INTRODUCTION

The issue of emerging risks knows an increasing interest amongst both public and private decision makers (OECD, 2003) [START_REF] Osha | Emerging risks. Sources, drivers and governance issues[END_REF] [START_REF] Enisa | ENISA emerging and future risks[END_REF] [START_REF] Irgc | Improving the management of emerging risks[END_REF]. The subprimes crisis in 2007, social unrests in Arab countries, or SARS epidemic are a few examples of how some systems' dynamics have dramatically challenged our previsions and understanding of their mechanisms and evolutions.

It also strongly shacked the foundations of our risk governance approaches that seem to be more adapted to routine based risks than to fast evolving, highly uncertain and multi stakeholders' ones. Furthermore, those risks seem to ignore all boundaries usually determined by governance structures and scientific disciplines (IRGC, 2010). The financial crisis has for example raised several issues at the whole European level that revealed all the economic and social discrepancies amongst EU countries' policies; the global warming and its potential consequences raise several economic, social and technical issues that require holistic approaches instead of disciplines based visions. On the other hand, the stakes at play are huge: possible collapse of whole systems as it was the case for financial institutions, dramatic limitations in access to water or ocean acidification in the case of global warming, worldwide epidemics in case of virus mutations…

In this context, what strikes at the very first sight is the great variety of risks that may be comprised under the umbrella of emerging risks. What are the common properties that are shared amongst them? Do those common properties allow a unified and still sophisticated approach? In other words, to which extent should those risks share a common definition and how such a definition may lead to develop adapted answers?

This paper will review 9 emerging risks definitions used by international organisations in several domains: occupational health and safety, food safety, insurance... Our aim is to question the homogeneity of the way emerging risks are treated by those high level organizations in order to identify common patterns that may be shared with smaller organizations. Our claim here is that emerging risks are not prerogatives of international organizations; they are also challenges to be dealt with at companies' levels, even the small ones like SMEs.

What do we really know about emerging risks? A literature review and some comments. ABSTRACT: The issue of emerging risks is today recognized as an important concern for risk governance. Several organisations have developed their own definitions of this concept and deduced from that a framework to deal with them. This paper aims to compare the various definitions associated today with this term and tries to infer a conceptualization of the concept of emergence in risk management. Through a detailed analysis, we will see that the heterogeneity of existing definitions and their inability to clearly distinguish the concept of emerging risks from other existing risk typologies require to explore more in depth the concept of emergence and its implications for risk management.

As [START_REF] Beck | Risk Society. Towards a new modernity[END_REF] stated it, our modern societies put risks repartition at the very center of the societal debate, especially regarding the questions raised by fast evolving technologies that continuously challenge the equilibriums we try to define. In this context, the idea of new risks that emerge as a consequence of various dynamics (economic, technological, demographic…) raise central questions as the issue of their repartition and management is to be correctly dealt with as early as possible.

According to the above, more and more international organizations have adopted definitions (see table 1 below) that describe the way they deal with the idea of emerging risks in their policies.

Table 1 Panorama of emerging risks definitions.

Authors Definition

1. European Agency on Health and Safety at Work (2005).

New and emerging risks are any occupational risk that is both new and increasing.

By new, we mean that: the risk did not previously exist and is caused by new processes, new technologies, new types of workplace, or social or organisational change; or, a long-standing issue is newly considered as a risk due to a change in social or public perceptions; or, New scientific knowledge allows a long-standing issue to be identified as a risk. The risk is increasing if: number of hazards leading to the risk is growing; or the exposure to the hazard leading to the risk is increasing (exposure level and/or the number of people exposed); or The effect of the hazard on workers' health is getting worse (seriousness of health effects and/or the number of people affected).

European Food and Safety Agency EFSA (2007)

A risk resulting from a newly identified hazard to which a significant exposure may occur or from an unexpected new or increased significant exposure and/or susceptibility to a known hazard. 3. Swiss Re [START_REF] Russell | Emerging risks: risk perception at Swiss Re[END_REF] Developing or changing risks which are difficult to quantify and may have a major impact on insurers' book of business.

International Risk Governance Council IRGC (2010)

Risk that is new or a familiar risk that becomes apparent in new or unfamiliar conditions.

European Network and Information Security Agency ENISA (2010).

