical and Aerosol pollution Multiscale approach Urban model (ADMS Urban) Prev’air (Chimere-continental 0.5°*0.5°)

  9. Boundary conditions from Prev’Air Input meteo COSMO-IT CORINAIR 2000(COVN ton/anno) The model domain has an extension of 640 km x 410 km, 10 km horizontal resolution, with eight vertical levels up to a height of 5000 m. This relatively coarse resolution allows the use of homogeneous emission inventories and meteorological data on the whole domain, and helps keeping computer times reasonably short. Prev’air (Chimere-continental-Europe-domain) NINFA (ER-Chimere-regional-Po valley domain)

  10. Numerical Air Quality forecast for northern Italy

  11. ARPA – SIM provide daily numerical air quality forecast over the Po valley basin by the NINFA integrated modelling system; NINFA is a main tool to prepare the subjective AQ forecast over the Emilia-Romagna Region; NINFA is also applied for long term runs (hincast by high resolution meteorological analysis, produced by the COSMO model assimilation cycle) hindcast results are stored and can be distributed (available April 2003 - Mar 2006), NINFA outputs provide boundary conditions for the high res. runs over the Emilia-Romagna (NINFA ER and Urban models). SUMMARIZING……..

  12. Disclaimer: At present POMI is not recognized by ER as a joint AQ assessment exercise. Which could be the contribution from ER ? Provide NINFA hindcast results already available (10 km res.) Run NINFA with POMI emissions and COSMO-IT meteo data (5 km res. possible); Provide observations: AQ and meteo data (already available by DEXTER) Topics to be better defined: Goals of the exercise ? (model comparison/validation or model ensemble ?) Which is the added value (after xx-Delta & CTN)? How the results will be evaluated and reported? For which purposes? Which data (input output) will be available from POMI? NINFA and POMI

  13. Some results

  14. 0% 0% inquinante inquinante indicatore indicatore soglia soglia regione regione popolazione esposta negli scenari popolazione esposta negli scenari 1-year hindcast simulation (apr 2003 – mar 2004), Meteorological input from COSMO (LAMA) re-analysis) BASE BPA BASE BPA CLE 2010 CLE 2010 EMR1 EMR1 CLE 2020 CLE 2020 PM10 PM10 numero di superamenti annui della soglia di 50mg/m3 sulla media giornaliera numero di superamenti annui della soglia di 50mg/m3 sulla media giornaliera 35 giorni 35 giorni Emilia - Romagna Emilia - Romagna 66% 66% 3% 3% 0% 0% Piemonte Piemonte 82% 82% 60% 60% fuori dominio fuori dominio 9% 9% Lombardia Lombardia 93% 93% 79% 79% 29% 29% Veneto Veneto 88% 88% 65% 65% 8% 8% Friuli – Venezia Giulia Friuli – Venezia Giulia 73% 73% 15% 15% 0% 0% PM10 PM10 media annuale media annuale 40mg/m3 40mg/m3 Emilia - Romagna Emilia - Romagna 2% 2% 0% 0% 0% 0% 0% 0% Piemonte Piemonte 40% 40% 0% 0% fuori dominio fuori dominio 0% 0% Lombardia Lombardia 60% 60% 0% 0% 0% 0% Veneto Veneto 39% 39% 0% 0% 0% 0% Friuli – Venezia Giulia Friuli – Venezia Giulia 0% 0% 0% 0% 0% 0% PM2.5 PM2.5 media annuale media annuale 25mg/m3 25mg/m3 Emilia - Romagna Emilia - Romagna 14% 14% 0% 0% 0% 0% 0% 0% Piemonte Piemonte 57% 57% 0% 0% fuori dominio fuori dominio 0% 0% Lombardia Lombardia 78% 78% 0% 0% 0% 0% Veneto Veneto 62% 62% 0% 0% 0% 0% Friuli – Venezia Giulia Friuli – Venezia Giulia 15% 15% 0% 0% 0% 0% ozono ozono numero di superamenti annui della soglia di 120mg/m3 sulla media su 8 ore numero di superamenti annui della soglia di 120mg/m3 sulla media su 8 ore 25 giorni 25 giorni Emilia - Romagna Emilia - Romagna 100% 100% 100% 100% 100% 100% Piemonte Piemonte 100% 100% 99% 99% fuori dominio fuori dominio 97% 97% Lombardia Lombardia 100% 100% 98% 98% 97% 97% Veneto Veneto 100% 100% 99% 99% 98% 98% Friuli – Venezia Giulia Friuli – Venezia Giulia 100% 100% 100% 100% 98% 98% 100% 100%

