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Environmental Pollution 157 (2009) 1407–1412

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Environmental Pollution

journal homepage: www.elsevier .com/locate/envpol

Assessment of present and future risk to Italian forests and human health:Modelling and mapping

Alessandra De Marco*

C.R. Casaccia ENEA, ACS-PROTATM, Via Anguillarese 301, 00123 Rome, Italy

AOT40 and SOMO35 are and will be high enough to affect forest and

human health all over Italy.

a r t i c l e i n f o

Article history:Received 15 September 2008Accepted 18 September 2008

Keywords:Air quality monitoring networkAOT40Tropospheric ozoneRAINS-ItalySOMO35

* Tel.: þ39 0630483910.E-mail address: alessandra.demarco@casaccia.enea

0269-7491/$ – see front matter � 2008 Elsevier Ltd.doi:10.1016/j.envpol.2008.09.047

a b s t r a c t

A review of ozone pollution in Italy shows levels largely above the thresholds established by EU regu-lation for vegetation and human health protection. The Italian air quality monitoring network appearsquantitatively inadequate to cover all the territorial surface, because of scarcity and unequal distributionof monitoring sites. By applying the integrated assessment model RAINS-Italy to the year 2000, thewhole of Italy exceeds the AOT40 critical level for forest, while Northern and central areas show strongpotential of O3 impact on human health with w11% of territory >10 O3-induced premature deaths. Twoscenarios for the year 2020, the Current Legislation and the Maximum Technical Feasible Reduction,show a reduction of AOT40Forest by 29% and 44%, SOMO35 by 31% and 47%, and O3-induced prematuredeaths by 32% and 48%, compared to 2000. RAINS-Italy can be used to improve the map quality and coverareas not reached by the national monitoring network.

� 2008 Elsevier Ltd. All rights reserved.

1. Introduction

Tropospheric ozone (O3) concentrations have reached very highlevels all over Europe, especially in the Mediterranean area (EEA,2007), and are expected to further increase in the next years(Collins et al., 2000), without appropriate emission reductionmeasures. While reduced peaks of O3 concentrations wereobserved in Sweden and Norway from 1996 to 2004, for the rest ofEurope O3 concentrations in this time frame was unchanged (EEA,2007). Coyle et al. (2003) reported a 30% decrease in O3 peaks overthe past decade in UK, but an increase in annual mean concentra-tions of about 0.1 ppb per year. This observation is in agreementwith US EPA (2006) that reports an increase in O3 minima duringthe ’90s because of a reduced titration of O3, by reaction with NO, inresponse to a reduction in NOx emissions. Emission reductionmeasures, in order to be effective, should take into account NOx orVOC limited regimes (Gabusi and Volta, 2005). This paradox is alsounderlined by EEA (2007) which shows an increase in O3 concen-trations, from 1990 till 2004, associated with a 36% reduction ofprecursor emissions all over Europe. EEA (2007) envisages a further30% reduction in O3 precursors, from 2000 till 2010.

A plenty of experimental work has been carried out in anattempt to establish threshold values for the protection of human

.it

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health (WHO, 2006), different kind of ecosystems (reviewed inPaoletti et al., 2007), and materials with particular attention tocultural heritage (reviewed in Screpanti and De Marco, 2009). A bigeffort has been addressed to define indicators suitable for riskassessment. The indicator suggested by WHO (2006) to estimate O3

effects on human health is SOMO35 (Sum of Ozone MeansOver 35 ppb), defined as the yearly sum of the daily maximum ofthe 8-hour running means over 35 ppb (Amann et al., 2005 CleanAir For Europe (CAFE) Programme Final Report, Laxenburg, Austria;Amann et al., 2005). The indicator for vegetation currently usedin Europe (ICP, 2004; Directive 2002/3/CE, 2002) is AOT40, definedas ‘‘Accumulated exposure Over a Threshold of 40 ppb, alonga given daily time interval’’. The critical level for forest protection(AOT40Forest), will be calculated from April to September, from8 to 20 h.

Ozone concentrations are higher in regions with high photo-chemical activity levels, like in the whole southern EuropeanRegion (Butkovic et al., 1990). The climate in Mediterranean regionsis characterised by hot summers with weak winds, high pressureand high solar radiation levels. These conditions are favourable tothe formation of O3 (Pleijel, 2000). Due to its central location in theMediterranean area, Italy may be considered as a hot-spot for O3

and representative of O3 effects on Mediterranean ecosystems(Paoletti, 2006).

