Modelling of Saharan dust transport to the Southern Italy

Transcription

1 Modelling of Saharan dust transport to the Southern Italy Mihaela Mircea a, *, Gino Briganti a, Antonella Malaguti a, Sandro Finardi b, Camillo Silibello b, Christos Spyrou c, Christina Kalogeri c, George Kallos c and Gabriele Zanini a a ENEA, Via Martiri di Monte Sole 4, Bologna, Italy b ARIANET s.r.l. via Gilino, 9, Milan, Italy c School of Physics, University of Athens, Athens, Greece * mihaela.mircea@enea.it

2 PARTICIPANTS: ENEA (UTVALAMB-AIR; UTMEA-TER) DIPARTMENT OF PHYSICS, SAPIENZA UNIVERSITY OF ROME ENDURO_KIT, EUFAR (http :// Trisaia experimental field campaign: 3 May 30 June 2010

3 MINNI project MINNI (International Integrated Model to support the international negotiation on atmospheric pollution) was developed on behalf of the Ministry of the Environment. MINNI is supporting air quality policy at national and regional level. The system is based on two main components: AMS: Atmospheric Modelling System GAINS-Italy: Greenhouse Gas - Air Pollution Interactions and Synergies linked through the Atmospheric Transfer Matrices (ATM) and RAINS Atmospheric Inventory Link (RAIL)

4 Study outline description of the modelling systems: AMS-MINNI and SKIRON description of the simulations setup Saharan dust transport during the experimental field campaign Effect of coupling two aerosol microphysical approaches on: horizontal and vertical dust concentration distribution: AMS-MINNI vs SKIRON dust concentrations dependence on horizontal model resolution: AMS-MINNI dust concentrations: AMS-MINNI vs experimental estimations summary

5 SKIRON SKIRON is a modeling system developed at the University of Athens from the AM&WFG [Kallos et al.,1997, 2006] in the framework of the nationally and European Union (EU) funded projects SKIRON, Mediterranean Dust Experiment (MEDUSE), Atmospheric Deposition and Impact on the Open Mediterranean Sea (ADIOS), and recently Climate Change and Impact Research (CIRCE). The atmospheric model: ETA/National Centers for Environmental Prediction (NCEP) model (Mesinger, 1984 and Janjic, 1984). Soil and vegetation characterization: 30s soil texture database based on the U.S. Department of Agriculture s State Soil Geographic Database (Miller and White, 1998) Dust model: dust particles are assumed to be mobilized through the process of saltation bombardment (Alfaro and Gomes, 2001; Bagnold, 2005). Dry deposition considers diffusion, impaction, gravitational settling. Particle deposition velocity is calculated using resistance approach similar to that implemented in UAM AERO (Kumar et al., 1996). Wet deposition: the particle scavenging process includes both in cloud and below cloud removal mechanisms. (Spyrou et al, 2010, J. Geophys.Res.)

6 AMS-MINNI Atmospheric Modelling System of MINNI project ECMWF fields Local data Ref. inventory Space, time, species info RAMS Emission Manager Meteorology Reference meteo year Emission scenario Emissions EMEP B.C. FARM Conc & dep. fields Air Quality (Mircea et al, in press Atm.Environ..)

7 FARM Emission of pollutants from area and point sources, with plume rise calculation and mass assignment to vertical grid cells Gas-phase mechanisms (SAPRC-99, POPs-Hg) through KPP (Kinetic Pre- Processor: Damian et al., 2002). Treatment of PM 10 and PM 2.5 (aero3 modal aerosol module as in CMAQ) Dry removal of pollutants dependent on local meteorology and land-use, resistance approach Wet removal both in-cloud and below-cloud LOCAL EROSION: simplified bulk scheme following Vautard et al. (2005, Atmos Environ) 50% of emissions are attributed to m aerosol mode 45% to m aerosol mode 5% to fine aerosol mode

