Economic damages of ozone air pollution to crops using combined air quality and GIS modelling

Atmospheric Environment 44 (2010) 3352e3361

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Economic damages of ozone air pollution to crops using combined air quality and GIS modelling Ch. Vlachokostas a, *, S.A. Nastis b, Ch. Achillas a, K. Kalogeropoulos a, I. Karmiris c, N. Moussiopoulos a, E. Chourdakis a, G. Banias a, N. Limperi a a b c

Laboratory of Heat Transfer and Environmental Engineering, Aristotle University of Thessaloniki, Box 483, 54124 Thessaloniki, Greece Department of Agricultural Economics, School of Agriculture, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece Laboratory of Range Science, Faculty of Forestry and Natural Environment, Aristotle University of Thessaloniki, Box 236, 54124 Thessaloniki, Greece

a r t i c l e i n f o

a b s t r a c t

Article history: Received 1 April 2010 Received in revised form 9 June 2010 Accepted 9 June 2010

This study aims at presenting a combined air quality and GIS modelling methodological approach in order to estimate crop damages from photochemical air pollution, depict their spatial resolution and assess the order of magnitude regarding the corresponding economic damages. The analysis is conducted within the Greater Thessaloniki Area, Greece, a Mediterranean territory which is characterised by high levels of photochemical air pollution and considerable agricultural activity. Ozone concentration fields for 2002 and for specific emission reduction scenarios for the year 2010 were estimated with the Ozone Fine Structure model in the area under consideration. Total economic damage to crops turns out to be significant and estimated to be approximately 43 MV for the reference year. Production of cotton presents the highest economic loss, which is over 16 MV, followed by table tomato (9 MV), rice (4.2 MV), wheat (4 MV) and oilseed rape (2.8 MV) cultivations. Losses are not spread uniformly among farmers and the major losses occur in areas with valuable ozone-sensitive crops. The results are very useful for highlighting the magnitude of the total economic impacts of photochemical air pollution to the area’s agricultural sector and can potentially be used for comparison with studies worldwide. Furthermore, spatial analysis of the economic damage could be of importance for governmental authorities and decision makers since it provides an indicative insight, especially if the economic instruments such as financial incentives or state subsidies to farmers are considered. Ó 2010 Elsevier Ltd. All rights reserved.

Keywords: Air quality modelling Ozone Agricultural production Yield loss Damage costs Mediterranean area

1. Introduction Most economic activities involving the use and conversion of energy are accompanied by emissions of air pollutants, thereby degrading the environment. Air pollution is recognised worldwide as having a significant influence on agricultural production. In general, air pollution adversely affects plants either by reducing yields or degrading the quality of agricultural products (Spash, 1997). Visible injury makes products unlikely to be saleable. The resulting direct economic loss and the indirect threat to other receptors have become an issue of political and scientific concern in many areas worldwide, especially where the expanding economy has lead to an increased emission of ambient Ozone (O3) precursors (e.g. Van Dingenen et al., 2009; Tong et al., 2007; Wang and Mauzerall, 2004; Holland et al., 2002; Fuhrer et al., 1997). Characteristically, either alone or in concurrence with acid rain precursors, * Corresponding author. Tel.: þ30 2310 996092; fax: þ30 2310 996012. E-mail address: [email protected] (Ch. Vlachokostas). 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.06.023

O3 has been reported to account for up to 90% of US crop losses resulting from exposure to all major air pollutants (Tong et al., 2007; Adams et al., 1986; Heck et al., 1982). O3 is also recognised as the most serious regional air pollutant problem in the European agricultural sector at the present time (Holland et al., 2005). The association between ambient O3 exposure and its detrimental effects on agricultural crops in Europe is well documented in the literature. Under UNECE ICP-Crops for the Convention on Long-Range Transboundary Air Pollution (CLRTAP), strong evidence has accumulated during the last 15 years that these effects on vegetation occur across Europe in most years, although the extent of damage varies between years and regions. O3 damages a plant after entering the stomatal leaf openings and then forming byproducts that reduce the efficiency of photosynthesis (Holdgate, 1972). Typical effects include chlorotic and necrotic lesions on the leaf surface of sensitive species (Benton et al., 2000), physiological changes such as reduced photosynthesis (Sanders et al., 1992), and reductions in both the quantity and quality of crop yield (e.g. Piikki et al., 2003; Gimeno et al., 1999; Fuhrer et al., 1997).