Risks that may have an impact between one and five years in the future, while the future risks…may have an impact in more than five years…

Price Waterhouse Coopers (2009)

Those large scale events or circumstances beyond one's direct capacity to control, that impact in ways difficult to imagine today. Emerging risks cover unknown costs of known events/risks, and unknown costs of unknown risks. 7. The prudential Insurance company of America [START_REF] Barney | Managing emerging risks. An oxymoron? Entreprise risk management symposium[END_REF].

A condition, situation or trend that could significantly impact the Company's financial strength, competitive position or reputation within the next 5 years.

Lloyds (2007)

An issue that is perceived to be potentially significant but which may not be fully understood or allowed for in insurance terms and conditions, pricing, reserving or capital setting.

National Academy of science (2012)

Refers to the likelihood that a new material will cause harm in ways that are not apparent, assessable, or manageable with current risk-assessment and risk-management approaches.

The long list of distinct definitions detailed above reveals the heterogeneity of significations and understandings various organisms associate to the concept of emerging risks. If such a heterogeneity can be explained, to a certain extent, by the variety of domains and applications associated to those sources (Food safety, insurance industry, consulting companies, international organisms…), we will see below that it is also the result of real discrepancies in the very conceptualization of the term "emerging". In order to be exhaustive and concise, we tried to extract those various conceptualizations and describe them through what we called descriptive factors. Those factors are meant to capture what we believe as an important property one or several definitions associate to the concept of emerging risks. In the followings, each of those descriptive factors is analyzed according to its capacity to characterize and differentiate emerging risks from other types of risks already described by former typologies [START_REF] Renn | A new approach to risk evaluation and management: risk based, precaution based and discourse based management[END_REF]) [START_REF] Renn | Towards an integrative approach[END_REF] (OECD, 2003). We are trying here to answer two distinct questions: How are emerging risks different from other types of risks? If yes, what could be, according to those initions, the shared properties of a specific category of risks called "emerging"?

Descriptive factor 1: Temporality of occurrence Definitions 5 and 7 relate the emerging character of a risk with its expected temporality of occurrence. In other words, emerging risks are existing or forthcoming risks which consequences may occur within the next five years. Temporality is here used as a segregating parameter allowing organizations to set priorities; emerging risks having the priority comparatively to longer term risks. [START_REF] Barney | Managing emerging risks. An oxymoron? Entreprise risk management symposium[END_REF] specifies that such a definition should orient organisations towards a forward looking and an early definition of mitigation opportunities. By insisting on the need to look forward and anticipate future evolutions, one can implicitly infer from this definition that emerging risks are evolving risks that need to be understood according to their dynamics and not only regarding their past behavior. The dynamic character of risks is also explicitly treated by the descriptive factor below.

Descriptive factor 2: Dynamic character of the risk Definitions 1, 2, 3 and 4 consecrate the dynamic character as the main property of an emerging risk. Emergence is here described as the result of one or several dynamics that may change the nature, probability, intensity or vulnerability associated to some risks. Various types of dynamics may lead to create/ reveal new risks or change existing ones:

Scientific dynamic: If a new knowledge puts into light new pieces of evidence that a risk exists. For example, new evidence that specific electromagnetic fields may increase likelihood of cancers will naturally change both expert risk assessments and social perceptions related to this technology. Technological dynamic: If new products or processes generate opportunities that may lead to new or change existing ratios of costs and benefits regarding exposure to risks. For example, nanotechnologies create new opportunities of innovative health treatments (Lloyds, 2007) and reduction of energy consumption (NAS, 2012) as well as important health and environmental threats (Maynard et al, 2007) (NAS, 2012). Those opportunities and threats create new configurations of costs and benefits within which new or existing risks need to be (re)studied.

Social and psychological dynamics: If new behaviors generate new forms of exposure or increases already known patterns of exposure. New or evolving perceptions can also create social dynamics that may change the perceived level of risk (IRGC, 2010). The most recent example is the evolution of nuclear risk perceptions after Fukushima accident, and more generally, after each nuclear accident (IRSN, 2012) [START_REF] Prati | The effect of the Fukushima nuclear accident on risk perception, antinuclear behavioral intentions, attitude, trust, environmental beliefs and values[END_REF]. Descriptive factor 3: Uncertainties Definitions 8, and 9 highlight uncertainties, and sometimes the ignorance, associated with the probability and/or magnitude of consequences an emerging risk may generate. More precisely, those definitions refer to the unavailability of past data when assessing those consequences as well as the limited character of our detection devices or techniques. According to those definitions, emerging risks are existing risks for which an important uncertainty still exists making the management and regulation arrangements always subject to potential re calibration in case of new pieces of scientific evidence or social evolutions in risk perception.

