1. POMI kick-off meeting Ispra, 7/3/2008 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. Contents The NINFA modelling system 1 year hindcast: apr 2003 – mar 2004 (model verification and scenarios) Model intercomparison 4 year hindcast: 2003-2006 (interannual variability)

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: Prevair (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) 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 NOx gridded emissions, year 2000 annual total The NINFA system is used for: -operational air-quality forecasts and hindcast (started in October 2005, available at www.arpa.emr.it/sim)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 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 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 see (http://cosmo-model.cscs.ch/public/various/operational/arpa/operationalAppsARPA.htm

6. Mixing height Average mixing height during winter months, estimated by Chimere pre-processor. Default configuration (left) and setup adapted to Northern Italy (right) PM10 annual average winter: + 5 - 7 g/m3 Summer: + 4 - 5 g/m3 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 ) 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 ) 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

7. NINFA Northern Italian Network to Forecast photochemical and Aerosol pollution 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. METEO: COSMO IT/LAMA CHIMERE EMISSIONS: CTN_ACE BOUNDARY CONDITIONS: (Prev'air) OUTPUT: O 3, NO 2, SO 2, PM 10 LANDUSE: CORINE2000+GL C2000 The modelling suite

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

9. NINFA (ER-Chimere-regional-Po valley domain) Prevair (Chimere-continental-Europe-domain) CORINAIR 2000 (COVN ton/anno) Input meteo COSMO-IT Boundary conditions from PrevAir 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.

10. Numerical Air Quality forecast for northern Italy

11. SUMMARIZING…….. 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).

12. NINFA and POMI 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?

13. Some results

14. 1-year hindcast simulation (apr 2003 – mar 2004), Meteorological input from COSMO (LAMA) re-analysis) 0% 100% inquina nte indicatoresogliaregionepopolazione esposta negli scenari BASE BPA CLE 2010 EMR1CLE 2020 PM10numero di superamenti annui della soglia di 50 g/m 3 sulla media giornaliera 35 giorniEmilia - Romagna66%3%0% Piemonte82%60% fuori dominio 9% Lombardia93%79%29% Veneto88%65%8% Friuli – Venezia Giulia73%15%0% PM10media annuale 40 g/m 3 Emilia - Romagna2%0% Piemonte40%0%fuori dominio 0% Lombardia60%0% Veneto39%0% Friuli – Venezia Giulia0% PM2.5media annuale 25 g/m 3 Emilia - Romagna14%0% Piemonte57%0% fuori dominio 0% Lombardia78%0% Veneto62%0% Friuli – Venezia Giulia15%0% ozononumero di superamenti annui della soglia di 120 g/m 3 sulla media su 8 ore 25 giorniEmilia - Romagna100% Piemonte100%99% fuori dominio 97% Lombardia100%98%97% Veneto100%99%98% Friuli – Venezia Giulia100% 98% 0% 100% inquina nte indicatoresogliaregionepopolazione esposta negli scenari BASE BPA CLE 2010 EMR1CLE 2020 PM10numero di superamenti annui della soglia di 50 g/m 3 sulla media giornaliera 35 giorniEmilia - Romagna66%3%0% Piemonte82%60% fuori dominio 9% Lombardia93%79%29% Veneto88%65%8% Friuli – Venezia Giulia73%15%0% PM10media annuale 40 g/m 3 Emilia - Romagna2%0% Piemonte40%0%fuori dominio 0% Lombardia60%0% Veneto39%0% Friuli – Venezia Giulia0% PM2.5media annuale 25 g/m 3 Emilia - Romagna14%0% Piemonte57%0% fuori dominio 0% Lombardia78%0% Veneto62%0% Friuli – Venezia Giulia15%0% ozononumero di superamenti annui della soglia di 120 g/m 3 sulla media su 8 ore 25 giorniEmilia - Romagna100% Piemonte100%99% fuori dominio 97% Lombardia100%98%97% Veneto100%99%98% Friuli – Venezia Giulia100% 98%

15. Model validation 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. Data-quality objectives PollutantAv. time Data-quality objectives for Modelling (*) Data-quality objectives for continuous measurement Italian lawEC Directive SO 2, NO, NO 2 1 h 1 d 1 y 50 – 60 % 50 % 30 % 15 % DM 2 aprile 2002, N. 60 1999/30/EC PM, lead1 y50 %25 % CO8 h50 %15 % 2000/69/EC Benzene1 y50 %25 % O 3, NO, NO 2 1 h day 8 h max 50 % 15 % To be received 2002/3/EC Data set: 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 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/m 3 ), although correlation coefficients for the daily mean are around 0.6 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) 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)

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

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

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

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

22. Composizione PM10 a Bologna

23. PM10 speciation and PM size distribution in Bologna 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 (Prevair 50 km) and regional (NINFA 10 km) simulations There is a general, although rough, agreement between observed and simulated size distribution

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

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

26. Air quality assessment in Northern Italy 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).

27. 4-year hindcast simulation (apr 2003 – mar 2007) 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) 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 7th EMS Annual Meeting

28. Results: O 3 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 O3, summer, day hours, all stations: average concentrations (left) and number of days with 8h average > 60ppb (right) in different years

29. Results: PM10 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 PM10, average concentrations in different years: winter months (left) and summer months (right)