Ludiwine Clouzot
email: ludiwine.clouzot.1@ulaval.ca

Frédéric Cloutier
email: frederic.cloutier.1@ulaval.ca

Nancy Love
email: nglove@umich.edu

Henryk Melcer
email: hmelcer@brwncald.com

Christophe Ort
email: christoph.ort@eawag.ch

Dominique Patureau
email: dominique.patureau@supagro.inra.fr

Benedek G Plósz

Maxime Pomiès
email: maxime.pomies@irstea.fr

Peter A Vanrolleghem
email: peter.vanrolleghem@gci.ulaval.ca

Jean-Marc Choubert
email: jean-marc.choubert@irstea.fr

Are we about to reliably predict the fate of micropollutants through wastewater treatment plants?

Keywords: Ecological Risk Assessment, Emerging contaminants, Metabolism, Modelling, Regulation, Uncertainty

Models for micropollutants (MPs) are required to provide engineers and decision-makers with reliable tools that will help increase MP removal through wastewater treatment plants (WWTPs) and minimize their impact on the receiving waters. This paper aims at identifying the modelling needs of research (fundamental) vs. practice (design) vs. regulation (compliance) with a focus on the interactions. Different model developments that have occurred recently are first presented with a discussion on the key-question of the 'optimum complexity'. Sampling strategies are also discussed since they influence the model uncertainty. Finally, the paper proposes suggestions for how models might be modified to incorporate our evolving knowledge about MP fate to improve model utility into the future. For instance, the relative role of heterotrophic bacteria versus ammonia oxidizing autotrophic bacteria towards the fate of MPs should be addressed, and the elaboration of a unified agreement on experimental protocols would be necessary to advance model calibration. The degree to which by-products that have ecotoxicological relevance are created during treatment is also a key issue that needs to be considered in future model as well as the use of ecological models as a means to benchmark modifications to wastewater treatment.

INTRODUCTION

Conventional wastewater treatment plants (WWTPs) were not designed for the elimination of micropollutants (MPs). Thus, only some of them are eliminated, transferred to sludge or biotransformed, whereas others are not altered by treatment (Clara et al., 2005a;[START_REF] Joss | Removal of pharmaceuticals and fragrances in biological wastewater treatment[END_REF][START_REF] Choubert | Limiting the emissions of micropollutants: what efficiency can we expect from wastewater treatment plants?[END_REF]Martin-Ruel et al., 2011). Upcoming regulations will be more and more stringent towards the release of MPs to the aquatic environment, especially since, among others, the publication of the European Water Framework Directive (WFD; EC, 2000EC, , 2008)), and also ongoing projects of regulation reinforcement (e.g. list of priority substances, sludge use for agriculture, etc.).

A more mechanistic knowledge, structured in mathematical models, is a potential means for optimizing treatment [START_REF] Gernaey | Activated sludge wastewater treatment plant modelling and simulation: state of the art[END_REF], either with existing infrastructure or by identifying additional treatment stages (tertiary treatment, sludge treatment). However, several gaps still remain and are ongoing topics of research all over the world: good quality data (with associated uncertainty), new concepts (e.g. co-metabolism), detailed data capturing time-dependent inputs, new inputs (organic matter and micro-organism characterization), etc. MP modelling is required to improve our knowledge but also to provide engineers and decision-makers with reliable tools that will help increase MP removal in WWTP and minimize their impact on receiving waters.

The present paper aims at identifying the modelling needs to reliably predict the fate of MP through WWTPs with a focus on the interactions between regulation, engineering practice and research. Different developments that have occurred recently are first presented with a discussion on those that hold the greatest promise for inclusion in future fate models (model structure, processes, calibration and validation). One of the key issues is the question of the 'optimum complexity' of process models. Sampling strategies are also discussed since they influence model uncertainty. Finally, the paper proposes a new modelling approach considering ecotoxicological characteristics of WWTP effluents to respond to the regulatory targets.

The main messages and questions raised in this paper are:

(1) Modelling is required for assessing MP emission into the environment and for optimizing WWTP design and operation. Improving MP modelling will increase the models' reliability and thus, their use by engineers and decision-makers.

(2) Different MP fate models are currently being developed with various definitions of terms and levels of detail regarding the variables and concepts. They concern regulated MPs and also ones of new concern like pharmaceuticals and personal care products. The model complexity (as evidenced by the number of parameters) and the data uncertainty influence the model calibration and its reliability. Do we need to go in further detail or do we need to simplify our models?

(3) What are the next steps in MP modelling? What about innovative and adaptive approaches?

WHAT SHOULD WE EXPECT FROM MP MODELLING? Regulatory point of view

There are many types of regulations concerning MPs that address chemical production, inventory assessment, source control, monitoring, and environmental quality standards. The thrust of environmental quality control to date in both North America and Europe has been the development of water quality criteria to protect flora and fauna in receiving waters. A consequence of the environmental quality focus is that it has led to the ban of only a few of the many thousands of MPs. For example, a ban exists in California on the penta-, octa-but not deca-forms of polybrominated diphenyl ethers (PDBE) in consumer products. In Connecticut, industry is required to stop the use of products containing alkylphenol ethoxylates, which have also been banned by the European Union and Canada (WEF, 2007). It has not, however, led to the widespread development of regulations concerned with MP control in discharges from WWTPs, with some notable exceptions such as the control of organic MPs in the U.S. organic chemical and petroleum refining industries through the limits set on benzene as a surrogate for other organic MPs.

In general, there is an absence of nationwide or statewide regulations for controlling MPs from WWTPs, which, from a modeling prospective, is a challenge since it is difficult to define an effluent target for the simulation of MPs in WWTPs. In the U.S., MPs are largely unregulated at the federal level; instead, a patchwork of regulations at the State and municipal levels exist to control MPs at source rather than at the end of pipe (IJC, 2009). Sometimes, control of specific metal and volatile organic MPs is implemented through municipal sewer ordinances. In Canada, the management and control of chemical substances is regulated at the federal level through the riskbased Canadian Environmental Protection Act, with provincial programs that focus on end of pipe measures (Anon, 2004). In Europe, the European Parliament Directive 2008/105/EC defined environmental quality standards for 33 priority substances for receiving waters in the European Community (EC, 2008). These thresholds are targeted concentrations to reach in receiving bodies, and that define the good 'chemical status'. However, these are not linked to any EC member country or municipal standard for discharging treated WWTP effluents.

The approach to MP control to date has largely focused on individual compounds. However, MPs rarely occur in isolation, making it difficult to determine the specific chemicals responsible for endocrine disruption or other effects [START_REF] North | Report on the San Francisco Bay Area's Safe Medicine Disposal Days, Bay Area Pollution Prevention Group[END_REF]. Aquatic organisms are exposed to numerous compounds that individually may have no noticeable effect, but, when present in mixtures, may have observable effects. Because organisms are not exposed to a single chemical stressor at a time, emphasis could shift to developing regulations based on biological effects rather than concentrations of suspected MPs. New regulations could also shift towards regulating MP as a class, based on a common mechanism for toxicity (i.e., endocrine disruption) or similar chemical structure, rather than by individual compound (WEF, 2007).

Nevertheless, there is a renewed commitment to the prevention of chemicals of concern. Numerous drivers are changing the way governments and industry think about chemicals in everyday products.