Descriptive factor 4: Magnitude of consequences Definitions 2, 3 and 6 characterize emerging risks through the potential large magnitude of consequences that may go beyond the limits and management scope of a unique organisation. The notion of uncertainty is here also present as well as the possibility that consequences may threaten a whole set of organisations that present the same vulnerabilities to the emerging threat. Such definitions are thus very close to the notion of systemic risks defined by the OECD (2003) as risks that affect the systems on which society depends-Health, transport, environment, telecommunications, etc. Emerging risks are here characterized regarding their ability to widespread in a very uncommon way (IRGC, 2011) that may impact not only one stakeholder but systems at a whole regarding their similarities or interconnections [START_REF] Irgc | Improving the management of emerging risks[END_REF].

ANALYSIS

A fast lecture of the various factors inferred from the set of definitions described in table.1 reflects the heterogeneity of the understandings lying behind the concept of emerging risks.

Depending on what one chooses to consider as a distinctive property, management of emerging risks may be oriented in various directions: development of knowledge to cope with uncertainties, reduction of systems interconnections to cope with magnitude of consequences… Our objective here is not to favor one definition over another; it is more to highlight how this term triggers today various understandings which, consequently, may generate ambiguities.

In addition to that, we believe it important to analyze also those factors according to their ability, alone or all together, to provide a sufficient basis to characterize a specific category of risks to be called "emerging". In other words, how do emerging risks distinguish from other types of risks already defined by existing typologies?

This second point is of importance. If emerging risks are really to be distinguished from other categories, this means that risk managers will need to develop dedicated management frameworks or enlarge existing ones to fit with new requirements. If not, this means that existing frameworks could be satisfactory and no further developments required.

Regarding descriptive factors 1 and 2, it seems natural to adopt the novelty and/or the evolving characters as distinctive properties of emerging risks. However, according to a managerial perspective, the novelty or the evolving characters of a risk can hardly be stated proactively, especially in case of risks which future behaviors are uncorrelated from past ones. Therefore, the deployment of a dedicated framework for emerging risks management cannot be performed before the irreversible observation that a new risk has emerged or that an existing risk has changed in a significant and unpredicted manner. Of course, actions still can be taken before the occurrence of the risk. However, we need to acknowledge that such actions might be taken in a context where other stakeholders, including the public, are aware of the existence of this risk and of the lack of a structured and coherent policy to deal with it. Therefore, we believe that adopting those properties to characterize emerging risks will more likely place decisionmakers, and the society in general, in reactive postures instead of proactive ones; which is an important drawback when it comes to risk management policies. Consequently, we believe those properties as more adapted to define already emerged risks that still require specific management approaches (Rossell, 2007) (IRGC, 2011) [START_REF] Jovanovic | Emerging risk management framework[END_REF] instead of risks in the process of emergence.

Regarding uncertainties (descriptive factor 3), We believe it difficult to consider the presence of uncertainties in risk assessment as a sufficient or systematic condition to consider a risk as emerging. Regardless of the various sources of uncertainties that may be generated either by a lack of knowledge or by the system's variability [START_REF] Van Asselt | Uncertainty and RIVM's environmental outlooks. Documenting a learning process[END_REF], It is widely acknowledged that risk assessment and management in general may comprise high uncertainties (Renn & Klinke,2003) [START_REF] Renn | Towards an integrative approach[END_REF] even for what are considered as familiar risks. Renn and Klinke (2003) have acknowledged this statement by suggesting a risk typology where risks characterized with high level of uncertainties are distinguished as uncertainty induced risks.

