A numerical study of the effect of sea breeze circulation on photochemical pollution over a highly...

METEOROLOGICAL APPLICATIONSMeteorol. Appl. 17: 19–31 (2010)Published online 10 August 2009 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/met.147

A numerical study of the effect of sea breeze circulation onphotochemical pollution over a highly industrialized

peninsula

Cristina Mangia,* Ilenia Schipa, Annalisa Tanzarella, Dario Conte, Gian Paolo Marra,Mario Marcello Miglietta and Umberto Rizza

Institute of Atmospheric and Climate Sciences ISAC -CNR-LECCE, 73100 Lecce, Italy

ABSTRACT: Numerical simulations compared with measurements are used to investigate the effect of sea breezecirculation on the ozone accumulation over a highly industrialized peninsula in southern Italy, where high levels ofozone concentration are often registered. A frequent meteorological phenomenon in this region during weak summersynoptic conditions is the development of complex sea breeze systems from the coastlines, with convergence areas withinthe peninsula. A case study characterized by strong winds alternating with sea breeze circulations was selected. Thesimulations show that during weak synoptic conditions, sea breezes transport ozone and its precursors over land from thesea, as well as from the coastlines where the largest industrialized districts are localized. The overlapping breezes lead toozone accumulation in the area where sea breeze convergence occurs. This may explain the high values of ozone registeredclose to the sea breeze convergence lines. The comparison between predictions and experimental data indicates that thenumerical system successfully reproduces both weather and ground level ozone concentration in different meteorologicalconditions, resulting in a fundamental tool for both scientific comprehension of the evolution of air contaminants andinterpretation of the monitoring data. Copyright 2009 Royal Meteorological Society

KEY WORDS mesoscale modelling; sea breeze circulations; mesoscale models; RAMS; Ozone.

Received 20 October 2008; Revised 4 February 2009; Accepted 26 February 2009

1. Introduction

Over the last few decades, air quality management hasbecome a major problem for decision makers, particularlywith respect to photochemical pollution, for which therelationships between emissions and atmospheric concen-trations can be quite complex due to non-linear interac-tions of meteorology, chemistry, emissions and land usepatterns. Air quality models combining meteorology andchemistry are fundamental to understanding the differentmechanisms leading to the accumulation of troposphericozone. Therefore, they can play an important role in boththe scientific evolution of air contaminants and support-ing strategies in emission control strategies (Russell andDennis, 2000).

Many air quality studies conducted in Mediterraneanareas have shown how these regions are often character-ized by elevated levels of ozone during the summer due tothe high solar radiation, the presence of large emissions,large areas of vegetation, and meteorological processesranging from the mesoscale to local circulations (Millanet al., 1996; Ziomas et al., 1998; Svensson, 1998; Millanet al., 2000; Alonso et al., 2000; Jimenez et al., 2005).

* Correspondence to: Cristina Mangia, Institute of Atmospheric andClimate Sciences ISAC -CNR-LECCE, 73100 Lecce, Italy.E-mail: [email protected]

Studies conducted in coastal areas have also demon-strated the role of sea breeze circulation in smog episodes(Lyons et al., 1995; Kambezidis et al.,1995, 1998; Melaset al., 1995; Clappier et al., 2000; Evtyugina et al., 2006;Pirovano et al., 2007). Due to the large horizontal andvertical variations in meteorological parameters causedby the different diurnal heating cycles at the sea/landboundary, coastal meteorology still represents a chal-lenge for air pollution modelling and assessment of ozonebehaviour.

The present work focuses on the photochemical pol-lution in a narrow Mediterranean peninsula located inthe southeast corner of Italy (Apulia Region), the oppo-site coastlines of which host two of the largest Euro-pean industrial sites (http://eper.eea.europa.eu). A fre-quent phenomenon that occurs in this area is the devel-opment of different sea breeze systems, which merge inthe middle of the peninsula (Mangia et al., 2004).

The aim of the present study was to investigate theinfluence of sea breeze circulation on ozone accumu-lation along the modelling domain. In this context, asummer episode (2–8 July 2005) characterized by strongnortherly winds alternating with a weak synoptic forcingwas selected.

The chemistry/transport models used in this studyare the meteorological models RAMS (Pielke et al.,1992) and CALMET (Scire et al., 2000) coupled with

Copyright 2009 Royal Meteorological Society

20 C. MANGIA ET AL.

the photochemical model CALGRID (Yamartino et al.,1992a, 1992b).

Predicted meteorological and concentration data werecompared with routine measurements from the regionalair quality network in order to investigate the capability ofthe models to reproduce the ozone concentration patternsin the area and to interpret the monitoring data.

2. Description of the area

The domain investigated is located in the MediterraneanSea in the southeastern corner of Italy. It is borderedby the Adriatic Sea to the east, the Ionian Sea to thesoutheast, and the Strait of Otranto and Gulf of Tarantoto the south (Figure 1). The region is 180 km long in thenorthwest–southeast direction, and has an average widthof 60 km. It is generally flat, with small hills (less than200 m) in the southeastern area and moderately high hills(about 500 m) in the northern part.

When anticyclonic conditions affect the centralMediterranean basin, the Apulia region is dominated bya northwesterly synoptic wind, intensified by the chan-nelling effect of the Otranto channel (that is about 70 kmwide) separating southeastern Italy from Albania. Duringweak synoptic conditions, the region is influenced on itsentire coastal perimeter (about 300 km) by complex sea-land breeze systems, caused by the diurnal heating cycle.