Regulations such as the European Union's Registration, Evaluation, and Authorization of Chemicals (REACH) legislation are inducing a cultural shift in industrial chemicals management by requiring data on chemical toxicity and uses, requiring preventive action for classes of chemicals, and shifting the burden of proof to industry to demonstrate safety for high concern chemicals. In preparation for when this initiative results in WWTP discharge regulations that specify effluent requirements for specific MPs, researchers and modelers should continue the development of information that improves our understanding of the mechanism of MP removals in WWTPs and how the plants might be able to achieve effluent requirements of differing complexity, ranging from monthly averages and daily maxima to annual average or never to be exceed values.

Engineering point of view

Engineering practice uses models mainly to select the best process configuration for certain wastewater composition and effluent discharge requirements, to evaluate the efficiency of a design configuration under dynamic conditions and to find optimal operating conditions (aeration, sludge concentration), especially in North-America. As the understanding of the profession increases regarding MP fate and behaviour in different types of unit processes, a clear demand is coming from engineering practice to be able to tap into this knowledge through its incorporation in process models. Very important problems remain to be solved before these models will be usable in day-to-day practice. This is due to the variety of micropollutants to be considered, their variety in properties and the lack of knowledge regarding their behaviour in the complex systems that wastewater treatment plants are. However, models can already now help the design and optimization process, as they can already go beyond the use of empirical knowledge and rules in use today.

Considerable efforts are ongoing worldwide to compare/benchmark treatment technologies [START_REF] Choubert | Limiting the emissions of micropollutants: what efficiency can we expect from wastewater treatment plants?[END_REF][START_REF] Pileggi | Impact of wastewater treatment process configuration on effluent chemistry and biological responses[END_REF] and these give considerable insights in the strengths and weaknesses of certain treatment trains with respect to removal of (classes of) MPs. The comprehensive databases that have been developed [START_REF] Miège | Removal efficiency of pharmaceuticals and personal care products with varying wastewater treatment processes and operating conditions: conception of a database and first results[END_REF] are currently being mined to support model development in order to generalize beyond the particularities of individual treatment plants (Cloutier et al., accepted). It can be expected that in the near future an important increase will be achieved in available unit process models adapted to predict MP fate. The fate of an increasingly wide range of MPs is being characterized thanks to the important measuring programs in place today. While this carries the promise that these models will find their way in engineering practice, considerable research efforts are still needed to reach the stage where engineering practice will have the confidence in models similar to the confidence that practicing engineers now have in nutrient removal models.

Research point of view

In the field of MP modelling, research questions focus on the determination of the suitable mechanistic knowledge, i.e. concepts and equations involved in structured mathematical models. In the academic context, process modelling is useful to test hypothesis, and is a means to identify the right process. One of the key issues is the fate of MPs due to the very low concentration level at which they occur: is co-metabolism a key process to be included in MP models, or is the concept of growth and decay of biomass sufficient? Other questions deal with the biotransformation pathways involved: Is an initiating adsorption step onto sludge necessary? Or does biotransformation occur directly from the soluble fraction? Finally, the consideration of bioavailability of MPs in biokinetic models and prediction of the generation of by-products remain unclear.

Sampling strategies (frequency, location) remain an important issue that draws attention by engineers and is important considering the requirements of regulators. Research work in academia should pursue progress in how to consider modelling results, and how to compare them with threshold values given their overall inaccuracy (sampling + analysis). The importance of sample analysis at higher temporal resolution should be considered to better understand the influent variability, and thus predict the performance of treatment systems.

HOW DO WE CURRENTLY MODEL MP FATE THROUGH WWTPs?

State-of-the-art and limits of existing models Biotic and abiotic phenomena influence the distribution and the fate of MP throughout the treatment system. This distribution/fate is governed by the physicochemical properties of the pair MP and matrix, together with the process design and operating conditions of the treatment plant. MPs are removed from WWTPs by various processes such as volatilization, biodegradation, sorption, air stripping, photolysis, etc. [START_REF] Rogers | Sources, behaviour and fate of organic contaminants during sewage treatment and in sewage sludges[END_REF]. State-of-the-art models use the chemical and physicochemical properties of MPs that are relevant to each process in order to assess their fate in WWTPs. A certain degree of complexity has to be reached if models are to be used as a tool to increase the removal of MPs in WWTPs.

The development of models that can predict the fate of MPs in WWTPs started in the 80's, after the development of the fugacity concept [START_REF] Mackay | Finding fugacity feasible[END_REF]. Several models were then created, most notably SimpleTreat [START_REF] Struijs | A spreadsheet-based box-model to predict the fate of xenobiotics in a municipal wastewater treatment plant[END_REF], WW-TREAT (Cowen et al., 1993), and TOXCHEM [START_REF] Melcer | Modeling Volatile Organic contaminants' fate in wastewater treatment plants[END_REF]. Meanwhile, the development of the state-of-the-art Activated Sludge Models (ASM) [START_REF] Henze | A general model for single-sludge wastewater treatment systems[END_REF] allowed modellers to create various add-on models or ASM-based models to simulate both the fate of traditional pollutants and specific MPs (e.g. Plósz et al., 2010a). Models published concern volatile organic carbons (VOCs), surfactants, and metals (particularly priority metals Cd, Pb, and Ni) with more than three different models for each [START_REF] Lee | Advanced steady-state model for the fate of hydrophobic and volatile compounds in activated sludge[END_REF][START_REF] Byrns | The fate of xenobiotic organic compounds in wastewater treatment plants[END_REF][START_REF] Dionisi | Theoretical and experimental analysis of the role of sludge age on the removal of adsorbed micropollutants in activated sludge processes[END_REF]. Moreover, PAHs (regulated in the WFD), bisphenol A (BPA), some pesticides (like DDT, dieldrin, lindane) are referenced in 2 or 3 models [START_REF] Lee | Advanced steady-state model for the fate of hydrophobic and volatile compounds in activated sludge[END_REF][START_REF] Byrns | The fate of xenobiotic organic compounds in wastewater treatment plants[END_REF][START_REF] Urase | Separate estimation of adsorption and degradation of pharmaceutical substances and estrogens in the activated sludge process[END_REF]. In recent works, a few papers dealt with emerging contaminants as hormones and pharmaceutical substances [START_REF] Urase | Separate estimation of adsorption and degradation of pharmaceutical substances and estrogens in the activated sludge process[END_REF][START_REF] Plósz | Diurnal variations in the occurrence and the fate of hormones and antibiotics in activated sludge wastewater treatment in Oslo, Norway[END_REF]. Only a few papers have defined the conditions for which these models have been set-up, for instance temperature range, mixed liquor suspended solids concentration, etc.