The Buncefield accident ( 2005) is a good illustration of such a situation. It occurred in an oil storage facility near Heartfordshire, England. Uncontrolled filling operations lead to oil overflowing from tank 912. A rich fuel/air mixture rapidly expanded and an ignition source lead to a massive explosion as a result of a physical phenomenon called Unconfined Vapor Cloud Explosion (UVCE). If UVCE in storage tanks is a widely known risk for several years, experts were surprised by the intensity of the explosion that did not match with analytical models and simulations widely shared in petroleum community [START_REF] Buston | Description and analysis of events leading to major atypical accidents[END_REF]. Furthermore, several UVCE already occurred in the past: Texas city (USA) in 2005, St Herblain (France) in 1991, Ufa (Russia) in 1989, Napoli (Italy) in 1985 allowing experts to calibrate their models according to past experiences. This example suggests that what can be considered as familiar risks can also comprise uncertainties that, when revealed through either scientific advances or because of an accident, may totally change our understanding of its probability or magnitude of consequences. Accordingly, it appears to us that uncertainty cannot be considered as a distinctive property to create a new category of risks called emerging. Otherwise, almost all existing and supposed well known risks are to be considered as candidates to this new category. However, this does not mean that emerging risks do not comprise uncertainties; we only admit that uncertainties are not a sufficient condition to label a risk as emerging.

Regarding descriptive factor 4 (magnitude of consequences), risks generating consequences which magnitude goes beyond the limits of one organization are already experienced at several stages in our societies by various stakeholders. A first example that still challenges our societies' organizational capacities is crisis management in case of an industrial accident. Thousands of dangerous plants around the world generate risk scenarios which consequences may go far beyond the plants' limits requiring the intervention of public forces according to local regulations (EC 96/82 directive, 1996), COMAH regulation in UK (1999)… The technical and organizational issues raised in such contexts are widely studied in the scientific community [START_REF] Penning | Signals from pre crisis discourse: Lessons from the UK flooding for global environmental policy change?[END_REF] [START_REF] Coria | Environmental crises' regulation, tradable permits and the adoption of new technologies[END_REF]) (Vascoukis et al, 2012) and still require further developments. At higher levels, the notion of systemic risks have been widely discussed (OECD, 2003) those last years, especially after the subprime crisis in 2007 that threatened the whole financial system. The issue of defining systemic risks as threats caused by unpredictable, highly improbable, exogenous stochastic events [START_REF] Albeverio | Extreme Events in Nature and Society[END_REF] or as results of endogenous structural weaknesses (Golding and Vogel, 2010) goes beyond the purpose of this paper. We will simply acknowledge the fact that systemic risks have the ability to threaten large systems on which our civilization relies (OECD, 2003) in several sectors: Contagion of dangerous financial products in banking systems, global collapse of multi-stakeholders electric grids , global failure of electronic systems in case of intense solar storms, global warming… According to this definition, we consider that risks impacting large systems, requiring multiple stakeholders' collaboration and resulting from complex interactions are already considered in existing risk typologies through the concept of systemic risks. Therefore, magnitude of consequences can hardly be considered as a determinant of emergence when dealing with risks. Once again, this does not mean that a systemic risk cannot be considered as emerging. Nevertheless, we contest the magnitude of consequences as a unique neither a sufficient condition to define a risk as emerging.

CONCLUSIONS

No property or combination of properties suggested by the various definitions in table.1 allow a complete differentiation of the concept of emerging risks comparatively to other risk types usually found in literature. Consequently, we believe the questions rose at the beginning of this analysis, i.e. the existence of a real distinction between emerging risks and other risks and the list of properties founding this distinction, are still unanswered at this level.

However, this does not mean that we deny the existence of emerging risks. We will rather plead for a better definition and characterization of this concept through a thorough analysis of the concepts of emergence as defined in the dense literature related to complexity science.

The stakes behind the construction of such a definition are high: Avoid ambiguities related to several definitions lying behind a single concept.

Better distinguish emerging risks comparatively to existing risk types and thus better identify specific needs in terms of management frameworks. Provide everyday risk managers in smaller organizations with clear and transposable definitions and methodologies for emerging risks screening and mitigation strategies.

Institut

  National de l'Environnement Industriel et des Risque INERIS. Parc Technologique ALATA, BP2 60550 Verneuil en Halatte, France.


Philippe Thunis
email: philippe.thunis@jrc.ec.europa.eu

Bertrand Bessagnet
email: bertrand.bessagnet@ineris.fr

Etienne Terrenoire
email: etienne.terrenoire@ineris.fr

Augustin Colette
email: augustin.colette@ineris.fr

Application of performance indicators based on observation uncertainty to evaluate a Europe-wide model simulation at urban scale

In the frame of the European Consortium for Modeling of Air Pollution and Climate Strategies (EC4MACS) the CHIMERE chemistry transport model has been run over Europe for the entire year 2009 with a spatial resolution of 7 km with the aim of assessing the urban impact on daily exceedances of PM and NO2 in European cities. In order to better capture these urban impacts improvements on urban scale meteorology, vertical resolution and emissions have been implemented. In the current work an evaluation of the model results against the AIRBASE European monitoring network measurements is done using model performance indicators (MPC) based on observation uncertainty.