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Furthermore, literature reviews for Mediterranean countries in Southern Europe, which are characterised by elevated levels of photochemical air pollution, explicitly state that ambient O3 episodes have been reported to cause visible injury on agricultural crops grown in commercial yields. Some reports have indicated the presence of visible injury on sensitive species over wide areas. Indicatively, Gimeno et al. (1995) reported O3 injury on watermelon grown in eastern Spain. On a more localised scale, effects can be catastrophic for farmers. Velissariou (1999) reported 100% crop loss for lettuce and chicory in the Acharnes area e near Athens, Greece e following an O3 episode in mid-October, 1998. In a nutshell, biomonitoring experiments, as well as yield observations, have indicated that O3 injury is widespread on numerous crops grown in the Mediterranean region. There is experimental evidence indicating that ambient O3 concentrations in the Mediterranean region induce yield losses on crops, such as wheat (20e27%), beans (17e31%), watermelon (19e39%) and tomatoes (17e24%) (Fumagalli et al., 2001). Moreover, monitor data for O3 concentrations to rural areas are generally limited and mostly deployed in non-rural areas. The methodological approach presented herein addresses this limitation through the combination of air quality and GIS-based modelling techniques. Within the framework of the present study, a methodology is presented in order to assess spatial impacts and economic damages of photochemical air pollution to crops. As a case study, this paper uses air quality modelling results to estimate crops’ exposure to O3 and resulting yield losses in the Greater Thessaloniki Area (GTA) for the year 2002 and on the basis of specific emission reduction scenarios of O3 precursors for 2010 reported in the work of Moussiopoulos et al. (2009). It should be emphasised that the quantification of the physical impacts of air pollution on crop yields together with the internalisation of external costs has never been realised for the GTA. The analysis mainly evaluates the economic impacts on 14 crops which are characterised as important cultivations based on cost and production criteria, namely; cotton, spring wheat, durum wheat, lettuce, processing tomato, table tomato, sugarbeet, oilseed rape, tobacco, rice, maize, grape, barley and sunflower. The true economic loss is potentially larger, since ambient pollutants may also have indirect effects on crop production elevating the actual loss of the agricultural sector, e.g. lower crop production due to an increase in pest and pathogen performance and reduced livestock production due to detrimental effects on forage production and quality (Ashmore, 1991). The importance of this paper lies in the demonstration of a flexible and reliable methodological approach presented herein, which can support local or national authorities’ planning schemes in order to analyse relevant benefits of policy interventions, focusing on the agricultural production. The results are very useful for highlighting the magnitude of the total economic impacts of photochemical air pollution to the area’s agricultural sector and can possibly be used for comparison with relevant studies worldwide. Furthermore, spatial analysis of the economic damage could be of prime importance for governmental authorities and decision makers since they provide an indicative insight, especially if the economic instruments such as financial incentives or state subsidies to farmers are considered. Last but not least, incorporating O3 impact in crop production forecasts can potentially improve the results of agricultural forecast by capturing the fluctuation in yield losses due to air pollution. 2. Methodology Over the last two decades, there have been many studies of the economic effects of reduced agricultural production due to O3 air

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pollution. The first studies were undertaken in the USA in the mid1980s (Heck et al., 1987). Two main methodological approaches have been employed: The first approach simply uses market prices to estimate the monetary value of crop losses due to exposure to O3. The second approach uses a model of supply and demand to estimate changes in producers’ and consumers’ surpluses. In theory, an approach that does not incorporate changes in prices with variation in yield will provide a less accurate estimate than a more comprehensive economic model, provided that the economic model is adequately parameterised. In general, the welfare effect of air pollution is a function of the value of crop production, the O3 sensitivity of the crops, the exposure to O3, the elasticity of demand for the crops and the constrained ability of producers to reallocate resources to less sensitive crops (Murphy et al., 1999). The failure to capture consumer surplus causes the simple model to underestimate the true welfare cost, but the failure to allow for the mitigating effects of producer reallocation of resources causes the simple method to overestimate the true welfare cost. The data requirement for an adequate parameterisation of an economic surplus model resulted in the analyses conducted by UNECE to be unable to apply a general equilibrium model for assessment of air pollution impacts on agriculture in Europe. Instead, they have simply applied market prices to estimate of yield change (Holland et al., 2002). This approach was accepted by the UNECE Task Force as the only practicable approach at the pan-European scale. The demand functions for all production outputs were assumed to be perfectly inelastic. Prices of several products are protected by the EU and regulated by the European Common Agricultural Policy (durum wheat, protein crops, rice, nuts, energy crops, starch potatoes, seeds, arable crops, grain legumes). Thus, a change in supply does not influence the price of these crops and a yield reduction is directly related to an economic loss for the farmers. The horticultural crops are unprotected, so supply influences price. However, the simple approach of market prices provides a transparent basis for analysis. More specifically, in the framework of this analysis, crop production is determined by the land area planted and the yield per unit area planted for each geographic region that constitutes the area under consideration. It is assumed that farmers’ choice of crops planted depends on past prices and yields of the crop planted and its’ substitute crops. Thus, in the long-run, adverse impacts of crop yields will affect farmers’ choice of the pattern of production. Economic estimates of crop yield loss attributable to O3 air pollution are estimated based on the production levels by geographic region and their recorded prices received by producers. It should be noted that it is possible other pollutants apart from O3 to negatively affect crop yields in the area. However, due to lack of data, their impact on crop yields has not been evaluated. Therefore, the estimated monetary valuation due to O3 air pollution can be considered a lower bound of the true economic damage due to all pollutants. The presented integrated approach follows the effects of air pollution emissions by predicting concentrations, crop exposures, resulting yield and corresponding monetary loss in rural areas where considerable farming activity is realised. The basic steps in order to estimate spatial physical impacts and the economic damages of O3 air pollution to crops include: (i) estimation of surface O3 concentrations and accumulative exposures, (ii) acquirement of information on the spatial distribution of crop production, by species (stock at risk), across the area under consideration, (iii) selection of the most important cultivations, based on the magnitude of production and cost criteria, (iv) coupling O3 accumulative exposure and crop distribution to calculate exposure using GIS-based techniques, (v) application of the adopted risk estimate e with the incorporation of the available

