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

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1 METEOROLOGICAL APPLICATIONS Meteorol. Appl. 17: (2010) Published online 10 August 2009 in Wiley InterScience ( DOI: /met.147 A numerical study of the effect of sea breeze circulation on photochemical 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, Lecce, Italy ABSTRACT: Numerical simulations compared with measurements are used to investigate the effect of sea breeze circulation on the ozone accumulation over a highly industrialized peninsula in southern Italy, where high levels of ozone concentration are often registered. A frequent meteorological phenomenon in this region during weak summer synoptic conditions is the development of complex sea breeze systems from the coastlines, with convergence areas within the peninsula. A case study characterized by strong winds alternating with sea breeze circulations was selected. The simulations show that during weak synoptic conditions, sea breezes transport ozone and its precursors over land from the sea, as well as from the coastlines where the largest industrialized districts are localized. The overlapping breezes lead to ozone accumulation in the area where sea breeze convergence occurs. This may explain the high values of ozone registered close to the sea breeze convergence lines. The comparison between predictions and experimental data indicates that the numerical system successfully reproduces both weather and ground level ozone concentration in different meteorological conditions, resulting in a fundamental tool for both scientific comprehension of the evolution of air contaminants and interpretation 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 Introduction Over the last few decades, air quality management has become a major problem for decision makers, particularly with respect to photochemical pollution, for which the relationships between emissions and atmospheric concentrations can be quite complex due to non-linear interactions of meteorology, chemistry, emissions and land use patterns. Air quality models combining meteorology and chemistry are fundamental to understanding the different mechanisms leading to the accumulation of tropospheric ozone. Therefore, they can play an important role in both the scientific evolution of air contaminants and supporting strategies in emission control strategies (Russell and Dennis, 2000). Many air quality studies conducted in Mediterranean areas have shown how these regions are often characterized by elevated levels of ozone during the summer due to the high solar radiation, the presence of large emissions, large areas of vegetation, and meteorological processes ranging from the mesoscale to local circulations (Millan et al., 1996; Ziomas et al., 1998; Svensson, 1998; Millan et al., 2000; Alonso et al., 2000; Jimènez et al., 2005). * Correspondence to: Cristina Mangia, Institute of Atmospheric and Climate Sciences ISAC -CNR-LECCE, Lecce, Italy. c.mangia@isac.cnr.it Studies conducted in coastal areas have also demonstrated the role of sea breeze circulation in smog episodes (Lyons et al., 1995; Kambezidis et al.,1995, 1998; Melas et al., 1995; Clappier et al., 2000; Evtyugina et al., 2006; Pirovano et al., 2007). Due to the large horizontal and vertical variations in meteorological parameters caused by the different diurnal heating cycles at the sea/land boundary, coastal meteorology still represents a challenge for air pollution modelling and assessment of ozone behaviour. The present work focuses on the photochemical pollution in a narrow Mediterranean peninsula located in the southeast corner of Italy (Apulia Region), the opposite coastlines of which host two of the largest European industrial sites ( A frequent phenomenon that occurs in this area is the development of different sea breeze systems, which merge in the middle of the peninsula (Mangia et al., 2004). The aim of the present study was to investigate the influence of sea breeze circulation on ozone accumulation along the modelling domain. In this context, a summer episode (2 8 July 2005) characterized by strong northerly winds alternating with a weak synoptic forcing was selected. The chemistry/transport models used in this study are the meteorological models RAMS (Pielke et al., 1992) and CALMET (Scire et al., 2000) coupled with Copyright 2009 Royal Meteorological Society

