Mihaela Mircea1, Massimo D'Isidoro1, Alberto Maurizi1, Maria Gabriella Villani1, Andrea Buzzi1, Sandro Fuzzi1, Francesco Tampieri1

Gabriele Zanini2, Fabio Monforti2, Lina Vitale2

1Istituto di Scienze dell’Atmosfera e del Clima, CNR, Bologna, Italy2ENEA,Italian Agency for New Technologies, Energy and the Environment, Via

Martiri di Monte Sole 4, 40129 Bologna, Italy

Ozone modeling over Italy: a sensitivity analysis to precursors using BOLCHEM air quality model

Objectives

• to investigate the sensitivity of ozone concentration to the reduction of NOx and VOC for few periods during the years 1999 and 2003 over the whole Italy

• to asses the relative importance of precursors in reducing the ozone levels identifying the regions of Italy where local emissions strategies could not be effective

Motivation there is no such study – only studies over Northern Italy

in some areas, the ozone concentrations can be controlled by transboundary processes and that it cannot be ruled out by simulating a small area.

the topography of Italy is very complex and leads to very complicate circulations features.

Italy is in a geographic position (from ca. 37o to 47o lat North) that lead to various climate features.

The ozone episodes to simulate were chosen for the year 1999 because it is used as a reference year in meteorological studies and for the summer 2003 because it was characterized by very high temperatures for a many days.

BOLCHEM flow chartBOLCHEM flow chart

Gas Chemistry(SAPRC90/CB4)

Emissions Dry Deposition

MeteorologicalModel

(BOLAM)Winds,

T, qTransport/dispersion

Chemistry&transport model(CTM)

Photochemical mechanismsCB-IV (Gery et al., 1989) : lumped-structure condensed mechanism-85 reactions and 30 chemical species

-organics are grouped according to bond type (for example, as carbon single bonds, carbon double bounds or carbonyl bounds)

-organic species are treated explicitly (e.g. formaldehyde, ethene, isoprene), represented by carbon bond (PAR – single bonded one carbon atom, OLE –two carbon atoms) or molecular (TOL and XYL aromatic hydrocarbons) surrogates according with their chemistry or importance in the environment.

SAPRC90 (Carter, 1990) : lumped-molecular condensed mechanism-131 reactions with 35 chemical species

-calculate the kinetic and mechanistic parameters for lumped species in the mechanism created for representative emissions profile (mole-weighted approach)

-organics species are treated explicitly (e.g. formaldehyde, acetaldehyde, etc) or represented by molecules as alkane, alkenes, aromatics, etc.

Chemistry is coupled “online” with meteorology

Meteorology and chemistry use:same transport scheme (WAF (Weighted Average Flux) 3-d advection scheme)

same grid: horizontal and vertical components (the vertical coordinate system is terrain-following (), with variables distributed on a non-uniformly spaced staggered Lorenz grid. the horizontal discretization uses geographical coordinates on an Arakawa C-grid)

same physics for the subgrid-scale transport (for example vertical diffusion in surface layer and PBL parameterization depend on the Richardson number)

same time step

Emissions, initial and boundary conditions

The chemical fields are driven by hourly surface emissions and 3 hourly lateral boundary conditions after the initialisation.

Emissions, initial and boundary conditions are produced by Thoscane model (Zanini et al., 2004).

The emission inventory includes the ship emissions and the point source emissions are also considered in the simulations.

Model configuration and meteorological inputs The model domain extends between: NW (20.77, 47.55); NE

(4.82 - 47.55); SW (6.17 - 35.79); SE (19.42 – 35.79).

The horizontal resolution used in the simulations is of 20 km. The vertical resolution includes 33 sigma vertical layers from surface to the tropopause. The lower layer is approximately 20m thick above the surface.

The meteorological fields are supplied by ECMWF. The lateral boundary conditions are updated every 6 hours. The weather fields were re-initialized every 48 hours with the analyses in order to avoid an excessive error growth in the meteorological forecast.

Evaluation of model performance

The model runs are on an grid between fine and coarse, therefore the model results are only compared with observations at rural and semi-rural locations.