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Crop-Response Functions (CRFs) e to the distribution of exposure experienced by the “target crop population”, recognising the variability in response between species, and (vi) monetary valuation. The great advantage of this air pollution “receptor category” is that it relies on the use of market prices to derive values rather than inferring values through indirect means, such as in the case of health impact assessment and premature mortality (Vlachokostas et al., 2009). Policy makers are able to use the logical flow presented in Fig. 1. For the formulation of an efficient policymaking scheme, the compilation of an accurate air pollutant emission inventory is crucial. It provides the input data for air quality simulations and therefore affects the reliability of concentrations fields and consequently accumulative exposure estimations to the cultivations under consideration. In order to enhance the reliability of an emission inventory it is important that all existing information is taken properly into account. In view of the emission inventory, meteorological data, land use and orography data, boundary conditions and background concentrations are also prerequisite in order to imprint the reference year status and analyse future concentrations in accordance with predefined emission reduction scenarios. The respective economic damages for the reference year reproduce a clear picture for the spatial relative yield losses in the area of interest.

Krupa and Kickert (1987) note that “A key issue in order to examine the relationship between air pollution stress and plant response is the existence of a biological time clock where plants respond differently to the pollutant stress at different stages of their growth. On the other hand, policy makers and regulatory personnel prefer a simple approach which would facilitate implementation and administration of ambient air quality standards”. The presented methodology aims to reinforce authorities or decision makers in their effort to estimate spatial impacts and the order of magnitude regarding economic damages of O3 air pollution to crops by means of a fast, simple and still reliable approach. Although any air quality model could be considered, high computational speed and a low output file size are the main reasons that the presented approach is built around the Ozone Fine Structure (OFIS) model, which has been proved to be an effective tool for air quality assessment and management in a wide variety of cases (Vlachokostas et al., 2009; Moussiopoulos et al., 2009; Cuvelier et al., 2007; Vautard et al., 2007; Moussiopoulos and Douros, 2005). The OFIS model belongs to the European Zooming Model (EZM) system which is a comprehensive model system for simulations of wind flow and pollutant transport and transformation (Moussiopoulos, 1995). OFIS was derived from the more sophisticated core models of the above mentioned EZM. Being closely related to the 3D photochemical dispersion model MARS, OFIS

Fig. 1. Economic evaluation of damages to agricultural crops attributed to air pollution.