2 20 C. MANGIA ET AL. the photochemical model CALGRID (Yamartino et al., 1992a, 1992b). Predicted meteorological and concentration data were compared with routine measurements from the regional air quality network in order to investigate the capability of the models to reproduce the ozone concentration patterns in the area and to interpret the monitoring data. 2. Description of the area The domain investigated is located in the Mediterranean Sea in the southeastern corner of Italy. It is bordered by the Adriatic Sea to the east, the Ionian Sea to the southeast, and the Strait of Otranto and Gulf of Taranto to the south (Figure 1). The region is 180 km long in the northwest southeast direction, and has an average width of 60 km. It is generally flat, with small hills (less than 200 m) in the southeastern area and moderately high hills (about 500 m) in the northern part. When anticyclonic conditions affect the central Mediterranean basin, the Apulia region is dominated by a northwesterly synoptic wind, intensified by the channelling effect of the Otranto channel (that is about 70 km wide) separating southeastern Italy from Albania. During weak synoptic conditions, the region is influenced on its entire coastal perimeter (about 300 km) by complex sealand breeze systems, caused by the diurnal heating cycle. A frequent meteorological phenomenon that occurs in the modelling domain is the development of different sea breeze systems, with an inland breeze penetration that may be tens of kilometres deep. These breeze systems merge in the middle of the peninsula (Mangia et al., 2004). This phenomenon is generally observed at midday during the summer, when factors such as high solar radiation and temperature may favour the formation of photochemical pollutants. Emissions in the area result primarily from urban, shipping and industrial activities. The largest industrial emissions are localized near the coasts: in the Taranto west coast and the Brindisi areas of the eastern coast (Figure 1). The industry in Taranto is dominated by iron and steel (among the largest in Europe), as well as oil refineries with hydrocarbon transformation processes. These industries make use of Taranto harbour to import raw material and export final products. In Brindisi, the main industrial activities are represented by two coal power plants and one petrochemical plant. Table I reports the emissions for the year 2000 of NOx, SOx, CO and NMVOC (Non-methanic Volatile Organic Compound) from different emission sources derived from the last available Italian CORINAIR database (2000) (APAT, 2004). Based on the data analysis, it is evident that the industrial division and transport sectors, including vehicles, airports, harbours and railroads, contribute greater emissions. Continuous measurements of the ozone concentration has only been performed since 2004 in three suburban/rural monitoring station areas handled by the Regional Environmental Protection Agency (ARPA) and other 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: (2010)

3 THE EFFECT OF SEA BREEZE CIRCULATION ON PHOTOCHEMICAL POLLUTION 21 Table I. Total emissions (Mg yr 1 ) of the main pollutants in the domain, from CORINAIR data referred to year Emission/Sector Industry Transport Biogenic Others NO x SO x CO NMVOC is shown in Figure 1, and their main characteristics are summarized in Table II. Stations AQ1 and AQ2 are located close to the east coast, while station AQ3 is located at the western side of the domain. In 2005, the ozone 8-h average concentration exceeded the health protection threshold prescribed by the European Commission (120 µg m 3 ) for 65 out of 86 days in all three monitoring stations. Furthermore, analysis of the data demonstrated that more than 50% of the photochemical episodes only affect either the eastern or the western side of the domain. In order to assess the meteorological fields, data from three weather stations were used (summarized in Table III). The position of each station is indicated in Figure 1. Station M1 (Brindisi) is an Air Force meteorological synoptic station: it is equipped with standard instruments and synoptic observations are routinely stored at intervals of 3 h. Station M2 is situated in the suburbs of the city of Lecce, and station M3 is close to the western coast in the city of Racale. The latter two stations belong to the University of Lecce. The spatial distribution of all monitoring stations allows for evaluation of the spatial distribution of both weather and concentration fields at critical points affected by different prevailing local wind breeze systems. 3. The case study The simulation period selected was from 2 to 8 July The evolution of the synoptic conditions during the whole period of the simulation is discussed hereafter. Figure 2 shows the NCEP reanalysis of the 500 hpa geopotential height and mean sea level pressure (MSLP) fields at different times. In the beginning of the period, 2 July 2005, 0000 LST, Figure 2(a) shows the presence of an area of high pressure that extends vertically from sea level to 500 hpa, and horizontally from the Atlantic Ocean to western Europe and, at its extreme northern portion, into Scandinavia. The MSLP field shows the Italian peninsula is on the eastern side of the ridge, and is thus affected by a low level northerly flow, which is more intense over the Adriatic. In the southeastern regions of Italy, the pressure gradient, associated with a pressure minimum located between Greece and Turkey, determines quite strong synoptic winds over the region. At