Presentation on theme: "Contributo delle polveri sahariane al PM10 sull Italia: approccio modellistico ed approccio statistico. 1 Pederzoli A., 2 Mircea M., 3 Finardi S. 1 JRC."— Presentation transcript:
Contributo delle polveri sahariane al PM10 sull Italia: approccio modellistico ed approccio statistico. 1 Pederzoli A., 2 Mircea M., 3 Finardi S. 1 JRC Ispra 2 ENEA Bologna 3. ARIANET Srl ENEA MEETING, 24 th March 2011
1.SCELTA DEGLI SCENARI E DELLEPISODIO 2.AGGIUNTA DI SKIRON-DUST ALLE BCs di FARM 20x 20km 2 3.RUNS DI FARM 4.CONFRONTO TRA I RISULTATI DELLE SIMULAZIONI a 20 x 20 km 2 5.CONFRONTO CON LE MISURE SUL DOMINIO 20 X 20 km 2 6. STATISTICAL EVALUATION MAIN STEPS: State of the art (March 2011): APPROCCIO MODELLISTICO: SKIRON + FARM
1. SCELTA DEGLI SCENARI E DELLEPISODIO NDC (No Dust Case) Concentrations from the EMEP model are used as LBCs in FARM. EMEP concentrations are interpolated from 50 x 50 km 2 to 20 x 20 km 2 and re-projected onto the four sides of the FARM domain. DC (Dust Case) Hourly SKIRON fields are interpolated to 20 x 20 km 2 and re-projected onto the four sides of the FARM grid. Size-resolved dust concentrations by SKIRON are then added to the LBCs used in the previous case (NDC). DC1.3 (Dust*1.3 Case) SKIRON dust concentrations are multiplied by a factor 1.3. According to Kallos et al. (2007) the model underestimates low dust concentrations by approximately 30%. The same study suggests to multiply SKIRON dust fields by a factor 1.3 in order to reduce the bias of the model. The same procedure used in the DC is then applied and new LBCs are created. DUST EVENT: JULY 2005 (Meloni et al. 2007, Gariazzo et al., 2007, Griffin et al., 2007)
SIZE DISTRIBUTION IN SKIRON AEROSOL SPECIES IN FARM 2. ADDING SKIRON DUST TO FARM 20x 20km 2 RESOLUTION BCs PMC1 A25J PMC2 ASOIL CLASSNAMECATEG ORY RADIUS 1PMC1CLAY< 1 um 2PMC2SMALL SILT 1 um - 10 um 3PMC3LARGE SILT 10 um - 25 um 4PMC4SAND> 25 um Contribution from the last two classes (PMC3 and PMC4) was not considered (no addition was performed) as particles of large silt and sand, due to their large dimensions, are rapidly removed from the atmosphere (dry deposition processes).
2. ADDING SKIRON DUST TO FARM 20x 20km 2 RESOLUTION BCs LATERAL BOUNDARY BONDITIONS (LBC) IN FARM 120-hours air mass backward trajectories ending over S.Antioco, Monrupino and Febbio on July the 29 th at 2300 UTC at both 1500 m (red) and 2500 m (blue). Starting time: 25 th March 2005 at 0000 UTC. DC and DC1.3 show higher concentrations (up to 50 mgm -3 ) along both South and West boundaries, which reflect the intrusion of dust from SW. The vertical dust layer extends from surface up to 3.5 km. An isolated hot spot, approximately between 1 and 3 km high, is also visible, indicating a residual dust layer due to a previous episode (23 rd - 25 th July). Daily Lateral Boundary Conditions (LBC) as a function of altitude for the crustal component (c_ASOIL) on day 29 th July 2005.
3. RUNS DI FARM 27 th July: DC–NDC between 5 and 10 gm -3 over Central and Southern Italy and over the islands (Sicily and Southern Sardinia). The difference is not significant over the Po Valley. 28 th July: a similar amount of dust is predicted over the islands, Southern Italy and partially over the Central Italy. DC-NDC is between 2 gm -3 and 8 gm -3 over the Po Valley. 29 th July: The contribution of dust becomes significant almost everywhere, ranging between 5 and 15 ugm -3 across Northern Italy and Sardinia. DDC1.3 - NDC is in the same range as DC-NDC for all days.
4. CONFRONTO CON LE MISURE Modelled hourly surface PM 10 concentrations extracted in single grid cells and compared to ground measurements from rural background monitoring stations across Italy. Analysis of the vertical dust distribution Modeled volume vertical profiles are compared to Lidar measurements at Tor Vergata site (41.83° N, 12.65° E) (Gobbi et al., 2004). The volume of total suspended aerosols is computed by using model derived relationships between backscatter coefficients and aerosol volume (Barnaba and Gobbi, 2001). Comparison to ground measurements
5. STATISTICAL EVALUATION The introduction of dust onto the boundaries reduces the FB for the same episode from 1.3 to approximately The NMB for NDC varies from approximately -70% to -90%, reduced to the range -40% - -80% in DC and DC1.3. The averaged NMB for all stations is -77%, -65%,-59% for NDC, DC and DC1.3 respectively.