The partitioning of MPs (i.e. the sorption and desorption processes), onto suspended solids, X TSS [g.L -1 as TSS] (e.g. activated sludge), can be characterised with an equilibrium state between the concentrations of the dissolved parent compound, S XOC [g.L -1 ], and that in the solids phase, X XOC [g.L -1 ] that gives: ¸

where the partitioning or sorption coefficient is denoted as K D [L.gX TSS -1 ]. The equilibrium partitioning can also be characterised with the ratio k Sor/ k Des = K D , where k Sor and k Des are the sorption and desorption rate coefficients, respectively (e.g., [START_REF] Joss | Biological degradation of pharmaceuticals in municipal wastewater treatment: Proposing a classification scheme[END_REF][START_REF] Lindblom | Dynamic experiments with high bisphenol-A concentrations modelled with an ASM model extended to include a separate XOC degrading microorganism[END_REF]. Sorption and desorption can be assumed to be in close equilibrium if the sorption substance mass flux is about ten times higher than the biodegradation flux [START_REF] Ternes | Human Pharmaceuticals, Hormones and Fragrances: The challenge of micropollutants in urban water management[END_REF]. Consequently, a K D value is usually measured instead of using an equilibrium constant The main limit identified in the existing models is the variety of protocols used for the direct determination of the K D . The common procedure consists in spiking biological sludge by chemical addition, and then measuring the equilibrium concentrations in the liquid and solid phases of sludge. The occurrence of biotransformation during the experiment is limited by using different protocols, like a prior aeration period to reduce the residual substrate (COD and NH 4 -N), washing of the sludge with distilled water, sterilization, inhibition with chemical agents or sparging with argon gas. However, the authors of those studies did not prove that these methods have no influence on sludge properties.

Sorption also occurs on colloids [START_REF] Holbrook | Investigation of sorption behavior between pyrene and colloidal organic carbon from activated sludge processes[END_REF], and the concentration and the nature of these colloids (varying from one sludge to another) influences greatly the sorption/desorption capacity on suspended solids [START_REF] Vinken | Bioavailability of a nonylphenol in dependence on the association to dissolved humic substances[END_REF]) and the commonly measured K D [START_REF] Barret | A three-compartment model for micropollutants sorption in sludge: Methodological approach and insights[END_REF]. In addition, several MPs, such as most of the pharmaceuticals and personal care products present in municipal sewage, are ionizing substances, i.e. anionic, cationic or zwitterionic, and their partitioning behaviour thus is impacted by pH and ionic interactions. In WWTP models, one way to account for the impact of pH on partitioning behavior is to use different K D values estimated under typical pH conditions, prevailing in aerobic and anoxic reactors (Plósz et al., 2010a).

Biotransformation comprises processes, such as the cleavage of chemical moieties (e.g., methyl) or oxidation processes (e.g., carboxylation), catalysed by microbial enzyme activities. This has also been shown by several studies, which implies that biotransformation is a more appropriate term to use instead of complete biodegradation [START_REF] Kim | Degradation of bisphenol A and nonylphenol by nitrifying activated sludge[END_REF]. The biotransformation of MPs can be modeled in several manners. [START_REF] Lindblom | Dynamic experiments with high bisphenol-A concentrations modelled with an ASM model extended to include a separate XOC degrading microorganism[END_REF] used a specific biomass which grows on MPs with a maximum specific growth rate, while other models did not involve specific bacteria, but instead, the biotransformation was a first [START_REF] Byrns | The fate of xenobiotic organic compounds in wastewater treatment plants[END_REF] or pseudo-first order reaction proportional to X TSS [START_REF] Joss | Biological degradation of pharmaceuticals in municipal wastewater treatment: Proposing a classification scheme[END_REF]. Different hypotheses exist concerning the compartment in which biotransformation can prevail. Dissolved MP biotransformation is generally considered. In this case, kinetic parameters are determined by measuring the concentration evolution of the dissolved MP. Models by [START_REF] Lee | Advanced steady-state model for the fate of hydrophobic and volatile compounds in activated sludge[END_REF], [START_REF] Byrns | The fate of xenobiotic organic compounds in wastewater treatment plants[END_REF][START_REF] Peev | Modelling the degradation of low concentration pollutants in membrane bioreactors[END_REF] include biotransformation for both compartments. However, the experimental evidence related to the biotransformation of sorbed fractions is limited as shown by [START_REF] Delgadillo-Mirquez | A new dynamic model for bioavailability and cometabolism of micropollutants during anaerobic digestion[END_REF].

Parameter values are mainly available for aerobic conditions, for PAHs, surfactants, and a few pharmaceutical compounds [START_REF] Urase | Separate estimation of adsorption and degradation of pharmaceutical substances and estrogens in the activated sludge process[END_REF][START_REF] Joss | Biological degradation of pharmaceuticals in municipal wastewater treatment: Proposing a classification scheme[END_REF]. But values for numerous other MPs are lacking. A few papers studied the effect of redox conditions on the transformation rates of MPs. Plósz et al. (2010a) and [START_REF] Suarez | Removal of Pharmaceutical and Personal Care Products (PPCPs) under nitrifying and denitrifying conditions[END_REF] have compared aerobic and anoxic conditions for some pharmaceutical compounds, and obtained different biological rates. As for the biotranformation rate, its value can significantly vary under aerobic and anaerobic/anoxic conditions (Plósz et al., 2010a;[START_REF] Vezzaro | Unit Process Models for Fate of Priority Pollutants[END_REF]. This is due to the different MP biotransformation capacity of reactions catalysed by heterotrophic and autotrophic bacteria populations in activated sludge. Numerous papers have also reported that the degradation of MPs is only possible in the presence of another compound used as carbon and energy source [START_REF] Clara | The solids retention time-A suitable design parameter to evaluate the capacity of wastewater treatment plants to remove micropollutants[END_REF].

Co-metabolism is a mechanism in which the removal of the MP does not result in any significant biomass growth, i.e. the biomass yield attributed to MP degradation is insignificant. Recently, Delgadillo-Mirquez et al. ( 2011) used such co-metabolism kinetic in a four-compartment (gas, aqueous, suspended solids and colloidal matter) dynamic model to describe the fate of polycyclic aromatic hydrocarbons (PAHs) in anaerobic digestion. In their model, biotransformation is described using the co-metabolic model of [START_REF] Criddle | The kinetics of cometabolism[END_REF] and [START_REF] Chang | Model for the cometabolic biodegradation of chlorinated organics[END_REF]. Conversely, readily biodegradable growth substrates can also competitively inhibit MP transformation by limiting the access to the non-specific enzyme sites [START_REF] Chang | Model for the cometabolic biodegradation of chlorinated organics[END_REF]Plósz et al., 2010a).

Volatilization is modeled along with air stripping using a kinetic that is proportional to the k L a value [START_REF] Lee | Advanced steady-state model for the fate of hydrophobic and volatile compounds in activated sludge[END_REF]. Models also consider that the fraction of MP that is sorbed to the TSS is not available for mass transfer across the water/air interface [START_REF] Byrns | The fate of xenobiotic organic compounds in wastewater treatment plants[END_REF]. Volatilization can also be a relevant process in treatment trains with large surface area (e.g., waste stabilization ponds). In these large ponds, but also in tertiary treatments where irradiation is involved, photolysis becomes another relevant removal mechanism. Direct photolysis occurs when light is absorbed by the MP while indirect photolysis refers to processes initiated through the absorption of light by intermediary compounds [START_REF] Schnoor | Environmental Modelling. Fate and Transport of Pollutants in Water, Air, and Soil[END_REF]. Therefore, it is mainly affected by UV radiation in stabilization ponds (from the sun) and in tertiary treatment units (from UV lights), the suspended solids concentration which limits the penetration of light in the tank, and the absorbance characteristics of the MP. A possible modelling approach is described in [START_REF] Vezzaro | Unit Process Models for Fate of Priority Pollutants[END_REF].

Models predicting the fate of MPs through WWTPs are a mean to evaluate the emissions, that is to say how much MPs will be removed with the sludge, volatilized, photolyzed or released in the receiving waters. In addition, recent development and experimental investigations lead to a proposal of more suitable concepts to use. However, our understanding as included in MPs models is still not sufficiently advanced for these models to be used for the design and optimization of treatment processes. The understanding of the mechanisms implied and the influence of local and operating conditions still needs to be improved.