The aims of this paper are to review SOMO35 and AOT40Forestdistribution over the Italian territory from monitoring data andfrom two scenarios of modelled data, comparing their results, in

A. De Marco / Environmental Pollution 157 (2009) 1407–14121408

order to evaluate the possibility to use a modelling approach toimprove the quality of O3 indicator maps and to investigate theeffect of altitude, latitude, distance from the sea on these O3 indi-cators, in order establish the relationship of this pollutant withdifferent geographical environments.

2. The Italian ozone monitoring network

Monitoring networks of ambient air pollution measurementsprovide an important source of information on pollution concen-trations and their trends, as exposure estimation. These networksare established with the purpose of early warnings in case ofpollution peak episodes, as well as for identification of areas at riskand for estimation of pollution concentration trends over time.However, careful considerations are needed, since each station isrepresentative of the local conditions for a specific site, while atnearby sites, conditions might be significantly different (Bell, 2006).The Italian network comprises regional and local databases(Regional Authority and Local Environment Protection Agencies(ARPA, 2004)). Over the Italian territory, 124 stations to monitor O3

concentrations, whose data are accessible and supplied to APAT(National Agency for Territory Protection), have been installed atdifferent years, beginning from 1996. Location and density of theavailable Italian O3 monitoring network in the year 2004 are shownin Fig. 1. Three southern regions have no station. The density isgenerally higher in the northern part except in Piedmont (PIE) andVeneto (VEN). The highest density is in Liguria (LIG) with a value of0.184 stations/100 km2. The average area covered by one stationover Italy is around 2.500 km2.

The monitoring stations are classified according to the followingcriteria (Criteria for EUROAIRNET, 1999; de’ Munari et al., 2004):type of stations (traffic, industrial and background); type of zones(urban, suburban, rural). In total the monitoring network in theyear 2000 was composed by 59 urban stations, located in the maincities, divided in traffic (along the main roads) and backgroundstations (far from the main roads); 27 suburban stations, locatedprincipally in the vicinity of cities or in urban green areas, further

Fig. 1. Location of the available Italian monitoring stations in the year 2004 identified by tystations per 100 km2.

divided in traffic, industrial (near industrial sources of pollution) orbackground stations; and 38 rural stations located far away fromthe cities, further divided in industrial or background stations.

From a qualitative point of view, not in all the Italian stations thedata capture reaches 75% of the time coverage, i.e. the valueestablished for checking validity when aggregating data andcalculating statistical parameters (Directive 2002/3/CE, 2002). Forexample, in the years considered in this paper (2000–2004) only76% of the stations (rural and suburban background stations) rea-ches the 75% of sampling efficiency. From a quantitative point ofview, the distribution of monitoring stations across the Italianterritory is inhomogeneous because of a serious lack of data in thesouthern regions, while a larger amount of sampling sites is locatedin the northern parts (Fig. 1). In addition, a monitoring network ofO3 passive samplers is located at 26 forest sites (Gerosa et al., 2007).Passive sampling typically results in time-integrated data, such asweekly-to-monthly O3 concentrations (Tuovinen, 2002; Ferrettiand Gerosa, 2002). Data coming from these passive monitors havebeen compared to data obtained from monitoring stations, after theelaboration of AOT40, and used together to increase the number ofsampling sites in Italy (Bussotti and Ferretti, 2009). Moreover,passive samplers give only an estimation of AOT40Forest andcannot be used to calculate other O3 indicators.

A dataset of AOT40Forest values from the active (40 rural andsuburban background stations) was analysed as average of theyears 2000–2004. As for a previous study on only 14 remotestations across Italy (Paoletti et al., 2007), all the stations exceededthe critical level of 5000 ppb h set by UNECE for the protection offorests (ICP, 2004). Latitude and altitude don’t correlate signifi-cantly with AOT40Forest values, even though there was anincreasing trend in their relationship (the r2 values are 0.0079 and0.0018 respectively). Concerning altitude this result is not inagreement with literature observations where a relationshipbetween O3 concentration and elevation was observed (Loibl et al.,2004). Also, AOT40Forest increased with increasing distance fromthe sea, although just for stations <200 km from the sea and<600 m elevation (the r2 value is 0.25). This could be explained by

pe of zones (A). Density of monitoring stations per region (B) expressed as number of

A. De Marco / Environmental Pollution 157 (2009) 1407–1412 1409

the potential interaction between sea salt particles and ozone thatmay provide an ozone loss mechanism (Adeeb and Shooter, 2004).