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simulates concentration changes due to both advection and chemical transformation of species in its computational domain. The concentration values outside this domain are assumed to coincide with the regional background concentrations used for the calculation of the boundary conditions. Meteorological input may be derived and fed into the model from either available measurements or the output of a larger scale meteorological model. It is essential to clarify that the model’s aim is not to precisely reproduce the concentration values for each species at every timestep and each cell of the domain, but to describe consistently the statistics of air pollution levels. The emphasis is given on estimates for the exceedances of each species’ threshold values based on meteorological data and regional background concentrations and the emission patterns of the area under investigation. For a full calendar year, typical executing times of OFIS are of the order of 8 h. Regarding CRFs, which are prerequisite for the monetary valuation of damages (Fig. 1), it should be emphasised that the biological response of a plant to air pollutants is a combined function of several biological, environmental and climatic factors, making relative model construction much more complex. Among others, such factors include: level and duration of pollution exposure, age of plant, genetic sensitivity of the plant, light, relative humidity, soil moisture and fertility, and general health of the plant. Since the 1980s, extensive field studies in the US (National Crop Loss Assessment Network, NCLAN) and in Europe (European Open Top Chamber Programme, EOTCP) have attempted to establish crop specific functions which relate a quantifiable O3-exposure indicator to a reduction in the crop yield (e.g. Heck et al., 1987; Legge et al., 1995; Fuhrer et al., 1997). Mauzerall and Wang (2001) give a comprehensive overview of the various indicators that have been developed and applied in Europe and the US since the NCLAN and EOTCP studies. Most frequently, indicators used are seasonal 7 h and 12 h mean O3 concentration during daylight (M7 and M12 respectively) and seasonal cumulative exposure over a threshold such as 60 ppb and 40 ppb (SUM06 and AOT40 respectively). Recently, Mills et al. (2007) re-compiled a large number of cropresponse data from existing literature for 19 crops, many of which originally based on 7 h and 24 h means, in order to derive all CRFs as a function of AOT40. The selection of the appropriate index for crop exposure to O3 has been thoroughly discussed in the scientific literature, and AOT40 is considered a valid “Level I” index producing sound and valuable results, especially for Mediterranean countries such as Greece. However, it should also be noted that flux-based critical levels (“Level II”) are also being developed and will soon append or replace AOT40 into the LRTAP Convention Modelling and Mapping Manual (Wieser and Tausz, 2006; Working Group on Effects, 2006). In any case, any accumulative exposure metric for crop exposure to O3 can be adopted in the presented methodology. Coupling O3-exposure indicators with crop distributions can be accomplished by using GIS modelling. Based on NOx and NMVOC annual emissions, OFIS calculates O3-exposure indicators such as AOT40 for the growing seasons of crops. The domain of the area under consideration forms a geo-referred grid where background distribution maps of the plants under consideration are coupled with the adopted exposure metric distribution fields. The GIS tool allows the application of the available CRFs to the distribution of exposure experienced by the target crop population. On this basis for every cell (or groups of cells) the corresponding yield loss can be obtained and consequently spatial analysis of physical impacts on crops can be imprinted on maps. Furthermore, in order to provide a user-friendly interface to the decision maker, maps of economic losses to the cultivations under consideration are produced, based on the market value of the affected crops. In this context, spatial analysis can support local or national authorities’ planning schemes

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by highlighting the magnitude of economic impacts to agricultural sector. An indicative insight for governmental authorities and decision makers is prerequisite, especially if the economic instruments such as financial incentives or state subsidies to farmers are considered. 3. Application to the greater Thessaloniki Area 3.1. The domain Fig. 2 depicts the domain under consideration, which is an area of 142 km  90 km including the city of Thessaloniki and the surrounding valley, hereafter referred as the Greater Thessaloniki Area (GTA). With more than one million inhabitants (approximately 10% of the Greek population) the city of Thessaloniki, situated in the northern part of Greece, is the second largest city in the country and one of the largest urban agglomerations in the Balkans. On top of that, the GTA presents considerable agricultural activity in the rural areas surrounding the urban and suburban territories. The fertile valley surrounding the city of Thessaloniki is a major agricultural production centre, mainly of cereals, industrial plants, legumes and plantations. In total, the GTA contains approximately 130,000 ha (approximately 1% of the country’s total area) attributing to nearly 7% of annual crop production in the country (NSSG, 2001). Personal communication with the National Statistical Service of Greece (NSSG), provided with all the necessary data regarding production quantities, market values and spatial distribution of the cultivations in the municipalities of the GTA. Selection of targeted crops within the study area was based upon both the magnitude of crop production and the current market values. The selected crops characterise the main agricultural activity in the area. Among them, tobacco had by far the highest market price, followed by table tomato, cotton and lettuce, whereas concerning yield production, maize, wheat (durum and spring), rice, tomato and cotton in declining order constituted the most important crops in the study area during the year 2002 (Table 1). 3.2. Crop-response functions and yield loss Although methods for the quantification of O3 effects on agricultural productivity are still under scientific study, adequate knowledge is now available to enable a reasonable estimate of its direct impacts to be made. Plants respond to the cumulative impact of exposure and peak concentrations have been shown to be more important than sustained lower concentrations. Thus, the concept of AOT is considered and a threshold was tentatively set at 40 ppb (AOT40) for crops in a European scale. As already discussed, Mills et al. (2007) recently re-compiled a large number of cropresponse data from existing literature for 19 crops as a function of AOT40, which are adopted in the framework of this analysis since there is no available relative data for the GTA. Crops under consideration, cultivated area, yield quantities, market prices and CRFs are summarised in Table 1. It should be noted that the CRF adopted for sunflower cultivation sources from ExternE (2005) as there was no available relative function in Mills et al. (2007). Moreover, the CRF for durum wheat is adopted from the work of De Marco et al. (2009) since the available relative function in Mills et al. (2007) is related to spring wheat, which is more sensitive to O3 pollution than durum wheat. AOT40 should be calculated for 3 months, during the period of active growth and centred around the start of anthesis (Mills et al., 2007). For the GTA, the period from the beginning of May to the end of July is considered appropriate for most of the crops under consideration, except wheat, lettuce, oilseed rape and barley for