The MPC used in this approach, constructed on the hypothesis that model results are allowed the same margin of uncertainty as measurements, are developed for four statistical indicators (Root Mean Square Error, Normalized Mean Bias, Normalized Mean Standard Deviation and correlation) to summarize the model-observation errors in terms of phase, amplitude and bias. The utility of this approach is to provide a performance scale to inform the user on the expected value an indicator should reach for a particular modeling application. These indicators are then used to identify the strengths and weaknesses of the model application in terms of geographical areas, cities, pollutants and/or period of the year.

Introduction

As Chemistry-Transport Models (CTMs) were initially designed to simulate ozone concentrations within the lower troposphere, a coarse horizontal resolution was sufficient to reach this objective. But during the last decade, air quality legislation has focussed more and more on particulate matter (PM) and CTMs were required to refine their resolution to capture the urban signals as high PM concentrations usually occur in urban areas. In this work a fine resolution (0.0625x0.125°i .e. about 7 km) simulation is performed over Europe for the meteorological year 2009 with the CHIMERE CTM [START_REF] Bessagnet | Regional modeling of carbonaceous aerosols over Europe -Focus on Secondary Organic Aerosols[END_REF]. For the evaluation model performance indicators based on the observation uncertainty [START_REF] Thunis | Performance criteria to evaluate air quality modeling applications[END_REF]) are used to identify the main strengths and weaknesses of the CHIMERE application in terms of geographical area, pollutant and period of the year. Results are presented here for NO2 and PM10 based on a comparison with the AIRBASE monitoring network measurements.

Methodology

The offline Chemistry Transport Model CHIMERE model is fed with ECMWF meteorological fields. Despite their relatively coarse horizontal resolution (16 km) this meteorological input dataset has been preferred to the higher resolution WRF fields since the latter tends to overestimate significantly the magnitude of the wind fields [START_REF] Miglietta | Evaluation of WRF model performance in different European regions with the DELTA-FAIRMODE evaluation tool[END_REF]. Anthropogenic emission fields were derived using a top-down approach over the entire domain extending from 10°W to 30°E in longitude and from 36°N to 62°N in latitude. Boundary conditions were obtained from the monthly mean LMDz-INCA climatology for gaseous species and from the GOCART model for aerosols. Biogenic species are calculated using the MEGAN model while wildfire emissions are issued from GFED3. Some important modifications were made to the code itself or to the input dataset:

•

The relatively coarse resolution of the ECMWF meteorological fields prevents a good representation of the urban effects. Based on literature overview wind fields intensities in urban centres have arbitrarily been decreased by a factor two. Similarly the turbulent diffusion coefficients have been modified within the urban canopy by .. (Bertrand?) •

Based on a comparison between bottom-up and top-down approaches on one hand and on expert judgments on the other, anthropogenic emissions in some Eastern country regions have been increased significantly to reflect the larger effective residential heating emissions in these regions. (factor 2) •

In addition new temporal profiles for residential emissions based on a "degree-days" concept have been generated to account for the fact that emissions related to heating would increase during the colder winter days (same total emissions distributed according to temperature).

The evaluation of the model performances is based on a comparison with the AIRBASE monitoring stations which are classified in terms of urban, suburban and rural background. The evaluation is performed by grouping stations around a series of cities (30 city areas). For each of these 30 cities the monitoring stations within a circle of radius 200 km are used for the evaluation. In total about 650 stations are used both for PM10 and NO2.

The evaluation itself is based on performance indicators normalised by the observation uncertainty [START_REF] Thunis | Performance criteria to evaluate air quality modeling applications[END_REF]). The main performance indicator is constructed as the ratio between the model to observed root mean square error and a function of the observation uncertainty. As discussed in [START_REF] Thunis | Performance criteria to evaluate air quality modeling applications[END_REF], values of this ratio between 0 and 0.5 indicate that, in average, differences between model results and measurement are within the range of their associated uncertainties. Conversely values larger than 1 indicate statistically significant differences between model and measured values. Based on measurement inter-comparison exercises measurement uncertainties values for NO2 and PM10 are provided (Pernigotti et al., 2103). As observation uncertainties generally exhibit much larger relative uncertainties at low concentration levels the required on model performance within tis range of concentrations becomes less stringent. In this way the less certain the measurements are the less stringent the model performances requested should be.