A frequent meteorological phenomenon that occurs inthe modelling domain is the development of differentsea breeze systems, with an inland breeze penetration

that may be tens of kilometres deep. These breeze sys-tems merge in the middle of the peninsula (Mangia et al.,2004).

This phenomenon is generally observed at middayduring the summer, when factors such as high solarradiation and temperature may favour the formation ofphotochemical pollutants.

Emissions in the area result primarily from urban,shipping and industrial activities. The largest industrialemissions are localized near the coasts: in the Tarantowest coast and the Brindisi areas of the eastern coast(Figure 1). The industry in Taranto is dominated by ironand steel (among the largest in Europe), as well asoil refineries with hydrocarbon transformation processes.These industries make use of Taranto harbour to importraw material and export final products. In Brindisi, themain industrial activities are represented by two coalpower plants and one petrochemical plant.

Table I reports the emissions for the year 2000 of NOx ,SOx , CO and NMVOC (Non-methanic Volatile OrganicCompound) from different emission sources derived fromthe last available Italian CORINAIR database (2000)(APAT, 2004). Based on the data analysis, it is evidentthat the industrial division and transport sectors, includ-ing vehicles, airports, harbours and railroads, contributegreater emissions.

Continuous measurements of the ozone concentra-tion has only been performed since 2004 in threesuburban/rural monitoring station areas handled by theRegional Environmental Protection Agency (ARPA) andother local public authorities. The position of each station

Figure 1. Modelling domain. Elevation contours (in m) are derived from topography used in the meteorological model. M1, M2 and M3(triangles) indicate the meteorological stations, while AQ1, AQ2 and AQ3 (circles) the air quality monitoring stations used in the modelling

assessment.

Copyright 2009 Royal Meteorological Society Meteorol. Appl. 17: 19–31 (2010)

THE EFFECT OF SEA BREEZE CIRCULATION ON PHOTOCHEMICAL POLLUTION 21

Table I. Total emissions (Mg yr−1) of the main pollutants inthe domain, from CORINAIR data referred to year 2000.

Emission/Sector Industry Transport Biogenic Others

NOx 35 244 45 402 3462SOx 62 204 2143 506CO 327 757 234 534 54 781NMVOC 11 131 50 688 2036 19 781

is shown in Figure 1, and their main characteristics aresummarized in Table II. Stations AQ1 and AQ2 arelocated close to the east coast, while station AQ3 islocated at the western side of the domain.

In 2005, the ozone 8-h average concentration exceededthe health protection threshold prescribed by the Euro-pean Commission (120 µg m−3) for 65 out of 86 daysin all three monitoring stations. Furthermore, analysis ofthe data demonstrated that more than 50% of the pho-tochemical episodes only affect either the eastern or thewestern side of the domain.

In order to assess the meteorological fields, datafrom three weather stations were used (summarized inTable III). The position of each station is indicated inFigure 1. Station M1 (Brindisi) is an Air Force mete-orological synoptic station: it is equipped with stan-dard instruments and synoptic observations are routinelystored at intervals of 3 h. Station M2 is situated in thesuburbs of the city of Lecce, and station M3 is close tothe western coast in the city of Racale. The latter twostations belong to the University of Lecce.

The spatial distribution of all monitoring stationsallows for evaluation of the spatial distribution of bothweather and concentration fields at critical points affectedby different prevailing local wind breeze systems.

3. The case study

The simulation period selected was from 2 to 8 July 2005.The evolution of the synoptic conditions during the wholeperiod of the simulation is discussed hereafter. Figure 2shows the NCEP reanalysis of the 500 hPa geopotentialheight and mean sea level pressure (MSLP) fields atdifferent times. In the beginning of the period, 2 July2005, 0000 LST, Figure 2(a) shows the presence of anarea of high pressure that extends vertically from sea level

to 500 hPa, and horizontally from the Atlantic Ocean towestern Europe and, at its extreme northern portion, intoScandinavia. The MSLP field shows the Italian peninsulais on the eastern side of the ridge, and is thus affected bya low level northerly flow, which is more intense overthe Adriatic. In the southeastern regions of Italy, thepressure gradient, associated with a pressure minimumlocated between Greece and Turkey, determines quitestrong synoptic winds over the region. At upper levels(500 hPa) a strong anticyclone prevents the pressureminima from moving across the Mediterranean; a ridgemain axis extends from Morocco to the British Isles,while the Italian peninsula is affected by a cycloniccirculation associated with a trough that extends fromCroatia to Sicily.

After 24 h, the circulation pattern moved eastward(Figure 2(b)): an area of high MSLP extended fromnorthern Italy to Scandinavia, while the MSLP mini-mum over the eastern Mediterranean remained localizedover the same region and it deepened further: as a conse-quence, the pressure gradient over the southern Adriaticand Ionian Seas became more intense. At 500 hPa, theridge moved northward and eastward, up to southernScandinavia and Poland, also affecting northern Italy. Aresidual cyclonic circulation affected southern Italy, inparticular the southern Adriatic and Ionian regions: infact, the weak wave located 24 h earlier over Croatiadeepened as it moved southeastward and became centredover Albania.

During the following 24 h (not shown), the high pres-sure centre moved farther to the east so that the main axisof the ridge directly affected all of the Italian peninsula:a weak cyclonic northerly circulation affected the Apuliaregion close to the ground, as the mean sea level pressureminimum moved from Turkey to the Black Sea and thepressure gradient over southern Italy weakened.