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Fig. 2. GTA and the municipalities, in the domain under consideration. The location and names of the stations belonging to the network of Central Macedonia Region are also shown (dots). (The nomenclature used for the stations is AGS: Ag. Sofias Sqr., AUT: Aristotle University Thessaloniki, KAL: Kalamaria, KOR: Kordelio, NEO: Neochorouda, PAN: Panorama, SIN: Sindos.)

which the spring (MarcheMay) is considered more appropriate due to their chronically earlier development and maturation. It is important to appreciate that the AOT40 index was developed within the context of the UNECE, as a critical level, defined as “the concentration in the atmosphere above which direct adverse effects on receptors such as plants and ecosystems may occur according to present knowledge”. “Level I” approach, defines a single critical level value which is assigned to a particular receptor, regardless of the local growth conditions. The need to move to a more sophisticated “Level II” approach, in which the

Table 1 Crops, cultivated area, yield, market value and relative CRFs for the GTA. Crop

Area (ha)

Sensitive to O3 Cotton Spring wheat Sunflower Lettuce Processing tomato Table tomato

16,599 14,713 3811 470 904 455

Moderately sensitive to O3 Durum wheat 59,694 Sugarbeet 986 Oilseed rape 1208 Tobacco 2178 Rice 12,569 Maize 10,633 Grape 3894 Resistant to O3 Barley

5165

Market price (V t1)

(y ¼ relative yield, x ¼ AOT40 in ppm h)

61,033 29,069 3067 5476 42,945 27,598

880 140 180 690 260 2000

y y y y y

¼ ¼ ¼ ¼ ¼

0.016x þ 1.07 0.0161x þ 0.99 0.012x þ 0.997 0.0108x þ 1.04 0.0083x þ 1.0

156,389 57,713 35,235 10,971 101,120 252,631 23,546

150 40 370 2730 280 150 370

y y y y y y y

¼ ¼ ¼ ¼ ¼ ¼ ¼

0.0078x þ 1.0 0.0058x þ 1.0 0.0056x þ 0.9 0.0055x þ 1.04 0.0039x þ 0.94 0.0036x þ 1.02 0.003x þ 0.99

11,995

140

Yield (t)

y ¼ 0.0006x þ 0.96

effects of local factors e such as climate and soil characteristics e on plant response to O3 are quantified, has long been recognised. Fluxbased critical levels have been developed within the framework of the ICP Vegetation body and are already included in the LRTAP Convention Modelling and Mapping Manual. In addition, the fluxbased critical levels are being updated after the 2005 Task Force Meeting of ICP Vegetation and are going to append or replace AOT40 in the LRTAP Manual1 (Wieser and Tausz, 2006; Working Group on Effects, 2006). However, adequate scientific information is not available for the needs of our analysis and therefore a fluxbased approach constitutes a future change for the authors. 3.3. Ozone pollution status and trends The measurements conducted by the Air Pollution Monitoring Network of Central Macedonia Region, also available through Airbase (Official Airbase web site, 2010), are used to primarily assess O3 pollution status and trends in the GTA. Moreover, available measurements are used to validate OFIS results. Location and names of monitoring stations are presented in Fig. 2. A stabilising trend can be seen for O3 levels during the period 2001e2006. Considerably elevated levels are observed at Panorama (PAN) and Neochorouda (NEO), which are characterised as suburban and rural areas, respectively. In these areas, the built-up of O3 is favoured due to the distance from the city centre and hence the low concentration levels of NOx (Moussiopoulos et al., 2009). Lower concentration levels of O3 are observed in urban and industrial areas, such as at the Ag. Sofias Sqr. (AGS) and Kordelio (KOR) (Fig. 3 (left)). Further details on air quality levels in the GTA can be found also in Touloumi

1

The authors wish to thank an anonymous referee for this information.

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Fig. 3. Interannual trend of the annual average O3 concentrations (mg m3) for the available measurements (left) and comparison between OFIS results and the observed values for the annual average concentrations (mg m3) for the year 2002 (right).

et al. (2006), although monitor data for O3 concentrations to rural areas is generally limited.