For visualization of this indicator the target diagram proposed by Joliff et al. ( 2009) has been modified by normalizing all quantities by the observation uncertainty (Figure 1, top and bottom rows). The X and Y axis of the diagram represent the observation normalized centered root mean square error (CRMSE) and bias (BIAS), respectively. The distance from the origin then represents the observation uncertainty normalized root mean square error (RMSE). The green area circle identifies the fulfillment of the performance criteria while the dashed green area represents the zone for which model results are within the observation uncertainty range. The negative and positive sides of the X axis are used to identify observed-model differences which are dominated by amplitude (standard deviation) or phase (correlation), respectively. The negative and positive sides of the Y axis identify negative and positive biases, respectively.

Results

Figure 1 provides an overview of the results for PM10 (right) and NO2 (left) where results have been averaged by city areas (each circle represents a city area) and where all station types (urban, suburban and rural) have been included in the analysis. The PM10 target diagram points to an underestimation of the levels, especially in Eastern country cities (Prague, Warsaw, Sofia and Krakow) with an error dominated by a lack of amplitude. Despite the increase of domestic emissions in these countries and the new temporal profiles based on degree days, the amplitude of the signal is yet underestimated. For many of the Mediterranean cities (e.g. Rome, Valencia, Milan, Sevilla, Athens) the model results show a lack of temporal correlation which might be caused by to the difficulty to adequately capture the local scale effects (e.g. sea-breezes) with the 16 km meteorology. The scatter plot (bottom) provides some information on the absolute concentrations and confirms the Eastern Europe underestimation while other city areas do not show significant biases. For NO2 model concentrations exhibit a significant underestimation at almost all cities. According to the Target diagram, the error is dominated by a lack of temporal correlation. The general underestimation could be explained by the fact that NOx emissions generally occur at street level, scale which cannot be sufficiently captured with the current spatial resolution. The lack of temporal correlation might be due to a lack of sufficiently accurate temporal emission profiles for the NOx traffic emissions, as these are emissions are directly linked to the NO2 concentrations.

Conclusion

CTMs are currently able to simulate air quality over large domains with a refined resolution. This allows assessing model performances with respect to PM10 and NO2 in urban areas in different geographical areas in the frame of a single simulation. In this work the CHIMERE CTM has been run over the entire European territory with a spatial resolution of about 7 km. To better capture urban scale effects some improvements have been made to the model itself (e.g. urban sub-scale paramerisations) but also to some input datasets (e.g. emissions). Model performances have been evaluated around 30 European city areas against the AIRBASE measurements. To perform this evaluation model performance criteria based on observation uncertainty have been used.

Despite corrections made to the PM anthropogenic emissions in some eastern country areas (increase of overall emitted totals, degree days corrections) CHIMERE still underestimates the concentration levels. Although the timing of the peaks is quite well reproduced the peaks amplitude is underestimated. Problems also arise in some Mediterranean areas where the model faces difficulties in reproducing the temporal variations of the concentrations. For NO2 the concentration levels are generally underestimated and show a lack of temporal correlation probably due to the still insufficient horizontal resolution.

The proposed evaluation methodology has proved its usefulness to screen model performances. The same methodology can then be used to check the model behaviour in more details (seasonal variations, station details.). Further work will also be done to relate model performances in terms of meteorological and air quality fields.

Figure 1 :

 1 Figure 1: PM10 (left) and NO2 (right) model-observed evaluation. Target diagrams (top) and scatter plots (bottom) provide information for statistics averaged by city areas. For the Target diagrams, stations statistics are sorted and the 10% worst ones are eliminated. The circle on the plot represents the worst remaining statistic. Acronyms are: Du (Dublin), Ro (Rome), Va (Valencia), At (Athens), Mi (Milan), Wa (Warsaw), Kr (Krakow), So (Sofia), Na (Napoli), Ms (Marseille), Se (Sevilla), Pr (Prague). For more information on target diagrams, refer to text.