At 0000 LST, 5 July, the weak pressure gradientover the whole Italian peninsula (Figure 2(c)) favouredthe triggering of local breeze circulations, althougha minimum centred to the southeast of Greece wasresponsible for the weak synoptic southeasterly flowover Apulia. The 500 hPa ridge was still elongatedin a north–south direction along central Europe. Thelarge-scale circulation remained quasi-stationary duringthe following 24 h (not shown): however, a mean sealevel pressure minimum of about 1010 hPa, locatedover the central Tyrrhenian Sea determined a prevailing

Table II. Main characteristics of the monitoring stations; ozone data refer to 2005.

Station XUTM

(km)YUTM

(km)Spatial scale Eight-hour maximum O3

(µg m−3)Times exceeding the health

protection threshold prescribedby the European Commission

AQ1-Cerrate 764.2 4483.4 Rural 166 86AQ2-Campi 769.0 4476.2 Suburban 176 82AQ3-Grottaglie 705.3 4469.0 Suburban 193 65

X, Y coordinates are expressed in the Universal Transverse Mercator (UTM) coordinate system.


22 C. MANGIA ET AL.

Table III. Locations of the meteorological stations.

Station XUTM (km) YUTM (km) Z (m)

M1-Brindisi 749.4241 4504.089 10M2- Lecce 766.8741 4468.848 23M3-Racale 778.7016 4427.046 49

X, Y coordinates are expressed in the Universal Transverse Mercator(UTM) coordinate system.Z is the height relative to mean sea level

low level southerly circulation over most of southernItaly.

At 0000 LST, 7 July (Figure 2(d)), the pressureminimum moved southeastward and was centred overGreece. Thus, a northeasterly flow prevailed over theItalian Ionian regions. At 500 hPa, the ridge overwestern Europe began to weaken along its northernside, due to the presence of a deep pressure mini-mum, centred between England and the Netherlands,and also responsible for a cyclonic circulation over theMediterranean regions. Finally, at 0000 LST, 8 July (notshown), the pressure gradient weakened over central andsouthern Italy, favouring the development of sea/landbreeze circulation along the coasts.

4. The air quality modelling system

Tropospheric ozone (O3) is the result of various reac-tions between precursors coming from biogenic andanthropogenic emissions, mainly nitrogen oxides (NOx)and volatile organic compounds (VOC). Ozone concen-tration depends upon photochemical production via itsprecursors, upon horizontal transport on a regional andsynoptic scale, upon vertical transport from the strato-sphere and finally upon sinks such as surface deposi-tion and photochemical destruction by nitrogen monoxide(NO) and other chemical constituents.

Photochemical grid models combining meteorologyand chemistry are fundamental to understanding thedifferent mechanisms leading to the accumulation oftropospheric ozone. The basis of these models is theatmospheric diffusion equation, which represents a massbalance in which emissions, transport, diffusion, chem-ical reactions and removal processes are expressed inmathematical terms. The three-dimensional structure ofthese models needs a large number of inputs suchas full three-dimensional meteorological fields, emis-sions, initial and boundary conditions and grid struc-ture. Typically, inputs are specified at hourly inter-vals for each computational cell in the modellingdomain.

Figure 2. NCEP reanalysis of geopotential height (colours) and mean sea level pressure (white contours) at: 0000 UTC, 2 July 2005 (a); 0000UTC, 3 July 2005 (b); 0000 UTC, 5 July 2005 (c); 0000 UTC, 7 July 2005 (d).



Figure 3. Modelling domain and the three meteorological nested grids.

The photochemical model used in this study is thegrid model CALGRID. The emission inputs were pre-pared by an emission ad hoc pre-processor. The initialand boundary conditions were obtained by the conti-nental chemistry-transport model known as CHIMERE(Schmidt et al., 2001; Menut et al., 2005).

4.1. Meteorological system

The meteorological fields necessary for dispersion simu-lations were derived by the meteorological models RAMSand CALMET.

The RAMS simulations were performed using a two-way nested run with three grids (Figure 3). The verticalgrid was subdivided into 25 levels with different thick-nesses, starting at 70 m near the surface and graduallyreaching a maximum of 1000 m at the top. For initialand boundary conditions, the Isentropic Analysis Sys-tem package was used. Initially, the analysed fields werebased on the ECMWF (European Centre for Medium-Range Weather Forecasts) grid datasets. Every 6 h, thelateral and top boundary conditions were updated in theouter modelling domain using the ECMWF grid datasets.In this domain, a nudging towards the data is applied tothe three grid points closest to the lateral boundaries andto the upper five grid levels. Horizontal domains and gridsizes were designed taking into account both computa-tional time limitations and the capability of the model toresolve essential mesoscale features.

The prognostic meteorological fields from RAMS wereused as input in the CALMET model to calculateall boundary layer parameters necessary input for thedispersion model CALGRID.

CALMET ran in the ‘no observations’ (No-Obs) mode,which involves the use of RAMS gridded data as theinitial guess field. Figure 4 shows the scheme of themeteorological system.

Figure 4. Scheme of the meteorological system.