upper levels (500 hpa) a strong anticyclone prevents the pressure minima from moving across the Mediterranean; a ridge main axis extends from Morocco to the British Isles, while the Italian peninsula is affected by a cyclonic circulation associated with a trough that extends from Croatia to Sicily. After 24 h, the circulation pattern moved eastward (Figure 2(b)): an area of high MSLP extended from northern Italy to Scandinavia, while the MSLP minimum over the eastern Mediterranean remained localized over the same region and it deepened further: as a consequence, the pressure gradient over the southern Adriatic and Ionian Seas became more intense. At 500 hpa, the ridge moved northward and eastward, up to southern Scandinavia and Poland, also affecting northern Italy. A residual cyclonic circulation affected southern Italy, in particular the southern Adriatic and Ionian regions: in fact, the weak wave located 24 h earlier over Croatia deepened as it moved southeastward and became centred over Albania. During the following 24 h (not shown), the high pressure centre moved farther to the east so that the main axis of the ridge directly affected all of the Italian peninsula: a weak cyclonic northerly circulation affected the Apulia region close to the ground, as the mean sea level pressure minimum moved from Turkey to the Black Sea and the pressure gradient over southern Italy weakened. At 0000 LST, 5 July, the weak pressure gradient over the whole Italian peninsula (Figure 2(c)) favoured the triggering of local breeze circulations, although a minimum centred to the southeast of Greece was responsible for the weak synoptic southeasterly flow over Apulia. The 500 hpa ridge was still elongated in a north south direction along central Europe. The large-scale circulation remained quasi-stationary during the following 24 h (not shown): however, a mean sea level pressure minimum of about 1010 hpa, located over the central Tyrrhenian Sea determined a prevailing Table II. Main characteristics of the monitoring stations; ozone data refer to Station X UTM (km) Y UTM (km) Spatial scale Eight-hour maximum O 3 (µg m 3 ) Times exceeding the health protection threshold prescribed by the European Commission AQ1-Cerrate Rural AQ2-Campi Suburban AQ3-Grottaglie Suburban X, Y coordinates are expressed in the Universal Transverse Mercator (UTM) coordinate system.

4 22 C. MANGIA ET AL. 4. Table III. Locations of the meteorological stations. Station M1-Brindisi M2- Lecce M3-Racale XUTM (km) YUTM (km) Z (m) 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 southern Italy. At 0000 LST, 7 July (Figure 2(d)), the pressure minimum moved southeastward and was centred over Greece. Thus, a northeasterly flow prevailed over the Italian Ionian regions. At 500 hpa, the ridge over western Europe began to weaken along its northern side, due to the presence of a deep pressure minimum, centred between England and the Netherlands, and also responsible for a cyclonic circulation over the Mediterranean regions. Finally, at 0000 LST, 8 July (not shown), the pressure gradient weakened over central and southern Italy, favouring the development of sea/land breeze circulation along the coasts. The air quality modelling system Tropospheric ozone (O3 ) is the result of various reactions between precursors coming from biogenic and anthropogenic emissions, mainly nitrogen oxides (NOx ) and volatile organic compounds (VOC). Ozone concentration depends upon photochemical production via its precursors, upon horizontal transport on a regional and synoptic scale, upon vertical transport from the stratosphere and finally upon sinks such as surface deposition and photochemical destruction by nitrogen monoxide (NO) and other chemical constituents. Photochemical grid models combining meteorology and chemistry are fundamental to understanding the different mechanisms leading to the accumulation of tropospheric ozone. The basis of these models is the atmospheric diffusion equation, which represents a mass balance in which emissions, transport, diffusion, chemical reactions and removal processes are expressed in mathematical terms. The three-dimensional structure of these models needs a large number of inputs such as full three-dimensional meteorological fields, emissions, initial and boundary conditions and grid structure. Typically, inputs are specified at hourly intervals for each computational cell in the modelling domain. Figure 2. NCEP reanalysis of geopotential height (colours) and mean sea level pressure (white contours) at: 0000 UTC, 2 July 2005 (a); 0000 UTC, 3 July 2005 (b); 0000 UTC, 5 July 2005 (c); 0000 UTC, 7 July 2005 (d). Copyright 2009 Royal Meteorological Society Meteorol. Appl. 17: (2010)