3.4. Spatial variability in O3 exposures on the basis of AOT40 metric In order to achieve a better insight into photochemical air pollution in the GTA, the use of computational methods is essential. Fig. 3 (right) depicts the comparison between OFIS results and the observed values for the annual average concentrations (mg m3) for the year 2002. The results of the model are in good agreement with the concentrations which are derived from the measurement data. Regarding input data fed into OFIS model a detailed emission inventory has been constructed for the year 2002, providing temporally and spatially disaggregated information for the emissions of NOx, NMVOCs and PM10. The geographically distributed gridded emission data are used for the relevant application. Vehicle and industrial emissions are the two main sources of air pollutants in the GTA. Although the emission model used is presented analytically elsewhere (Moussiopoulos et al., 2009; Tsilingiridis et al., 2002), some interesting insights regarding O3 precursor emissions are provided synoptically. More specifically the total annual NOx emission rate is 24,720 (t a1). Road transport is the dominant source of NOx emissions (64.5%), with industrial activities also having a considerable share (18.9%). Road transport is also responsible for most NMVOC emissions (52.4%), with solvent use (23%) and industry (14.4%) to impose a significant burden. NOx emissions are mainly concentrated within the urban area of Thessaloniki and along the main road leading in and out of the city. NMVOC emissions are more uniformly distributed, since major NMVOC sources are allocated all around and within the urban area. Locally, NOx and NMVOC emission densities are higher close to large industrial point sources. However, apart from local anthropogenic sources, the area is characterised by significant background O3 concentrations which originate from transboundary transport (Gauss et al., 2008; Kalabokas and Repapis, 2003), either attributed to anthropogenic or biogenic sources, e.g. forests surrounding the GTA (Tsilingiridis et al., 2002). O3 concentration fields were estimated with OFIS both for the reference year and predefined emission scenarios in the area under consideration and reported analytically in Moussiopoulos et al. (2009). In order to imprint the spatial distribution of AOT40 calculated from hourly O3 concentrations in each grid cell, both initial and lateral boundary conditions were derived from 3-hourly concentration averages predicted by the Unified EMEP model at a spatial resolution of 50 km. Typical meteorological data consists

of hourly values of basic parameters such as temperature, wind speed and direction, which are averaged to 3-hourly values in order to be compatible with the standard model setup. In the case under consideration the meteorological data that were used originated from surface level measurements conducted at Kordelio during 2002 (Moussiopoulos et al., 2009). Due to the modular structure of OFIS, chemical transformations can be treated by any suitable chemical reaction mechanism, the default being the EMEP MSC-W chemistry (Simpson, 1993). Fig. 4 displays the spatial distribution of AOT40 metric between 08:00 and 19:59 h (local time) over the GTA from 1 March to 31 May 2002 (up) and from 1 May to 31 July 2002 (down). AOT40 emphasises both exposure duration and peak O3 concentrations. It is evident that spring season presents as expected lower AOT40 peaks compared to the corresponding (01/05e31/07) three month

Fig. 4. AOT40 metric (ppb h) between 08:00 and 19:59 h (local time) over the GTA from 1 March to 31 May 2002 (up) and from 1 May to 31 July 2002 (down).

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Table 2 Monetary valuation of crop yield loss attributable to O3 air pollution for the reference year 2002 and specific emission reduction scenarios for 2010. Cultivation

Cotton Spring wheat Sunflower Lettuce Processing tomato Table tomato Durum wheat Sugarbeet Oilseed rape Tobacco Rice Maize Grape Barley Total

Damage cost (kV) Reference year

Reductions 5% NOx, 25% NMVOC

Reductions 5% NOx, 50% NMVOC

16,142 1031 154 427 1866 9068 2708 280 2807 1533 4256 1595 556 73 42,496

15,457 981 146 394 1782 8369 2515 268 2661 1470 4053 1498 520 69 40,185

14,478 920 136 361 1664 7802 2390 250 2476 1379 3777 1408 487 64 37,595

period. AOT40 exceeds 16,000 ppb h for the period (01/05e31/07) in almost all rural areas, where agricultural activities occur. This implies significant negative impacts on crop production in the area under consideration. 4. Results and discussion In order to assess the economic damages using the aforementioned AOT40 distribution fields, apart from the reference year 2002 calculations, two emission reduction scenarios regarding photochemical precursors of O3 are used. The scenarios that are investigated for indicative monetary evaluation purposes are analytically presented in Moussiopoulos et al. (2009) and briefly

concern: (i) 5% NOx and 25% NMVOC anthropogenic emission reductions for 2010 compared to reference year 2002, (ii) 5% NOx and 50% NMVOC anthropogenic emission reductions for 2010 compared to reference year 2002. For crops sensitive to O3, the crop losses (%) for the reference year are significant. Damages to annual production of cotton, spring wheat and sunflower cultivations are estimated 16.2%, 21.4% and 16.1% respectively. Lettuce and tomato cultivations, which are also characterised as sensitive to O3, encounter lower damage levels, 9.8% and 11.5% respectively. Regarding durum wheat, sugarbeet, oilseed rape, tobacco, rice, maize and grape, which are crops that are characterised as moderately sensitive to O3, the losses are 10.6%, 8.2%, 17.9%, 2.5%, 11.4%, 2.6%, 4.9% respectively. For barley, a more resistant to O3 cultivation, the yield loss is approximately 4.7%. Our results are similar to figures reported elsewhere. Booker et al. (2009) find yield losses that range from 5% to 15% in the US among sensitive crops. Simulations of cumulative O3 concentrations in China estimate wheat yield losses by 12e19% (Wang and Mauzerall, 2004), analogous to our estimates and rice yield losses of 3e5% in 1990, lower than our estimates. Kuik et al. (2000), using a spatial economic model of the Dutch farm sector, estimated yield losses of 13.9% on cereals and between 11.2% and 19.9% for vegetables, which are in the range of our estimates. In addition, Chameides et al. (1994) have reported estimated yield reductions by 5e10% for cereals worldwide. However, for decision makers the crucial question lies in the monetary valuation of crop yield loss attributable to air pollution and not to the absolute quantities or percentages of crop damages. In this context, the economic estimation of agricultural damage attributable to O3 pollution for the reference year 2002, as well as for the two indicative scenarios, are depicted in Table 2. The agricultural loss is estimated to be approximately 43 MV for all cultivations under consideration in the domain which can be attributed to O3 air pollution for the reference year 2002 in GTA. It is evident that