4.2. Photochemical modelling system

CALGRID is a Eulerian photochemical grid model. Itimplements an accurate advection–diffusion scheme inthe terrain following coordinates with vertical variablespacing; a resistance-based dry deposition algorithmtakes into account pollutant properties, local meteorologyand terrain features. The adopted chemical scheme isbased on the SAPRC-90 chemical mechanism (Carter,1990), including 54 chemical species with 129 reactionsand the quasi steady-state approximations solver forthe integration of kinetic equations. A lumped volatileorganic compound method is used to represent a class ofVOC with similar structure and reactivity.

The boundary conditions were provided using thechemical fields derived from the CHIMERE simulationson a continental scale (http://euler.lmd.polytechnique.fr/chimere) with a 0.5° of horizontal resolution and sixvertical levels. The domain size and grid spacing speci-fications are provided in Table IV.

4.3. Emission data

Emissions from industrial point sources were derivedfrom a regional database (www.arpa.puglia.it). Emissionsfrom all other sectors were estimated based on the ItalianCORINAIR databases and elaborated by means of anemission pre-processor.

The CORINAIR inventory (EEA, 2001) consists ofannual emission data for CO, NMVOC, CH4, NOX, SOX,N2O, CO2, NH3 and TSP (Total Suspended Particulate)at the level of province. The pollutants emissions areclassified by means of SNAP (Selected Nomenclature forAir Pollution) code. Table V reports the 11 main sourcessectors. In order to provide the appropriate detail in timeand space, the inventory has to be disaggregated to hourlyemissions for every cell of the simulation domain, and


24 C. MANGIA ET AL.

Table IV. Main characteristics of the three domains used in RAMS and of the one used in CALMET and CALGRID models.

Domain Lx (km) Ly (km) Lz (km) Nx Ny Nz �x, �y (km)

RAMS-1 960 960 15 40 40 25 24RAMS-2 492 420 15 82 70 25 6RAMS-3 162 180 15 108 120 25 1.5CALMET/CALGRID 162 180 5 108 120 10 1.5

Lx, Ly and Lz are domain sizes in the x, y and z directions, respectively.Nx, Ny and Nz are the number of mesh points in the x, y and z directions, respectively.�x and �y are the mesh spacing in the x and y directions, respectively.

Figure 5. Spatial distribution of the annual emissions of (a) NOX , (b) anthropogenic NMVOC, (c) biogenic NMVOC. Units are Mg y−1.

the class of the organic compounds (NMVOC) must bespeciated according to the chemical scheme used by thephotochemical model CALGRID. This task requires aproper spatial/temporal modulation and a chemical pro-file for each emission source. Therefore, CORINAIRdata were pre-processed using the software tool GEM-PPdeveloped by Conte et al. (2008) following the method-ology suggested by EPA (2002), APAT (2004), Monfortiand Pederzoli (2005), Carnevale et al., (2006). Figure 5depicts the distribution of annual NOX, biogenic and non-biogenic NMVOC emissions for all macro-sectors, overthe simulated domain. The figures demonstrate that thelargest emissions of ozone precursors are localized inthe Taranto and Brindisi areas due to the industrial andenergy production activities located there. Figure 6 showsthe scheme of the photochemical model.

5. Results and discussion

5.1. Meteorological simulations and comparison withobservations

In order to avoid potential effects related to the ini-tial condition, the simulations started 1 day before, on1 July 2005. Figure 7 shows the simulated surface tem-perature and wind fields for each day of the periodconsidered at 1500 LST, when the conditions are con-sidered representative of maximum ozone concentrations.Figure 8 shows the time variation for the modelled and

Table V. Main CORINAIR sectors.

Main SNAP sector Description

01 Combustion in energy andtransformation industries

02 Nonindustrial combustion plants03 Combustion in manufacturing industries04 Production processes05 Extraction and distribution of fossil

fuels/geothermal energy06 Solvent and other product use07 Road transport08 Other mobile sources and machinery09 Waste treatment and disposal10 Agriculture11 Other

observed wind speed, wind direction, and temperature atthe three meteorological stations considered. The thickline indicates RAMS/CALMET simulations and the cir-cles the experimental data.

During the first period of 2–4 July, a northerly flow,which was more intense over the Adriatic, affected thearea. Wind intensity reached a maximum value higherthan 9 m s−1 on the eastern side of the peninsula, andtended to decrease in the beginning of 4 July when thesynoptic conditions became weaker and winds started torotate from the south. One can appreciate that on the



Figure 6. Scheme of the photochemical modelling system.

western side of the numerical domain (grid 3), alongthe Taranto Gulf, the surface temperature was higherand the wind intensity lower. This is confirmed by boththe model results and the measurements at the threestations.

During the second period of 5–8 July a weak synopticsoutheasterly flow prevailed. The wind intensity dimin-ished and the surface temperatures tended to increasedifferently between the eastern and western side of thegrid depending on the day. These conditions favour thedevelopment of different sea breeze circulations over thedomain, with a convergence area observed during 5, 7,and 8 July, in particular.

It is important to point out that, for all simulated days,differences between the eastern and western sides of thedomain were evident and, in general, reproduced well bythe model.

The wind map in Figure 7(d) shows that at 1500on the 5 July two breeze systems developed. Onewas initiated in the Adriatic Sea and moved inland,where near surface winds blew from east-southeast tonorthwest with a prevailing direction of about 140°.A second cell developed from the southwest to west,with a prevailing direction of about 220°. This wasconfirmed by the wind direction data measured at theM1 and M3 meteorological stations. The two breeze cellsmerged in the middle of the peninsula where the surfacetemperatures also reached their highest values as shownat station M2.