Model uncertainty related to the sampling strategy

To assess the overall performance of a WWTP related to MP removal we need to know at least influent and effluent loads. Significant knowledge about matrix effects is required when analyzing MPs to minimize the uncertainty associated with the data obtained and thus, the resulting model uncertainty. The sampling step also has a significant impact on the quality of the data that will be implemented in the model. Indeed, the sampling strategy has to be designed taking into account the questions that need to be answered and the site-specific setting (e.g., inflow, recirculation, etc.). For the removal of MPs associated with the solid phase, the design and performance of primary and secondary clarifiers are important. The affinity of a compound to different solid matter must be characterized (maybe sufficiently in laboratory experiments) and in the WWTP the solid matter and its removal must be assessed. Influent loads to primary clarifiers can be highly dynamic (Ort et al., 2010a). This must be considered with an appropriate, typically precautionary high sampling frequency as demonstrated by [START_REF] Ort | Sampling for PPCPs and illicit drugs in wastewater systems: Are your conclusions valid? A critical review[END_REF] -note that the sampling frequency (determined by the substances' fluctuations) is not directly linked to the frequency of the model analysis (that is determined by the research questions). If a primary clarifier exists, most of the high intra-hour fluctuations in the influent to biological treatment steps are attenuated, which implies that the sampling frequency can be reduced to obtain representative average influent concentrations. For the sampling location it is important to know whether internal recirculation can be captured or not (both hydraulically as well as for the impact on average concentrations).

Different temporal resolutions and sampling approaches have been proposed in the past to better understand influent variability and the effect on the performance of treatment systems: three 8-h composite samples (e.g. [START_REF] Göbel | Occurrence and sorption behaviour of sulfonamides, macrolides, and trimethroprim in activated sludge treatment[END_REF] (one day, flow-proportional); [START_REF] Joss | Removal of pharmaceuticals and fragrances in biological wastewater treatment[END_REF] (one day, time-proportional); Plósz et al., 2010a, b (three days, flow-proportional)). Recently, samples were collected even at higher temporal resolution (only influent: e.g. Gerrity et al., 2011 (two 12-h periods, 30-minute composites, continuous time-prop.); [START_REF] Salgado | Assessing the diurnal variability of pharmaceutical and personal care products in a full-scale activated sludge plant[END_REF] (four days, grab samples every 2h). This contrasts with suggestions to reliably determine average removal rates by averaging influent loads over longer periods (e.g. [START_REF] Joss | Removal of pharmaceuticals and fragrances in biological wastewater treatment[END_REF][START_REF] Wick | Fate of beta blockers and psycho-active drugs in conventional wastewater treatment[END_REF] (3 weeks, flow-proportional mixed two 2-day composites and one 3-day composite to 7-consecutive-day composites); Majewsky et al., 2011 (4-conseuctive-day influent composite and 1-day effluent composite)).

Assessing the sampling error for a particular sampling campaign (e.g. a given individual 24-hour period) is not possible. It can only be assessed on average for a well characterized urban drainage system and compound of interest. Therefore, it should be the primary goal to minimize sampling uncertainty by using a non-biased sampling mode (i.e. flow-proportional, or at least volumeproportional) with a sampling frequency to capture the relevant dynamics. If the dynamics are unknown, the sampling frequency should be precautionary high e.g. (Ort et al., 2010a). Conventional sampling equipment is required to collect at least 200 individual samples in a 24-hour period.

Experimentally determining/predicting the average removal of a compound in a given treatment system under stable conditions (i.e. wastewater composition and dry weather conditions) may be possible fairly reliably by evaluating a large number of samples (typically 24-h composites), even if sampled loads are subject to random variation. Another approach would be to evaluate longer time periods as suggested by [START_REF] Majewsky | Xenobiotic removal efficiencies in wastewater treatment plants: Residence time distributions as a guiding principle for sampling strategies[END_REF]. Note, this does not hold true if a bias exists (e.g. caused by time-proportional sampling with pollutant concentrations positively or negatively correlated to wastewater flows). In contrast, gaining more mechanistic insight at higher spatial/temporal resolution is only possible with non-biased, representative samples. It will be decisive if relevant degradation mechanisms and corresponding variables (i.e. operational parameters) can be reliably measured at sufficiently high spatial and temporal resolution to improve mechanistic models.

In conclusion, there is no general answer to deal with the sampling strategy, it depends on the question: research (fundamental) vs. practice (design) vs. regulation (compliance). It also depends on the dynamics of the processes that are observed, the hydraulic residence time, the assigned objective, and probably the MP that is considered. A consequent spatio-temporal resolution for samples to be analyzed is then determined. The sites' characteristics determine how the samples need to be obtained to be representative. It should be noted that shorter period composite samples meant to gain more insight in the dynamics require shorter sampling intervals to avoid sampling errors -e.g. the collection of a 24-hour composite sample with sampling intervals of 30 minutes may be appropriate while the collection of a representative 1-hour composite sample at the same site may require significantly shorter sampling intervals [START_REF] Ort | Sampling for representative micropollutant loads in sewer systems[END_REF].

HOW SHOULD WE MODEL MP FATE IN FUTURE?

As detailed elsewhere in this paper, researchers have made significant progress in developing mathematical structures for deterministic models that predict the fate of MPs through various processes in WWTPs, particularly in the biological treatment process. The question to be answered now is: What information is not helpful in our quest for better predictive models of MP fate, and what data are needed to inform the development of better models? There are several challenges faced by modellers, and suggestions for how models might be modified to incorporate our evolving knowledge about MPs to improve model utility into the future need to be formulated.

The different needs risen by regulators, engineers and modellers on MPs should be integrated in an overall approach. Therefore, of equal importance to predicting the fate of MPs through WWTPs is translating the model outputs into an ecotoxicological framework responding to regulation based on biological effects of MPs.

Model structure

Our knowledge about factors that are causal in the fate of MPs and the mathematical structure that those factors require when being modelled is evolving. For example, many of the early experimental papers that reported on MP fate did not provide enough information about the process engineering parameters that differed between the various sites that were sampled. Eventually, the loss of some MPs was correlated with changes in solids retention time (SRT) across a range of treatment configurations [START_REF] Clara | The solids retention time-A suitable design parameter to evaluate the capacity of wastewater treatment plants to remove micropollutants[END_REF]. For other MPs, e.g. diclofenac, carbamazepine, however, SRT proved to be an insufficient predictor of MP fate [START_REF] Strenn | Carbamazepine, diclofenac, ibuprofen and bezafibrateinvestigations on the behaviour of selected pharmaceuticals during wastewater treatment[END_REF]. More recently, research on the relative role of heterotrophic bacteria versus ammonia oxidizing autotrophic bacteria on the fate of selected MPs has shown that these groups of bacteria probably play different roles in defining MP fate [START_REF] Khunjar | Elucidating the relative roles of ammonia oxidizing and heterotrophic bacteria during the biotransformation of 17αethinylestradiol and trimethoprim[END_REF][START_REF] Love | Understanding Microaerobic Metabolism in a Sustainable World, Status Report[END_REF]. Therefore, a lack of correlation by SRT may be due more to our lack of knowledge about the physiological condition of various ecological groups that are present in biological processes. Current model structures do not consider this evolving knowledge, but if it did our ability to more consistently model the fate of MPs would improve. It is helpful to consider that we have a long and relatively successful history of modelling processes that combine these ecological groups.