To translate those results to the whole Italian territory and applyscenarios of future emissions, the following modelling andmapping approach was used.

3. Ozone modelling

Data from air quality models can often be complementary tomonitoring data, when pollution concentration mapping is difficultto obtain because of a few monitoring sites. With the objective,among others, of overcoming this limit, Zanini et al. (2005) devel-oped the MINNI Project. The MINNI Project is based upon a doubledeck system:

1) AMS (Atmospheric Modelling System): a model chaincomposed by a meteorological model (RAMS), an emission pre-processor (Emission Manager) and a multiphase chemicaltransformation model (FARM) (ARIANET, 2004);

2) RAINS-Italy: an IAM model derived from RAINS-Europe(Regional Air pollution INformation and Simulation) (Amann,2004a,b). By building Atmospheric Transfer Matrices (ATMs)on the basis of Atmospheric Modelling System (AMS), itprovides emission scenarios, abatement costs, impact onenvironment and human health, assessment scenarios.

AMS has been applied to estimate deposition for the mainpollutants including O3 (as AOT40Forest and SOMO35) over Italy,with a spatial resolution of 20� 20 km2 (Vialetto et al., submittedfor publication). AMS includes meteorology, emissions andpollutant dispersion. The emission subsystem is based upon Italianand European inventories, including the maritime emissions (Via-letto et al., 2005). The emissions coming from the neighbouringcountries are taken, as boundary conditions, from the EMEP UnifiedModel. ATMs are the link between RAINS-Italy and AMS.

Fig. 2. AOT40Forest for the year 2000 (A) and for the year 2020 in the Current LEgislation sceRAINS-Italy model. The threshold indicated for forest health risk is below 5000 ppb h (in li

RAINS-Italy computes emission scenarios on the basis of anthro-pogenic activity levels, emission factors and a long list of applicableabatement technologies. Through RAINS-Italy, maps of AOT40Forestand SOMO35 were developed for impact assessment (Figs. 2 and 3).Two scenarios were applied to the year 2020: (1) the CurrentLEgislation (CLE), which assumes the implementation of all presentemission-related regulations in all countries of the EU-25; and (2)the Maximum Technical Feasible Reduction (MTFR), which assumesfull implementation of the presently available most advancedtechnical emission control measures (Amann et al., 2004b; Den-tener et al., 2006). In the year 2000 AOT40 exceeded the thresholdestablished for forest protection in the whole Italian territory, witha minimum and maximum value of 16000 and 57000 ppb h,respectively, i.e., 3.2 and 11.4 times higher than the UNECE criticallevel. The CLE scenario estimated a 29% average AOT40Forestreduction over Italy compared to 2000. The MTFR scenario led toa reduction of around 44%. It is interesting to note that the MTFRscenario will result in AOT40 values near the critical level for forestprotection (5000 ppb h) in a few areas (around 1% of the territory)along the eastern coast (Adriatic Sea) (Fig. 2). In northern Italy andin some regions of central Italy, especially along the coastlines,there is evidence of a major potential impact of O3 on human healthin the year 2000 (Fig. 3A). The CLE and MTFR scenarios result ina 31% and 47% reduction of SOMO35, respectively, in the year 2020(Fig. 3B,C). Total of premature deaths in Italy in 2000 was 6336 witharound 11% of territory reaching more than 10 premature deathsdue to ozone exposure (Fig. 5). RAINS-Europe, still in the year 2000,estimated about 4500 cases of premature deaths due to O3 expo-sure in Italy (Amann et al., 2004b). This figure is slightly lower thanthe 6336 cases calculated here, and was the highest number of allEuropean countries. The reduction of SOMO35 implies a reductionin the number of premature deaths due to O3 (WHO, 2006). In fact,the CLE and MTFR scenarios result in a 32% and 48% reduction,respectively, in the premature deaths compared to the year 2000(Fig. 4).

nario (B), and the Maximum Technical Feasible Reduction scenario (C), obtained by theght blue).

Fig. 3. SOMO35 for the year 2000 (A) and for the year 2020 in the Current LEgislation scenario (B), and the Maximum Technical Feasible Reduction scenario (C), obtained by theRAINS-Italy model.