During 6 July at 1500 LST (Figure 7(e)), a conver-gence area was identified in the northern part of thepeninsula. The temperature reached 31 °C at the M1 sta-tion, but did not exceed 26.5 °C at the M3 station.

During 7 July, at 1500 LST, three sea breeze systemswere identified (Figure 7(f)). One was from the AdriaticSea moving inland, where near surface winds blewfrom a prevailing direction of 60° –90°. A second celldeveloped with a direction of about 180° and differentinland penetration. In this case, two convergence areaswere formed close to the western side of the domain.During 8 July, again at 1500, two sea breeze systemscould be identified, but in this case the convergencecovered most of the area closer to the eastern side. Thehighest temperature was observed and predicted at theM2 station.

The poor reproduction of the wind intensity cycle atthe M2 station during 5 and 8 July may be due to thefact that the M2 station was close to the convergencearea during those days, making the spatial and temporalmatching between the model and observations quitedifficult.

To evaluate the model performance over the entireperiod considered and for all stations the following sta-tistical indices for wind speed and temperature were con-sidered (Hanna and Yang, 2001; Cai et al., 2007); theseare the mean bias as an absolute as well as a relativevalue, the root mean square error, the correlation coef-ficient, and the index of agreement, which is a measureof how well the resulting simulated simulation result dis-tance from the observed mean Xo matches the observedresult from Xo.

Due to the rotating scale of the wind direction, onlyabsolute indices were considered for wind direction. Fur-thermore, for these indicators, a threshold value was setfor the wind speed of the corresponding data points sincethe wind direction cannot be determined under calmconditions. Thus, data points corresponding only to an


26 C. MANGIA ET AL.

Figure 7. Simulated near surface wind fields and temperature at 1500 LST (a) 2 July, (b) 3 July, (c) 4 July, (d) 5 July, (e) 6 July, (f) 7 July,(g) 8 July. Grey scale indicates the temperature. Arrows indicate the wind vector.

observed wind speed exceeding 1.3 m s−1 were takeninto account (Papalexiou and Moussiopolus, 2006).

Mean Bias- MB = 1

N

N∑i=1

(Xp − Xo) (1)

Mean Normalized Bias Error- MNBE

= 1

N

N∑i=1

(Xp − Xo)

Xo× 100 (2)

Mean Normalised Gross Error- MNGE

= 1

N

N∑i=1

|Xp − Xo|Xo

× 100 (3)

Root mean squared error -RMSE

=√√√√ 1

N

N∑i=1

(Xp − Xo)2 (4)

Index of Agreement IOA = 1

−

N∑i=1

(Xp − Xo)2

N∑i=1

(|Xp − Xo| + |Xo − Xo|)2

(5)

Correlation coefficient -COR

=

N∑i=1

(Xp − Xp)(Xo − Xo)

√√√√ N∑i=1

(Xp − Xp)2(Xo − Xo)2

(6)

where Xo and Xp are the observed and predicted vari-ables, respectively, N is the number of pairs of predic-tions and observations at the same time. The overbardenotes averages.



Figure 8. Temporal variation of near surface wind speed (ws) (m s−1), wind direction (wdir) (deg) and temperature (temp) (°C) at themeteorological stations. (a) refers to M1 station, (b) refers to M2 station, and (c) refers to M3 stations. Solid line: model results; circles:

observations.

Table VI summarizes the statistical indices for eachstation. For the wind speed, the average mean biasand corresponding mean normalized bias are close to0.4 m s−1, while the RMSE to 1.6 m s−1. The indexof agreement is 0.87, while the correlation coefficient at0.77. For the wind direction, the mean direction bias is6° and the RMSE at 38°.

The worst statistical indices were obtained for the M1station, where the wind intensity was underestimated, onaverage, by about 1.1 m s−1, and the temperature by−0.6 °C. Regardless, all values obtained for the statisticalindices were in the range of values typical for mesoscalemodels, as shown in many studies (see Papalexiou andMoussiopoulos, 2006, for a brief review). This indicatesthat the meteorological models reproduce both the windand temperature fields quite well.

5.2. Photochemical simulations and comparisons withmeasurements

Figure 9 (a)–(c) shows the comparison between ozonepredictions and the measured concentrations for 2–4 July2005.

As seen in the previous section, the strong synopticnorthern flow during these days did not enable thedevelopment of a sea breeze. Uniform ozone levels werecalculated over the entire domain. This was confirmed bythe measurements for all the three stations (AQ1–AQ3),

Table VI. Meteorological model performance measures forhourly average wind speed, wind direction and temperature at

the surface.

Statistical indices M2 M3 M1 All stations

Wind speed meanobserved (m s−1)

4.13 3.22 5.35 3.90

Wind speed MB (m s−1) 0.82 0.38 −1.08 0.37Wind speed MNBE (%) 46 35 9 0.35Wind speed RMSE(m s−1)

1.75 1.13 2.15 1.58

Wind speed IOA 0.90 0.85 0.82 0.87Wind speed COR 0.81 0.75 0.85 0.77Wind direction MB(degrees)

12 7 4 6

Wind direction RMSE(degrees)

30 19 42 38

Temperature meanobserved (°C)

25.0 24.7 25.0 24.9

Temperature MB (°C) −0.4 −0.2 −0.6 −0.3Temperature RMSE (°C) 1.1 1.2 1.5 1.2Temperature IOA 0.96 0.94 0.86 0.99Temperature COR 0.93 0.90 0.78 0.9

The averaging time is 1 h.MB stands for mean bias, MNBE for mean normalized bias error,RMSE for root mean squared error, IOA for index of agreement, CORfor correlation coefficient.