Fig. 5. Spatial distribution of total agricultural damage cost to municipalities in the GTA for the reference year 2002.

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Fig. 6. Spatial distribution of total agricultural damage cost per area of cultivation (kV ha1) to municipalities in the GTA for the reference year 2002.

production of cotton presents the highest economic loss, which is over 16 MV, followed by table tomato (9 MV) and rice (4.2 MV) cultivations. Wheat (both durum and spring) also presents considerable economic losses, which are approximately 4 MV. It should be noted that tobacco cultivations do not present so much damage cost compared to cotton, table tomato or rice although tobacco has the highest market price (2730 V t1) among all cultivations. In addition to the above discussion another issue should be raised at this point. One of the basic problems that the local decision maker confronts is the selection of control measures at emission sources considering the limited influence on background transboundary air pollution (Vlachokostas et al., 2009). Furthermore, it should be noted that compliance with O3 EU legislative limit values is not achieved at suburban and rural areas of the GTA for any of the scenarios studied in the work of Moussiopoulos et al. (2009). Taking into consideration that GTA is characterised by significant background O3 concentrations the benefit of the emission scenarios studied in the framework of this analysis is notable, but not remarkable enough compared e.g. to potential benefits to public health. Regarding the 5% NOx, 25% NMVOC emission reduction scenario the benefit to agricultural sector is approximately 2.3 MV compared to the reference year. The 5% NOx, 50% NMVOC emission reductions scenario is more effective as expected since the benefit reaches 5 MV. Apart from the monetary valuation of crop yield loss for the whole area under consideration, it is interesting for decision makers to allocate damages to specific municipalities especially if the use of economic instruments, such as financial incentives or subsidies to farmers is considered. In order to satisfy this specific need, the grid-based AOT40 is converted into municipality-average data using the GIS software ArcInfo. The analytical power of GIS constitutes an effective means for graphically conveying and clearly illustrating complex information which is the output of a mix of process models. The latter becomes even more crucial when

considering relevant stakeholders who often seek transparency for proper interpretation of the results in the decision making process. The total damage cost to agricultural production in the GTA for the reference year is spatially allocated to municipalities in Fig. 5. The spatial allocation is analytically presented in the Annex. Similarly, Fig. 6 presents spatial allocation to municipalities of damage cost per area of cultivation (kV ha1) for the reference year. The results presented in Figs. 5 and 6 suggest that losses are not spread uniformly among farmers. Regionally, the major losses occur in areas with valuable ozone-sensitive crops. Policy makers and agricultural economists can use this information in developing measures that promote substitution of ozone-sensitive crops with more ozone-resistant ones through farmer education and training, at the regional level, and through support schemes and policies, at the national and EU level. The present study, by estimating the external costs of O3 on agricultural production, provides a valuable policy tool which can assist taking actions that compensate farmers for the economic losses they incur, thus internalizing the externalities of O3 pollution.

5. Conclusions O3 is recognised as the most serious regional air pollutant problem for the agricultural sector in Europe at the present time. Although, methods for quantification of O3 effects on productivity are still under scientific discussion, adequate knowledge is now available to enable a reasonable estimate of its direct impacts to be made. Although it seems clear that O3 effect can be serious for individual farmers, there is a lack of systematic recording of episodes where farmers lose considerable amounts of their yields. However, such episodes are rather common in some parts of Europe, especially in the Mediterranean. The total external cost of O3 pollution on agricultural production for only a limited