A. De Marco / Environmental Pollution 157 (2009) 1407–14121410

4. Ozone mapping

One of the first ozone maps in Italy was obtained by Gerosa andBallarin-Denti (2002), for the Lombardy Region, where the densityof monitoring stations is elevated. AOT40Forest maps for Lombardy,in the years 1999 and 2000, were generated using an interpolation

Fig. 4. Premature deaths due to ozone pollution estimated for the year 2000 (A) and for theReduction scenario (C), obtained by the RAINS-Italy model.

statistical method based on elevation, as suggested by Loibl et al.(1994). The elevation values were incorporated in the estimation ofO3 concentrations, using the algorithm developed by Fowler et al.(1995). Interpolation was performed by ordinary kriging (Goo-vaerts, 1997). The maps showed that AOT40Forest values wereranging between 20,000 and 30,000 ppb h in the 79% of the

year 2020 in the Current LEgislation scenario (B), and the Maximum Technical Feasible

Fig. 5. Maps of AOT40Forest in the year 2000 obtained by ordinary kriging of moni-toring stations data (number of stations¼ 40) implemented by modelled data fromRAINS-Italy.

A. De Marco / Environmental Pollution 157 (2009) 1407–1412 1411

Lombardy territory, i.e., 4–6 times higher than the critical level forforest protection (ICP, 2004).

In 2004, the regional environmental protection agency of EmiliaRomagna (ARPA), applied the CALGRID model (Yamartino et al.,1992) and obtained maps for Emilia Romagna (EMR) of average O3

concentrations in summer 2003 and maps of exceedances over thethreshold established for human health protection (daily maximumaverage on 8 hours of 60 ppb h not to be exceeded more than 25times a year, Directive 2002/3/CE, 2002). Peaks of maximum O3

concentrations were observed in the central lowland, while thehigher average summer concentration was found on the AppenniniMountains area. The threshold for human health effects wasexceeded in the whole region, therefore resulting in the exposure ofpopulation to high risk (WHO, 2006).

In recent years, geographical information systems (GIS) havebeing integrated into many monitoring programs, as tools gener-ating maps of air pollution concentrations, both in urban and ruralareas (Wong et al., 2004; Horalek et al., 2005; Hartkamp et al.,1999). GIS requires a huge and uniform monitoring network, whichimplies high implementation costs. The Italian air quality moni-toring network is deemed too scarce, from a qualitative andquantitative point of view, making the GIS approach hardly appli-cable (De Marco et al., in press).

Recently, maps from monitoring data in the years from 2000 to2004 were elaborated for AOT40Forest and AOT40Crops (Ferrettiet al., 2006) and SOMO35 in the year 2000 (De Marco et al., in press)in Italy. Concerning AOT40, O3 was recorded in Italy at 40 back-ground automatic stations (only rural and suburban background,that reach the sampling efficiency of 75% in the considered years)of the national air quality network and 26 passive sampling sites ofthe forest monitoring programme CONECOFOR, co-ordinatedby the National Forest Service. For crops, the 3-month AOT40reached peak values of 30,000–40,000 ppb h. AOT40 for forestsreached 50000 ppb h at individual sites and 30000 ppb h at 47% ofthe sites. The critical levels of 3000 ppb h for agricultural crops andsemi-natural vegetation, of 5000 ppb h for forests were exceedednearly everywhere, i.e., at 100% and 98% of the sites for crops andforests, respectively.

A comparison between measured and modelled data in theyear 2000 was performed for human health effects. SOMO35values derived from measured data were generally lower thanvalues obtained by statistical interpolation of the ones comingfrom RAINS model (De Marco et al., in press). Moreover, in theRAINS-Italy map the southern coastal zone shows higher ozonelevels, while in the measured map the central area shows higherSOMO35 values. The differences were reasonably due to thestarting data. In fact, RAINS-Italy model spreads the pollutants onall the national territory including the Mediterranean Sea, while inmonitoring mapping the results are very sensitive to the distri-bution of the measurements stations, obviously not present onthe sea. Moreover, the Directive 2002/3/CE (2002) establishesa sampling efficiency threshold of 75% of available data to considerthe dataset validated. This consideration implies a possibleunderestimation of the SOMO35 measured value, resulting inunderestimated SOMO35 maps. The lack of monitored data ina large part of the Italian territory, particularly in all the southernItalian areas, makes the modelling outcome as the unique sourceof results in these areas.