CONCLUSIONS Differences in PM 10 surface concentrations between both DC and DC1.3 scenarios and the default case NDC are approximately between 5 and 15 mg m -3. The correction (30%) introduced in DC1.3 and suggested by Kallos et al. (2007) for reducing SKIRON underestimation of dust concentration over Greece is not suitable over Italy: differences in PM 10 concentrations between DC1.3 and NDC are in the same ranges as DC-NDC for all days. A new correction factor needs to be introduced for the Italian contest. The analysis of the vertical dust distribution shows that the dust contribution at surface is low compared to the one at high altitudes: most of dust particles remains above 2000 m and does not reach the ground. This suggests a process of sedimentation and deposition of aerosol particles too slow with respect to the transport process inside the model. Further investigations need to be carried out in the future in order to verify this hypothesis. The comparison with ground measurements reveals that the addition of dust improves FARM aerosol mass concentration predictions: the underestimation of PM 10 concentration is reduced from approximately 77% to 59%. The use of these new LBCs also improves the performance of FARM compared to the results achieved by Gariazzo et al. (2007) which makes use of climatology fields: the fractional bias is reduced from 1.3 to 0.8.
1.Identification of dust episodes in Estimates of dust contribution ( g m -3 ) to PM10 concentrations 3.Calculation of the reduction in the number of exceedances of 50 g m -3 MAIN STEPS: State of the art (March 2011): APPROCCIO STATISTICO: METODO DI ESCUDERO Quantification of Saharan dust contribution to PM10 concentrations over Italy during A. Pederzoli, M. Mircea, S. Finardi, A. di Sarra, G. Zanini, Atmospheric Environment 44 (2010)
1. Identification of dust episodes SOURCES: 1. Monitoring network measurements 2. Satellite retrievals 3. Mesurements of optical properties 4. Air mass backward trajectory analysis 5. Numerical dust models (SKIRON) Rural background stations
1. Identification of dust episodes SOURCES: Air mass backward trajectory analysis (HYSPLIT) Satellite retrievals (i.e. MODIS) Ground measurements of optical properties and PM10 concentrations Modelled data
2. Stima del contributo alla concentrazione di PM10 Escudero M., Querol X., Avila A., Cuevas E. (2007). Origin of the exceedances of the European daily PM limit value in the regional background areas of Spain. Atmos. Environ. 41, 730–744. Per stazioni remote-rurali (no anthropogenic contribution): i=rural site j=dust day Calculated over the month excluding j-days
Example of methodology application for a daily episode 29 th July 2005 Trieste Monrupino 30 th daily moving percentile calculated over all days of July 2005 apart the dust day (29 th ) = 20 g m -3 Average of PM10 concentration = 59 gm -3 Dust contribution for 29 th July 2005 = = 39 g m -3 Average monthly contribution for July 2005 = 1.4 g m Stima del contributo alla concentrazione di PM10
3. Riduzione del numero di eccedenze (> 50 g m -3 )
Le intrusioni Sahariane sono maggiormente concentrate in primavera (40%-45%) ed estate (35%-55%). In inverno ed autunno i siti nel Nord Italia hanno registrato un numero significativo di eventi (tra il 7% e il 10% in Gennaio e Febbraio). Il numero maggiore di intrusioni è stato registrato sullisola di Lampedusa in estate (57% del totale). il contributo mensile delle polveri sahariane alle concentrazioni di PM10 varia tra 1 g m -3 e 10 g m -3 nel 2005 e tra 1 g m -3 e 8 g m -3 nel 2003; nel 2004 è < 5 gm -3 per tutti i siti. La riduzione (%) nel numero di eccedenze del limite giornaliero varia da stazione a stazione: tra il 20% e il 50% nel in 2005 e tra il 5% e 25% nel 2003 e Conclusioni
AKNOWLEDGEMENTS This work is part of the MINNI (Integrated National Model in support to the International Negotiation on Air Pollution) project, funded by the Italian Ministry for Environment, Territory and Sea and carried out by ENEA. This work was supported and coordinated by ENEA and ARIANET Srl. Special thanks to: Professor G. Kallos and the Atmospheric Modeling and Weather Forecasting Group at the University of Athens. The Environmental Protection Agency of Friuli Venezia Giulia for providing PM 10 measurements at Trieste Monrupino. Dr. Gian Paolo Gobbi, of the Institute of Atmospheric Sciences and Climate of Rome for the Lidar measurements at Tor Vergata. The authors finally gratefully aknowledge the Air Resources Laboratory (ARL) for the provision of the HYSPLIT model.