28 C. MANGIA ET AL.

Figure 9. Temporal variation of ground level ozone concentration(µg m−3) for 2–4 July 2005 at AQ1 (a), AQ2 (b) and AQ3 (c) air

quality stations. Solid line: model results; circles: observations.

which showed similar profiles, without large excursionsbetween night and day, Ozone tended to diminish overthe peninsula between 2 and 4 July, and the highestozone values were calculated over the sea, where largeamount of NOX and VOC emitted in Taranto city weretransported.

During 5–8 July the synoptic situation changed. Thewind circulation became weaker and a south-easterlyflow prevailed. The ozone concentration became higherthan on previous days, and differences between the threestations (AQ1–AQ3) were evident. Figure 10 (a)–(c)shows the 5–8 July.

On 5 July the general circulation became very weakduring the night, as confirmed by wind measurements(all <2 m s−1). A wind component from the East-South-East carried NOX and VOC emitted from the Tarantoindustrial site inland. At midday, two sea breeze frontsdeveloped that merged inland within the domain leadingto an accumulation of ozone close to the convergencearea. Figure 11 (a)–(c) shows the ozone map at 1300,1500, and 1800 LST. In particular, it can be seen how theconvergence line moved to the eastern side between 1300and 1800 LST, expanding the convergence area. This wasconfirmed as a tendency by the monitoring data. At 1300LST, the AQ3 station registered 143 µg m−3, while, onthe opposite side, the AQ1 station registered 125 µg m−3.At 1700 LST, the AQ3 station registered 135 µg m−3,while the AQ1 ozone concentration was 155 µg m−3.

Figure 10. As in Figure 9, but for 5–8 July 2005.

Figure 12 (a)–(c) shows the ozone maps at 1500 LSTfor 6–8 July. During these days it can be observed howdifferent see breeze fronts form and converge in differentparts of the domain, leading to areas with high ozonevalues.

On 6 July, at 1500 LST, an area of high ozone valueswas very confined to the eastern side of the peninsula.Here, the AQ1 and AQ2 stations registered ozone valuesof 142 and 135.4 µg m−3 respectively, at 1500 LST,while AQ3 registered 114 µg m−3 at the same time.

On 7 July at 1500 LST, three sea breeze systems couldbe identified. In this case, two convergence areas withhigh ozone concentration formed close to the western sideof the domain. In this case, the AQ3 station was veryclose to the northern convergence area, and registered132 µg m−3 of ozone at 1500 LST and 143 µg m−3 at1600 LST, whereas the stations close to the eastern sidesregistered about 113 µg m−3 of ozone.

On 8 July at 1500 LST, two sea breeze systems couldagain be identified, but in this case the convergencearea and the corresponding high ozone level were foundclose to the eastern side of the domain. In this case, theAQ1 station was very close to the convergence area andregistered an ozone value of 143 µg m−3 at 1600 LST,whereas the stations close to the western side did notexceed 105 µg m−3 all day.

Similarly to the evaluation of meteorological results,the statistical performance for the measured and predictedhourly ozone concentrations were assessed. The statisti-cal indices of MB, MNBE, RMSE, and COR, as wellas IOA were used. Apart from these estimators, three



Figure 11. Simulated ground level ozone concentration (µg m−3) for 5 July at (a) 1300 LT, (b) 1500 LT, (c) 1800 LT.

Figure 12. Simulated ground level ozone concentration (µg m−3) at 1500 LT (a) 6 July, (b) 7 July, (c) 8 July.

indicators recommended by the US EPA (2007) were cal-culated: the Average Peak Prediction Bias (APPB) andError (APPE), which measure the ability of the modelto predict daily ozone peaks. They were calculated asthe mean normalized bias and error (Equations (1) and(2)), except that they only considered the daily max-ima values (predicted vs observed) at each monitoringlocation. Although there was no objective criterion setforth for satisfactory model performance, US EPA sug-gests values of 5–15% for the mean normalized biaserror, 30–35% for the mean normalized gross errorand 15–20% for the unpaired peak prediction accuracyto be met by the modelling simulations for regulatoryapplications.

In Table VII, the results of the statistical evaluationfor each monitoring stations are reported. It can benoted from the Table that the model tends to slightlyoverestimate ozone concentration, with a mean posi-tive bias for each station and an average MNBE of12%. The MNGE was about 20% for all stations.The index of agreement and the correlation coeffi-cient were both 0.7, which are satisfactory values. TheAverage Peak Prediction Error was about 7%. BothMNBE and MNGE were within the ranges set bythe EPA.

6. Conclusions

The air quality modelling system RAMS/CALMET/CALGRID was used to simulate photochemical pollutionover the Apulia Region in southern Italy during selected(2–8 July 2005) summer conditions characterized byalternating strong and weak synoptic meteorologicalforcing. During the first 3 days (2–4 July), strong syn-optic situations dominated with a rather uniform ozoneconcentration, and without large differences between dayand night values, were observed at the monitoring sta-tions. During the last four simulated days (5–8 July), thesynoptic situation became weaker, the observed ozoneconcentrations became higher, and various daily ozonepatterns were registered in the different areas of thedomain.