5 THE EFFECT OF SEA BREEZE CIRCULATION ON PHOTOCHEMICAL POLLUTION 23 Figure 3. Modelling domain and the three meteorological nested grids. The photochemical model used in this study is the grid model CALGRID. The emission inputs were prepared by an emission ad hoc pre-processor. The initial and boundary conditions were obtained by the continental chemistry-transport model known as CHIMERE (Schmidt et al., 2001; Menut et al., 2005) Meteorological system The meteorological fields necessary for dispersion simulations werederivedby the meteorological models RAMS and CALMET. The RAMS simulations were performed using a twoway nested run with three grids (Figure 3). The vertical grid was subdivided into 25 levels with different thicknesses, starting at 70 m near the surface and gradually reaching a maximum of 1000 m at the top. For initial and boundary conditions, the Isentropic Analysis System package was used. Initially, the analysed fields were based on the ECMWF (European Centre for Medium- Range Weather Forecasts) grid datasets. Every 6 h, the lateral and top boundary conditions were updated in the outer modelling domain using the ECMWF grid datasets. In this domain, a nudging towards the data is applied to the three grid points closest to the lateral boundaries and to the upper five grid levels. Horizontal domains and grid sizes were designed taking into account both computational time limitations and the capability of the model to resolve essential mesoscale features. The prognostic meteorological fields from RAMS were used as input in the CALMET model to calculate all boundary layer parameters necessary input for the dispersion model CALGRID. CALMET ran in the no observations (No-Obs) mode, which involves the use of RAMS gridded data as the initial guess field. Figure 4 shows the scheme of the meteorological system. Figure 4. Scheme of the meteorological system Photochemical modelling system CALGRID is a Eulerian photochemical grid model. It implements an accurate advection diffusion scheme in the terrain following coordinates with vertical variable spacing; a resistance-based dry deposition algorithm takes into account pollutant properties, local meteorology and terrain features. The adopted chemical scheme is based on the SAPRC-90 chemical mechanism (Carter, 1990), including 54 chemical species with 129 reactions and the quasi steady-state approximations solver for the integration of kinetic equations. A lumped volatile organic compound method is used to represent a class of VOC with similar structure and reactivity. The boundary conditions were provided using the chemical fields derived from the CHIMERE simulations on a continental scale ( chimere) with a 0.5 of horizontal resolution and six vertical levels. The domain size and grid spacing specifications are provided in Table IV Emission data Emissions from industrial point sources were derived from a regional database ( Emissions from all other sectors were estimated based on the Italian CORINAIR databases and elaborated by means of an emission pre-processor. The CORINAIR inventory (EEA, 2001) consists of annual emission data for CO, NMVOC, CH 4,NO X,SO X, N 2 O, CO 2,NH 3 and TSP (Total Suspended Particulate) at the level of province. The pollutants emissions are classified by means of SNAP (Selected Nomenclature for Air Pollution) code. Table V reports the 11 main sources sectors. In order to provide the appropriate detail in time and space, the inventory has to be disaggregated to hourly emissions for every cell of the simulation domain, and

6 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 L x (km) L y (km) L z (km) N x N y N z x, y (km) RAMS RAMS RAMS CALMET/CALGRID L x, L y and L z are domain sizes in the x, y and z directions, respectively. N x, N y and N z 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) NO X, (b) anthropogenic NMVOC, (c) biogenic NMVOC. Units are Mg y 1. the class of the organic compounds (NMVOC) must be speciated according to the chemical scheme used by the photochemical model CALGRID. This task requires a proper spatial/temporal modulation and a chemical profile for each emission source. Therefore, CORINAIR data were pre-processed using the software tool GEM-PP developed by Conte et al. (2008) following the methodology suggested by EPA (2002), APAT (2004), Monforti and Pederzoli (2005), Carnevale et al., (2006). Figure 5 depicts the distribution of annual NO X, biogenic and nonbiogenic NMVOC emissions for all macro-sectors, over the simulated domain. The figures demonstrate that the largest emissions of ozone precursors are localized in the Taranto and Brindisi areas due to the industrial and energy production activities located there. Figure 6 shows the scheme of the photochemical model. Main SNAP sector Table V. Main CORINAIR sectors. Description 01 Combustion in energy and transformation industries 02 Nonindustrial combustion plants 03 Combustion in manufacturing industries 04 Production processes 05 Extraction and distribution of fossil fuels/geothermal energy 06 Solvent and other product use 07 Road transport 08 Other mobile sources and machinery 09 Waste treatment and disposal 10 Agriculture 11 Other 5. Results and discussion 5.1. Meteorological simulations