  15. Model validation Data-quality objectives Data set: Fonte: APAT- CTN-ACE 2004 (*) the accuracy for modelling is defined as the maximum deviation of the measured and calculated concentration levels, over the period considered by the limit value, without taking into account the timing of events. • 51 stations: • 8 rural background • 24 urban background • 11 urban traffic • 6 suburban background • 1 urban industrial • 1 suburban industrial

  16. Model validation: RESULTS

  17. Model verification O3: good agreement for UB and RB stations, urban effect not reproduced (coarse resolution) PM10: generally underestimated, better for the RB, less for the UB (coarse resolution), good correlation (R  0.6) • daytime ozone concentrations (1-hour and maximum daily 8-hour mean) agree very well with the observed ones, with correlation coefficients higher than 0.7 and low bias • PM10 annual mean levels are underestimated (the bias is approximately -20 μg/m3), although correlation coefficients for the daily mean are around 0.6

  18. Models intercomparison: O3 summer period Source: CTN-ACE report 2007

  19. Models intercomparison: PM10 winter period Source: CTN-ACE report 2007

  20. NINFAmodelApril – Sept 2003 Model verification: Ozone mean day in the plane, in the hills and in the mountain Observed: MOTAP The daily cycle is well reproduced by NINFA: Plane: high peak values during the day, minimum during the night, Mountain: little diurnal cycle…

  21. Model verification: PM10 speciation * Data from CNR-ISAC (S.Fuzzi, C. Facchini) Bologna, annual mean* Warning: 2003 vs 2003-2004 !

  22. Composizione PM10 a Bologna

  23. There is a lack of experimental data, a very rough comparison indicate that: organic seems to be strongly underestimated Inorganic is underestimated Dust agreement Salt: sea salt can be neglect in Bologna, other sources..? Similar results for continental (Prev’air 50 km) and regional (NINFA 10 km) simulations There is a general, although rough, agreement between observed and simulated size distribution PM10 speciation and PM size distribution in Bologna

  24. COSMO IT: Some problems Wind calm are underestimated Strong nocturnal inversions are underestimated Thermal inversion strength (00GMT), frequency distribution, S.Pietro Capofiume station Wind velocity 10 m, BIAS frequency distribution,

  25. EMISSIONS: Annual total from different data sources over Lombardia and Emilia Romagna regions CityDelta: http://aqm.jrc.it/citydelta/ CTN 2000: http://www.sinanet.apat.it

  26. NINFA has been run over 1 year period in the hindcast mode to simulate ozone and PM10 concentration. The hindcast run is helpful to estimate the size of the polluted area and to analyze the spatial patterns of the atmospheric pollution in Northern Italy The spatial structure of the simulated fields reproduces the mountain-plain concentration gradients of pollutants. spatial patterns are linked to wind regimes, characterized by frequently stagnation of air masses in the Po Valley and to the emissions distribution, Ozone: large amount of exceedances of the target value for the protection of human health (120 μg/m3 maximum daily 8-hour mean) are in the sub alpine region and in the plane. Most exceedances (up to 120 per year) are located downwind of the main urban agglomerates (Milano and Torino). PM10: annual average reaches its highest value in the plain area, extending from the west sub alpine region to the North-East Adriatic coast. The highest values are located in and around the main urban agglomerates (Milano and Torino). Air quality assessment in Northern Italy

  27. 4-year hindcast simulation(apr 2003 – mar 2007) Background: Most studies on air quality assessment and emission reduction scenarios (eg. City-Delta), are based on annual CTM simulations. The particular year to be simulated is normally chosen “a priori”, mostly depending on data availability • Objectives: • Study the interannual variability in air quality due to meteorological conditions • Remove the meteorological variability from observed concentrations to see if there is a real trend in emissions • Investigate the uncertainty in emission reduction scenarios introduced by meteorological variability Focus on fulfilment of EU legislation requirements for air quality (maximum 8h average for O3, daily average for PM10) 7th EMS Annual Meeting

  28. Results: O3 O3, summer, day hours, all stations: average concentrations (left) and number of days with 8h average > 60ppb (right) in different years • Summer 2003 is exceptional (especially the number of exceedances) • The model reproduces very well the differences between years (only a small overestimation of day-time average) • Inter-annual variability is about 20% for average and 40% for exeedances • Model bias is constant  in the “real world” there is no significant change in emissions

  29. Results: PM10 PM10, average concentrations in different years: winter months (left) and summer months (right) • Annual average concentrations are almost constant (summer compensate winter); inter-annual variability is less than 15% • Observed variability in seasonal average can be explained by meteorology alone (no appreciable effect of changes in emissions) • Model underestimation is rather homogeneous in time

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