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Mediterranean area such as the GTA is estimated in the order of 40 MV for the year 2002. It should be emphasised that there are inherent uncertainties to the estimates of the monetary valuation of crop yield loss attributable to O3 air pollution due to specific assumptions in the present analysis. General sources of individual uncertainties could come from data series uncertainties, emission and atmospheric dispersion and chemistry model uncertainties, uncertainty about the future, synergies and idiosyncrasies of the analyst in the interpretation of ambiguous or incomplete information. Moreover, assumptions about the slope and form of CRFs (e.g. with or without threshold) contribute to the inherent uncertainties. In order to construct a reasonable, physical doseeresponse function the biological response of a plant to fumigation by photochemical air pollution should be studied in the area under consideration. This biological response is a function of a complex mix of biological, environmental and climatic factors. Such factors include, among others; level and duration of pollution exposure, age of plant, genetic sensitivity of the plant, light, relative humidity, soil moisture and fertility, and general health of the plant. Recently there has been extensive debate about the possibility of replacing the current critical-level based exposure indices (Level I), by flux-orientated limiting values (Level II). Furthermore, indirect effects of these pollutants may also be significant. This is mainly because air pollution could stimulate the performance of insects and other agricultural pests, enabling them to impact more severely on crop yield than in the absence of air pollution. Although the inherent uncertainties are well defined, for the decision maker even rough estimations using the presented

methodological framework is of great importance. Therefore the use of CRFs that elicit implicit values in policy decisions to monetise the impacts of photochemical air pollution to crops could be taken into account. That is to say that incorporating O3 impact in crop production forecasts can potentially improve the results of agricultural forecast by capturing the order of fluctuation in yield losses due to air pollution. Undoubtedly, the current state of knowledge has still gaps and uncertainties. The purpose of ongoing research is to reduce gaps and in addition refine the methodology to reduce uncertainties, especially those regarding the CRFs. Clarity in defining these issues is a prerequisite for proper interpretation of the results in the policy arena. It is the authors’ strong belief that the considerable figures of damage costs estimated in the present study would justify instant implementation of measures to reduce O3 in a regional scale, while increase public awareness to enhance environmental protection. There is still much to learn about subtle, chronic, low-level-pollution yield effects. Until some of all the above aforementioned areas of uncertainty are investigated further, the vegetation loss estimate can only be used with an understanding of its many deficiencies.

Acknowledgements We would like to thank the anonymous reviewers for their valuable comments, which significantly improved the quality of the manuscript.

Annex. Spatial distribution of total agricultural damage cost to municipalities in the GTA for the reference year 2002 (in kV). Cultivation Municipality

Cotton

Spring wheat

Sunflower Lettuce Processing Table tomato tomato

Chalkidona Koufalia Axios Chalastra Echedoros Lagadas Vasilika Epanomi Mikra Ag. Athanasios Apollonia Michaniona Sochos Egnatia Thermi Kallindion Koroneia Arethousa Migdonia Lachanas Madytos Assiros Pilaia Vertiskos Thermaikos Other Total

6.939 4.107 1.866 1.088 303 13 39 661 227 637 0 29 51 5 125 3 30 0 0 0 17 0 0 0 0 1 16.142

2 0 22 0 0 0 2 0 23 0 113 2 2 1 0 0 0 0 0 0 48 2 0 0 152 12 42 4 1 0 199 9 94 14 22 5 111 0 66 69 15 0 63 1 0 0 28 17 0 0 26 19 1.031 154

11 56 8 5 182 49 9 29 2 3 1 14 0 27 12 0 2 0 0 0 1 0 15 0 0 1 427

459 222 285 349 124 0 7 142 0 241 0 0 0 4 21 0 0 8 0 0 4 0 0 0 0 0 1.866

18 462 0 31 1.544 2.101 2.321 448 143 85 39 681 140 221 199 7 80 15 46 54 63 22 148 43 23 133 9.068

Durum wheat

Sugarbeet Oilseed rape

7 72 90 125 3 13 0 12 17 13 57 0 125 0 195 0 1.341 0 131 32 39 0 27 0 39 0 22 0 45 0 78 0 34 0 65 12 105 0 25 0 26 0 98 0 6 0 26 0 21 0 87 0 2.708 280

Tobacco Rice

979 2 586 311 0 0 23 0 1.183 45 0 1.728 446 0 533 64 29 0 12 0 0 730 0 0 31 0 0 42 0 227 3 1.125 0 38 0 0 4 100 0 16 57 0 17 0 0 0 53 0 4 24 0 7 65 0 2 0 0 1 17 0 12 12 0 1 19 0 10 0 0 1 14 0 7 0 0 2 16 0 2.807 1.533 4.256

Maize

Grape Barley Total

107 2 9 14 60 0 109 0 119 0 568 13 0 4 26 52 0 129 59 200 9 2 0 8 2 6 106 7 40 41 13 8 59 4 134 2 50 0 4 0 76 0 13 1 2 4 2 9 3 51 26 1 1.595 556

0 0 0 0 2 6 3 0 1 2 3 0 15 0 3 4 6 7 3 0 4 1 0 2 0 10 73

9.183 5.419 3.441 3.370 3.307 3.015 2.524 2.283 1.875 1.658 1.271 797 521 510 505 372 351 344 318 235 229 219 184 141 104 323 42.496

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