1999: 4 clear sky periods selected based on Meteosat Images of Europe:•January: 20-25

•June: 1-4

•July: 1-5

•August: 5-8

Time series of observed and predicted O3 mixing ratio of gases at sites (1)

Atmospheric Chemistry

ISPRA - O3

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Motta Visconti - O3

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Gambara - O3

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Atmospheric Chemistry

Time series of observed and predicted O3 mixing ratio of gases at sites (2)Ferrara - Gherardi - O3

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Fontechiari - O3

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Montelibretti - O3

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1999080617

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Quantitative performance statistics for hourly concentrations of O3O3(ug/m3) SAPRC90 CB4 Station S_MAGE S_Std.Dev. O_MAGE O_Std.Dev. S_MAGE S_Std.Dev. 20-25 January Monte Gazza 48.12 11.52 92.1 3.58 47.07 12.01 Ispra 25.79 15.43 14.47 17.47 24.64 15.04 M.S. Pantaleone 32.52 7.35 8.08 5.67 31.44 7.36 Gambara 11.67 19.57 11.24 2.96 8.56 13.2 Pieve di Teco 59.92 9.25 18.41 17.18 54.56 10.8 Montelibretti 60.09 29.98 20.69 20.05 39.9 23.61 Fontechiari 62.81 20.16 62.37 30.36 50.7 16.07 Bocca di Falco 45.42 14.11 64.38 14.81 41.98 12.11 1-4 June Monte Gazza 110.4 19.4 88.12 10.19 103.12 16.87 Ispra 99.46 46.07 90.8 53.81 88.78 38.76 Motta Visconti 87.24 53.74 108.57 42.78 74.28 41.9 Gambara 105.2 56.53 111.95 52.77 88.84 43.48 Ferrara-Gherardi 96.2 35.32 86.68 44.85 85.54 30.03 Pieve di Teco 88.95 17.42 52.89 32.93 86.93 17.19 Chiaravalle 88.36 27.15 18.15 11.38 79.86 22.22 Gabbro 83.78 37.38 95 16.7 74.06 29.97 Montelibretti 99.28 49.46 63.79 63.14 82.32 42.32 Fontechiari 107.13 26.7 103.66 36.92 96.2 21.9 Bocca di Falco 101.61 32.31 106.02 56.62 86.92 25.57 1-5 July Monte Gazza 106.92 33.25 99.31 18.68 94.39 26.9 Ispra 114.7 47.56 88.04 67.35 98.64 39.25 Motta Visconti 105.46 74.92 107.11 65.87 84.38 57.41 Gambara 120.25 61.86 129.74 72.18 95.83 48.77 Pieve di Teco 100.16 27.35 66.26 42.75 95.25 24.03 Chiaravalle 104.95 27.59 17.84 13.38 94.89 24.5 Gabbro 61.55 33.32 96.93 14.43 50.55 19.06 Montelibretti 100.22 35.48 60.14 40.62 90.13 31.2 Bocca di Falco 102.36 23.05 110.89 39.69 99.21 19.18 5-8 August Monte Gazza 97.34 12.95 94.57 12.17 90.23 13.15 Ispra 88.26 36.21 57.51 45.27 78.94 31.28 M.S. Pantaleone 103.35 31.06 31.67 10.5 89.13 25.63 Motta Visconti 78.41 46.66 84.88 44.84 65.72 37.04 Gambara 85.47 42.15 79.69 48.62 72.18 33.34 Ferrara-Gherardi 72.59 39.06 60.26 36.55 60.92 32 Pieve di Teco 82.88 16.75 41.64 28.86 75.89 17.17 Chiaravalle 71.65 26.33 33.79 21.72 62.47 20.79 Gabbro 68.3 35.91 64.84 19.09 57.91 30.07 Montelibretti 79.69 43.27 61.2 49.86 67.25 37.62 Fontechiari 78.26 24.07 106.32 37.04 71.35 21.7

SAPRC90 CB4 BIAS RMSE Corr.Coeff. IOA BIAS RMSE Corr.Coeff. IOA

-43.98 45.61 0 0.1 -45.03 46.8 -0.06 0.1 11.32 22.66 0.29 0.56 10.17 22.4 0.25 0.54 24.44 25.69 0.22 0.25 23.36 24.8 0.2 0.25

0.54 19.13 0.23 0.2 -2.67 13.21 0.2 0.26 41.51 46.32 -0.13 0.33 36.15 42.25 -0.18 0.33

39.4 45.2 0.68 0.56 19.2 27.86 0.61 0.69 0.43 20.46 0.74 0.82 -11.67 23.98 0.76 0.73

-28.96 33.21 0.37 0.41 -32.4 36.3 0.27 0.38

22.28 31.18 0.01 0.27 15 24.32 0.06 0.32 8.67 36.56 0.76 0.85 -2.01 35.01 0.76 0.84