Model calibration and validation

The quality of data that is used to both develop the mathematical structure for MP models and to adequately calibrate the models is insufficient. Methods between research groups are inconsistent, and very often insufficient information is provided by experimentalists about factors that may be causal and should be included in models. Furthermore, it is important to perform well controlled laboratory studies in order to better understand causal mechanisms of MP fate. However, many of these studies are analytically constrained so that MP concentrations that are higher than what is typically encountered in full-scale systems are used (e.g. [START_REF] Lindblom | Dynamic experiments with high bisphenol-A concentrations modelled with an ASM model extended to include a separate XOC degrading microorganism[END_REF]. Doing so allows experimentalists to test hypotheses about fate and causal factors, but it is questionable whether model structures that evolve from those experiments are relevant to full-scale systems. Therefore, modellers have to carefully scrutinize which experimental results to use in structuring and parameterizing their models, and experimentalists need to conduct experiments at environmentally relevant conditions. A unified agreement on protocols that are to be used when conducting experiments on MP fate would be helpful toward this end.

In addition, the frequency of data collected during experiments is often inadequate for model calibration and validation. This occurs because MP analysis is time consuming and expensive. It requires expensive analytical equipment, detailed protocols that require costly consumable supplies, and analysts with significant knowledge about matrix effects when analyzing MPs. Furthermore, we have very limited to no knowledge about how the ecology of biological processes evolves or adapts to the presence of MPs. Therefore, we need both frequent data collection over short (<1 day) time periods appropriate for model calibration, but then repeated sampling events over long time periods (e.g., a sampling event once a month for a few years) to see how parameters change with time and season. To reduce costs when executing such a plan, some are proposing the use of surrogate MPs that serve to represent classes of MPs, and to use the fate of those surrogates to predict the fate of a broader range of compounds [START_REF] Drewes | Development of Indicators and Surrogates for Chemical Contaminant Removal during Wastewater Treatment and Reclamation[END_REF]. This reduces the scale of the analytical problem and should be considered by the modelling community, in cooperation with experimentalists, when planning future experimental campaigns.

Adaptation to future wastewater treatment technologies

Thinking beyond current-day WWTP technology, there are dramatic changes underway in the WWT industry that are motivated by a desire to develop more sustainable (Triple Bottom Line: less environmental impact, more cost effective, acceptable by society) strategies for wastewater management. The WWT sustainability movement considers treatment technology options (e.g., mainstream anaerobic treatment, source separation with physical chemical treatment of yellow water where many MPs reside) that will dramatically change the pathways through which MPs are stabilized, and in some cases a WWT design that is touted as being more sustainable may have poorer overall removal of MPs and be more of an ecotoxicological threat. Therefore, the structured models we are developing today to predict MP fate should be vetted against these new generation WWT strategies to consider if modified model structures are needed.

Limit of fate models

There is a lack of information on important by-products and their physico-chemical, biotransformation, and ecotoxicological characteristics. Identifying by-products involves complicated analytical techniques that often can't be rendered on full-scale systems. This is because use of radiolabeled forms of MPs and tracing the fate of the radiolabeled carbon is one of the best methods for detecting and identifying by-products, using a range of analytical instruments. There is also value in identifying by-products because not doing so renders the biological reactor to being a black box. An example of the importance of by-product identification was reported by [START_REF] Khunjar | Elucidating the relative roles of ammonia oxidizing and heterotrophic bacteria during the biotransformation of 17αethinylestradiol and trimethoprim[END_REF]. In that study, the authors showed that 17Į-ethinylestradiol (EE2) was converted into the conjugated form, sulfo-EE2, that can ultimately be deconjugated to the highly estrogenic EE2, rendering the impact of the effluent more estrogenic than would have been predicted based on the composition of the effluent as it left the treatment plant. This is very important, because almost no analytical campaigns on MP fate in full-scale WWTPs monitors for sulfo-EE2.

We have inadequate knowledge of the degree to which by-products that have ecotoxicological relevance are created during WWT, and therefore models do not (or rarely) consider these effects. No current MP models consider the role that the WWTP biota plays in transforming MPs into ecotoxicologically-relevant by-products, and how WWTP design and operation can be modified to reduce the ecological risk imposed by the effluent. As stated previously in this paper, regulation is more oriented toward the overall biological effect of MPs rather than concentrations of every suspected MPs. Indeed, ecological risk assessment (ERA) is widely used by decision-makers to predict potential adverse effects of anthropogenic activities on various ecosystems [START_REF] Newman | Fundamentals of ecotoxicology[END_REF]. ERA is evolving from experimental measurements to ecological models that predict the effect of contaminants on the aquatic organisms. Therefore, the combination of fate models with ecological models would allow accurate prediction of WWTP's impact on the receiving waters. Integrated urban wastewater systems (IUWS) already consider the system from the sewers to the receiving waters by combining hydrological, geomorphological and fate models [START_REF] De Keyser | Combining multimedia models with integrated urban water system models for micropollutants[END_REF]. Why not add ecological models to complete this integrated system?

Adaptation to regulatory targets:

Benchmarking is commonly applied in treatment modeling to objectively compare different wastewater treatment technologies, designs and operating strategies. Model simulations provide the benchmark criteria for energy use, effluent quality and greenhouse gas emissions [START_REF] Flores-Alsina | Including greenhouse gas emissions during benchmarking of wastewater treatment plant control strategies[END_REF]. What about a simulation benchmark criterion for the ecological impact?

A typical aquatic ecosystem model is currently developed to calculate a benchmarking criterion for WWTPs [START_REF] De Laender | Validation of an ecosystem modelling approach as a tool for ecological effect assessment[END_REF]Clouzot et al., accepted). The model inputs are the concentrations of selected MP indicators or overall estrogenicity predicted or measured in WWTP effluents. The model outputs are the biological effects of WWTP effluents on the receiving waters considering direct effects of MPs on aquatic organisms (e.g., endocrine disruption, acute toxicity, etc.) as well as the consequences on the whole ecosystem through ecological interactions i.e. feeding and competition relationships. Therefore, the ecosystem model can be used to benchmark various WWT technologies within an ecotoxicological context.

In future, MP regulation will probably require ecological models as a means to support decisionmaking regarding modifications to wastewater treatment and thus, researchers should start now considering the integration of such models in WWTP benchmarking.

CONCLUSION

Upcoming regulations towards MPs are one of the driving forces that could increase the use of modeling by engineers and decision-makers. Also research work must still be carried out to improve our mechanistic knowledge and to structure it into mathematical models. Experimental data with associated uncertainty, sample analysis at higher temporal resolution, new concepts (e.g. co-metabolism), new inputs (organic matter characterization) are the key-issues for improving the knowledge included of fate model. The relative role of heterotrophic bacteria versus ammonia oxidizing autotrophic bacteria towards the fate of MPs should be addressed, and the elaboration of a unified agreement on experimental protocols would be also necessary to progress on model calibration. In future, MP regulation will probably require ecological models as a means to support decision-making regarding modifications to wastewater treatment and thus, researchers should start now considering the integration of such models in WWTP benchmarking. The degree to which byproducts that have ecotoxicological relevance are created during treatment is also a key issue that needs to be considered in future models.