To show the ozone oncentration in areas not covered by themonitoring network, the use of RAINS-Italy model is proposed(Fig. 5). The map has been obtained integrating and interpolatingthe measured data where available with modelled data. For theinterpolation the measured data in a circle with 25 km radius hasbeen considered. The result shows the potentiality of modelled datain order to cover all the Italian territory, even when measured O3

concentrations are not available.

5. Conclusions

Although the inhomogeneous distribution of the monitoringstations over Italy must be underlined, some conclusions are clear.In the year 2000, the whole Italy exceeded the AOT40 critical levelfor forest protection, while northern and central areas showedstrong potential of O3 impact on human health with w11% ofterritory >10 O3-induced premature deaths. The modellingapproach helped to obtain a picture of O3 levels on the whole Italianterritory, giving information also in areas not covered by themonitoring network, included coastal areas. This considerationleads to the improvement of maps for predicting O3 concentrations,and consequently area at risk also on the coastal area and thesouthern part of Italy (Fig. 5). The modelling approach also showedthat the Current Legislation and the Maximum Technical FeasibleReduction scenarios show a reduction (2020 vs. 2000) of AOT40-Forest by 29% and 44%, SOMO35 by 31% and 47%, and O3-inducedpremature deaths by 32% and 48%, respectively. Anyway furtherwork has to be done to establish the uncertainties of the RAINS-Italy model. Moreover, the evaluation of the uncertainties has beencarried out for RAINS-Europe model and is assessed around 10–20%(Alcamo and Bartnicki, 1990). It is interesting to discuss theconsideration that also in the more optimistic scenario (MFR) theminimum value obtained for AOT40Forest is above the thresholdestablished for forest protection of 5000 ppb h. This could indicatethe need to review the established threshold efficiency in a Medi-terranean environment (Ferretti et al., 2006).

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In conclusion, Italy is at elevated O3 risk. The features of theozone monitoring network highlight the need to improve thepresent monitoring network by both increasing the number ofmeasurement stations and re-allocating the stations in a morehomogeneous way. An air quality model, such as MINNI, however,can be a useful tool to improve ozone mapping in areas not coveredby the national monitoring network instead.

References

Adeeb, F., Shooter, D., 2004. Variation in the ambient air of Auckland, New Zealand.Environmental Monitoring and Assessment 95, 201–220.

Alcamo, J., Bartnicki, J., 1990. The uncertainties of atmospheric source–receptorrelationship in Europe. Atmospheric Environment 24A, 2169–2189.

Amann, M., Cofala, J., Heyes, C., Klimont, Z. Mechler, R., Posch, M., Shopp, W., 2004a.The Regional Air Pollution Information and Simulation (RAINS) model: review2004. Interim Reports, February 2004. International Institute for AppliedSystem Analysis (IIASA), Laxenburg, Austria.

Amann, M., Cabala, R., Cofala, J., Heyes, C., Klimont, Z., Shopp, W., Tarrason, L., Simpson,D., Wind, P., Jonson, J.E., 2004b. ‘‘Current Legislation’’ and the ‘‘Maxumum Tech-nical Feasible Reduction’’ cases for the CAFE baseline emission projections.International Institute for Applied System Analysis (IIASA), Laxenburg, Austria.

Amann, M., Bertok, I., Cofala, J., Gyarfas, F., Heyes, C., Klimont, Z., Schopp, W.,Winiwater, W., 2005. Clean Air For Europe (CAFE) Programme Final Report,International Institute for Applied System Analysis (IIASA), Laxenburg, Austria.

ARIANET, 2004. FARM (Flexible Air quality Regional Model) Model formulation anduser manual. Arianet report R2004.04, Milano, Italy.

ARPA, 2004. Ozono: stato dei prodotti e prospettive. ARPA rivista, No. 3, May–June 2004.Bell, M.L., 2006. The use of ambient air quality modelling to estimate individual and

population exposure for human health research: a case study of ozone in theNorthern Georgia Region of the United States. Environment International 32,586–593.

Bussotti, F., Ferretti, M., 2009. Visible injury, crown condition, and growth responsesof selected Italian forests in relation to ozone exposure. Environmentalpollution 157 (5), 1427–1437.

Butkovic, V., Cvitas, T., Klasinc, L., 1990. Photochemical ozone in the Mediterranean.Science of Total Environment 99, 145–151.