and comparison with observations In order to avoid potential effects related to the initial condition, the simulations started 1 day before, on 1 July Figure 7 shows the simulated surface temperature and wind fields for each day of the period considered at 1500 LST, when the conditions are considered representative of maximum ozone concentrations. Figure 8 shows the time variation for the modelled and observed wind speed, wind direction, and temperature at the three meteorological stations considered. The thick line indicates RAMS/CALMET simulations and the circles the experimental data. During the first period of 2 4 July, a northerly flow, which was more intense over the Adriatic, affected the area. Wind intensity reached a maximum value higher than 9 m s 1 on the eastern side of the peninsula, and tended to decrease in the beginning of 4 July when the synoptic conditions became weaker and winds started to rotate from the south. One can appreciate that on the

7 THE EFFECT OF SEA BREEZE CIRCULATION ON PHOTOCHEMICAL POLLUTION 25 Figure 6. Scheme of the photochemical modelling system. western side of the numerical domain (grid 3), along the Taranto Gulf, the surface temperature was higher and the wind intensity lower. This is confirmed by both the model results and the measurements at the three stations. During the second period of 5 8 July a weak synoptic southeasterly flow prevailed. The wind intensity diminished and the surface temperatures tended to increase differently between the eastern and western side of the grid depending on the day. These conditions favour the development of different sea breeze circulations over the domain, 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 the domain were evident and, in general, reproduced well by the model. The wind map in Figure 7(d) shows that at 1500 on the 5 July two breeze systems developed. One was initiated in the Adriatic Sea and moved inland, where near surface winds blew from east-southeast to northwest with a prevailing direction of about 140. A second cell developed from the southwest to west, with a prevailing direction of about 220. This was confirmed by the wind direction data measured at the M1 and M3 meteorological stations. The two breeze cells merged in the middle of the peninsula where the surface temperatures also reached their highest values as shown at station M2. During 6 July at 1500 LST (Figure 7(e)), a convergence area was identified in the northern part of the peninsula. The temperature reached 31 C atthem1station, but did not exceed 26.5 C at the M3 station. During 7 July, at 1500 LST, three sea breeze systems were identified (Figure 7(f)). One was from the Adriatic Sea moving inland, where near surface winds blew from a prevailing direction of A second cell developed with a direction of about 180 and different inland penetration. In this case, two convergence areas were formed close to the western side of the domain. During 8 July, again at 1500, two sea breeze systems could be identified, but in this case the convergence covered most of the area closer to the eastern side. The highest temperature was observed and predicted at the M2 station. The poor reproduction of the wind intensity cycle at the M2 station during 5 and 8 July may be due to the fact that the M2 station was close to the convergence area during those days, making the spatial and temporal matching between the model and observations quite difficult. To evaluate the model performance over the entire period considered and for all stations the following statistical indices for wind speed and temperature were considered (Hanna and Yang, 2001; Cai et al., 2007); these are the mean bias as an absolute as well as a relative value, the root mean square error, the correlation coefficient, and the index of agreement, which is a measure of how well the resulting simulated simulation result distance from the observed mean X o matches the observed result from X o. Due to the rotating scale of the wind direction, only absolute indices were considered for wind direction. Furthermore, for these indicators, a threshold value was set for the wind speed of the corresponding data points since the wind direction cannot be determined under calm conditions. Thus, data points corresponding only to an

8 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 taken into account (Papalexiou and Moussiopolus, 2006). Mean Bias- MB = 1 N (X p X o ) (1) N i=1 Mean Normalized Bias Error- MNBE = 1 N (X p X o ) 100 (2) N X i=1 o Mean Normalised Gross Error- MNGE = 1 N X p X o 100 (3) N X i=1 o Root mean squared error -RMSE = 1 N (X p X o ) N 2 (4) i=1 Index of Agreement IOA = 1 N (X p X o ) 2 i=1 (5) N ( X p X o + X o X o ) 2 i=1 Correlation coefficient -COR N (X p X p )(X o X o ) i=1 = N (X p X p ) 2 (X o X o ) 2 i=1 (6) where X o and X p are the observed and predicted variables, respectively, N is the number of pairs of predictions and observations at the same time. The overbar denotes averages.