ACKNOWLEDGEMENT

Maxime Pomiès is a PhD student at Irstea, financed by Onema (French National Agency for Water and Aquatic Ecosystems), and directed by Dr. Marina Coquery (Irstea, UR MALY) and Professor Christelle Wisniewski (UMR Qualisud, UFR des Sciences Pharmaceutiques et Biologiques, université Montpellier 1). Peter Vanrolleghem holds the Canada Research Chair on Water Quality Modelling and receives funding from the Canadian Water Network for the EC-2 project.


B Knechtle

P Knechtle

R Lepers

Participation and performance trends in ultra-triathlons from 1985 to 2009

Keywords:  gender,  sex difference,  swimming,  cycling

3. Material and methods 4. Results 5. Discussion 6. Perspectives 7. Acknowledgements 8. References We examined the changes in participation and performance trends in ultra-triathlons, from the Double Iron (7.6 km swimming, 360 km cycling, 84.4 km running) to the Deca Iron (38 km swimming, 1800 km cycling, 422 km running), between 1985 (first year of a Double Iron)

and 2009 (25 years). The mean finish rate for all distances and races was 75.8%. Women accounted for ∼8-10% of the ultra-triathlons starters. For Double and Triple Iron, the number of finishers per year increased, from 17 to 98 and from 7 to 41, respectively. In the Deca Iron, the finishers per race have remained <20 since the first event was held, up to 2009.

Concerning World best performances, the men were ∼19% faster than the women in both the Double and Triple Iron, and ∼30% faster in a Deca Iron. With the increasing length of ultratriathlons, the best women became relatively slower compared with the best men. Further investigations are required to understand why this gender difference in total performance time increased with the distance in ultra-triathlons.

The discipline of triathlon involves successively swimming, cycling and running. The traditional distances vary from the short (or Olympic) distance, i.e.1.5 km swimming, 40 km cycling and 10 km running, to the Ironman distance, i.e. of 3.8 km swimming, 180 km cycling and 42.2 km running (Lepers, 2008). Triathlons of greater distances than the Ironman are usually called "ultra-triathlons," such as the Double Ironman triathlon (2IMT) distance of 7.6 km swimming, 360 km cycling and 84.4 km running (Lehmann et al., 1995;Gastmann et al., 1998), the Triple Ironman triathlon (3IMT) distance of 11.4 km swimming, 540 km cycling and 126.6 km running (Knechtle et al., 2007a;Knechtle & Kohler, 2009), and the Deca Ironman triathlon (10IMT) distance of 38 km swimming, 1800 km cycling and 420 km running (Knechtle et al., 2007b).

The Ironman distance is the most popular long-distance triathlon. Since the first event was held in 1978, participation has increased, and nowadays thousands of triathletes compete in about 25 Ironman races all over the world in order to qualify for the Ironman Hawaii, where each year more than 1700 triathletes participate in the Ironman triathlon World Championship (Lepers, 2008;Lepers et al., 2010). The first ultra-triathlons, of distances greater than the In the last two decades the ultra-endurance events, especially ultra-running and Ironman triathlon, have become more and more popular throughout the world (Lepers, 2008;Hoffman & Wegelin, 2009;Hoffman, 2010;Lepers et al., 2010). Participation and performance trends have previously been investigated for ultra-marathons (Hoffman & Wegelin, 2009;Hoffman, 2010) and triathlons (Lepers, 2008), but not for ultra-triathlons. Hoffmann and colleagues analyzed participation and performance trends in 100-mile ultra-marathons (Hoffman & Wegelin, 2009;Hoffman, 2010). In the "Western States 100-Mile Endurance Run," they found an increase in starters from 1974 to 1986, and thereafter a stable entry. In 1986, the first women competed in that race. The participation among women has increased from 10-12% in the late 1980s to 20-22% where it has remained since 2001 (Hoffman & Wegelin, 2009). In the Hawaii Ironman triathlon, nowadays, 27% of the participants are women. The number of total starters at the Western States Endurance Run and at the Hawaii Ironman triathlon have been stable since 1990s because of a limitation in the number of entries than can be accepted rather than a lack of interest in participating in these events.

It has been suggested that gender differences in running would disappear over longer distances. Sparling et al. (1998) analyzed the World best times from the 1980s to 1996 for the 1500 m and marathon distances. In 1980, the marathon was a fairly new event for women.

After an initial improvement, the best times for women have leveled off and remain ∼11% behind the men. Coast et al. (2004)investigated the distances between 100 m and 200 km. Also over longer distances speeds were different, with men being ∼12% faster than women. Indeed, longer distances were associated with greater differences. Regarding the ultramarathon distances, the finishing times of women improved relative to men throughout the 1980s, but then stablized over the past two decades with the fastest women running ∼20% slower than the fastest men. Interestingly, the average times of the fastest runners have not changed over the past two decades for either gender (Hoffman, 2010).

The aim of this study was to investigate the participation and performance trends of both men and women over the ultra-triathlon distances, ranging from 2IMT to 10IMT, from the beginning in 1985 until 2009. In the analysis of Ironman performances at the Ironman Hawaii from 1981 to 2007, performance times decreased in the beginning, but reached a plateau within 10 years (Lepers, 2008). Gender differences for the split times in swimming, cycling and running, and the total race time, were ∼10% (Lepers, 2008). We hypothesized that for longer distances up to the 10IMT, there would be an improvement in performance over the 25 years, and male athletes would remain faster compared with women athletes.

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Material and methods

1. Top of page 2. Abstract 3. Material and methods 4. Results 5. Discussion 6. Perspectives 7. Acknowledgements 8. References All the race results from 2IMT to 10IMT, showing total times and the split times for swimming, cycling and running, were collected from 1985 to 2009 by two of the authors. All the race results were provided by the organizers of the different races. The total number of starts and finishes were considered even if some people likely completed multiple events. Linear regressions were used for estimating the changes of selected variables (total, swimming, cycling and running performances for both men and women winner of each race) across years. One-way ANOVA was used to compare gender difference in swimming, cycling, running and total time across ultra-triathlons. Data analysis was approved by the Ethical Committee of the Canton, St. Gallen, Switzerland.

Participation

The total number of starts and finishes for both men and women in the different ultratriathlons, since the first year each event was held, are given in Table 1. The 4IMT and 5IMT appear to have been considered as "experimental" on the way to the longest ultra-triathlon distance, i.e. the 10IMT, and so the number of race starts and finishes in these events have been very small compared with the other ultra-triathlon distances. Therefore, we focused only on the 2IMT, 3IMT and 10IMT in the following results. Figure 1 shows the changes in women and men finishers in the 2IMT, 3IMT and 10IMT distances since the first year of each race. For the 2IMT and 3IMT the total number of finishers has increased since the first year. In contrast, in the 10IMT, the total number of finishers decreased after the first year, until 1998, and then increased thereafter, until stabilizing from 2004.

Performance

Table 2presents the actual World best times in the 2IMT, 3IMT and 10IMT, for both men and women, together with the corresponding swim, cycle and run split times. When the World best total performance times were compared, gender differences were similar for the 2IMT and 3IMT (∼19%), while it was greatest for 10IMT (∼30%).

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Figure 4 shows the changes in performance times for both the women and men winners in the ten 10IMT held since 1992. The total swimming, cycling and running performances have not significantly changed since the first event for either men or women.