Collins, W.J., Stevenson, D.S., Jonson, C.E., Dervent, R.G., 2000. The Europeanregional ozone distribution and its links with the global scale for the years 1992and 2015. Atmospheric Environment 346, 255–267.

Coyle, M., Fowler, D., Ashmore, M., 2003. New directions: implications of increasingtropospheric background ozone concentration for vegetation. AtmosphericEnvironment 37, 153–154.

Criteria for EUROAIRNET – The EEA Air Quality Monitoring and InformationNetwork, 1999. Technical Report No. 12. EEA (European Environment Agency).

De Marco, A., Screpanti, A., Racalbuto, S., Pignatelli, T., Vialetto G., Monforti, F.,Zanini, G. Comparison between ozone monitoring data in Italy in theperspective of health indicators assessment. Air Pollution, WIT Press, in press.

Dentener, F., Stevenson, D., Ellingsen, K., Van Noije, T., Schultz, M., Amann, M.,Atherton, C., Bell, N., Bergmann, D., Bey, I., Bouwman, L., Butler, T., Cofala, J.,Collins, B., Drevet, J., Doherty, R., Eickhout, B., Eskes, H., Fiore, A., Gauss, M.,Hauglustaine, D., Horowitz, L., Isaksen, I.S., Josse, B., Lawrence, M., Krol, M.,Lamarque, J.F., Montanaro, V., Muller, J.F., Peuch, V.H., Pitari, G., Pyle, J., Rast, S.,Rodriguez, I., Sanderson, M., Savage, N.H., Shindell, D., Strahan, S., Szopa, S., Sudo, K.,Van Dingenen, R., Wild, O., Zeng, G., 2006. The global atmospheric environment forthe next generation. Environmental Science and Technologies 40, 3586–3594.

Directive 2002/3/CE of the European Parliament and of the Council. Official Journalof the European Communities 67, 2002, 14–30.

EEA, 2007. Air pollution in Europe 1990–2004. EEA Report, No. 2/2007.Ferretti, M., Gerosa, G., 2002. Ozone concentrations data requirements at national to

plot scale. In: Anonymous (Ed.), Background Documents of the UN/ECE Ad-hocExpert Panel Meeting on Modelling and Mapping of Ozone Flux and Depositionto Vegetation, pp. 16–19. Harrogate, UK.

Ferretti, M., Fagnano, M., Amoriello, T., Badiani, M., Ballarin-Denti, A., Buffoni, A.,Bussotti, F., Castagna, A., Cieslik, S., Costantini, A., De Marco, A., Gerosa, G.,Lorenzini, G., Manes, F., Merola, G., Nali, C., Paoletti, E., PetriccioneRacalbuto, S.,Rana, G., Ranieri, A., Tagliaferri, A., Vialetto, G., Vitale, M., 2006. Measuring,modelling and testing ozone exposure, flux and effects on vegetation insouthern European conditions – what does not work? A review from Italy.Environmental Pollution 146, 648–658.

Fowler, D., Smith, R.I., Coyle, M., Weston, K.J., Davies, T.D., Ashmore, M.R., Brown, M.,1995. Quantifying the fine scale (1 km� 1 km) exposure and effects of ozone.Part 1. Methodology and application for effects on forests. Water, Air and SoilPollution 85, 1479–1484.

Gabusi, V., Volta, M., 2005. Seasonal modelling assessment of ozone sensitivity toprecursors in northern Italy. Atmospheric Environment 39, 2795–2804.

Gerosa, G., Ballarin-Denti, A., 2002. Ozono e foreste in Lombardia: verso unavalutazione di II livello. Informatore fitopatologico 3, 41–45.

Gerosa, G., Ferretti, M., Bussotti, F., Rocchini, D., 2007. Estimates of ozone AOT40from passive sampling in forest sites in south-western Europe. EnvironmentalPollution 145, 629–635.

Goovaerts, P., 1997. Geostatistic for Natural Resources Evaluation. Oxford UniversityPress, New York, 483 pp.

Hartkamp, A.D., De Beurs, K., Stein, A., White, J.W., 1999. Interpolation Techniquesfor Climate Variable. In: NRG-GIS Series 99-01. CIMMYT, Mexico, D.F.