9 THE EFFECT OF SEA BREEZE CIRCULATION ON PHOTOCHEMICAL POLLUTION 27 Figure 8. Temporal variation of near surface wind speed (ws) (m s 1 ), wind direction (wdir) (deg) and temperature (temp) ( C) at the meteorological 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 each station. For the wind speed, the average mean bias and corresponding mean normalized bias are close to 0.4 m s 1, while the RMSE to 1.6 m s 1. The index of agreement is 0.87, while the correlation coefficient at For the wind direction, the mean direction bias is 6 and the RMSE at 38. The worst statistical indices were obtained for the M1 station, where the wind intensity was underestimated, on average, by about 1.1 m s 1, and the temperature by 0.6 C. Regardless, all values obtained for the statistical indices were in the range of values typical for mesoscale models, as shown in many studies (see Papalexiou and Moussiopoulos, 2006, for a brief review). This indicates that the meteorological models reproduce both the wind and temperature fields quite well Photochemical simulations and comparisons with measurements Figure 9 (a) (c) shows the comparison between ozone predictions and the measured concentrations for 2 4 July As seen in the previous section, the strong synoptic northern flow during these days did not enable the development of a sea breeze. Uniform ozone levels were calculated over the entire domain. This was confirmed by the measurements for all the three stations (AQ1 AQ3), Table VI. Meteorological model performance measures for hourly average wind speed, wind direction and temperature at the surface. Statistical indices M2 M3 M1 All stations Wind speed mean observed (m s 1 ) Wind speed MB (m s 1 ) Wind speed MNBE (%) Wind speed RMSE (m s 1 ) Wind speed IOA Wind speed COR Wind direction MB (degrees) Wind direction RMSE (degrees) Temperature mean observed ( C) Temperature MB ( C) Temperature RMSE ( C) Temperature IOA Temperature COR 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, COR for correlation coefficient.

10 28 C. MANGIA ET AL. Figure 10. As in Figure 9, but for 5 8 July 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 excursions between night and day, Ozone tended to diminish over the peninsula between 2 and 4 July, and the highest ozone values were calculated over the sea, where large amount of NO X and VOC emitted in Taranto city were transported. During 5 8 July the synoptic situation changed. The wind circulation became weaker and a south-easterly flow prevailed. The ozone concentration became higher than on previous days, and differences between the three stations (AQ1 AQ3) were evident. Figure 10 (a) (c) shows the 5 8 July. On 5 July the general circulation became very weak during the night, as confirmed by wind measurements (all <2 ms 1 ). A wind component from the East-South- East carried NO X and VOC emitted from the Taranto industrial site inland. At midday, two sea breeze fronts developed that merged inland within the domain leading to an accumulation of ozone close to the convergence area. Figure 11 (a) (c) shows the ozone map at 1300, 1500, and 1800 LST. In particular, it can be seen how the convergence line moved to the eastern side between 1300 and 1800 LST, expanding the convergence area. This was confirmed as a tendency by the monitoring data. At 1300 LST, the AQ3 station registered 143 µg m 3, while, on the 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 12 (a) (c) shows the ozone maps at 1500 LST for 6 8 July. During these days it can be observed how different see breeze fronts form and converge in different parts of the domain, leading to areas with high ozone values. On 6 July, at 1500 LST, an area of high ozone values was very confined to the eastern side of the peninsula. Here, the AQ1 and AQ2 stations registered ozone values of 142 and 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 could be identified. In this case, two convergence areas with high ozone concentration formed close to the western side of the domain. In this case, the AQ3 station was very close to the northern convergence area, and registered 132 µg m 3 of ozone at 1500 LST and 143 µg m 3 at 1600 LST, whereas the stations close to the eastern sides registered about 113 µg m 3 of ozone. On 8 July at 1500 LST, two sea breeze systems could again be identified, but in this case the convergence area and the corresponding high ozone level were found close to the eastern side of the domain. In this case, the AQ1 station was very close to the convergence area and registered an ozone value of 143 µg m 3 at 1600 LST, whereas the stations close to the western side did not exceed 105 µg m 3 all day. Similarly to the evaluation of meteorological results, the statistical performance for the measured and predicted hourly ozone concentrations were assessed. The statistical indices of MB, MNBE, RMSE, and COR, as well as IOA were used. Apart from these estimators, three