8 Aerosol models: SKIRON and FARM FARM SKIRON - eight size bins with effective radii of 0.15, 0.25, 0.45, 0.78, 1.3, 2.2, 3.8, and 7.1 m - transport, dry and wet deposition are applied separately for each particle size bin (Spyrou et al., 2010) Gas precursors: HNO3, NH3, H2SO4 toluene, xylene isoprene, monoterpene n u c l e a t i o n Modal N i, D pgi, s i sulfate,ammonium nitrate, OC,EC unspecified anthropogenic condensation/evaporat ion coagulation sulfate ammonium nitrate, OC,EC sea-salt unspecified anthropogenic unspecified anthropogenic Aitken AERO3 (Binkowski and Roselle, 2003) ISORROPIA (Nenes et al., 1998) SORGAM (Schell et al., 2001)

9 20 km Simulations setup (1) Simulations with 20, 4 and 1 km horizontal resolutions Simulation with 20 km horizontal resolution: METEOROLOGY: 20km horizontal spatial resolution ic/bc from ECMWF: 50km every 6hrs AIR QUALITY: 20km horizontal spatial resolution ic/bc from EMEP: 50km every 6hrs and from SKIRON 25 km every 3 hrs EMISSIONS: GAINS-ITALY+ EMEP+GEIA 4 km Simulation with 4 and 1 km horizontal resolutions: METEOROLOGY: ic/bc from RAMS 20 km and 4 km respectively AIR QUALITY: ic/bc from: AMS-MINNI 20 km and 4 km respectively EMISSIONS: GAINS-ITALY+main streets+ EMEP+GEIA 1 km

10 Simulations setup (2) AERO3 model was extended to include dust in accumulation mode 3 set of simulations with 20, 4 and 1 km horizontal resolutions were carried out: - without Saharan dust at boundaries - with 4 th bin SKIRON in accumulation mode FARM (4f) - with 4 th bin SKIRON in coarse mode FARM (4c)

11 Emissions in AMS-MINNI simulations: PM10 20km x 20km 4km x 4km 1km x 1km Trisaia

12 Validation of meteorology: AMS-MINNI 2D validation: - meteorological statistics ISPRA-SCIA - meteorological data from Basilicata region Meteorological data from Trisaia campaign: - data from VAISALAMAWS00 - humidity and temperature profiler HATPRO

13 Identification of dust events during the campaign 3-5 May May May June June 12 June 13 June

14 Horizontal fine dust concentrations: SKIRON vs AMS-MINNI 4f 4 th bin SKIRON in accumulation mode FARM 13 June c 4 th bin SKIRON in coarse mode FARM

15 Vertical fine dust concentrations: SKIRON vs AMS-MINNI 13 June f 4c

16 Horizontal coarse dust concentrations: SKIRON vs AMS-MINNI 4f 13 June c

17 Vertical coarse dust concentrations: SKIRON vs AMS-MINNI 13 June f 4c

18 Effect of horizontal model resolution on concentration vertical profiles AMS-MINNI 20km 1 km 4f 4c

19 Effect of horizontal model resolution on fine dust concentrations: AMS-MINNI 20km 1 km 4f 13 June c

20 Effect of horizontal model resolution on coarse dust concentrations: AMS-MINNI 20km 1 km 4f 13 June c

21 Fine dust concentrations: AMS-MINNI vs experimental estimations obs red 20 km blue 4 km green 1 km black

22 Coarse dust concentrations: AMS-MINNI vs experimental estimations obs red 20 km blue 4 km green 1 km black

23 Contribution of dust concentrations to PM2.5 obs red 20 km blue 4 km green 1 km black

24 Contribution of dust concentrations to PM10 obs red 20 km blue 4 km green 1 km black

25 Summary and future plans surface dust concentrations modelled by AMS-MINNI are higher than those simulated by SKIRON, for both fine and coarse dust fractions the increase of horizontal spatial resolution from 20 km to 1 km lead to an increase of surface dust concentrations, for both fine and coarse dust fractions AMS-MINNI: 4f simulations give better results with respect to the observations for fine dust fraction and low intensity dust events; both 4f and 4c simulations overestimates coarse dust fraction for all events and anticipate June dust event the effect of coupling the two aerosol models is important as much as increasing horizontal resolution of the simulations for fine dust fraction PM2.5 and PM10 cannot be reproduced without Saharan dust contribution Further work will be carried out to better assess the effect of aerosol microphysical coupling and of horizontal resolution on simulated dust concentrations.

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