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The average differences in the total, and in the swimming, cycling and running, times between the women and men winners at 2IMT, 3IMT and 10IMT are presented in Table 3. Gender differences for the total distances, together with the three disciplines, are in the same range for both the 2IMT and 3IMT, but are significantly greater for the 10IMT when compared with the 2IMT and 3IMT, except for running. 10IMT (n=7) 40.3 ± 11.8 *,$$ 47.4 ± 19.3 *,$ 39.5 ± 12.5 **,$$ 45.9 ± 33.9

The trends in the top five men's times' spreads are shown in Fig. 5 for all the 2IMT, 3IMT and 10IMT held since 1985. The gradients of the regression lines demonstrate that the top five times' spreads have not changed since the first event over any distance. The aim of this study was to investigate the participation and performance trends at ultratriathlon events from 1985 to 2009. We hypothesized that an improvement in performance over the 25 years would occur, and that male athletes would be faster compared with female athletes. Apart from the study of Lepers ( 2008) at the Ironman Hawaii, and the studies from Hoffman and colleagues (Hoffman & Wegelin, 2009;Hoffman, 2010) in the 100-miles ultramarathons in North America, this is the first study to describe the development of ultraendurance events over 25 years, where the athletes pushed themselves to the limits of ultraendurance performance. Interestingly, the length of the races for ultra-triathletes increased within a short period of time.

Participation and finisher rates

In 1985 the first 2IMT was held. Within 7 years the athletes had moved up to the 3IMT, the 4IMT and the 5IMT, and ultimately to the 10IMT. Owing to the low number of races over the 4IMT and 5IMT distances, we focused our analysis on the 2IMT, 3IMT and 10IMT. While the organization of a 2IMT and 3IMT is not an insoluble logistic problem, distance of more than the 3IMT require a closed circuit to traffic regarding the rules of the International Ultra-Triathlon Association. The only organizer who is able to offer for weeks a closed circuit is the organizer in Monterrey, Mexico, where nearly all races of 5IMT and longer were held (Knechtle et al., 2007b).

With the increasing length of the races, the number of participants dramatically decreased.

The rate of male finishers in all distances was ∼80%, whereas it varieddue to the low number of participantsin females, between 55% for the 10IMT and 100% for the 4IMT.

In the "Western States 100-Mile Endurance Run" the participation of women increased between 1986 and 2007 to ∼20% of all starters (Hoffman & Wegelin, 2009). The overall yearly finish rates have ranged from 48% to 80% since 1978, and the overall finish rate for men (64%) was comparable to that of women (62%). When all the 100-mile ultra-marathons in North America were analyzed, the average finisher rate was ∼60% (Hoffman, 2010). For the ultra-triathletes, however, the mean finish rate was higher for both men (∼78%) and women (∼72%) over all the ultra-triathlon distances, compared with the 100-mile ultramarathoners.

In the "Western States 100-Mile Endurance Run," the average yearly number of starters was around 300 men and 50 women runners (Hoffman & Wegelin, 2009). With a total distance of 161 km and an average finish time between 25 and 30 h, the "Western States 100-Mile Endurance Run" is comparable to the shortest distance in ultra-triathlon, i.e. the 2IMT. Most of the ultra-triathletes participated in the 2IMT and in 3IMT. These two distances can be completed without sleep and at a rather high speed (Knechtle et al., 2007a(Knechtle et al., , 2008a)).

In ultra-triathlons, female participation was lower compared with the 100-mile ultramarathoners (Hoffman & Wegelin, 2009). The number of women starters in the 2IMT has slightly increased and accounted for ∼10% in the last 10 years; for the 3IMT and 10IMT, women starters were, from the beginning of the events, at 8% and 10%, respectively. In the Ironman Hawaii, however, the number of women increased progressively from 20 in 1981 (6% of the participants) to more than 450 in 2007 (27% of the participants). Also in the "Western States 100-Mile Endurance Run" there was an increasing participation among women, to ∼20% of all starters between 1986 and 2007 (Hoffman & Wegelin, 2009).

Performance trends

Regarding gender differences in performance, Lippi et al. ( 2008) analyzed the World records, including runs up to the marathon distance, from 1900 to 2007 where, overall, the relative improvement in athletic performance was higher in women than in men. Considering the World best performances in marathon running, men were ∼11% faster then women from 1980 to 1996 (Sparling et al., 1998).

In ultra-endurance performances, there is still a debate about whether women could be faster than men. Bam et al. (1997) evaluated both men and women ultra-marathon performances and found that men ran faster over 5-42.2 km, but not in a 90 km ultra-marathon. It is, indeed, possible that a women ultra-runner can win a multi-stage ultra-marathon ahead of all the male competitors (Knechtle et al., 2008b). When performances over 100 m to 200 km between men and women were compared, men were, however, averaged 12.4% faster compared with women (Coast et al., 2004). Also in a 100-mile ultra-marathon, the fastest women were ∼20% slower compared with the fastest men (Hoffman, 2010).

In the 2IMT and 3IMT, the World best performance times for men were 19.3% and 19.2%

faster compared with women. For all winners, men were 25.6% faster in the 2IMT and 21.4%

faster in the 3IMT compared with women. For the 10IMT, World best performance was 29.7% faster for men. For all winners, men were 40.3% faster than women. The winner gender differences in performance times for both the 2IMT and 3IMT (∼19%) are slightly greater comparable to those found for an Ironman (∼13%) (Lepers, 2008). In all distances greater than the Ironman distance, women became progressively slower than men. This finding is in accordance with the findings for runners when distances between 100 m and 200 km were investigated (Coast et al., 2004). It is possible that non-physiological factors may have contributed to these observations. For example, the widening of gender difference with increase distance could be due at least in part to the fewer number of women triathletes in 10IMT.

Performance in swimming, cycling and running

Considering total race time and the split times, male winners were ∼20% faster for total race time, including split times, in the 2IMT and 3IMT. In the run split for the 2IMT, male winners were ∼30% faster than female winners. In the 10IMT, male winners were ∼40% faster than females. In both the swim and run splits, male winners were ∼45% faster than female winners. Regarding performance, neither split times nor the total race time became faster for men in the 2IMT, 3IMT and 10IMT during these 25 years. For the split times in the 3IMT, from 1988 to 2009, running performance increased for female winners and swimming performance increased for male winners. In the 2IMT and 10IMT, no changes for split times and total performances were found. For ultra-runners, Hoffman (2010) showed that the average times of the fastest runners had not changed over the last 20 years in the 161 km ultra-marathon in the United States. Also for the top five finishers among all men, or within different age groups, no changes could be detected (Hoffman & Wegelin, 2009).

The sport of triathlon involves three separate disciplines. In the Ironman Hawaii, between 1998 and 2007, the gender difference remained stable and practically identical for swimming (+0.1% per decade), increased a little for cycling (+0.8% per decade) and decreased somewhat more for running (-2.8%) (Lepers, 2008). In the 2IMT and 3IMT, the gender difference in time was the smallest in swimming and the greatest in running for both men and women winners. In the 10IMT, however, gender difference was the smallest in cycling.

In the Ironman Hawaii, time performances for the three disciplines, and for both sexes, decreased during the 1980s and tended to stabilize over the last two decades (Lepers, 2008).

From 1988 to 2007, the change in swimming, cycling, running and total performance for both men and women was <1.4% per decade, except for the women's run time, which decreased by 3.8% per decade. So while running performance for women improved in the Ironman Hawaii, it declined in the 3IMT.