Horalek, J., Kurfurst, P., Denby, B., de Smet, P., de Leeuw, F., Brabec, M., Fiala, J., 2005.Interpolation and assimilation methods for European scale air quality assess-ment and mapping. Part II: Development and testing new methodologies. ETC/ACC Technical Paper 2005/8, December 2005.

ICP, 2004. In: Spranger, T., Lorenz, U., Gregor, H.D. (Eds.), Manual on Methodologiesand Criteria for Modelling and Mapping Critical Loads & Levels and Air PollutionEffects, Risks and Trends. Federal Environmental Agency, Berlin. http://www.umweltbundesamt.de.

Loibl, W., Winiwarter, W., Kopcsa, A., Zuger, J., Baumann, R., 1994. Estimating thespatial distribution of ozone concentrations in complex terrain using a functionof elevation and day time and kriging techniques. Atmospheric Environment28, 2557–2566.

Loibl, W., Bolhar-Nordenkampf, H.R., Herman, F., Smidt, S., 2004. Modelling criticallevels of ozone for the forested area of Austria. Modifications of the AOT40concept. Environmental Science and Pollution Research International 11,171–180.

de’ Munari, E., Allegrini, I., Bardizza, N., Carfagno, N., Di Carlo, N., Gaeta, A., Lanzani,G., Malaguti, M., Marson, G., Melegari, C., Moricci, F., Fagotto, P., Ramponi, L.,2004. Linee guida per la predisposizione delle reti di monitoraggio della qualitadell’aria in Italia. APAT CTN-ACE.

Paoletti, E., 2006. Impact of ozone on Mediterranean forests: a review. Environ-mental Pollution 144, 463–474.

Paoletti, E., De Marco, A., Racalbuto, S., 2007. Why should we calculate complexindices of ozone exposure? Results from Mediterranean background sites.Environmental Monitoring and Assessment 128, 19–30.

Pleijel, H., 2000. Ground level ozone. A problem largely ignored in southern Europe.In: Air Pollution and Climate Series No. 12. Swedish Secretariat on Acid Rain,Goteborg, Sweden, 26 pp.

Screpanti, A., De Marco, A., 2009. Corrosion on cultural heritage buildings in Italy:a role for ozone? Environmental Pollution 157 (5), 1513–1520.

Tuovinen, J.P., 2002. Assessing vegetation exposure to ozone: it is possible to esti-mate AOT40 by passive sampling? Environmental Pollution 119, 203–214.

US EPA (US Environmental Protection Agency), 2006. Air Quality Criteria for Ozoneand Related Photochemical Oxidants. Office of Research and Development,Research Triangle Park, NC. Report no. EPA/600/R-05/004af.

Vialetto, G., Contaldi, M., De Lauretis, R., Lelli, M., Mazzotta, V., Pignatelli, T., 2005.Emission scenarios of air pollutants in Italy using integrated assessmentmodels. Pollution Atmospherique 185, 71.

Vialetto, G., Calori, G., Contardi, C., D’Elia, I., Contaldi, M., Sordi, F., Radice, P., Truffo,G., Arduino, G., Pittini, T., Ciancarella, L., Pignatelli, T. Integrated assessment ofair quality plans through national and regional models: the case of Piemonte,Italy. Journal of Environmental Management, submitted for publication.

WHO, 2006. WHO air quality guidelines for particulate matter, ozone, nitrogendioxide and sulphur dioxide. Global update 2005, summary of risk assessment.WHO/SDE/PHE/OEH06.02.

Wong, D.W., Yuan, L., Perlin, S.A., 2004. Comparison of spatial interpolationmethods for the estimation of air quality data. Journal of Exposure Analysis andEnvironmental Epidemiology 14, 404–415.

Yamartino, R.J., Scire, J.S., Charmichael, G.R., Chang, Y.S., 1992. The CALGRIDmesoscale photochemical grid model I. Model formulation. AtmosphericEnvironment 26A, 1493–1512.

Zanini, G., Pignatelli, T., Monforti, F., Vialetto, G., Vitali, L., Brusasca, G., Calori, G.,Finardi, S., Radice, P., Silibello, C., December, 2005. The MINNI Project: anintegrated assessment modelling system for policy making. In: Zerger, A.,Argent, R.M. (Eds.), MODSIM 2005. International Congress on Modelling andSimulation. Modelling and Simulation Society of Australia and New Zealand,ISBN 0-9758400-2-9, pp. 2005–2011. http://www.mssanz.org.au/modsim05/papers/zanini.pdf.