11 THE EFFECT OF SEA BREEZE CIRCULATION ON PHOTOCHEMICAL POLLUTION 29 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 calculated: the Average Peak Prediction Bias (APPB) and Error (APPE), which measure the ability of the model to predict daily ozone peaks. They were calculated as the mean normalized bias and error (Equations (1) and (2)), except that they only considered the daily maxima values (predicted vs observed) at each monitoring location. Although there was no objective criterion set forth for satisfactory model performance, US EPA suggests values of 5 15% for the mean normalized bias error, 30 35% for the mean normalized gross error and 15 20% for the unpaired peak prediction accuracy to be met by the modelling simulations for regulatory applications. In Table VII, the results of the statistical evaluation for each monitoring stations are reported. It can be noted from the Table that the model tends to slightly overestimate ozone concentration, with a mean positive bias for each station and an average MNBE of 12%. The MNGE was about 20% for all stations. The index of agreement and the correlation coefficient were both 0.7, which are satisfactory values. The Average Peak Prediction Error was about 7%. Both MNBE and MNGE were within the ranges set by the EPA. 6. Conclusions The air quality modelling system RAMS/CALMET/ CALGRID was used to simulate photochemical pollution over the Apulia Region in southern Italy during selected (2 8 July 2005) summer conditions characterized by alternating strong and weak synoptic meteorological forcing. During the first 3 days (2 4 July), strong synoptic situations dominated with a rather uniform ozone concentration, and without large differences between day and night values, were observed at the monitoring stations. During the last four simulated days (5 8 July), the synoptic situation became weaker, the observed ozone concentrations became higher, and various daily ozone patterns were registered in the different areas of the domain. Simulations showed that when the synoptic meteorological conditions are strong enough to prevent the formation of a local sea breeze circulation, the predicted ozone concentration is uniform, with relatively low values (less than 120 µg m 3 ). The maximum ozone concentration was computed over the Gulf of Taranto where large amounts of NO X and VOC emitted from Taranto city were transported.

12 30 C. MANGIA ET AL. Table VII. Statistical analysis of hourly observed (index o) versus predicted (index p) concentrations time series. C o (µg m 3 ) C p (µg m 3 ) C o max (µg m 3 ) C p max (µg m 3 ) MB (µg m 3 ) MNBE (%) MNGE (%) IOA COR APPB (%) APPE (%) AQ2 - Campi AQ3 - Grottaglie AQ1 - Cerrate All stations 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 mean normalized bias error (%), MNGE for mean normalized gross error (%), IOA for index of agreement, COR for correlation coefficient, APPB for average peak prediction bias (%) and APPE for average peak prediction error (%). In contrast, the maximum ground level concentration of ozone was modelled/registered during weak synoptic conditions when local sea breeze systems were not inhibited. The ozone maps showed how the locations of maximum ozone concentration are strongly linked to the degree of penetration of these sea breeze systems into the Peninsula and the resulting sea breeze convergence zone. During these days, the maximum predicted and observed ozone concentrations were recorded close to the convergence lines, which varied in location on a day-today basis. Regarding the comparison between field measurements and model results, the statistical analyses showed that the modelling system RAMS/CALMET/CALGRID is able to reproduce the main characteristics of the photochemical pollution over the entire 2 8 July 2005 period for the simulated domain. In addition, the model simulations aided the interpretation of field measurements in different meteorological conditions. Overall, the model tended to overestimate the measured ozone concentration, but, in general, all statistical indices were in the range of the state-of-the-art-model. Some discrepancies were apparent between predictions and observations for some hours essentially due to merging of different breeze systems, which are critical for the space-time matching between the model and the observations, those small-scale effects that cannot be reproduced by mesoscale simulations. The study showed the important contribution of sea breeze transportation of air masses along the two coasts and the contribution of 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 Lecce and the Apulia Region. The authors acknowledge the working group of the PREV AIR system and particularly Cécile Honore of INERIS, for supplying concentration data used for the boundary conditions of the simulated domain, Dr. Lorenzo Angiuli and Dr. Alessandra Nocioni Environmental Protection Regional Agency (ARPA) of Puglia and Dr. Salvatore Francioso, Province of Lecce, for supplying Environmental data; also Gennaro Rispoli, University of Lecce, for supplying meteorological data. Appendix List of Acronyms. 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