Simulations showed that when the synoptic meteoro-logical conditions are strong enough to prevent the for-mation of a local sea breeze circulation, the predictedozone concentration is uniform, with relatively low val-ues (less than 120 µg m−3). The maximum ozone con-centration was computed over the Gulf of Taranto wherelarge amounts of NOX and VOC emitted from Tarantocity were transported.


30 C. MANGIA ET AL.

Table VII. Statistical analysis of hourly observed (index o) versus predicted (index p) concentrations time series.

Co

(µg m−3)Cp

(µg m−3)Co max

(µg m−3)Cp max

(µg m−3)MB

(µg m−3)MNBE

(%)MNGE

(%)IOA COR APPB

(%)APPE(%)

AQ2 - Campi 99 104 140 160 6 11 19 0.8 0.7 5 9AQ3 - Grottaglie 102 108 143 162 6 14 20 0.6 0.7 −1 8AQ1 - Cerrate 107 110 155 162 4 9 19 0.6 0.7 −2 6All stations 103 107 140 160 6 12 20 0.7 0.7 1 7

The overbar stands for mean values (µg m−3), the index max for the maximum values (µg m−3), MB for mean bias (µg m−3), MNBE for meannormalized bias error (%), MNGE for mean normalized gross error (%), IOA for index of agreement, COR for correlation coefficient, APPB foraverage peak prediction bias (%) and APPE for average peak prediction error (%).

In contrast, the maximum ground level concentrationof ozone was modelled/registered during weak synopticconditions when local sea breeze systems were notinhibited. The ozone maps showed how the locations ofmaximum ozone concentration are strongly linked to thedegree of penetration of these sea breeze systems intothe Peninsula and the resulting sea breeze convergencezone. During these days, the maximum predicted andobserved ozone concentrations were recorded close to theconvergence lines, which varied in location on a day-to-day basis.

Regarding the comparison between field measurementsand model results, the statistical analyses showed that themodelling system RAMS/CALMET/CALGRID is able toreproduce the main characteristics of the photochemicalpollution over the entire 2–8 July 2005 period for thesimulated domain. In addition, the model simulationsaided the interpretation of field measurements in differentmeteorological conditions. Overall, the model tended tooverestimate the measured ozone concentration, but, ingeneral, all statistical indices were in the range of thestate-of-the-art-model.

Some discrepancies were apparent between predictionsand observations for some hours essentially due tomerging of different breeze systems, which are criticalfor the space-time matching between the model and theobservations, those small-scale effects that cannot bereproduced by mesoscale simulations. The study showedthe important contribution of sea breeze transportationof air masses along the two coasts and the contributionof sea breeze convergence to a high ozone level result,depending on where the convergence zone forms.

Acknowledgements

This work was supported by the Province of Lecceand the Apulia Region. The authors acknowledge theworking group of the PREV’AIR system and particu-larly Cecile Honore of INERIS, for supplying concentra-tion data used for the boundary conditions of the simu-lated domain, Dr. Lorenzo Angiuli and Dr. AlessandraNocioni – Environmental Protection Regional Agency(ARPA) of Puglia and Dr. Salvatore Francioso, Provinceof Lecce, for supplying Environmental data; also GennaroRispoli, University of Lecce, for supplying meteorologi-cal data.

Appendix List of Acronyms.

CALMET: CALifornian METeorological modelCALGRID: CALifornian GRID modelCORINAIR: COordination INformation AIRGEM-PP: Gis Emission Pre-ProcessorMSLP: Mean Sea Level PressureNCEP: National Centers for Environmental PredictionNMVOC: Non-Methanic Volatile Organic CompoundRAMS: Regional Atmospheric Modelling SystemSAPRC: Statewide Air Pollution Research CentreVOC: Volatile Organic Compounds

ReferencesAlonso L, Gangoiti G, Navazo M, Millan MM, Mantilla E. 2000.

Transport of tropospheric ozone over the Bay of Biscay and theeastern Cantabrian coast of Spain. Journal of Applied Meteorology39: 475–486.

APAT. 2004. The counties level disaggregation of the national emissioninventory (in Italian).

Cai XM, McGregory GR, Harrison RM, Ryall D. 2007. Modelling ofmeteorological conditions at urban scale for the PUMA campaigns.Meteorological Applications 14: 311–326.

Carnevale C, Gabusi V, Volta M. 2006. POEM-PM: an emission modelfor secondary pollution control scenarios. Environmental Modellingand Software 21: 320–329.

Carter W. 1990. A detailed mechanism for the gas-phase atmosphericreactions of organic compounds. Atmospheric Environment 27A:481–518.

Clappier A. 2000. Effect of sea breeze on air pollution in the greaterAthens area. Part I: numerical simulations and field observations.Journal of Applied Meteorology 39: 546–562.

Conte D, Marra GP, Mangia C, Rizza U, Schipa I, Tanzarella A.2008. GEM-PP: a GIS EMissions Pre-Processor to ingest Euro-pean emission inventory (EMEP/CORINAIR) into photochemi-cal transport models. Proceedings of the academic track of the2008 Free and Open Source Software for Geospatial (FOSS4G)Conference, 29 September–3 October 2008, Cape Town, SouthAfrica. ISBN 978-0-620-42117-1. http://conference.osgeo.org/index.php/foss4g/2008/paper/view/182/67.