A further important finding was that with the exception of the World best time in the 3IMT

for men, all the World best times were achieved in the early or mid 1990s. In 2000 no improvement in the World best times occurred for any distance. This finding is in line with global findings in the development of World records. When all the World records for Olympic disciplines were evaluated, a major global fading of World record progression was found (Berthelot et al., 2008).

How could these gender differences be explained?

It is well known that women are different regarding anthropometry, with women having more fat mass and less skeletal muscle mass compared with men (Sanborn & Jankowski, 1994).

During ultra-endurance races such as the 3IMT, a loss of both muscle mass and fat mass has been described (Knechtle et al., 2008a). The decrease in skeletal muscle mass occurs in both women (Knechtle et al., 2008a) and men (Knechtle et al., 2008b) ultra-endurance athletes.

When we consider that performance times increased for women with the increasing length in the ultra-distance races, we must assume that the smaller muscle mass at the start, and the decrease during the ultra-race, must be the limiting factor. Already in 1998, when the World best times from 1500 m to the marathon were compared, Sparling et al. (1998) concluded that the differences in performance will remain due to the biological differences between men and women. seemed to be experimental toward a move to the 10IMT. Since performance in the 2IMT, 3IMT and 10IMT showed no improvement over 25 years, we assume that only ultraendurance athletes of a very high level started ultra-triathlons from the initiation of the sport.

Presumably, ultra-triathletes enter the scene in distances such as the 2IMT and 3IMT and then move later to the 10IMT. However, it is not known whether the best ultra-triathletes competing in the 2IMT and 3IMT will also be the best in the 10IMT. The relationship between the 2IMT and 3IMT performances to the 10IMT performances for the same athletes need to be further investigated. The mean finish rate for all distances was 75%, which is high compared with a 161 km ultra-marathon. With the increasing length of the races, women became relatively slower compared with men. So women seem not to be able to compete faster than men in ultra-endurance performances. With the increasing length of the races, the smaller skeletal muscle mass in women may be a limiting factor. Further investigations are required in order to understand why gender differences in total performance times increase with the distance in ultra-triathlons.

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  Ironman distance, appeared since 1985. Historically, the first 2IMT was held in 1985 in Huntsville (USA) where 23 athletes started. Then, in 1988, the first 3IMT was organized in Le Fontanil (France) with 11 starters. The first Quadruple Ironman triathlon (4IMT) was held in 1989 in Den Haag (the Netherland) where two athletes started, and the first Quintuple Ironman triathlon (5IMT) was held in 1991, again in Den Haag (the Netherland), with 10 starters. In 1992 the ultra-triathlon scene moved to Monterrey (Mexico) where 20 athletes started in the first 10IMT.

Figure 1 .

 1 Figure 1. Total number of finishers, and finishers of each sex, in the 2IMT from 1985 to 2009 (a), in the 3IMT from 1988 to 2009 (b) and in the 10IMT from 1992 to 2008 (c). The 10IMT was only held on even years. 2IMT, Double Ironman triathlon; 3IMT, Triple Ironman triathlon; 10IMT, Deca Ironman triathlon.

Figure 2

 2 Figure2shows the historical performance trends of the women and men winners in the 2IMT from 1985 to 2009. Regression lines are presented for the total time, together with the three

Figure 2 .

 2 Figure 2. Total, swimming, cycling and running performance times in the 2IMT, for both men and women winners, from 1985 to 2009. All winners were considered when several races took place the same year. Regression lines are presented as a bold line for men and a dotted line for women. 2IMT, Double Ironman triathlon.

Figure 3 .

 3 Figure 3. Total, swimming, cycling and running performance times in the 3IMT, for both men and women winners, from 1988 to 2009. Regression lines are presented as a bold line for men and a dotted line for women. 3IMT, Triple Ironman triathlon.

Figure 4 .

 4 Figure 4. Total, swimming, cycling and running performance times in the 10IMT, for both men and women winners, from 1992 to 2008. No race took place in 2009. Regression lines are presented as a bold line for men and a dotted line for women. 10IMT, Deca Ironman triathlon.

Figure 5 .

 5 Figure 5. Top five times' spreads for men across the times for the 2IMT, 3IMT and 10IMT with linear regressions shown by the solid lines. The gradients of the three distances were not different from zero. 2IMT, Double Ironman triathlon; 3IMT, Triple Ironman triathlon; 10IMT, Deca Ironman triathlon.

Table 1 . Total number of races, year of first race, total number of starts and finishes, and mean finish rate (± SD) for 2IMT, 3IMT, 4IMT, 5IMT and 10IMT from the year of the first race to 2009, inclusive

 1 The number of starts in the 2IMT has progressively increased from 1985 to 2009 (number of starts in 1985: 23 men, 0 women; number of starts in 2009: 109 men, 12 women). Women accounted for ∼10% of the 2IMT starts since 2000. The number of starts in the 3IMT has also increased since 1988: men starts ranging from 11(in 1998) to 69 (in 2000) and women starts

	Numbe Yea Overall	Men	Women

Table 2 . World best total event performance times for men and women with corresponding split times for swimming, cycling and running for 2IMT, 3IMT and 10IMT

 2

			Country	Total race	Swim time	Cycle time	Run time
			(year)	time (min)	(min)	(min)	(min)
	1.	Gender difference is expressed as a percentage of the men's time.
	2.	2IMT, Double Ironman triathlon; 3IMT, Triple Ironman triathlon;
	10IMT, Deca Ironman triathlon.
	2IMT
	Men		Belgium (1993)	1194	124	628	441
	Women	Belgium (1992)	1425	165	761	499
	Difference (%)		19.3	33.1	21.2	13.2
	3IMT
	Men		Germany (2003)	1907	229	987	690
	Women	Germany (1996)	2274	236	1215	823
	Difference (%)		19.2	3.1	23.1	19.3
	10IMT
	Men		Mexico (1997)	11 528	671	6026	4829
	Women	Mexico (1992)	14 954	1383	7194	6376
	Difference (%)		29.7	106.1	19.4	32.0

Table 3 . Differences in total swimming, cycling and running times between the female and male winners for all 2IMT, 3IMT and 10IMT races with at least one women finisher since the first event (mean ± CI 95%)

 3

			Total (%)	Swim (%)	Cycle (%)	Run (%)
	1.	Gender differences are expressed as a percentage of men's time.
	P<0.05, ** P<0.01: significantly different from 2IMT.$P<0.05, $$P<0.01:
	significantly different from 3IMT. 2IMT, Double Ironman triathlon; 3IMT,
	Triple Ironman triathlon; 10IMT, Deca Ironman triathlon.
	2IMT (n=56)	25.2 ± 3.8	21.8 ± 7.8	23.3 ± 3.3	30.3 ± 6.6
	3IMT (n=36)	21.4 ± 4.9	19.3 ± 6.1	20.3 ± 4.2	24.6 ± 8.1

*

Table 3 . Differences in total swimming, cycling and running times between the female and male winners for all 2IMT, 3IMT and 10IMT races with at least one women finisher since the first event (mean

 3

	± CI

  7 years, from 1985 to 1992, ultra-triathletes had moved progressively from the 2IMT to the 10IMT. While the 2IMT and 3IMT are of increasing popularity, the 4IMT and 5IMT

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	Perspectives
	1. Top of page
	2. Abstract
	3. Material and methods
	4. Results
	5. Discussion
	6. Perspectives
	7. Acknowledgements
	8. References

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Acknowledgements

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