-21.33 35.51 0.85 0.87 -34.29 46.74 0.72 0.73 -6.75 29.03 0.87 0.93 -23.11 32.7 0.9 0.89 9.52 30.19 0.77 0.85 -1.14 31.44 0.71 0.81

36.05 43.37 0.7 0.6 34.04 42.22 0.67 0.6 70.21 73.05 0.74 0.27 61.71 63.94 0.68 0.29

-11.22 36.61 0.37 0.52 -20.94 36.04 0.32 0.49 35.48 46.68 0.88 0.84 18.52 38.88 0.86 0.86

3.47 32.01 0.54 0.7 -7.45 32.9 0.5 0.64 -4.41 40.68 0.71 0.77 -19.1 49.78 0.6 0.64

7.61 41.33 -0.15 0.3 -4.92 36.34 -0.22 0.31

28.66 50.78 0.79 0.81 12.6 46.36 0.77 0.8 -1.65 34.94 0.89 0.94 -22.72 37.03 0.9 0.91 -9.49 36.2 0.87 0.92 -33.91 50.84 0.87 0.83 33.9 41.37 0.86 0.74 28.99 38.55 0.86 0.74

87.11 89.9 0.6 0.25 77.06 79.99 0.48 0.27 -35.38 46.35 0.26 0.4 -46.38 50.64 0.29 0.35 40.08 46.81 0.81 0.71 29.99 40.08 0.76 0.73 -8.53 27.46 0.78 0.8 -20.68 35.53 0.73 0.69

2.67 14.59 0.35 0.6 -4.34 13.89 0.46 0.67

30.75 43.37 0.74 0.76 21.43 38.99 0.69 0.75 61.68 76.17 0.63 0.23 57.47 61.35 0.57 0.26 -6.47 24.35 0.87 0.93 -19.15 32.5 0.81 0.84 5.68 21.28 0.91 0.94 -7.51 22.98 0.93 0.92

12.33 20.96 0.9 0.92 0.66 17.27 0.88 0.93 41.24 45.28 0.79 0.6 34.26 39.2 0.77 0.64 37.86 39.8 0.89 0.62 28.68 31.04 0.84 0.67 -6.54 26.88 0.71 0.75 -16.93 27.43 0.7 0.71 18.49 25.99 0.93 0.92 6.04 21.98 0.92 0.93

-28.06 36.93 0.77 0.69 -34.97 42.8 0.77 0.63

NOx reduced(-35%) VOC reduced(-35%)Base case

Differences in ozone concentrations: O3=O3(65%VOC)-O3(65%NOx)

CB4

O3 > 0 NOx limited areaO3 <0 VOC limited area

SAPRC90 ppb

Chemical regimes over Italy

ppb

Areas selected for the analysis

Center of the area

Lat Lon Size (km X km)

Milano 45o28’ 9o10’ 160x160

Genova 44o25’ 8o54’ 80x80

Venezia 45o26’ 12o19’ 240x240

Roma 41o54’ 12o28’ 80x80

Napoli 40o51’ 14o16’ 80x80

Taranto 40o25’ 17o14’ 80x80

Pachino 36o15’ 15o05’ 100x100

NOx reduced(-35%) VOC reduced(-35%)

O3=local-global reductions

O3(ppb)CB4

SAPRC90

Roma: 5 - 8 August 1999

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Extreme summer 2003

Montelibretti: 9-12 August 2003

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ug

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Fraction of VOC or NOx limited area

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99_SAPRC9099_CB403_SAPRC9099_SAPRC90_areas

Pachino

O3 increase due to the increase in isoprene emissionsppb

Preliminary conclusions

The differences in the predicted ozone concentrations due to the photochemical mechanisms are comparable to those obtained by reducing the emissions of NOx or VOC

The distribution and the “intensity” (differences in ozone concentration) of VOC or NOx limited areas depend on the photochemical mechanism. For example, in the same meteorological and environmental conditions, a region can be VOC or NOx sensitive according with the photochemical mechanism used.

The local reduction of VOC was efficient for Milano and Venice areas. In the other regions, significant increase in ozone concentration was observed by reducing locally both the NOx or VOC emissions.

The increase of isoprene leads to substantial increase in the concentration of ozone at some locations (up to 25%), therefore, the uncertainties in isoprene emissions can bias the air quality design.

Atmospheric Chemistry – ISAC (CNR)

ACKNOWLEDGEMENTS

GEMS and ACCENT: EU-projectsICTP Programme for Training and

Research in Italian Laboratories”, Trieste, Italy