EEA. 2001. EMEP/CORINAIR Atmospheric Emission InventoryGuidebook. Technical Report, 30, 3rd ed. EEA.

EPA. 2002. SPECIATE 3.2. Available from: http://www.epa.gov/ttn/chief/software/speciate.

EPA. 2007. Guidance on the Use of Models and Other Analyses forDemonstrating Attainment of Air Quality Goals for Ozone, PM2.5,and Regional Haze EPA Report EPA -454/B-07-002, 253p.

Evtyugina MG, Nunes T, Pio C, Costa CS. 2006. Photochemicalpollution under sea breeze conditions, during summer, at thePortuguese West Coast. Atmospheric Environment 40: 6277–6293.

Hanna SR, Yang R. 2001. Evaluation of mesoscale models’simulationof near-surface winds, temperature gradients,and mixing depths.Journal of Applied Meteorology 40: 1095–1104.

Jimenez P, Parra R, Gasso S, Baldasano JM. 2005. Modelling theozone weekend effect in very complex terrains: a case study in



the Northeastern Iberian Peninsula. Atmospheric Environment 39:429–444.

Kambezidis HD, Peppes AA, Melas D. 1995. An environmentalexperiment over Athens urban area under sea-breeze condition.Atmospheric Research 36: 139–156.

Kambezidis HD, Weidauer D, Melas D, Ulbricht M. 1998. Air qualityin the Athens basin during sea breeze and non-sea breeze daysusing laser-remote-sensing technique. Atmospheric Environment 32:2173–2182.

Lyons WA, Pielke RA, Tremback CJ, Walko RL, Moon DA, KeenCS. 1995. Modeling impacts of mesoscale vertical motions uponcoastal zone air pollution dispersion. Atmospheric Environment 29:283–301.

Mangia C, Martano P, Miglietta MM, Morabito A, Tanzarella A.2004. Modelling local circulations over the Salento Peninsula.Meteorological Applications 11: 231–244.

Melas D, Kambezidis HD, Walmsley JL, Moussiopoulos N, BornsteinRD, Klemm O, Asimakopoulos DN. 1995. Summary of meetingNATO/CCMS Pilot Study workshop on Air pollution transport anddiffusion over coastal urban areas. Atmospheric Environment 29:3713–3718.

Menut L, Coll I, Cautenet S. 2005. Impact of meteorologicaldata resolution on the forecasted ozone concentrations during theESCOMPTE IOP2a and IOP2b. Atmospheric Research 74: 241–279.

Millan M, Salvador R, Mantilla E, Artınano B. 1996. Meteorologyand photochemical air pollution in southern Europe: experimentalresults from EC Research Projects. Atmospheric Environment 30:1909–1996.

Millan M, Mantilla E, Salvador R, Carratala A, Sanz MJ. 2000.Ozone cycles in the Western Mediterranean Basin: interpretationof monitoring data in complex coastal terrain. Journal of AppliedMeteorology 39: 487–508.

Monforti F, Pederzoli A. 2005. THOSCANE: a tool to detailCORINAIR emission inventories. Environmental Modelling andSoftware 20: 505–508.

Papalexiou S, Moussiopoulos N. 2006. Wind flow and photochemicalair pollution in Thessaloniki, Greece. Part II: Statistical evaluationof European Zooming Model’s simulation results. EnvironmentalModelling and Software 21: 1752–1758.

Pielke RA, Cotton WR, Walko RL, Tremback CJ, Lyons WA, GrassoLD, Nicholls ME, Moran MD, Wesley DA, Lee TJ, Copeland JH.1992. A comprehensive meteorological modelling system – RAMS.Meteorology and Atmospheric Physics 49: 69–91.

Pirovano G, Coll I, Bedogni M, Alessandrini S, Costa MP, GabusiV, Lasry F, Menut LR, Vautard R. 2007. On the influence ofmeteorological input on photochemical modelling of a severe episodeover a coastal area. Atmospheric Environment 41: 6445–6464.

Russell A, Dennis R. 2000. NARSTO – critical review ofphotochemical models and modelling. Atmospheric Environment 34:2283–2324.

Schmidt H, Derognat C, Vautard R, Beekmann M. 2001. A comparisonof simulated and observed ozone mixing ratios for the summer of1998 in Western Europe. Atmospheric Environment 36: 6277–6297.

Scire JS, Insley EM, Yamartino R. 2000. Model Formulation anduser’s guide for the CALMET meteorological Model (Version 5.0).California Air Resource Board.

Svensson G. 1998. Model simulations of the air quality inAthens, Greece, during the MEDCAPHOT-TRACE. AtmosphericEnvironment 32: 2239–2268.

Yamartino RJ, Scire J, Carmichael GR, Chang YS. 1992a. TheCALGRID mesoscale photochemical grid model. AtmosphericEnvironment 26A: 1493–1512.

Yamartino RJ, Scire J, Hanna SR, Carmichael GR, Chang YS. 1992b.CALGRID: A Mesoscale Photochemical Grid Model Vol. I: ModelFormulation Document Sigma Research Report No. A6-215-74.PTSD, California Air Resources Board: Sacramento; 94814.

Ziomas IC, Tzoumaka P, Balis D, Melas D, Zerefos C, Klemm O. 1998.Ozone episodes in Athens, Greece. A modelling approach usingdata from the MEDCAPHOT-TRACE. Atmospheric Environment 32:2313–2321.


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